in the experimental and control group

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Control Group Definition and Examples

The control group is the set of subjects that does not receive the treatment in a study. In other words, it is the group where the independent variable is held constant. This is important because the control group is a baseline for measuring the effects of a treatment in an experiment or study. A controlled experiment is one which includes one or more control groups.

• The experimental group experiences a treatment or change in the independent variable. In contrast, the independent variable is constant in the control group.
• A control group is important because it allows meaningful comparison. The researcher compares the experimental group to it to assess whether or not there is a relationship between the independent and dependent variable and the magnitude of the effect.
• There are different types of control groups. A controlled experiment has one more control group.

Control Group vs Experimental Group

The only difference between the control group and experimental group is that subjects in the experimental group receive the treatment being studied, while participants in the control group do not. Otherwise, all other variables between the two groups are the same.

Control Group vs Control Variable

A control group is not the same thing as a control variable. A control variable or controlled variable is any factor that is held constant during an experiment. Examples of common control variables include temperature, duration, and sample size. The control variables are the same for both the control and experimental groups.

Types of Control Groups

There are different types of control groups:

• Placebo group : A placebo group receives a placebo , which is a fake treatment that resembles the treatment in every respect except for the active ingredient. Both the placebo and treatment may contain inactive ingredients that produce side effects. Without a placebo group, these effects might be attributed to the treatment.
• Positive control group : A positive control group has conditions that guarantee a positive test result. The positive control group demonstrates an experiment is capable of producing a positive result. Positive controls help researchers identify problems with an experiment.
• Negative control group : A negative control group consists of subjects that are not exposed to a treatment. For example, in an experiment looking at the effect of fertilizer on plant growth, the negative control group receives no fertilizer.
• Natural control group : A natural control group usually is a set of subjects who naturally differ from the experimental group. For example, if you compare the effects of a treatment on women who have had children, the natural control group includes women who have not had children. Non-smokers are a natural control group in comparison to smokers.
• Randomized control group : The subjects in a randomized control group are randomly selected from a larger pool of subjects. Often, subjects are randomly assigned to either the control or experimental group. Randomization reduces bias in an experiment. There are different methods of randomly assigning test subjects.

Control Group Examples

Here are some examples of different control groups in action:

Negative Control and Placebo Group

For example, consider a study of a new cancer drug. The experimental group receives the drug. The placebo group receives a placebo, which contains the same ingredients as the drug formulation, minus the active ingredient. The negative control group receives no treatment. The reason for including the negative group is because the placebo group experiences some level of placebo effect, which is a response to experiencing some form of false treatment.

Positive and Negative Controls

For example, consider an experiment looking at whether a new drug kills bacteria. The experimental group exposes bacterial cultures to the drug. If the group survives, the drug is ineffective. If the group dies, the drug is effective.

The positive control group has a culture of bacteria that carry a drug resistance gene. If the bacteria survive drug exposure (as intended), then it shows the growth medium and conditions allow bacterial growth. If the positive control group dies, it indicates a problem with the experimental conditions. A negative control group of bacteria lacking drug resistance should die. If the negative control group survives, something is wrong with the experimental conditions.

• Bailey, R. A. (2008).  Design of Comparative Experiments . Cambridge University Press. ISBN 978-0-521-68357-9.
• Chaplin, S. (2006). “The placebo response: an important part of treatment”.  Prescriber . 17 (5): 16–22. doi: 10.1002/psb.344
• Hinkelmann, Klaus; Kempthorne, Oscar (2008).  Design and Analysis of Experiments, Volume I: Introduction to Experimental Design  (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Pithon, M.M. (2013). “Importance of the control group in scientific research.” Dental Press J Orthod . 18 (6):13-14. doi: 10.1590/s2176-94512013000600003
• Stigler, Stephen M. (1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

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control group

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• Verywell Mind - What Is a Control Group?
• National Center for Biotechnology Information - PubMed Central - Control Group Design: Enhancing Rigor in Research of Mind-Body Therapies for Depression

control group , the standard to which comparisons are made in an experiment. Many experiments are designed to include a control group and one or more experimental groups; in fact, some scholars reserve the term experiment for study designs that include a control group. Ideally, the control group and the experimental groups are identical in every way except that the experimental groups are subjected to treatments or interventions believed to have an effect on the outcome of interest while the control group is not. Inclusion of a control group greatly strengthens researchers’ ability to draw conclusions from a study. Indeed, only in the presence of a control group can a researcher determine whether a treatment under investigation truly has a significant effect on an experimental group, and the possibility of making an erroneous conclusion is reduced. See also scientific method .

A typical use of a control group is in an experiment in which the effect of a treatment is unknown and comparisons between the control group and the experimental group are used to measure the effect of the treatment. For instance, in a pharmaceutical study to determine the effectiveness of a new drug on the treatment of migraines , the experimental group will be administered the new drug and the control group will be administered a placebo (a drug that is inert, or assumed to have no effect). Each group is then given the same questionnaire and asked to rate the effectiveness of the drug in relieving symptoms . If the new drug is effective, the experimental group is expected to have a significantly better response to it than the control group. Another possible design is to include several experimental groups, each of which is given a different dosage of the new drug, plus one control group. In this design, the analyst will compare results from each of the experimental groups to the control group. This type of experiment allows the researcher to determine not only if the drug is effective but also the effectiveness of different dosages. In the absence of a control group, the researcher’s ability to draw conclusions about the new drug is greatly weakened, due to the placebo effect and other threats to validity. Comparisons between the experimental groups with different dosages can be made without including a control group, but there is no way to know if any of the dosages of the new drug are more or less effective than the placebo.

It is important that every aspect of the experimental environment be as alike as possible for all subjects in the experiment. If conditions are different for the experimental and control groups, it is impossible to know whether differences between groups are actually due to the difference in treatments or to the difference in environment. For example, in the new migraine drug study, it would be a poor study design to administer the questionnaire to the experimental group in a hospital setting while asking the control group to complete it at home. Such a study could lead to a misleading conclusion, because differences in responses between the experimental and control groups could have been due to the effect of the drug or could have been due to the conditions under which the data were collected. For instance, perhaps the experimental group received better instructions or was more motivated by being in the hospital setting to give accurate responses than the control group.

In non-laboratory and nonclinical experiments, such as field experiments in ecology or economics , even well-designed experiments are subject to numerous and complex variables that cannot always be managed across the control group and experimental groups. Randomization, in which individuals or groups of individuals are randomly assigned to the treatment and control groups, is an important tool to eliminate selection bias and can aid in disentangling the effects of the experimental treatment from other confounding factors. Appropriate sample sizes are also important.

A control group study can be managed in two different ways. In a single-blind study, the researcher will know whether a particular subject is in the control group, but the subject will not know. In a double-blind study , neither the subject nor the researcher will know which treatment the subject is receiving. In many cases, a double-blind study is preferable to a single-blind study, since the researcher cannot inadvertently affect the results or their interpretation by treating a control subject differently from an experimental subject.

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• Control Groups and Treatment Groups | Uses & Examples

Control Groups & Treatment Groups | Uses & Examples

Published on 6 May 2022 by Lauren Thomas . Revised on 13 April 2023.

In a scientific study, a control group is used to establish a cause-and-effect relationship by isolating the effect of an independent variable .

Researchers change the independent variable in the treatment group and keep it constant in the control group. Then they compare the results of these groups.

Using a control group means that any change in the dependent variable can be attributed to the independent variable.

Table of contents

Control groups in experiments, control groups in non-experimental research, importance of control groups, frequently asked questions about control groups.

Control groups are essential to experimental design . When researchers are interested in the impact of a new treatment, they randomly divide their study participants into at least two groups:

• The treatment group (also called the experimental group ) receives the treatment whose effect the researcher is interested in.
• The control group receives either no treatment, a standard treatment whose effect is already known, or a placebo (a fake treatment).

The treatment is any independent variable manipulated by the experimenters, and its exact form depends on the type of research being performed. In a medical trial, it might be a new drug or therapy. In public policy studies, it could be a new social policy that some receive and not others.

In a well-designed experiment, all variables apart from the treatment should be kept constant between the two groups. This means researchers can correctly measure the entire effect of the treatment without interference from confounding variables .

• You pay the students in the treatment group for achieving high grades.
• Students in the control group do not receive any money.

Studies can also include more than one treatment or control group. Researchers might want to examine the impact of multiple treatments at once, or compare a new treatment to several alternatives currently available.

• The treatment group gets the new pill.
• Control group 1 gets an identical-looking sugar pill (a placebo).
• Control group 2 gets a pill already approved to treat high blood pressure.

Since the only variable that differs between the three groups is the type of pill, any differences in average blood pressure between the three groups can be credited to the type of pill they received.

• The difference between the treatment group and control group 1 demonstrates the effectiveness of the pill as compared to no treatment.
• The difference between the treatment group and control group 2 shows whether the new pill improves on treatments already available on the market.

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Although control groups are more common in experimental research, they can be used in other types of research too. Researchers generally rely on non-experimental control groups in two cases: quasi-experimental or matching design.

Control groups in quasi-experimental design

While true experiments rely on random assignment to the treatment or control groups, quasi-experimental design uses some criterion other than randomisation to assign people.

Often, these assignments are not controlled by researchers, but are pre-existing groups that have received different treatments. For example, researchers could study the effects of a new teaching method that was applied in some classes in a school but not others, or study the impact of a new policy that is implemented in one region but not in the neighbouring region.

In these cases, the classes that did not use the new teaching method, or the region that did not implement the new policy, is the control group.

Control groups in matching design

In correlational research , matching represents a potential alternate option when you cannot use either true or quasi-experimental designs.

In matching designs, the researcher matches individuals who received the ‘treatment’, or independent variable under study, to others who did not – the control group.

Each member of the treatment group thus has a counterpart in the control group identical in every way possible outside of the treatment. This ensures that the treatment is the only source of potential differences in outcomes between the two groups.

Control groups help ensure the internal validity of your research. You might see a difference over time in your dependent variable in your treatment group. However, without a control group, it is difficult to know whether the change has arisen from the treatment. It is possible that the change is due to some other variables.

If you use a control group that is identical in every other way to the treatment group, you know that the treatment – the only difference between the two groups – must be what has caused the change.

For example, people often recover from illnesses or injuries over time regardless of whether they’ve received effective treatment or not. Thus, without a control group, it’s difficult to determine whether improvements in medical conditions come from a treatment or just the natural progression of time.

Risks from invalid control groups

If your control group differs from the treatment group in ways that you haven’t accounted for, your results may reflect the interference of confounding variables instead of your independent variable.

Minimising this risk

A few methods can aid you in minimising the risk from invalid control groups.

• Ensure that all potential confounding variables are accounted for , preferably through an experimental design if possible, since it is difficult to control for all the possible confounders outside of an experimental environment.
• Use double-blinding . This will prevent the members of each group from modifying their behavior based on whether they were placed in the treatment or control group, which could then lead to biased outcomes.
• Randomly assign your subjects into control and treatment groups. This method will allow you to not only minimise the differences between the two groups on confounding variables that you can directly observe, but also those you cannot.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

A true experiment (aka a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment
• Random assignment of participants to ensure the groups are equivalent

Depending on your study topic, there are various other methods of controlling variables .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

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Experimental Design: Types, Examples & Methods

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Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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Understanding Control Groups for Research

Introduction

What are control groups in research, examples of control groups in research, control group vs. experimental group, types of control groups, control groups in non-experimental research.

A control group is typically thought of as the baseline in an experiment. In an experiment, clinical trial, or other sort of controlled study, there are at least two groups whose results are compared against each other.

The experimental group receives some sort of treatment, and their results are compared against those of the control group, which is not given the treatment. This is important to determine whether there is an identifiable causal relationship between the treatment and the resulting effects.

As intuitive as this may sound, there is an entire methodology that is useful to understanding the role of the control group in experimental research and as part of a broader concept in research. This article will examine the particulars of that methodology so you can design your research more rigorously .

Suppose that a friend or colleague of yours has a headache. You give them some over-the-counter medicine to relieve some of the pain. Shortly after they take the medicine, the pain is gone and they feel better. In casual settings, we can assume that it must be the medicine that was the cause of their headache going away.

In scientific research, however, we don't really know if the medicine made a difference or if the headache would have gone away on its own. Maybe in the time it took for the headache to go away, they ate or drank something that might have had an effect. Perhaps they had a quick nap that helped relieve the tension from the headache. Without rigorously exploring this phenomenon , any number of confounding factors exist that can make us question the actual efficacy of any particular treatment.

Experimental research relies on observing differences between the two groups by "controlling" the independent variable , or in the case of our example above, the medicine that is given or not given depending on the group. The dependent variable in this case is the change in how the person suffering the headache feels, and the difference between taking and not taking the medicine is evidence (or lack thereof) that the treatment is effective.

The catch is that, between the control group and other groups (typically called experimental groups), it's important to ensure that all other factors are the same or at least as similar as possible. Things such as age, fitness level, and even occupation can affect the likelihood someone has a headache and whether a certain medication is effective.

Faced with this dynamic, researchers try to make sure that participants in their control group and experimental group are as similar as possible to each other, with the only difference being the treatment they receive.

Experimental research is often associated with scientists in lab coats holding beakers containing liquids with funny colors. Clinical trials that deal with medical treatments rely primarily, if not exclusively, on experimental research designs involving comparisons between control and experimental groups.

However, many studies in the social sciences also employ some sort of experimental design which calls for the use of control groups. This type of research is useful when researchers are trying to confirm or challenge an existing notion or measure the difference in effects.

Workplace efficiency research

How might a company know if an employee training program is effective? They may decide to pilot the program to a small group of their employees before they implement the training to their entire workforce.

If they adopt an experimental design, they could compare results between an experimental group of workers who participate in the training program against a control group who continues as per usual without any additional training.

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Mental health research

Music certainly has profound effects on psychology, but what kind of music would be most effective for concentration? Here, a researcher might be interested in having participants in a control group perform a series of tasks in an environment with no background music, and participants in multiple experimental groups perform those same tasks with background music of different genres. The subsequent analysis could determine how well people perform with classical music, jazz music, or no music at all in the background.

Educational research

Suppose that you want to improve reading ability among elementary school students, and there is research on a particular teaching method that is associated with facilitating reading comprehension. How do you measure the effects of that teaching method?

A study could be conducted on two groups of otherwise equally proficient students to measure the difference in test scores. The teacher delivers the same instruction to the control group as they have to previous students, but they teach the experimental group using the new technique. A reading test after a certain amount of instruction could determine the extent of effectiveness of the new teaching method.

As you can see from the three examples above, experimental groups are the counterbalance to control groups. A control group offers an essential point of comparison. For an experimental study to be considered credible, it must establish a baseline against which novel research is conducted.

Researchers can determine the makeup of their experimental and control groups from their literature review . Remember that the objective of a review is to establish what is known about the object of inquiry and what is not known. Where experimental groups explore the unknown aspects of scientific knowledge, a control group is a sort of simulation of what would happen if the treatment or intervention was not administered. As a result, it will benefit researchers to have a foundational knowledge of the existing research to create a credible control group against which experimental results are compared, especially in terms of remaining sensitive to relevant participant characteristics that could confound the effects of your treatment or intervention so that you can appropriately distribute participants between the experimental and control groups.

There are multiple control groups to consider depending on the study you are looking to conduct. All of them are variations of the basic control group used to establish a baseline for experimental conditions.

No-treatment control group

This kind of control group is common when trying to establish the effects of an experimental treatment against the absence of treatment. This is arguably the most straightforward approach to an experimental design as it aims to directly demonstrate how a certain change in conditions produces an effect.

Placebo control group

In this case, the control group receives some sort of treatment under the exact same procedures as those in the experimental group. The only difference in this case is that the treatment in the placebo control group has already been judged to be ineffective, except that the research participants don't know that it is ineffective.

Placebo control groups (or negative control groups) are useful for allowing researchers to account for any psychological or affective factors that might impact the outcomes. The negative control group exists to explicitly eliminate factors other than changes in the independent variable conditions as causes of the effects experienced in the experimental group.

Positive control group

Contrasted with a no-treatment control group, a positive control group employs a treatment against which the treatment in the experimental group is compared. However, unlike in a placebo group, participants in a positive control group receive treatment that is known to have an effect.

If we were to use our first example of headache medicine, a researcher could compare results between medication that is commonly known as effective against the newer medication that the researcher thinks is more effective. Positive control groups are useful for validating experimental results when compared against familiar results.

Historical control group

Rather than study participants in control group conditions, researchers may employ existing data to create historical control groups. This form of control group is useful for examining changing conditions over time, particularly when incorporating past conditions that can't be replicated in the analysis.

Qualitative research more often relies on non-experimental research such as observations and interviews to examine phenomena in their natural environments. This sort of research is more suited for inductive and exploratory inquiries, not confirmatory studies meant to test or measure a phenomenon.

That said, the broader concept of a control group is still present in observational and interview research in the form of a comparison group. Comparison groups are used in qualitative research designs to show differences between phenomena, with the exception being that there is no baseline against which data is analyzed.

Comparison groups are useful when an experimental environment cannot produce results that would be applicable to real-world conditions. Research inquiries examining the social world face challenges of having too many variables to control, making observations and interviews across comparable groups more appropriate for data collection than clinical or sterile environments.

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What Is a Control Group?

Control Groups vs. Experimental Groups in Psychology Research

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

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Control Group vs. Experimental Group

Types of control groups.

In simple terms, the control group comprises participants who do not receive the experimental treatment. When conducting an experiment, these people are randomly assigned to this group. They also closely resemble the participants who are in the experimental group or the individuals who receive the treatment.

Experimenters utilize variables to make comparisons between an experimental group and a control group. A variable is something that researchers can manipulate, measure, and control in an experiment. The independent variable is the aspect of the experiment that the researchers manipulate (or the treatment). The dependent variable is what the researchers measure to see if the independent variable had an effect.

While they do not receive the treatment, the control group does play a vital role in the research process. Experimenters compare the experimental group to the control group to determine if the treatment had an effect.

By serving as a comparison group, researchers can isolate the independent variable and look at the impact it had.

The simplest way to determine the difference between a control group and an experimental group is to determine which group receives the treatment and which does not. To ensure that the results can then be compared accurately, the two groups should be otherwise identical.

Not exposed to the treatment (the independent variable)

Used to provide a baseline to compare results against

May receive a placebo treatment

Exposed to the treatment

Used to measure the effects of the independent variable

Identical to the control group aside from their exposure to the treatment

Why a Control Group Is Important

While the control group does not receive treatment, it does play a critical role in the experimental process. This group serves as a benchmark, allowing researchers to compare the experimental group to the control group to see what sort of impact changes to the independent variable produced.  

Because participants have been randomly assigned to either the control group or the experimental group, it can be assumed that the groups are comparable.

Any differences between the two groups are, therefore, the result of the manipulations of the independent variable. The experimenters carry out the exact same procedures with both groups with the exception of the manipulation of the independent variable in the experimental group.

There are a number of different types of control groups that might be utilized in psychology research. Some of these include:

• Positive control groups : In this case, researchers already know that a treatment is effective but want to learn more about the impact of variations of the treatment. In this case, the control group receives the treatment that is known to work, while the experimental group receives the variation so that researchers can learn more about how it performs and compares to the control.
• Negative control group : In this type of control group, the participants are not given a treatment. The experimental group can then be compared to the group that did not experience any change or results.
• Placebo control group : This type of control group receives a placebo treatment that they believe will have an effect. This control group allows researchers to examine the impact of the placebo effect and how the experimental treatment compared to the placebo treatment.
• Randomized control group : This type of control group involves using random selection to help ensure that the participants in the control group accurately reflect the demographics of the larger population.
• Natural control group : This type of control group is naturally selected, often by situational factors. For example, researchers might compare people who have experienced trauma due to war to people who have not experienced war. The people who have not experienced war-related trauma would be the control group.

Examples of Control Groups

Control groups can be used in a variety of situations. For example, imagine a study in which researchers example how distractions during an exam influence test results. The control group would take an exam in a setting with no distractions, while the experimental groups would be exposed to different distractions. The results of the exam would then be compared to see the effects that distractions had on test scores.

Experiments that look at the effects of medications on certain conditions are also examples of how a control group can be used in research. For example, researchers looking at the effectiveness of a new antidepressant might use a control group that receives a placebo and an experimental group that receives the new medication. At the end of the study, researchers would compare measures of depression for both groups to determine what impact the new medication had.

After the experiment is complete, researchers can then look at the test results and start making comparisons between the control group and the experimental group.

Uses for Control Groups

Researchers utilize control groups to conduct research in a range of different fields. Some common uses include:

• Psychology : Researchers utilize control groups to learn more about mental health, behaviors, and treatments.
• Medicine : Control groups can be used to learn more about certain health conditions, assess how well medications work to treat these conditions, and assess potential side effects that may result.
• Education : Educational researchers utilize control groups to learn more about how different curriculums, programs, or instructional methods impact student outcomes.
• Marketing : Researchers utilize control groups to learn more about how consumers respond to advertising and marketing efforts.

Malay S, Chung KC. The choice of controls for providing validity and evidence in clinical research . Plast Reconstr Surg. 2012 Oct;130(4):959-965. doi:10.1097/PRS.0b013e318262f4c8

National Cancer Institute. Control group.

Pithon MM. Importance of the control group in scientific research . Dental Press J Orthod. 2013;18(6):13-14. doi:10.1590/s2176-94512013000600003

Karlsson P, Bergmark A. Compared with what? An analysis of control-group types in Cochrane and Campbell reviews of psychosocial treatment efficacy with substance use disorders . Addiction . 2015;110(3):420-8. doi:10.1111/add.12799

Myers A, Hansen C. Experimental Psychology . Belmont, CA: Cengage Learning; 2012.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Statistics By Jim

Making statistics intuitive

Control Group in an Experiment

By Jim Frost 3 Comments

A control group in an experiment does not receive the treatment. Instead, it serves as a comparison group for the treatments. Researchers compare the results of a treatment group to the control group to determine the effect size, also known as the treatment effect.

Imagine that a treatment group receives a vaccine and it has an infection rate of 10%. By itself, you don’t know if that’s an improvement. However, if you also have an unvaccinated control group with an infection rate of 20%, you know the vaccine improved the outcome by 10 percentage points.

By serving as a basis for comparison, the control group reveals the treatment’s effect.

Related post : Effect Sizes in Statistics

Using Control Groups in Experiments

Most experiments include a control group and at least one treatment group. In an ideal experiment, the subjects in all groups start with the same overall characteristics except that those in the treatment groups receive a treatment. When the groups are otherwise equivalent before treatment begins, you can attribute differences after the experiment to the treatments.

Randomized controlled trials (RCTs) assign subjects to the treatment and control groups randomly. This process helps ensure the groups are comparable when treatment begins. Consequently, treatment effects are the most likely cause for differences between groups at the end of the study. Statisticians consider RCTs to be the gold standard. To learn more about this process, read my post, Random Assignment in Experiments .

Observational studies either can’t use randomized groups or don’t use them because they’re too costly or problematic. In these studies, the characteristics of the control group might be different from the treatment groups at the start of the study, making it difficult to estimate the treatment effect accurately at the end. Case-Control studies are a specific type of observational study that uses a control group.

For these types of studies, analytical methods and design choices, such as regression analysis and matching, can help statistically mitigate confounding variables. Matching involves selecting participants with similar characteristics. For each participant in the treatment group, the researchers find a subject with comparable traits to include in the control group. To learn more about this type of study and matching, read my post, Observational Studies Explained .

Control groups are key way to increase the internal validity of an experiment. To learn more, read my post about internal and external validity .

Randomized versus non-randomized control groups are just several of the different types you can have. We’ll look at more kinds later!

Related posts : When to Use Regression Analysis

Example of a Control Group

Suppose we want to determine whether regular vitamin consumption affects the risk of dying. Our experiment has the following two experimental groups:

• Control group : Does not consume vitamin supplements
• Treatment group : Regularly consumes vitamin supplements.

In this experiment, we randomly assign subjects to the two groups. Because we use random assignment, the two groups start with similar characteristics, including healthy habits, physical attributes, medical conditions, and other factors affecting the outcome. The intentional introduction of vitamin supplements in the treatment group is the only systematic difference between the groups.

After the experiment is complete, we compare the death risk between the treatment and control groups. Because the groups started roughly equal, we can reasonably attribute differences in death risk at the end of the study to vitamin consumption. By having the control group as the basis of comparison, the effect of vitamin consumption becomes clear!

Types of Control Groups

Researchers can use different types of control groups in their experiments. Earlier, you learned about the random versus non-random kinds, but there are other variations. You can use various types depending on your research goals, constraints, and ethical issues, among other things.

Negative Control Group

The group introduces a condition that the researchers expect won’t have an effect. This group typically receives no treatment. These experiments compare the effectiveness of the experimental treatment to no treatment. For example, in a vaccine study, a negative control group does not get the vaccine.

Positive Control Group

Positive control groups typically receive a standard treatment that science has already proven effective. These groups serve as a benchmark for the performance of a conventional treatment. In this vein, experiments with positive control groups compare the effectiveness of a new treatment to a standard one.

For example, an old blood pressure medicine can be the treatment in a positive control group, while the treatment group receives the new, experimental blood pressure medicine. The researchers want to determine whether the new treatment is better than the previous treatment.

In these studies, subjects can still take the standard medication for their condition, a potentially critical ethics issue.

Placebo Control Group

Placebo control groups introduce a treatment lookalike that will not affect the outcome. Standard examples of placebos are sugar pills and saline solution injections instead of genuine medicine. The key is that the placebo looks like the actual treatment. Researchers use this approach when the recipients’ belief that they’re receiving the treatment might influence their outcomes. By using placebos, the experiment controls for these psychological benefits. The researchers want to determine whether the treatment performs better than the placebo effect.

Learn more about the Placebo Effect .

Blinded Control Groups

If the subject’s awareness of their group assignment might affect their outcomes, the researchers can use a blinded experimental design that does not tell participants their group membership. Typically, blinded control groups will receive placebos, as described above. In a double-blinded control group, both subjects and researchers don’t know group assignments.

Waitlist Control Group

When there is a waitlist to receive a new treatment, those on the waitlist can serve as a control group until they receive treatment. This type of design avoids ethical concerns about withholding a better treatment until the study finishes. This design can be a variation of a positive control group because the subjects might be using conventional medicines while on the waitlist.

Historical Control Group

When historical data for a comparison group exists, it can serve as a control group for an experiment. The group doesn’t exist in the study, but the researchers compare the treatment group to the existing data. For example, the researchers might have infection rate data for unvaccinated individuals to compare to the infection rate among the vaccinated participants in their study. This approach allows everyone in the experiment to receive the new treatment. However, differences in place, time, and other circumstances can reduce the value of these comparisons. In other words, other factors might account for the apparent effects.

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Reader Interactions

December 19, 2021 at 9:17 am

Thank you very much Jim for your quick and comprehensive feedback. Extremely helpful!! Regards, Arthur

December 17, 2021 at 4:46 pm

Thank you very much Jim, very interesting article.

Can I select a control group at the end of intervention/experiment? Currently I am managing a project in rural Cambodia in five villages, however I did not select any comparison/control site at the beginning. Since I know there are other villages which have not been exposed to any type of intervention, can i select them as a control site during my end-line data collection or it will not be a legitimate control? Thank you very much, Arthur

December 18, 2021 at 1:51 am

You might be able to use that approach, but it’s not ideal. The ideal is to have control groups defined at the beginning of the study. You can use the untreated villages as a type of historical control groups that I talk about in this article. Or, if they’re awaiting to receive the intervention, it might be akin to a waitlist control group.

If you go that route, you’ll need to consider whether there was some systematic reason why these villages have not received any intervention. For example, are the villages in question more remote? And, if there is a systematic reason, would that affect your outcome variable? More generally, are they systematically different? How well do the untreated villages represent your target population?

If you had selected control villages at the beginning, you’d have been better able to ensure there weren’t any systematic differences between the villages receiving interventions and those that didn’t.

If the villages that didn’t receive any interventions are systematically different, you’ll need to incorporate that into your interpretation of the results. Are they different in ways that affect the outcomes you’re measuring? Can those differences account for the difference in outcomes between the treated and untreated villages? Hopefully, you’d be able to measure those differences between untreated/treated villages.

So, yes, you can use that approach. It’s not perfect and there will potentially be more things for you to consider and factor into your conclusions. Despite these drawbacks, it’s possible that using a pseudo control group like that is better than not doing that because at least you can make comparisons to something. Otherwise, you won’t know whether the outcomes in the intervention villages represent an improvement! Just be aware of the extra considerations!

Best of luck with your research!

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Control Group: The Key Elements In Experimental Research

Understand the design and interpretation of control group in research experiments for powerful conclusions

The control group constitutes a baseline for comparison, enabling researchers to assess the true effects of independent variables. Researchers can effectively assess the impact of independent variables and discern causation from correlation, by comparing the results of experimental groups to those of control groups. This article will highlight the significance and implementation of control groups in research experiments, and explain their role in ensuring scientific methodology and reliable findings. We will explore the fundamental principles of control groups, examine their types, and discuss their importance in minimizing biases and confounding factors.

What Is A Control Group?

A control group is a fundamental component of scientific experiments designed to compare and evaluate the effects of an intervention or treatment. It serves as a baseline against which the experimental group is measured. The control group consists of individuals or subjects who do not receive the experimental treatment but are otherwise subjected to the same conditions and procedures as the experimental group. Working with a control group, researchers can assess the specific impact of the intervention by comparing the outcomes between the experimental and control groups.

Related article: The Role Of Experimental Groups In Research

The Role Of A Control Group In Scientific Experiments

A control group plays a crucial role in scientific experiments as it enables researchers to establish a valid cause-and-effect relationship between the experimental treatment and the observed outcomes. By comparing the experimental group’s results with those of the control group, researchers can determine whether any observed effects are due to the treatment or other factors. The control group serves as a standard for comparison, helping to isolate the specific influence of the intervention being tested. It provides a baseline against which experimental group outcomes can be evaluated and allows researchers to draw accurate conclusions about the treatment’s efficacy or the impact of other variables being studied.

Why Is A Control Group Necessary?

Including a control group in scientific experiments is essential for ensuring the reliability and validity of the findings. Without a control group, it becomes challenging to determine whether any observed changes or effects are truly attributable to the intervention or simply a result of chance or other factors. The control group allows researchers to differentiate between the effects of the experimental treatment and background noise or confounding variables because it provides a reference point. A well-designed control group is crucial for generating reliable and meaningful results, intensifying the scientific rigor of the study, and supporting evidence-based decision-making in various fields of research.

Types Of Control Groups

In scientific experiments, different types of control groups are used to ensure accurate and meaningful results. These control groups help researchers compare the effects of an intervention or treatment against a reference point. Four common types of control groups are negative controls, positive controls, placebo controls, and randomized control groups.

Negative Controls

Negative controls are an integral part of scientific experiments, serving as a reference to establish the absence of a specific effect. In these control groups, no treatment is administered, allowing researchers to compare the outcomes with the experimental group. Researchers can identify and account for confounding variables and background effects that may influence the results when they include negative control groups. This ensures the specificity of the treatment and enhances the validity of the study. Negative controls can take various forms, such as placebos or control groups receiving no treatment, depending on the research question.

Positive controls

Positive controls are references to validate the reliability and sensitivity of the experimental setup. In these control groups, a known treatment or condition is applied to generate an expected response or outcome. By including positive controls, researchers can assess whether the experimental conditions and methodology are capable of detecting the desired effect. Positive controls act as a benchmark, providing evidence that the experimental system is functioning properly and capable of producing the anticipated results. This helps researchers ensure the validity and accuracy of their findings by confirming that the experimental conditions are conducive to detecting the intended response.

Placebo controls

Placebo controls play a significant role in medical and clinical research by providing a baseline for comparison and evaluating the effectiveness of a new treatment or intervention. In a placebo control group, participants receive an inactive substance or sham procedure that is indistinguishable from the active treatment being tested. The purpose of the placebo control is to assess the specific effects of the treatment by comparing it to the effects observed in the placebo group. By administering a placebo, researchers can account for the psychological and physiological responses that may occur simply due to the participants’ belief in receiving treatment. This helps determine the true efficacy of the active treatment, as any observed improvements in the treatment group can be attributed to the treatment itself, beyond the placebo effect. Placebo controls are essential in clinical trials and other studies to minimize bias, establish the true therapeutic benefits of treatment, and ensure the reliability of the results.

Randomized Control Group

Randomized control groups are an essential component of research studies as they introduce unpredictability to control factors. By randomly assigning participants to either the control or treatment group, researchers ensure that the variables not specifically tested are evenly distributed. This randomization helps eliminate bias and allows for accurate analysis of the independent variable. By using randomized control groups, researchers can draw reliable conclusions about the impact of the variables being studied. 

Quasi-Experimental Designs And Their Role In Social Policy Studies

Quasi-experimental designs in social policy studies often utilize control groups to assess the impact of interventions or policies on a target population. While these designs do not involve random assignment of participants to groups, they still incorporate a control group to establish a baseline for comparison. The control group consists of individuals who do not receive the intervention or policy being studied, allowing researchers to evaluate the effects of the intervention by comparing outcomes between the treatment group and the control group. This helps control for confounding variables and provides insights into a causal relationship between the intervention and the observed outcomes. 

Implementing Control Groups In Experimental Design And Analysis

Control groups serve as a reference point against which the effects of experimental interventions can be measured. They provide a baseline to compare with the treatment group, allowing researchers to determine the true impact of the variables under investigation. This approach helps establish causal relationships and increases the internal validity of the research. 

Randomized Controlled Experiments (RCTs) For Public Policy Studies

Randomized controlled experiments are widely used in public policy studies. RCTs involve randomly assigning participants to either a treatment group or a control group. The treatment group receives the intervention or policy being tested, while the control group does not. RCTs help ensure that any observed differences between the groups are not due to pre-existing factors, increasing the reliability of the study’s findings. RCTs are particularly valuable in evaluating the impact of public policies and interventions on a large scale.

Non-Experimental Research Vs. Actual Experimentation

When determining the baseline for comparison in research, researchers must consider whether to use non-experimental research or actual experimentation. Non-experimental research involves observing and analyzing existing data without manipulating any variables. This approach is helpful in situations where it is not feasible or ethical to conduct an experiment. On the other hand, actual experimentation involves actively manipulating variables and comparing groups with and without the intervention. While actual experimentation provides stronger causal evidence, non-experimental research can still provide valuable insights when experiments are not possible.

Identifying Confounding Variables And Factors

Confounding variables and factors are extraneous variables that can influence the relationship between the independent and dependent variables in a study. Identifying and controlling for confounding variables is crucial to ensure accurate and valid results. Researchers employ various techniques to address confounding variables, such as random assignment of participants to groups, matching participants based on relevant characteristics, or statistical techniques like regression analysis. By accounting for confounding variables, researchers can strengthen the internal validity of their studies and draw more accurate conclusions about the relationship between variables.

The Vital Role Of The Control Group In Scientific Methodology And Analysis

In experimental studies, the control group serves as a standard against which the effects of a particular intervention or treatment are measured. By keeping all variables constant except for the one being studied, researchers can isolate the true impact of the intervention. This helps to establish causality and determine whether the observed effects are indeed due to the intervention or simply a result of other factors.

In addition to experimental studies, control groups are also essential in observational and epidemiological research. They help researchers account for potential biases and confounding factors when analyzing the relationship between variables. By comparing a group exposed to a certain risk factor or condition with a similar group that is not exposed, researchers can better understand the true impact of the risk factor or condition on the outcome of interest.

Overall, the control group serves as a guide in scientific methodology and analysis. It allows researchers to draw valid and reliable conclusions, enhance the internal validity of their studies, and provide more robust evidence for decision-making in various fields, including medicine, psychology, biology, and social sciences.

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Frequently asked questions

What is the difference between a control group and an experimental group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

Frequently asked questions: Methodology

Attrition refers to participants leaving a study. It always happens to some extent—for example, in randomized controlled trials for medical research.

Differential attrition occurs when attrition or dropout rates differ systematically between the intervention and the control group . As a result, the characteristics of the participants who drop out differ from the characteristics of those who stay in the study. Because of this, study results may be biased .

Action research is conducted in order to solve a particular issue immediately, while case studies are often conducted over a longer period of time and focus more on observing and analyzing a particular ongoing phenomenon.

Action research is focused on solving a problem or informing individual and community-based knowledge in a way that impacts teaching, learning, and other related processes. It is less focused on contributing theoretical input, instead producing actionable input.

Action research is particularly popular with educators as a form of systematic inquiry because it prioritizes reflection and bridges the gap between theory and practice. Educators are able to simultaneously investigate an issue as they solve it, and the method is very iterative and flexible.

A cycle of inquiry is another name for action research . It is usually visualized in a spiral shape following a series of steps, such as “planning → acting → observing → reflecting.”

To make quantitative observations , you need to use instruments that are capable of measuring the quantity you want to observe. For example, you might use a ruler to measure the length of an object or a thermometer to measure its temperature.

Criterion validity and construct validity are both types of measurement validity . In other words, they both show you how accurately a method measures something.

While construct validity is the degree to which a test or other measurement method measures what it claims to measure, criterion validity is the degree to which a test can predictively (in the future) or concurrently (in the present) measure something.

Construct validity is often considered the overarching type of measurement validity . You need to have face validity , content validity , and criterion validity in order to achieve construct validity.

Convergent validity and discriminant validity are both subtypes of construct validity . Together, they help you evaluate whether a test measures the concept it was designed to measure.

• Convergent validity indicates whether a test that is designed to measure a particular construct correlates with other tests that assess the same or similar construct.
• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related. This type of validity is also called divergent validity .

You need to assess both in order to demonstrate construct validity. Neither one alone is sufficient for establishing construct validity.

• Discriminant validity indicates whether two tests that should not be highly related to each other are indeed not related

Content validity shows you how accurately a test or other measurement method taps  into the various aspects of the specific construct you are researching.

In other words, it helps you answer the question: “does the test measure all aspects of the construct I want to measure?” If it does, then the test has high content validity.

The higher the content validity, the more accurate the measurement of the construct.

If the test fails to include parts of the construct, or irrelevant parts are included, the validity of the instrument is threatened, which brings your results into question.

Face validity and content validity are similar in that they both evaluate how suitable the content of a test is. The difference is that face validity is subjective, and assesses content at surface level.

When a test has strong face validity, anyone would agree that the test’s questions appear to measure what they are intended to measure.

For example, looking at a 4th grade math test consisting of problems in which students have to add and multiply, most people would agree that it has strong face validity (i.e., it looks like a math test).

On the other hand, content validity evaluates how well a test represents all the aspects of a topic. Assessing content validity is more systematic and relies on expert evaluation. of each question, analyzing whether each one covers the aspects that the test was designed to cover.

A 4th grade math test would have high content validity if it covered all the skills taught in that grade. Experts(in this case, math teachers), would have to evaluate the content validity by comparing the test to the learning objectives.

Snowball sampling is a non-probability sampling method . Unlike probability sampling (which involves some form of random selection ), the initial individuals selected to be studied are the ones who recruit new participants.

Because not every member of the target population has an equal chance of being recruited into the sample, selection in snowball sampling is non-random.

Snowball sampling is a non-probability sampling method , where there is not an equal chance for every member of the population to be included in the sample .

This means that you cannot use inferential statistics and make generalizations —often the goal of quantitative research . As such, a snowball sample is not representative of the target population and is usually a better fit for qualitative research .

Snowball sampling relies on the use of referrals. Here, the researcher recruits one or more initial participants, who then recruit the next ones.

Participants share similar characteristics and/or know each other. Because of this, not every member of the population has an equal chance of being included in the sample, giving rise to sampling bias .

Snowball sampling is best used in the following cases:

• If there is no sampling frame available (e.g., people with a rare disease)
• If the population of interest is hard to access or locate (e.g., people experiencing homelessness)
• If the research focuses on a sensitive topic (e.g., extramarital affairs)

The reproducibility and replicability of a study can be ensured by writing a transparent, detailed method section and using clear, unambiguous language.

Reproducibility and replicability are related terms.

• Reproducing research entails reanalyzing the existing data in the same manner.
• Replicating (or repeating ) the research entails reconducting the entire analysis, including the collection of new data . 
• A successful reproduction shows that the data analyses were conducted in a fair and honest manner.
• A successful replication shows that the reliability of the results is high.

Stratified sampling and quota sampling both involve dividing the population into subgroups and selecting units from each subgroup. The purpose in both cases is to select a representative sample and/or to allow comparisons between subgroups.

The main difference is that in stratified sampling, you draw a random sample from each subgroup ( probability sampling ). In quota sampling you select a predetermined number or proportion of units, in a non-random manner ( non-probability sampling ).

Purposive and convenience sampling are both sampling methods that are typically used in qualitative data collection.

A convenience sample is drawn from a source that is conveniently accessible to the researcher. Convenience sampling does not distinguish characteristics among the participants. On the other hand, purposive sampling focuses on selecting participants possessing characteristics associated with the research study.

The findings of studies based on either convenience or purposive sampling can only be generalized to the (sub)population from which the sample is drawn, and not to the entire population.

Random sampling or probability sampling is based on random selection. This means that each unit has an equal chance (i.e., equal probability) of being included in the sample.

On the other hand, convenience sampling involves stopping people at random, which means that not everyone has an equal chance of being selected depending on the place, time, or day you are collecting your data.

Convenience sampling and quota sampling are both non-probability sampling methods. They both use non-random criteria like availability, geographical proximity, or expert knowledge to recruit study participants.

However, in convenience sampling, you continue to sample units or cases until you reach the required sample size.

In quota sampling, you first need to divide your population of interest into subgroups (strata) and estimate their proportions (quota) in the population. Then you can start your data collection, using convenience sampling to recruit participants, until the proportions in each subgroup coincide with the estimated proportions in the population.

A sampling frame is a list of every member in the entire population . It is important that the sampling frame is as complete as possible, so that your sample accurately reflects your population.

Stratified and cluster sampling may look similar, but bear in mind that groups created in cluster sampling are heterogeneous , so the individual characteristics in the cluster vary. In contrast, groups created in stratified sampling are homogeneous , as units share characteristics.

Relatedly, in cluster sampling you randomly select entire groups and include all units of each group in your sample. However, in stratified sampling, you select some units of all groups and include them in your sample. In this way, both methods can ensure that your sample is representative of the target population .

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

It’s often best to ask a variety of people to review your measurements. You can ask experts, such as other researchers, or laypeople, such as potential participants, to judge the face validity of tests.

While experts have a deep understanding of research methods , the people you’re studying can provide you with valuable insights you may have missed otherwise.

Face validity is important because it’s a simple first step to measuring the overall validity of a test or technique. It’s a relatively intuitive, quick, and easy way to start checking whether a new measure seems useful at first glance.

Good face validity means that anyone who reviews your measure says that it seems to be measuring what it’s supposed to. With poor face validity, someone reviewing your measure may be left confused about what you’re measuring and why you’re using this method.

Face validity is about whether a test appears to measure what it’s supposed to measure. This type of validity is concerned with whether a measure seems relevant and appropriate for what it’s assessing only on the surface.

Statistical analyses are often applied to test validity with data from your measures. You test convergent validity and discriminant validity with correlations to see if results from your test are positively or negatively related to those of other established tests.

You can also use regression analyses to assess whether your measure is actually predictive of outcomes that you expect it to predict theoretically. A regression analysis that supports your expectations strengthens your claim of construct validity .

When designing or evaluating a measure, construct validity helps you ensure you’re actually measuring the construct you’re interested in. If you don’t have construct validity, you may inadvertently measure unrelated or distinct constructs and lose precision in your research.

Construct validity is often considered the overarching type of measurement validity ,  because it covers all of the other types. You need to have face validity , content validity , and criterion validity to achieve construct validity.

Construct validity is about how well a test measures the concept it was designed to evaluate. It’s one of four types of measurement validity , which includes construct validity, face validity , and criterion validity.

There are two subtypes of construct validity.

• Convergent validity : The extent to which your measure corresponds to measures of related constructs
• Discriminant validity : The extent to which your measure is unrelated or negatively related to measures of distinct constructs

Naturalistic observation is a valuable tool because of its flexibility, external validity , and suitability for topics that can’t be studied in a lab setting.

The downsides of naturalistic observation include its lack of scientific control , ethical considerations , and potential for bias from observers and subjects.

Naturalistic observation is a qualitative research method where you record the behaviors of your research subjects in real world settings. You avoid interfering or influencing anything in a naturalistic observation.

You can think of naturalistic observation as “people watching” with a purpose.

A dependent variable is what changes as a result of the independent variable manipulation in experiments . It’s what you’re interested in measuring, and it “depends” on your independent variable.

In statistics, dependent variables are also called:

• Response variables (they respond to a change in another variable)
• Outcome variables (they represent the outcome you want to measure)
• Left-hand-side variables (they appear on the left-hand side of a regression equation)

An independent variable is the variable you manipulate, control, or vary in an experimental study to explore its effects. It’s called “independent” because it’s not influenced by any other variables in the study.

Independent variables are also called:

• Explanatory variables (they explain an event or outcome)
• Predictor variables (they can be used to predict the value of a dependent variable)
• Right-hand-side variables (they appear on the right-hand side of a regression equation).

As a rule of thumb, questions related to thoughts, beliefs, and feelings work well in focus groups. Take your time formulating strong questions, paying special attention to phrasing. Be careful to avoid leading questions , which can bias your responses.

Overall, your focus group questions should be:

• Open-ended and flexible
• Impossible to answer with “yes” or “no” (questions that start with “why” or “how” are often best)
• Unambiguous, getting straight to the point while still stimulating discussion
• Unbiased and neutral

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when: 

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyze your data quickly and efficiently.
• Your research question depends on strong parity between participants, with environmental conditions held constant.

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias is the tendency for interview participants to give responses that will be viewed favorably by the interviewer or other participants. It occurs in all types of interviews and surveys , but is most common in semi-structured interviews , unstructured interviews , and focus groups .

Social desirability bias can be mitigated by ensuring participants feel at ease and comfortable sharing their views. Make sure to pay attention to your own body language and any physical or verbal cues, such as nodding or widening your eyes.

This type of bias can also occur in observations if the participants know they’re being observed. They might alter their behavior accordingly.

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions.
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualize your initial thoughts and hypotheses.
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order. 
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

Deductive reasoning is commonly used in scientific research, and it’s especially associated with quantitative research .

In research, you might have come across something called the hypothetico-deductive method . It’s the scientific method of testing hypotheses to check whether your predictions are substantiated by real-world data.

Deductive reasoning is a logical approach where you progress from general ideas to specific conclusions. It’s often contrasted with inductive reasoning , where you start with specific observations and form general conclusions.

Deductive reasoning is also called deductive logic.

There are many different types of inductive reasoning that people use formally or informally.

Here are a few common types:

• Inductive generalization : You use observations about a sample to come to a conclusion about the population it came from.
• Statistical generalization: You use specific numbers about samples to make statements about populations.
• Causal reasoning: You make cause-and-effect links between different things.
• Sign reasoning: You make a conclusion about a correlational relationship between different things.
• Analogical reasoning: You make a conclusion about something based on its similarities to something else.

Inductive reasoning is a bottom-up approach, while deductive reasoning is top-down.

Inductive reasoning takes you from the specific to the general, while in deductive reasoning, you make inferences by going from general premises to specific conclusions.

In inductive research , you start by making observations or gathering data. Then, you take a broad scan of your data and search for patterns. Finally, you make general conclusions that you might incorporate into theories.

Inductive reasoning is a method of drawing conclusions by going from the specific to the general. It’s usually contrasted with deductive reasoning, where you proceed from general information to specific conclusions.

Inductive reasoning is also called inductive logic or bottom-up reasoning.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Triangulation can help:

• Reduce research bias that comes from using a single method, theory, or investigator
• Enhance validity by approaching the same topic with different tools
• Establish credibility by giving you a complete picture of the research problem

But triangulation can also pose problems:

• It’s time-consuming and labor-intensive, often involving an interdisciplinary team.
• Your results may be inconsistent or even contradictory.

There are four main types of triangulation :

• Data triangulation : Using data from different times, spaces, and people
• Investigator triangulation : Involving multiple researchers in collecting or analyzing data
• Theory triangulation : Using varying theoretical perspectives in your research
• Methodological triangulation : Using different methodologies to approach the same topic

Many academic fields use peer review , largely to determine whether a manuscript is suitable for publication. Peer review enhances the credibility of the published manuscript.

However, peer review is also common in non-academic settings. The United Nations, the European Union, and many individual nations use peer review to evaluate grant applications. It is also widely used in medical and health-related fields as a teaching or quality-of-care measure. 

Peer assessment is often used in the classroom as a pedagogical tool. Both receiving feedback and providing it are thought to enhance the learning process, helping students think critically and collaboratively.

Peer review can stop obviously problematic, falsified, or otherwise untrustworthy research from being published. It also represents an excellent opportunity to get feedback from renowned experts in your field. It acts as a first defense, helping you ensure your argument is clear and that there are no gaps, vague terms, or unanswered questions for readers who weren’t involved in the research process.

Peer-reviewed articles are considered a highly credible source due to this stringent process they go through before publication.

In general, the peer review process follows the following steps: 

• First, the author submits the manuscript to the editor.
• Reject the manuscript and send it back to author, or 
• Send it onward to the selected peer reviewer(s) 
• Next, the peer review process occurs. The reviewer provides feedback, addressing any major or minor issues with the manuscript, and gives their advice regarding what edits should be made. 
• Lastly, the edited manuscript is sent back to the author. They input the edits, and resubmit it to the editor for publication.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Explanatory research is used to investigate how or why a phenomenon occurs. Therefore, this type of research is often one of the first stages in the research process , serving as a jumping-off point for future research.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Explanatory research is a research method used to investigate how or why something occurs when only a small amount of information is available pertaining to that topic. It can help you increase your understanding of a given topic.

Clean data are valid, accurate, complete, consistent, unique, and uniform. Dirty data include inconsistencies and errors.

Dirty data can come from any part of the research process, including poor research design , inappropriate measurement materials, or flawed data entry.

Data cleaning takes place between data collection and data analyses. But you can use some methods even before collecting data.

For clean data, you should start by designing measures that collect valid data. Data validation at the time of data entry or collection helps you minimize the amount of data cleaning you’ll need to do.

After data collection, you can use data standardization and data transformation to clean your data. You’ll also deal with any missing values, outliers, and duplicate values.

Every dataset requires different techniques to clean dirty data , but you need to address these issues in a systematic way. You focus on finding and resolving data points that don’t agree or fit with the rest of your dataset.

These data might be missing values, outliers, duplicate values, incorrectly formatted, or irrelevant. You’ll start with screening and diagnosing your data. Then, you’ll often standardize and accept or remove data to make your dataset consistent and valid.

Data cleaning is necessary for valid and appropriate analyses. Dirty data contain inconsistencies or errors , but cleaning your data helps you minimize or resolve these.

Without data cleaning, you could end up with a Type I or II error in your conclusion. These types of erroneous conclusions can be practically significant with important consequences, because they lead to misplaced investments or missed opportunities.

Data cleaning involves spotting and resolving potential data inconsistencies or errors to improve your data quality. An error is any value (e.g., recorded weight) that doesn’t reflect the true value (e.g., actual weight) of something that’s being measured.

In this process, you review, analyze, detect, modify, or remove “dirty” data to make your dataset “clean.” Data cleaning is also called data cleansing or data scrubbing.

Research misconduct means making up or falsifying data, manipulating data analyses, or misrepresenting results in research reports. It’s a form of academic fraud.

These actions are committed intentionally and can have serious consequences; research misconduct is not a simple mistake or a point of disagreement but a serious ethical failure.

Anonymity means you don’t know who the participants are, while confidentiality means you know who they are but remove identifying information from your research report. Both are important ethical considerations .

You can only guarantee anonymity by not collecting any personally identifying information—for example, names, phone numbers, email addresses, IP addresses, physical characteristics, photos, or videos.

You can keep data confidential by using aggregate information in your research report, so that you only refer to groups of participants rather than individuals.

Research ethics matter for scientific integrity, human rights and dignity, and collaboration between science and society. These principles make sure that participation in studies is voluntary, informed, and safe.

Ethical considerations in research are a set of principles that guide your research designs and practices. These principles include voluntary participation, informed consent, anonymity, confidentiality, potential for harm, and results communication.

Scientists and researchers must always adhere to a certain code of conduct when collecting data from others .

These considerations protect the rights of research participants, enhance research validity , and maintain scientific integrity.

In multistage sampling , you can use probability or non-probability sampling methods .

For a probability sample, you have to conduct probability sampling at every stage.

You can mix it up by using simple random sampling , systematic sampling , or stratified sampling to select units at different stages, depending on what is applicable and relevant to your study.

Multistage sampling can simplify data collection when you have large, geographically spread samples, and you can obtain a probability sample without a complete sampling frame.

But multistage sampling may not lead to a representative sample, and larger samples are needed for multistage samples to achieve the statistical properties of simple random samples .

These are four of the most common mixed methods designs :

• Convergent parallel: Quantitative and qualitative data are collected at the same time and analyzed separately. After both analyses are complete, compare your results to draw overall conclusions. 
• Embedded: Quantitative and qualitative data are collected at the same time, but within a larger quantitative or qualitative design. One type of data is secondary to the other.
• Explanatory sequential: Quantitative data is collected and analyzed first, followed by qualitative data. You can use this design if you think your qualitative data will explain and contextualize your quantitative findings.
• Exploratory sequential: Qualitative data is collected and analyzed first, followed by quantitative data. You can use this design if you think the quantitative data will confirm or validate your qualitative findings.

Triangulation in research means using multiple datasets, methods, theories and/or investigators to address a research question. It’s a research strategy that can help you enhance the validity and credibility of your findings.

Triangulation is mainly used in qualitative research , but it’s also commonly applied in quantitative research . Mixed methods research always uses triangulation.

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

No, the steepness or slope of the line isn’t related to the correlation coefficient value. The correlation coefficient only tells you how closely your data fit on a line, so two datasets with the same correlation coefficient can have very different slopes.

To find the slope of the line, you’ll need to perform a regression analysis .

Correlation coefficients always range between -1 and 1.

The sign of the coefficient tells you the direction of the relationship: a positive value means the variables change together in the same direction, while a negative value means they change together in opposite directions.

The absolute value of a number is equal to the number without its sign. The absolute value of a correlation coefficient tells you the magnitude of the correlation: the greater the absolute value, the stronger the correlation.

These are the assumptions your data must meet if you want to use Pearson’s r :

• Both variables are on an interval or ratio level of measurement
• Data from both variables follow normal distributions
• Your data have no outliers
• Your data is from a random or representative sample
• You expect a linear relationship between the two variables

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your sampling methods or criteria for selecting subjects
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

Questionnaires can be self-administered or researcher-administered.

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

The third variable and directionality problems are two main reasons why correlation isn’t causation .

The third variable problem means that a confounding variable affects both variables to make them seem causally related when they are not.

The directionality problem is when two variables correlate and might actually have a causal relationship, but it’s impossible to conclude which variable causes changes in the other.

Correlation describes an association between variables : when one variable changes, so does the other. A correlation is a statistical indicator of the relationship between variables.

Causation means that changes in one variable brings about changes in the other (i.e., there is a cause-and-effect relationship between variables). The two variables are correlated with each other, and there’s also a causal link between them.

While causation and correlation can exist simultaneously, correlation does not imply causation. In other words, correlation is simply a relationship where A relates to B—but A doesn’t necessarily cause B to happen (or vice versa). Mistaking correlation for causation is a common error and can lead to false cause fallacy .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

Random error  is almost always present in scientific studies, even in highly controlled settings. While you can’t eradicate it completely, you can reduce random error by taking repeated measurements, using a large sample, and controlling extraneous variables .

You can avoid systematic error through careful design of your sampling , data collection , and analysis procedures. For example, use triangulation to measure your variables using multiple methods; regularly calibrate instruments or procedures; use random sampling and random assignment ; and apply masking (blinding) where possible.

Systematic error is generally a bigger problem in research.

With random error, multiple measurements will tend to cluster around the true value. When you’re collecting data from a large sample , the errors in different directions will cancel each other out.

Systematic errors are much more problematic because they can skew your data away from the true value. This can lead you to false conclusions ( Type I and II errors ) about the relationship between the variables you’re studying.

Random and systematic error are two types of measurement error.

Random error is a chance difference between the observed and true values of something (e.g., a researcher misreading a weighing scale records an incorrect measurement).

Systematic error is a consistent or proportional difference between the observed and true values of something (e.g., a miscalibrated scale consistently records weights as higher than they actually are).

On graphs, the explanatory variable is conventionally placed on the x-axis, while the response variable is placed on the y-axis.

• If you have quantitative variables , use a scatterplot or a line graph.
• If your response variable is categorical, use a scatterplot or a line graph.
• If your explanatory variable is categorical, use a bar graph.

The term “ explanatory variable ” is sometimes preferred over “ independent variable ” because, in real world contexts, independent variables are often influenced by other variables. This means they aren’t totally independent.

Multiple independent variables may also be correlated with each other, so “explanatory variables” is a more appropriate term.

The difference between explanatory and response variables is simple:

• An explanatory variable is the expected cause, and it explains the results.
• A response variable is the expected effect, and it responds to other variables.

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

• A control group that receives a standard treatment, a fake treatment, or no treatment.
• Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

There are 4 main types of extraneous variables :

• Demand characteristics : environmental cues that encourage participants to conform to researchers’ expectations.
• Experimenter effects : unintentional actions by researchers that influence study outcomes.
• Situational variables : environmental variables that alter participants’ behaviors.
• Participant variables : any characteristic or aspect of a participant’s background that could affect study results.

An extraneous variable is any variable that you’re not investigating that can potentially affect the dependent variable of your research study.

A confounding variable is a type of extraneous variable that not only affects the dependent variable, but is also related to the independent variable.

In a factorial design, multiple independent variables are tested.

If you test two variables, each level of one independent variable is combined with each level of the other independent variable to create different conditions.

Within-subjects designs have many potential threats to internal validity , but they are also very statistically powerful .

Advantages:

• Only requires small samples
• Statistically powerful
• Removes the effects of individual differences on the outcomes

Disadvantages:

• Internal validity threats reduce the likelihood of establishing a direct relationship between variables
• Time-related effects, such as growth, can influence the outcomes
• Carryover effects mean that the specific order of different treatments affect the outcomes

While a between-subjects design has fewer threats to internal validity , it also requires more participants for high statistical power than a within-subjects design .

• Prevents carryover effects of learning and fatigue.
• Shorter study duration.
• Needs larger samples for high power.
• Uses more resources to recruit participants, administer sessions, cover costs, etc.
• Individual differences may be an alternative explanation for results.

Yes. Between-subjects and within-subjects designs can be combined in a single study when you have two or more independent variables (a factorial design). In a mixed factorial design, one variable is altered between subjects and another is altered within subjects.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

Random assignment is used in experiments with a between-groups or independent measures design. In this research design, there’s usually a control group and one or more experimental groups. Random assignment helps ensure that the groups are comparable.

In general, you should always use random assignment in this type of experimental design when it is ethically possible and makes sense for your study topic.

To implement random assignment , assign a unique number to every member of your study’s sample .

Then, you can use a random number generator or a lottery method to randomly assign each number to a control or experimental group. You can also do so manually, by flipping a coin or rolling a dice to randomly assign participants to groups.

Random selection, or random sampling , is a way of selecting members of a population for your study’s sample.

In contrast, random assignment is a way of sorting the sample into control and experimental groups.

Random sampling enhances the external validity or generalizability of your results, while random assignment improves the internal validity of your study.

In experimental research, random assignment is a way of placing participants from your sample into different groups using randomization. With this method, every member of the sample has a known or equal chance of being placed in a control group or an experimental group.

“Controlling for a variable” means measuring extraneous variables and accounting for them statistically to remove their effects on other variables.

Researchers often model control variable data along with independent and dependent variable data in regression analyses and ANCOVAs . That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Control variables help you establish a correlational or causal relationship between variables by enhancing internal validity .

If you don’t control relevant extraneous variables , they may influence the outcomes of your study, and you may not be able to demonstrate that your results are really an effect of your independent variable .

A control variable is any variable that’s held constant in a research study. It’s not a variable of interest in the study, but it’s controlled because it could influence the outcomes.

Including mediators and moderators in your research helps you go beyond studying a simple relationship between two variables for a fuller picture of the real world. They are important to consider when studying complex correlational or causal relationships.

Mediators are part of the causal pathway of an effect, and they tell you how or why an effect takes place. Moderators usually help you judge the external validity of your study by identifying the limitations of when the relationship between variables holds.

If something is a mediating variable :

• It’s caused by the independent variable .
• It influences the dependent variable
• When it’s taken into account, the statistical correlation between the independent and dependent variables is higher than when it isn’t considered.

A confounder is a third variable that affects variables of interest and makes them seem related when they are not. In contrast, a mediator is the mechanism of a relationship between two variables: it explains the process by which they are related.

A mediator variable explains the process through which two variables are related, while a moderator variable affects the strength and direction of that relationship.

There are three key steps in systematic sampling :

• Define and list your population , ensuring that it is not ordered in a cyclical or periodic order.
• Decide on your sample size and calculate your interval, k , by dividing your population by your target sample size.
• Choose every k th member of the population as your sample.

Systematic sampling is a probability sampling method where researchers select members of the population at a regular interval – for example, by selecting every 15th person on a list of the population. If the population is in a random order, this can imitate the benefits of simple random sampling .

Yes, you can create a stratified sample using multiple characteristics, but you must ensure that every participant in your study belongs to one and only one subgroup. In this case, you multiply the numbers of subgroups for each characteristic to get the total number of groups.

For example, if you were stratifying by location with three subgroups (urban, rural, or suburban) and marital status with five subgroups (single, divorced, widowed, married, or partnered), you would have 3 x 5 = 15 subgroups.

You should use stratified sampling when your sample can be divided into mutually exclusive and exhaustive subgroups that you believe will take on different mean values for the variable that you’re studying.

Using stratified sampling will allow you to obtain more precise (with lower variance ) statistical estimates of whatever you are trying to measure.

For example, say you want to investigate how income differs based on educational attainment, but you know that this relationship can vary based on race. Using stratified sampling, you can ensure you obtain a large enough sample from each racial group, allowing you to draw more precise conclusions.

In stratified sampling , researchers divide subjects into subgroups called strata based on characteristics that they share (e.g., race, gender, educational attainment).

Once divided, each subgroup is randomly sampled using another probability sampling method.

Cluster sampling is more time- and cost-efficient than other probability sampling methods , particularly when it comes to large samples spread across a wide geographical area.

However, it provides less statistical certainty than other methods, such as simple random sampling , because it is difficult to ensure that your clusters properly represent the population as a whole.

There are three types of cluster sampling : single-stage, double-stage and multi-stage clustering. In all three types, you first divide the population into clusters, then randomly select clusters for use in your sample.

• In single-stage sampling , you collect data from every unit within the selected clusters.
• In double-stage sampling , you select a random sample of units from within the clusters.
• In multi-stage sampling , you repeat the procedure of randomly sampling elements from within the clusters until you have reached a manageable sample.

Cluster sampling is a probability sampling method in which you divide a population into clusters, such as districts or schools, and then randomly select some of these clusters as your sample.

The clusters should ideally each be mini-representations of the population as a whole.

If properly implemented, simple random sampling is usually the best sampling method for ensuring both internal and external validity . However, it can sometimes be impractical and expensive to implement, depending on the size of the population to be studied,

If you have a list of every member of the population and the ability to reach whichever members are selected, you can use simple random sampling.

The American Community Survey  is an example of simple random sampling . In order to collect detailed data on the population of the US, the Census Bureau officials randomly select 3.5 million households per year and use a variety of methods to convince them to fill out the survey.

Simple random sampling is a type of probability sampling in which the researcher randomly selects a subset of participants from a population . Each member of the population has an equal chance of being selected. Data is then collected from as large a percentage as possible of this random subset.

Quasi-experimental design is most useful in situations where it would be unethical or impractical to run a true experiment .

Quasi-experiments have lower internal validity than true experiments, but they often have higher external validity  as they can use real-world interventions instead of artificial laboratory settings.

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Blinding is important to reduce research bias (e.g., observer bias , demand characteristics ) and ensure a study’s internal validity .

If participants know whether they are in a control or treatment group , they may adjust their behavior in ways that affect the outcome that researchers are trying to measure. If the people administering the treatment are aware of group assignment, they may treat participants differently and thus directly or indirectly influence the final results.

• In a single-blind study , only the participants are blinded.
• In a double-blind study , both participants and experimenters are blinded.
• In a triple-blind study , the assignment is hidden not only from participants and experimenters, but also from the researchers analyzing the data.

Blinding means hiding who is assigned to the treatment group and who is assigned to the control group in an experiment .

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

In scientific research, concepts are the abstract ideas or phenomena that are being studied (e.g., educational achievement). Variables are properties or characteristics of the concept (e.g., performance at school), while indicators are ways of measuring or quantifying variables (e.g., yearly grade reports).

The process of turning abstract concepts into measurable variables and indicators is called operationalization .

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

A confounding variable is closely related to both the independent and dependent variables in a study. An independent variable represents the supposed cause , while the dependent variable is the supposed effect . A confounding variable is a third variable that influences both the independent and dependent variables.

Failing to account for confounding variables can cause you to wrongly estimate the relationship between your independent and dependent variables.

To ensure the internal validity of your research, you must consider the impact of confounding variables. If you fail to account for them, you might over- or underestimate the causal relationship between your independent and dependent variables , or even find a causal relationship where none exists.

Yes, but including more than one of either type requires multiple research questions .

For example, if you are interested in the effect of a diet on health, you can use multiple measures of health: blood sugar, blood pressure, weight, pulse, and many more. Each of these is its own dependent variable with its own research question.

You could also choose to look at the effect of exercise levels as well as diet, or even the additional effect of the two combined. Each of these is a separate independent variable .

To ensure the internal validity of an experiment , you should only change one independent variable at a time.

No. The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both!

You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .

• The type of soda – diet or regular – is the independent variable .
• The level of blood sugar that you measure is the dependent variable – it changes depending on the type of soda.

Determining cause and effect is one of the most important parts of scientific research. It’s essential to know which is the cause – the independent variable – and which is the effect – the dependent variable.

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

Using careful research design and sampling procedures can help you avoid sampling bias . Oversampling can be used to correct undercoverage bias .

Some common types of sampling bias include self-selection bias , nonresponse bias , undercoverage bias , survivorship bias , pre-screening or advertising bias, and healthy user bias.

Sampling bias is a threat to external validity – it limits the generalizability of your findings to a broader group of people.

A sampling error is the difference between a population parameter and a sample statistic .

A statistic refers to measures about the sample , while a parameter refers to measures about the population .

Populations are used when a research question requires data from every member of the population. This is usually only feasible when the population is small and easily accessible.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

There are seven threats to external validity : selection bias , history, experimenter effect, Hawthorne effect , testing effect, aptitude-treatment and situation effect.

The two types of external validity are population validity (whether you can generalize to other groups of people) and ecological validity (whether you can generalize to other situations and settings).

The external validity of a study is the extent to which you can generalize your findings to different groups of people, situations, and measures.

Cross-sectional studies cannot establish a cause-and-effect relationship or analyze behavior over a period of time. To investigate cause and effect, you need to do a longitudinal study or an experimental study .

Cross-sectional studies are less expensive and time-consuming than many other types of study. They can provide useful insights into a population’s characteristics and identify correlations for further research.

Sometimes only cross-sectional data is available for analysis; other times your research question may only require a cross-sectional study to answer it.

Longitudinal studies can last anywhere from weeks to decades, although they tend to be at least a year long.

The 1970 British Cohort Study , which has collected data on the lives of 17,000 Brits since their births in 1970, is one well-known example of a longitudinal study .

Longitudinal studies are better to establish the correct sequence of events, identify changes over time, and provide insight into cause-and-effect relationships, but they also tend to be more expensive and time-consuming than other types of studies.

Longitudinal studies and cross-sectional studies are two different types of research design . In a cross-sectional study you collect data from a population at a specific point in time; in a longitudinal study you repeatedly collect data from the same sample over an extended period of time.

There are eight threats to internal validity : history, maturation, instrumentation, testing, selection bias , regression to the mean, social interaction and attrition .

Internal validity is the extent to which you can be confident that a cause-and-effect relationship established in a study cannot be explained by other factors.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

Discrete and continuous variables are two types of quantitative variables :

• Discrete variables represent counts (e.g. the number of objects in a collection).
• Continuous variables represent measurable amounts (e.g. water volume or weight).

Quantitative variables are any variables where the data represent amounts (e.g. height, weight, or age).

Categorical variables are any variables where the data represent groups. This includes rankings (e.g. finishing places in a race), classifications (e.g. brands of cereal), and binary outcomes (e.g. coin flips).

You need to know what type of variables you are working with to choose the right statistical test for your data and interpret your results .

You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause , while a dependent variable is the effect .

In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. For example, in an experiment about the effect of nutrients on crop growth:

• The  independent variable  is the amount of nutrients added to the crop field.
• The  dependent variable is the biomass of the crops at harvest time.

Defining your variables, and deciding how you will manipulate and measure them, is an important part of experimental design .

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

I nternal validity is the degree of confidence that the causal relationship you are testing is not influenced by other factors or variables .

External validity is the extent to which your results can be generalized to other contexts.

The validity of your experiment depends on your experimental design .

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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Experimental Group

Reviewed by: BD Editors

Experimental Group Definition

In a comparative experiment, the experimental group (aka the treatment group) is the group being tested for a reaction to a change in the variable. There may be experimental groups in a study, each testing a different level or amount of the variable. The other type of group, the control group , can show the effects of the variable by having a set amount, or none, of the variable. The experimental groups vary in the level of variable they are exposed to, which shows the effects of various levels of a variable on similar organisms.

In biological experiments, the subjects being studied are often living organisms. In such cases, it is desirable that all the subjects be closely related, in order to reduce the amount of genetic variation present in the experiment. The complicated interactions between genetics and the environment can cause very peculiar results when exposed to the same variable. If the organisms being tested are not related, the results could be the effects of the genetics and not the variable. This is why new human drugs must be rigorously tested in a variety of animals before they can be tested on humans. These different experimental groups allow researchers to see the effects of their drug on different genetics. By using animals that are closer and closer in their relation to humans, eventually human trials can take place without severe risks for the first people to try the drug.

Examples of Experimental Group

A simple experiment.

A student is conducting an experiment on the effects music has on growing plants. The student wants to know if music can help plants grow and, if so, which type of music the plants prefer. The students divide a group of plants in to two main groups, the control group and the experimental group. The control group will be kept in a room with no music, while the experimental group will be further divided into smaller experimental groups. Each of the experimental groups is placed in a separate room, with a different type of music.

Ideally, each room would have many plants in it, and all the plants used in the experiment would be clones of the same plant. Even more ideally, the plant would breed true, or would be homozygous for all genes. This would introduce the smallest amount of genetic variation into the experiment. By limiting all other variables, such as the temperature and humidity, the experiment can determine with validity that the effects produced in each room are attributable to the music, and nothing else.

Bugs in the River

To study the effects of variable on many organisms at once, scientist sometimes study ecosystems as a whole. The productivity of these ecosystems is often determined by the amount of oxygen they produce, which is an indication of how much algae is present. Ecologists sometimes study the interactions of organisms on these environments by excluding or adding organisms to an experimental group of ecosystems, and test the effects of their variable against ecosystems with no tampering. This method can sometimes show the drastic effects that various organisms have on an ecosystem.

Many experiments of this kind take place, and a common theme is to separate a single ecosystem into parts, with artificial divisions. Thus, a river could be separated by netting it into areas with and without bugs. The area with no nets allows bugs into the water. The bugs not only eat algae, but die and provide nutrients for the algae to grow. Without the bugs, various effects can be seen on the experimental portion of the river, covered by netting. The levels of oxygen in the water in each system can be measured, as well as other indicators of water quality. By comparing these groups, ecologists can begin to discern the complex relationships between populations of organisms in the environment.

Related Biology Terms

• Control Group – The group that remains unchanged during the experiment, to provide comparison.
• Scientific Method – The process scientists use to obtain valid, repeatable results.
• Comparative Experiment – An experiment in which two groups, the control and experiment groups, are compared.
• Validity – A measure of whether an experiment was caused by the changes in the variable, or simply the forces of chance.

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Major Differences

5 differences between control group and experimental group with example.

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• Published: 02 May 2024

Effectiveness of social media-assisted course on learning self-efficacy

• Jiaying Hu 1 ,
• Yicheng Lai 2 &
• Xiuhua Yi 3  

Scientific Reports volume  14 , Article number:  10112 ( 2024 ) Cite this article

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• Human behaviour

The social media platform and the information dissemination revolution have changed the thinking, needs, and methods of students, bringing development opportunities and challenges to higher education. This paper introduces social media into the classroom and uses quantitative analysis to investigate the relation between design college students’ learning self-efficacy and social media for design students, aiming to determine the effectiveness of social media platforms on self-efficacy. This study is conducted on university students in design media courses and is quasi-experimental, using a randomized pre-test and post-test control group design. The study participants are 73 second-year design undergraduates. Independent samples t-tests showed that the network interaction factors of social media had a significant impact on college students learning self-efficacy. The use of social media has a significant positive predictive effect on all dimensions of learning self-efficacy. Our analysis suggests that using the advantages and value of online social platforms, weakening the disadvantages of the network, scientifically using online learning resources, and combining traditional classrooms with the Internet can improve students' learning self-efficacy.

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Introduction.

Social media is a way of sharing information, ideas, and opinions with others one. It can be used to create relationships between people and businesses. Social media has changed the communication way, it’s no longer just about talking face to face but also using a digital platform such as Facebook or Twitter. Today, social media is becoming increasingly popular in everyone's lives, including students and researchers 1 . Social media provides many opportunities for learners to publish their work globally, bringing many benefits to teaching and learning. The publication of students' work online has led to a more positive attitude towards learning and increased achievement and motivation. Other studies report that student online publications or work promote reflection on personal growth and development and provide opportunities for students to imagine more clearly the purpose of their work 2 . In addition, learning environments that include student publications allow students to examine issues differently, create new connections, and ultimately form new entities that can be shared globally 3 , 4 .

Learning self-efficacy is a belief that you can learn something new. It comes from the Latin word “self” and “efficax” which means efficient or effective. Self-efficacy is based on your beliefs about yourself, how capable you are to learn something new, and your ability to use what you have learned in real-life situations. This concept was first introduced by Bandura (1977), who studied the effects of social reinforcement on children’s learning behavior. He found that when children were rewarded for their efforts they would persist longer at tasks that they did not like or had low interest in doing. Social media, a ubiquitous force in today's digital age, has revolutionized the way people interact and share information. With the rise of social media platforms, individuals now have access to a wealth of online resources that can enhance their learning capabilities. This access to information and communication has also reshaped the way students approach their studies, potentially impacting their learning self-efficacy. Understanding the role of social media in shaping students' learning self-efficacy is crucial in providing effective educational strategies that promote healthy learning and development 5 . Unfortunately, the learning curve for the associated metadata base modeling methodologies and their corresponding computer-aided software engineering (CASE) tools have made it difficult for students to grasp. Addressing this learning issue examined the effect of this MLS on the self-efficacy of learning these topics 6 . Bates et al. 7 hypothesize a mediated model in which a set of antecedent variables influenced students’ online learning self-efficacy which, in turn, affected student outcome expectations, mastery perceptions, and the hours spent per week using online learning technology to complete learning assignments for university courses. Shen et al. 8 through exploratory factor analysis identifies five dimensions of online learning self-efficacy: (a) self-efficacy to complete an online course (b) self-efficacy to interact socially with classmates (c) self-efficacy to handle tools in a Course Management System (CMS) (d) self-efficacy to interact with instructors in an online course, and (e) self-efficacy to interact with classmates for academic purposes. Chiu 9 established a model for analyzing the mediating effect that learning self-efficacy and social self-efficacy have on the relationship between university students’ perceived life stress and smartphone addiction. Kim et al. 10 study was conducted to examine the influence of learning efficacy on nursing students' self-confidence. The objective of Paciello et al. 11 was to identify self-efficacy configurations in different domains (i.e., emotional, social, and self-regulated learning) in a sample of university students using a person-centered approach. The role of university students’ various conceptions of learning in their academic self-efficacy in the domain of physics is initially explored 12 . Kumar et al. 13 investigated factors predicting students’ behavioral intentions towards the continuous use of mobile learning. Other influential work includes 14 .

Many studies have focused on social networking tools such as Facebook and MySpace 15 , 16 . Teachers are concerned that the setup and use of social media apps take up too much of their time, may have plagiarism and privacy issues, and contribute little to actual student learning outcomes; they often consider them redundant or simply not conducive to better learning outcomes 17 . Cao et al. 18 proposed that the central questions in addressing the positive and negative pitfalls of social media on teaching and learning are whether the use of social media in teaching and learning enhances educational effectiveness, and what motivates university teachers to use social media in teaching and learning. Maloney et al. 3 argued that social media can further improve the higher education teaching and learning environment, where students no longer access social media to access course information. Many studies in the past have shown that the use of modern IT in the classroom has increased over the past few years; however, it is still limited mainly to content-driven use, such as accessing course materials, so with the emergence of social media in students’ everyday lives 2 , we need to focus on developing students’ learning self-efficacy so that they can This will enable students to 'turn the tables and learn to learn on their own. Learning self-efficacy is considered an important concept that has a powerful impact on learning outcomes 19 , 20 .

Self-efficacy for learning is vital in teaching students to learn and develop healthily and increasing students' beliefs in the learning process 21 . However, previous studies on social media platforms such as Twitter and Weibo as curriculum support tools have not been further substantiated or analyzed in detail. In addition, the relationship between social media, higher education, and learning self-efficacy has not yet been fully explored by researchers in China. Our research aims to fill this gap in the topic. Our study explored the impact of social media on the learning self-efficacy of Chinese college students. Therefore, it is essential to explore the impact of teachers' use of social media to support teaching and learning on students' learning self-efficacy. Based on educational theory and methodological practice, this study designed a teaching experiment using social media to promote learning self-efficacy by posting an assignment for post-course work on online media to explore the actual impact of social media on university students’ learning self-efficacy. This study examines the impact of a social media-assisted course on university students' learning self-efficacy to explore the positive impact of a social media-assisted course.

Theoretical background

• Social media

Social media has different definitions. Mayfield (2013) first introduced the concept of social media in his book-what is social media? The author summarized the six characteristics of social media: openness, participation, dialogue, communication, interaction, and communication. Mayfield 22 shows that social media is a kind of new media. Its uniqueness is that it can give users great space and freedom to participate in the communication process. Jen (2020) also suggested that the distinguishing feature of social media is that it is “aggregated”. Social media provides users with an interactive service to control their data and information and collaborate and share information 2 . Social media offers opportunities for students to build knowledge and helps them actively create and share information 23 . Millennial students are entering higher education institutions and are accustomed to accessing and using data from the Internet. These individuals go online daily for educational or recreational purposes. Social media is becoming increasingly popular in the lives of everyone, including students and researchers 1 . A previous study has shown that millennials use the Internet as their first source of information and Google as their first choice for finding educational and personal information 24 . Similarly, many institutions encourage teachers to adopt social media applications 25 . Faculty members have also embraced social media applications for personal, professional, and pedagogical purposes 17 .

Social networks allow one to create a personal profile and build various networks that connect him/her to family, friends, and other colleagues. Users use these sites to stay in touch with their friends, make plans, make new friends, or connect with someone online. Therefore, extending this concept, these sites can establish academic connections or promote cooperation and collaboration in higher education classrooms 2 . This study defines social media as an interactive community of users' information sharing and social activities built on the technology of the Internet. Because the concept of social media is broad, its connotations are consistent. Research shows that Meaning and Linking are the two key elements that make up social media existence. Users and individual media outlets generate social media content and use it as a platform to get it out there. Social media distribution is based on social relationships and has a better platform for personal information and relationship management systems. Examples of social media applications include Facebook, Twitter, MySpace, YouTube, Flickr, Skype, Wiki, blogs, Delicious, Second Life, open online course sites, SMS, online games, mobile applications, and more 18 . Ajjan and Hartshorne 2 investigated the intentions of 136 faculty members at a US university to adopt Web 2.0 technologies as tools in their courses. They found that integrating Web 2.0 technologies into the classroom learning environment effectively increased student satisfaction with the course and improved their learning and writing skills. His research focused on improving the perceived usefulness, ease of use, compatibility of Web 2.0 applications, and instructor self-efficacy. The social computing impact of formal education and training and informal learning communities suggested that learning web 2.0 helps users to acquire critical competencies, and promotes technological, pedagogical, and organizational innovation, arguing that social media has a variety of learning content 26 . Users can post digital content online, enabling learners to tap into tacit knowledge while supporting collaboration between learners and teachers. Cao and Hong 27 investigated the antecedents and consequences of social media use in teaching among 249 full-time and part-time faculty members, who reported that the factors for using social media in teaching included personal social media engagement and readiness, external pressures; expected benefits; and perceived risks. The types of Innovators, Early adopters, Early majority, Late majority, Laggards, and objectors. Cao et al. 18 studied the educational effectiveness of 168 teachers' use of social media in university teaching. Their findings suggest that social media use has a positive impact on student learning outcomes and satisfaction. Their research model provides educators with ideas on using social media in the education classroom to improve student performance. Maqableh et al. 28 investigated the use of social networking sites by 366 undergraduate students, and they found that weekly use of social networking sites had a significant impact on student's academic performance and that using social networking sites had a significant impact on improving students' effective time management, and awareness of multitasking. All of the above studies indicate the researcher’s research on social media aids in teaching and learning. All of these studies indicate the positive impact of social media on teaching and learning.

• Learning self-efficacy

For the definition of concepts related to learning self-efficacy, scholars have mainly drawn on the idea proposed by Bandura 29 that defines self-efficacy as “the degree to which people feel confident in their ability to use the skills they possess to perform a task”. Self-efficacy is an assessment of a learner’s confidence in his or her ability to use the skills he or she possesses to complete a learning task and is a subjective judgment and feeling about the individual’s ability to control his or her learning behavior and performance 30 . Liu 31 has defined self-efficacy as the belief’s individuals hold about their motivation to act, cognitive ability, and ability to perform to achieve their goals, showing the individual's evaluation and judgment of their abilities. Zhang (2015) showed that learning efficacy is regarded as the degree of belief and confidence that expresses the success of learning. Yan 32 showed the extent to which learning self-efficacy is viewed as an individual. Pan 33 suggested that learning self-efficacy in an online learning environment is a belief that reflects the learner's ability to succeed in the online learning process. Kang 34 believed that learning self-efficacy is the learner's confidence and belief in his or her ability to complete a learning task. Huang 35 considered self-efficacy as an individual’s self-assessment of his or her ability to complete a particular task or perform a specific behavior and the degree of confidence in one’s ability to achieve a specific goal. Kong 36 defined learning self-efficacy as an individual’s judgment of one’s ability to complete academic tasks.

Based on the above analysis, we found that scholars' focus on learning self-efficacy is on learning behavioral efficacy and learning ability efficacy, so this study divides learning self-efficacy into learning behavioral efficacy and learning ability efficacy for further analysis and research 37 , 38 . Search the CNKI database and ProQuest Dissertations for keywords such as “design students’ learning self-efficacy”, “design classroom self-efficacy”, “design learning self-efficacy”, and other keywords. There are few relevant pieces of literature about design majors. Qiu 39 showed that mobile learning-assisted classroom teaching can control the source of self-efficacy from many aspects, thereby improving students’ sense of learning efficacy and helping middle and lower-level students improve their sense of learning efficacy from all dimensions. Yin and Xu 40 argued that the three elements of the network environment—“learning content”, “learning support”, and “social structure of learning”—all have an impact on university students’ learning self-efficacy. Duo et al. 41 recommend that learning activities based on the mobile network learning community increase the trust between students and the sense of belonging in the learning community, promote mutual communication and collaboration between students, and encourage each other to stimulate their learning motivation. In the context of social media applications, self-efficacy refers to the level of confidence that teachers can successfully use social media applications in the classroom 18 . Researchers have found that self-efficacy is related to social media applications 42 . Students had positive experiences with social media applications through content enhancement, creativity experiences, connectivity enrichment, and collaborative engagement 26 . Students who wish to communicate with their tutors in real-time find social media tools such as web pages, blogs, and virtual interactions very satisfying 27 . Overall, students report their enjoyment of different learning processes through social media applications; simultaneously, they show satisfactory tangible achievement of tangible learning outcomes 18 . According to Bandura's 'triadic interaction theory’, Bian 43 and Shi 44 divided learning self-efficacy into two main elements, basic competence, and control, where basic competence includes the individual's sense of effort, competence, the individual sense of the environment, and the individual's sense of control over behavior. The primary sense of competence includes the individual's Sense of effort, competence, environment, and control over behavior. In this study, learning self-efficacy is divided into Learning behavioral efficacy and Learning ability efficacy. Learning behavioral efficacy includes individuals' sense of effort, environment, and control; learning ability efficacy includes individuals' sense of ability, belief, and interest.

In Fig.  1 , learning self-efficacy includes learning behavior efficacy and learning ability efficacy, in which the learning behavior efficacy is determined by the sense of effort, the sense of environment, the sense of control, and the learning ability efficacy is determined by the sense of ability, sense of belief, sense of interest. “Sense of effort” is the understanding of whether one can study hard. Self-efficacy includes the estimation of self-effort and the ability, adaptability, and creativity shown in a particular situation. One with a strong sense of learning self-efficacy thinks they can study hard and focus on tasks 44 . “Sense of environment” refers to the individual’s feeling of their learning environment and grasp of the environment. The individual is the creator of the environment. A person’s feeling and grasp of the environment reflect the strength of his sense of efficacy to some extent. A person with a shared sense of learning self-efficacy is often dissatisfied with his environment, but he cannot do anything about it. He thinks the environment can only dominate him. A person with a high sense of learning self-efficacy will be more satisfied with his school and think that his teachers like him and are willing to study in school 44 . “Sense of control” is an individual’s sense of control over learning activities and learning behavior. It includes the arrangement of individual learning time, whether they can control themselves from external interference, and so on. A person with a strong sense of self-efficacy will feel that he is the master of action and can control the behavior and results of learning. Such a person actively participates in various learning activities. When he encounters difficulties in learning, he thinks he can find a way to solve them, is not easy to be disturbed by the outside world, and can arrange his own learning time. The opposite is the sense of losing control of learning behavior 44 . “Sense of ability” includes an individual’s perception of their natural abilities, expectations of learning outcomes, and perception of achieving their learning goals. A person with a high sense of learning self-efficacy will believe that he or she is brighter and more capable in all areas of learning; that he or she is more confident in learning in all subjects. In contrast, people with low learning self-efficacy have a sense of powerlessness. They are self-doubters who often feel overwhelmed by their learning and are less confident that they can achieve the appropriate learning goals 44 . “Sense of belief” is when an individual knows why he or she is doing something, knows where he or she is going to learn, and does not think before he or she even does it: What if I fail? These are meaningless, useless questions. A person with a high sense of learning self-efficacy is more robust, less afraid of difficulties, and more likely to reach their learning goals. A person with a shared sense of learning self-efficacy, on the other hand, is always going with the flow and is uncertain about the outcome of their learning, causing them to fall behind. “Sense of interest” is a person's tendency to recognize and study the psychological characteristics of acquiring specific knowledge. It is an internal force that can promote people's knowledge and learning. It refers to a person's positive cognitive tendency and emotional state of learning. A person with a high sense of self-efficacy in learning will continue to concentrate on studying and studying, thereby improving learning. However, one with low learning self-efficacy will have psychology such as not being proactive about learning, lacking passion for learning, and being impatient with learning. The elements of learning self-efficacy can be quantified and detailed in the following Fig.  1 .

Learning self-efficacy research structure in this paper.

Research participants

All the procedures were conducted in adherence to the guidelines and regulations set by the institution. Prior to initiating the study, informed consent was obtained in writing from the participants, and the Institutional Review Board for Behavioral and Human Movement Sciences at Nanning Normal University granted approval for all protocols.

Two parallel classes are pre-selected as experimental subjects in our study, one as the experimental group and one as the control group. Social media assisted classroom teaching to intervene in the experimental group, while the control group did not intervene. When selecting the sample, it is essential to consider, as far as possible, the shortcomings of not using randomization to select or assign the study participants, resulting in unequal experimental and control groups. When selecting the experimental subjects, classes with no significant differences in initial status and external conditions, i.e. groups with homogeneity, should be selected. Our study finally decided to select a total of 44 students from Class 2021 Design 1 and a total of 29 students from Class 2021 Design 2, a total of 74 students from Nanning Normal University, as the experimental subjects. The former served as the experimental group, and the latter served as the control group. 73 questionnaires are distributed to measure before the experiment, and 68 are returned, with a return rate of 93.15%. According to the statistics, there were 8 male students and 34 female students in the experimental group, making a total of 44 students (mirrors the demographic trends within the humanities and arts disciplines from which our sample was drawn); there are 10 male students and 16 female students in the control group, making a total of 26 students, making a total of 68 students in both groups. The sample of those who took the course were mainly sophomores, with a small number of first-year students and juniors, which may be related to the nature of the subject of this course and the course system offered by the university. From the analysis of students' majors, liberal arts students in the experimental group accounted for the majority, science students and art students accounted for a small part. In contrast, the control group had more art students, and liberal arts students and science students were small. In the daily self-study time, the experimental and control groups are 2–3 h. The demographic information of research participants is shown in Table 1 .

Research procedure

Firstly, the ADDIE model is used for the innovative design of the teaching method of the course. The number of students in the experimental group was 44, 8 male and 35 females; the number of students in the control group was 29, 10 male and 19 females. Secondly, the classes are targeted at students and applied. Thirdly, the course for both the experimental and control classes is a convenient and practice-oriented course, with the course title “Graphic Design and Production”, which focuses on learning the graphic design software Photoshop. The course uses different cases to explain in detail the process and techniques used to produce these cases using Photoshop, and incorporates practical experience as well as relevant knowledge in the process, striving to achieve precise and accurate operational steps; at the end of the class, the teacher assigns online assignments to be completed on social media, allowing students to post their edited software tutorials online so that students can master the software functions. The teacher assigns online assignments to be completed on social media at the end of the lesson, allowing students to post their editing software tutorials online so that they can master the software functions and production skills, inspire design inspiration, develop design ideas and improve their design skills, and improve students' learning self-efficacy through group collaboration and online interaction. Fourthly, pre-tests and post-tests are conducted in the experimental and control classes before the experiment. Fifthly, experimental data are collected, analyzed, and summarized.

We use a questionnaire survey to collect data. Self-efficacy is a person’s subjective judgment on whether one can successfully perform a particular achievement. American psychologist Albert Bandura first proposed it. To understand the improvement effect of students’ self-efficacy after the experimental intervention, this work questionnaire was referenced by the author from “Self-efficacy” “General Perceived Self Efficacy Scale” (General Perceived Self Efficacy Scale) German psychologist Schwarzer and Jerusalem (1995) and “Academic Self-Efficacy Questionnaire”, a well-known Chinese scholar Liang 45 .  The questionnaire content is detailed in the supplementary information . A pre-survey of the questionnaire is conducted here. The second-year students of design majors collected 32 questionnaires, eliminated similar questions based on the data, and compiled them into a formal survey scale. The scale consists of 54 items, 4 questions about basic personal information, and 50 questions about learning self-efficacy. The Likert five-point scale is the questionnaire used in this study. The answers are divided into “completely inconsistent", “relatively inconsistent”, “unsure”, and “relatively consistent”. The five options of “Completely Meet” and “Compliant” will count as 1, 2, 3, 4, and 5 points, respectively. Divided into a sense of ability (Q5–Q14), a sense of effort (Q15–Q20), a sense of environment (Q21–Q28), a sense of control (Q29–Q36), a sense of Interest (Q37–Q45), a sense of belief (Q46–Q54). To demonstrate the scientific effectiveness of the experiment, and to further control the influence of confounding factors on the experimental intervention. This article thus sets up a control group as a reference. Through the pre-test and post-test in different periods, comparison of experimental data through pre-and post-tests to illustrate the effects of the intervention.

Reliability indicates the consistency of the results of a measurement scale (See Table 2 ). It consists of intrinsic and extrinsic reliability, of which intrinsic reliability is essential. Using an internal consistency reliability test scale, a Cronbach's alpha coefficient of reliability statistics greater than or equal to 0.9 indicates that the scale has good reliability, 0.8–0.9 indicates good reliability, 7–0.8 items are acceptable. Less than 0.7 means to discard some items in the scale 46 . This study conducted a reliability analysis on the effects of the related 6-dimensional pre-test survey to illustrate the reliability of the questionnaire.

From the Table 2 , the Cronbach alpha coefficients for the pre-test, sense of effort, sense of environment, sense of control, sense of interest, sense of belief, and the total questionnaire, were 0.919, 0.839, 0.848, 0.865, 0.852, 0.889 and 0.958 respectively. The post-test Cronbach alpha coefficients were 0.898, 0.888, 0.886, 0.889, 0.900, 0.893 and 0.970 respectively. The Cronbach alpha coefficients were all greater than 0.8, indicating a high degree of reliability of the measurement data.

The validity, also known as accuracy, reflects how close the measurement result is to the “true value”. Validity includes structure validity, content validity, convergent validity, and discriminative validity. Because the experiment is a small sample study, we cannot do any specific factorization. KMO and Bartlett sphericity test values are an important part of structural validity. Indicator, general validity evaluation (KMO value above 0.9, indicating very good validity; 0.8–0.9, indicating good validity; 0.7–0.8 validity is good; 0.6–0.7 validity is acceptable; 0.5–0.6 means poor validity; below 0.45 means that some items should be abandoned.

Table 3 shows that the KMO values of ability, effort, environment, control, interest, belief, and the total questionnaire are 0.911, 0.812, 0.778, 0.825, 0.779, 0.850, 0.613, and the KMO values of the post-test are respectively. The KMO values are 0.887, 0.775, 0.892, 0.868, 0.862, 0.883, 0.715. KMO values are basically above 0.8, and all are greater than 0.6. This result indicates that the validity is acceptable, the scale has a high degree of reasonableness, and the valid data.

In the graphic design and production (professional design course), we will learn the practical software with cases. After class, we will share knowledge on the self-media platform. We will give face-to-face computer instruction offline from 8:00 to 11:20 every Wednesday morning for 16 weeks. China's top online sharing platform (APP) is Tik Tok, micro-blog (Micro Blog) and Xiao hong shu. The experiment began on September 1, 2022, and conducted the pre-questionnaire survey simultaneously. At the end of the course, on January 6, 2023, the post questionnaire survey was conducted. A total of 74 questionnaires were distributed in this study, recovered 74 questionnaires. After excluding the invalid questionnaires with incomplete filling and wrong answers, 68 valid questionnaires were obtained, with an effective rate of 91%, meeting the test requirements. Then, use the social science analysis software SPSS Statistics 26 to analyze the data: (1) descriptive statistical analysis of the dimensions of learning self-efficacy; (2) Using correlation test to analyze the correlation between learning self-efficacy and the use of social media; (3) This study used a comparative analysis of group differences to detect the influence of learning self-efficacy on various dimensions of social media and design courses. For data processing and analysis, use the spss26 version software and frequency statistics to create statistics on the basic situation of the research object and the basic situation of the use of live broadcast. The reliability scale analysis (internal consistency test) and use Bartlett's sphericity test to illustrate the reliability and validity of the questionnaire and the individual differences between the control group and the experimental group in demographic variables (gender, grade, Major, self-study time per day) are explained by cross-analysis (chi-square test). In the experimental group and the control group, the pre-test, post-test, before-and-after test of the experimental group and the control group adopt independent sample T-test and paired sample T-test to illustrate the effect of the experimental intervention (The significance level of the test is 0.05 two-sided).

Results and discussion

Comparison of pre-test and post-test between groups.

To study whether the data of the experimental group and the control group are significantly different in the pre-test and post-test mean of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. The research for this situation uses an independent sample T-test and an independent sample. The test needs to meet some false parameters, such as normality requirements. Generally passing the normality test index requirements are relatively strict, so it can be relaxed to obey an approximately normal distribution. If there is serious skewness distribution, replace it with the nonparametric test. Variables are required to be continuous variables. The six variables in this study define continuous variables. The variable value information is independent of each other. Therefore, we use the independent sample T-test.

From the Table 4 , a pre-test found that there was no statistically significant difference between the experimental group and the control group at the 0.05 confidence level ( p  > 0.05) for perceptions of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. Before the experiment, the two groups of test groups have the same quality in measuring self-efficacy. The experimental class and the control class are homogeneous groups. Table 5 shows the independent samples t-test for the post-test, used to compare the experimental and control groups on six items, including the sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief.

The experimental and control groups have statistically significant scores ( p  < 0.05) for sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief, and the experimental and control groups have statistically significant scores (t = 3.177, p  = 0.002) for a sense of competence. (t = 3.177, p  = 0.002) at the 0.01 level, with the experimental group scoring significantly higher (3.91 ± 0.51) than the control group (3.43 ± 0.73). The experimental group and the control group showed significance for the perception of effort at the 0.01 confidence level (t = 2.911, p  = 0.005), with the experimental group scoring significantly higher (3.88 ± 0.66) than the control group scoring significantly higher (3.31 ± 0.94). The experimental and control groups show significance at the 0.05 level (t = 2.451, p  = 0.017) for the sense of environment, with the experimental group scoring significantly higher (3.95 ± 0.61) than the control group scoring significantly higher (3.58 ± 0.62). The experimental and control groups showed significance for sense of control at the 0.05 level of significance (t = 2.524, p  = 0.014), and the score for the experimental group (3.76 ± 0.67) would be significantly higher than the score for the control group (3.31 ± 0.78). The experimental and control groups showed significance at the 0.01 level for sense of interest (t = 2.842, p  = 0.006), and the experimental group's score (3.87 ± 0.61) would be significantly higher than the control group's score (3.39 ± 0.77). The experimental and control groups showed significance at the 0.01 level for the sense of belief (t = 3.377, p  = 0.001), and the experimental group would have scored significantly higher (4.04 ± 0.52) than the control group (3.56 ± 0.65). Therefore, we can conclude that the experimental group's post-test significantly affects the mean scores of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. A social media-assisted course has a positive impact on students' self-efficacy.

Comparison of pre-test and post-test of each group

The paired-sample T-test is an extension of the single-sample T-test. The purpose is to explore whether the means of related (paired) groups are significantly different. There are four standard paired designs: (1) Before and after treatment of the same subject Data, (2) Data from two different parts of the same subject, (3) Test results of the same sample with two methods or instruments, 4. Two matched subjects receive two treatments, respectively. This study belongs to the first type, the 6 learning self-efficacy dimensions of the experimental group and the control group is measured before and after different periods.

Paired t-tests is used to analyze whether there is a significant improvement in the learning self-efficacy dimension in the experimental group after the experimental social media-assisted course intervention. In Table 6 , we can see that the six paired data groups showed significant differences ( p  < 0.05) in the pre and post-tests of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. There is a level of significance of 0.01 (t = − 4.540, p  = 0.000 < 0.05) before and after the sense of ability, the score after the sense of ability (3.91 ± 0.51), and the score before the Sense of ability (3.41 ± 0.55). The level of significance between the pre-test and post-test of sense of effort is 0.01 (t = − 4.002, p  = 0.000). The score of the sense of effort post-test (3.88 ± 0.66) will be significantly higher than the average score of the sense of effort pre-test (3.31 ± 0.659). The significance level between the pre-test and post-test Sense of environment is 0.01 (t = − 3.897, p  = 0.000). The average score for post- Sense of environment (3.95 ± 0.61) will be significantly higher than that of sense of environment—the average score of the previous test (3.47 ± 0.44). The average value of a post- sense of control (3.76 ± 0.67) will be significantly higher than the average of the front side of the Sense of control value (3.27 ± 0.52). The sense of interest pre-test and post-test showed a significance level of 0.01 (− 4.765, p  = 0.000), and the average value of Sense of interest post-test was 3.87 ± 0.61. It would be significantly higher than the average value of the Sense of interest (3.25 ± 0.59), the significance between the pre-test and post-test of belief sensing is 0.01 level (t = − 3.939, p  = 0.000). Thus, the average value of a post-sense of belief (4.04 ± 0.52) will be significantly higher than that of a pre-sense of belief Average value (3.58 ± 0.58). After the experimental group’s post-test, the scores for the Sense of ability, effort, environment, control, interest, and belief before the comparison experiment increased significantly. This result has a significant improvement effect. Table 7 shows that the control group did not show any differences in the pre and post-tests using paired t-tests on the dimensions of learning self-efficacy such as sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief ( p  > 0.05). It shows no experimental intervention for the control group, and it does not produce a significant effect.

The purpose of this study aims to explore the impact of social media use on college students' learning self-efficacy, examine the changes in the elements of college students' learning self-efficacy before and after the experiment, and make an empirical study to enrich the theory. This study developed an innovative design for course teaching methods using the ADDIE model. The design process followed a series of model rules of analysis, design, development, implementation, and evaluation, as well as conducted a descriptive statistical analysis of the learning self-efficacy of design undergraduates. Using questionnaires and data analysis, the correlation between the various dimensions of learning self-efficacy is tested. We also examined the correlation between the two factors, and verifies whether there was a causal relationship between the two factors.

Based on prior research and the results of existing practice, a learning self-efficacy is developed for university students and tested its reliability and validity. The scale is used to pre-test the self-efficacy levels of the two subjects before the experiment, and a post-test of the self-efficacy of the two groups is conducted. By measuring and investigating the learning self-efficacy of the study participants before the experiment, this study determined that there was no significant difference between the experimental group and the control group in terms of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. Before the experiment, the two test groups had homogeneity in measuring the dimensionality of learning self-efficacy. During the experiment, this study intervened in social media assignments for the experimental group. The experiment used learning methods such as network assignments, mutual aid communication, mutual evaluation of assignments, and group discussions. After the experiment, the data analysis showed an increase in learning self-efficacy in the experimental group compared to the pre-test. With the test time increased, the learning self-efficacy level of the control group decreased slightly. It shows that social media can promote learning self-efficacy to a certain extent. This conclusion is similar to Cao et al. 18 , who suggested that social media would improve educational outcomes.

We have examined the differences between the experimental and control group post-tests on six items, including the sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. This result proves that a social media-assisted course has a positive impact on students' learning self-efficacy. Compared with the control group, students in the experimental group had a higher interest in their major. They showed that they liked to share their learning experiences and solve difficulties in their studies after class. They had higher motivation and self-directed learning ability after class than students in the control group. In terms of a sense of environment, students in the experimental group were more willing to share their learning with others, speak boldly, and participate in the environment than students in the control group.

The experimental results of this study showed that the experimental group showed significant improvement in the learning self-efficacy dimensions after the experimental intervention in the social media-assisted classroom, with significant increases in the sense of ability, sense of effort, sense of environment, sense of control, sense of interest and sense of belief compared to the pre-experimental scores. This result had a significant improvement effect. Evidence that a social media-assisted course has a positive impact on students' learning self-efficacy. Most of the students recognized the impact of social media on their learning self-efficacy, such as encouragement from peers, help from teachers, attention from online friends, and recognition of their achievements, so that they can gain a sense of achievement that they do not have in the classroom, which stimulates their positive perception of learning and is more conducive to the awakening of positive effects. This phenomenon is in line with Ajjan and Hartshorne 2 . They argue that social media provides many opportunities for learners to publish their work globally, which brings many benefits to teaching and learning. The publication of students' works online led to similar positive attitudes towards learning and improved grades and motivation. This study also found that students in the experimental group in the post-test controlled their behavior, became more interested in learning, became more purposeful, had more faith in their learning abilities, and believed that their efforts would be rewarded. This result is also in line with Ajjan and Hartshorne's (2008) indication that integrating Web 2.0 technologies into classroom learning environments can effectively increase students' satisfaction with the course and improve their learning and writing skills.

We only selected students from one university to conduct a survey, and the survey subjects were self-selected. Therefore, the external validity and generalizability of our study may be limited. Despite the limitations, we believe this study has important implications for researchers and educators. The use of social media is the focus of many studies that aim to assess the impact and potential of social media in learning and teaching environments. We hope that this study will help lay the groundwork for future research on the outcomes of social media utilization. In addition, future research should further examine university support in encouraging teachers to begin using social media and university classrooms in supporting social media (supplementary file 1 ).

The present study has provided preliminary evidence on the positive association between social media integration in education and increased learning self-efficacy among college students. However, several avenues for future research can be identified to extend our understanding of this relationship.

Firstly, replication studies with larger and more diverse samples are needed to validate our findings across different educational contexts and cultural backgrounds. This would enhance the generalizability of our results and provide a more robust foundation for the use of social media in teaching. Secondly, longitudinal investigations should be conducted to explore the sustained effects of social media use on learning self-efficacy. Such studies would offer insights into how the observed benefits evolve over time and whether they lead to improved academic performance or other relevant outcomes. Furthermore, future research should consider the exploration of potential moderators such as individual differences in students' learning styles, prior social media experience, and psychological factors that may influence the effectiveness of social media in education. Additionally, as social media platforms continue to evolve rapidly, it is crucial to assess the impact of emerging features and trends on learning self-efficacy. This includes an examination of advanced tools like virtual reality, augmented reality, and artificial intelligence that are increasingly being integrated into social media environments. Lastly, there is a need for research exploring the development and evaluation of instructional models that effectively combine traditional teaching methods with innovative uses of social media. This could guide educators in designing courses that maximize the benefits of social media while minimizing potential drawbacks.

In conclusion, the current study marks an important step in recognizing the potential of social media as an educational tool. Through continued research, we can further unpack the mechanisms by which social media can enhance learning self-efficacy and inform the development of effective educational strategies in the digital age.

Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request. The data are not publicly available due to privacy or ethical restrictions.

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Acknowledgements

This work is supported by the 2023 Guangxi University Young and middle-aged Teachers' Basic Research Ability Enhancement Project—“Research on Innovative Communication Strategies and Effects of Zhuang Traditional Crafts from the Perspective of the Metaverse” (Grant Nos. 2023KY0385), and the special project on innovation and entrepreneurship education in universities under the “14th Five-Year Plan” for Guangxi Education Science in 2023, titled “One Core, Two Directions, Three Integrations - Strategy and Practical Research on Innovation and Entrepreneurship Education in Local Universities” (Grant Nos. 2023ZJY1955), and the 2023 Guangxi Higher Education Undergraduate Teaching Reform General Project (Category B) “Research on the Construction and Development of PBL Teaching Model in Advertising” (Grant Nos.2023JGB294), and the 2022 Guangxi Higher Education Undergraduate Teaching Reform Project (General Category A) “Exploration and Practical Research on Public Art Design Courses in Colleges and Universities under Great Aesthetic Education” (Grant Nos. 2022JGA251), and the 2023 Guangxi Higher Education Undergraduate Teaching Reform Project Key Project “Research and Practice on the Training of Interdisciplinary Composite Talents in Design Majors Based on the Concept of Specialization and Integration—Taking Guangxi Institute of Traditional Crafts as an Example” (Grant Nos. 2023JGZ147), and the2024 Nanning Normal University Undergraduate Teaching Reform Project “Research and Practice on the Application of “Guangxi Intangible Cultural Heritage” in Packaging Design Courses from the Ideological and Political Perspective of the Curriculum” (Grant Nos. 2024JGX048),and the 2023 Hubei Normal University Teacher Teaching Reform Research Project (Key Project) -Curriculum Development for Improving Pre-service Music Teachers' Teaching Design Capabilities from the Perspective of OBE (Grant Nos. 2023014), and the 2023 Guangxi Education Science “14th Five-Year Plan” special project: “Specialized Integration” Model and Practice of Art and Design Majors in Colleges and Universities in Ethnic Areas Based on the OBE Concept (Grant Nos. 2023ZJY1805), and the 2024 Guangxi University Young and Middle-aged Teachers’ Scientific Research Basic Ability Improvement Project “Research on the Integration Path of University Entrepreneurship and Intangible Inheritance - Taking Liu Sanjie IP as an Example” (Grant Nos. 2024KY0374), and the 2022 Research Project on the Theory and Practice of Ideological and Political Education for College Students in Guangxi - “Party Building + Red”: Practice and Research on the Innovation of Education Model in College Student Dormitories (Grant Nos. 2022SZ028), and the 2021 Guangxi University Young and Middle-aged Teachers’ Scientific Research Basic Ability Improvement Project - "Research on the Application of Ethnic Elements in the Visual Design of Live Broadcast Delivery of Guangxi Local Products" (Grant Nos. 2021KY0891).

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The contribution of H. to this paper primarily lies in research design and experimental execution. H. was responsible for the overall framework design of the paper, setting research objectives and methods, and actively participating in data collection and analysis during the experimentation process. Furthermore, H. was also responsible for conducting literature reviews and played a crucial role in the writing and editing phases of the paper. L.'s contribution to this paper primarily manifests in theoretical derivation and the discussion section. Additionally, author L. also proposed future research directions and recommendations in the discussion section, aiming to facilitate further research explorations. Y.'s contribution to this paper is mainly reflected in data analysis and result interpretation. Y. was responsible for statistically analyzing the experimental data and employing relevant analytical tools and techniques to interpret and elucidate the data results.

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Hu, J., Lai, Y. & Yi, X. Effectiveness of social media-assisted course on learning self-efficacy. Sci Rep 14 , 10112 (2024). https://doi.org/10.1038/s41598-024-60724-0

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The story linking nutrition and health has unexpected twists.

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This article was first published in  The Montreal Gazette.

“Become a vegetarian!” was Swami Satchidananda’s advice to young Dean Ornish, who asked about improving his health. In 1972, Ornish was stricken with mononucleosis followed by a bout with depression that forced him to drop out of Rice University. While at home, he met the swami who had been teaching meditation techniques to his sister. That sparked Ornish’s interest and prompted a discussion that resulted in the advice not only to embrace vegetarianism, but to explore yoga and meditation as well. Ornish did, and soon felt well enough to continue his education, this time at the Baylor College of Medicine.

Seeing patients who were suffering from heart disease, often accompanied by depression, Ornish began to wonder if the program that had solved his problems would work for others. He enlisted a small group of patients who were willing to try his regimen and taught them about diet, exercise and yoga. Within weeks, they felt better, experienced less chest pain and had lower cholesterol. This turned out to be a pivotal moment in Ornish’s life and one that would define his career. He would dedicate himself to studying whether heart disease can be prevented and even reversed by diet and other lifestyle factors.

Of course, the idea that food and health are intimately connected was not novel. As early as the second century AD, the Greek physician Galen described the relation between food and health in his work On the Power of Foods. Neither was the connection between diet and heart disease new. In 1908, Russian physician Alexander Ignatowski published a paper in which he described the arteries of rabbits fed a diet of full-fat milk, eggs and meat being blocked by a buildup of fats and cholesterol. Then in 1913, Nikolai Anichkov, another Russian, showed that feeding cholesterol to rabbits causes atherosclerosis, the hardening of arteries due to a buildup of fatty deposits known as “plaque.” The question of whether dietary fats and cholesterol can also affect human arteries was not raised until the 1940s.

Lester Morrison graduated from McGill University, earned a medical degree from Temple University in Philadelphia and went on to practise medicine in Los Angeles. Reading about Anichkov’s experiments sparked an interest in heart disease that was furthered by noting records that showed a drop in the disease during wartime when food supplies became critical, but increased in peacetime when food was plentiful. Since he had plenty of heart disease patients in his practice, Morrison decided to carry out an experiment. In 1946, he assigned 86 men and 14 women who had suffered a heart attack to a low-cholesterol, low-fat diet and a similar number of patients who would serve as controls were asked to follow their normal diet.

Although this was not a double-blind, properly randomized trial, it was the first experimental test of the relationship between diet and heart disease in humans. After three years, body weight and blood cholesterol had dropped in the test group, anginal symptoms decreased while exercise tolerance increased. More significantly, 30 per cent of the subjects in the control group died and only 14 per cent in the test group. Morrison followed both groups for another five years, after which the mortality rate was 44 per cent in the test group and 76 per cent in the control group. Judging that the study was too small and of questionable methodology, the medical establishment did not pay much attention.

In the meantime, researchers followed up on Anichkov’s rabbit feeding studies. John Gofman at the University of California centrifuged the blood serum of cholesterol-fed rabbits and identified two types of cholesterol, both of which were attached to proteins that enabled them to be transported through the blood. One type floated to the top of the serum sample and was termed low-density lipoprotein cholesterol (LDL) while the other deposited at the bottom, earning the name high-density lipoprotein cholesterol (HDL). The two types of cholesterol were also found in human blood samples and Gofman showed that men who had suffered a heart attack had elevated LDL and low HDL. Then in 1952 Laurence Kinsell published a study in the American Journal of Clinical Nutrition documenting a decrease in LDL with ingestion of plant foods and the avoidance of animal fats.

At this point, the story took an interesting twist, thanks to Nathan Pritikin, who was neither a physician nor a nutritionist. Pritikin had made a fortune as an entrepreneur and was totally shocked when in 1955, at age 40, a routine electrocardiogram revealed heart disease. The opinion at the time was that there was nothing to do except avoid both physical and mental stress. Pritikin decided to “do his own research,” something we are often advised to do by today’s self-appointed nutritional gurus. He came upon Morrison’s small study and since he lived nearby, made an appointment. A blood test found his cholesterol to be high and Morrison suggested he try the low-fat diet. Pritikin went all in, adopted a strict vegetarian regimen and, ignoring the recommendation about avoiding exertion, began a program of exercise. His blood cholesterol dropped dramatically and he became convinced he was beating his heart disease. A subsequent cardiac stress test during which he ran nearly 13 kilometres on a treadmill indeed showed normal heart function.

Thoroughly taken by his results, Pritikin became an evangelist for his diet and exercise regimen and began to fund medical studies. He educated himself thoroughly and gave talks about his experience, eventually garnering national attention, especially after the publication of his book, The Pritikin Program for Diet and Exercise, in 1979. Pritikin was even invited to speak at medical conferences and, although some criticized his diet as being too extreme, he earned a solid reputation in the medical community. Pritikin ended up punctuating the benefits of his program posthumously. In 1985, in the final clutches of leukemia, he died by suicide. As required by law, an autopsy was performed: There was a near absence of atherosclerosis. Furthermore, his heart’s pumping function had been completely uncompromised before death. But a proper, randomized trial to document the reversal of heart disease by diet and exercise was still missing.

It was such a trial that Ornish eventually carried out. Just a year after Pritikin’s death, 48 patients with coronary disease were randomized into an experimental group that would follow a strict vegetarian diet, a program of exercise as well as stress management training that involved yoga and meditation. After five years, the 28 patients who followed the program had fewer cardiac events and showed regression in the blockage of coronary arteries. Although the study was small, it demonstrated that lifestyle factors can at least in some cases play an important role in reversing heart disease.

Ornish always recognized Swami Satchidananda as having inspired his research and even wrote the forward to the swami’s Healthy Vegetarian cookbook. Vegetarianism and yoga worked for the swami who was healthy up to his death at age 88. Unfortunately, it seems he may have been into more than vegetarianism. His reputation is marred by allegations that he used his spiritual authority to coerce women into sexual relationships.

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What are Controlled Experiments?

Determining Cause and Effect

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A controlled experiment is a highly focused way of collecting data and is especially useful for determining patterns of cause and effect. This type of experiment is used in a wide variety of fields, including medical, psychological, and sociological research. Below, we’ll define what controlled experiments are and provide some examples.

Key Takeaways: Controlled Experiments

• A controlled experiment is a research study in which participants are randomly assigned to experimental and control groups.
• A controlled experiment allows researchers to determine cause and effect between variables.
• One drawback of controlled experiments is that they lack external validity (which means their results may not generalize to real-world settings).

Experimental and Control Groups

To conduct a controlled experiment , two groups are needed: an experimental group and a control group . The experimental group is a group of individuals that are exposed to the factor being examined. The control group, on the other hand, is not exposed to the factor. It is imperative that all other external influences are held constant . That is, every other factor or influence in the situation needs to remain exactly the same between the experimental group and the control group. The only thing that is different between the two groups is the factor being researched.

For example, if you were studying the effects of taking naps on test performance, you could assign participants to two groups: participants in one group would be asked to take a nap before their test, and those in the other group would be asked to stay awake. You would want to ensure that everything else about the groups (the demeanor of the study staff, the environment of the testing room, etc.) would be equivalent for each group. Researchers can also develop more complex study designs with more than two groups. For example, they might compare test performance among participants who had a 2-hour nap, participants who had a 20-minute nap, and participants who didn’t nap.

Assigning Participants to Groups

In controlled experiments, researchers use  random assignment (i.e. participants are randomly assigned to be in the experimental group or the control group) in order to minimize potential confounding variables in the study. For example, imagine a study of a new drug in which all of the female participants were assigned to the experimental group and all of the male participants were assigned to the control group. In this case, the researchers couldn’t be sure if the study results were due to the drug being effective or due to gender—in this case, gender would be a confounding variable.

Random assignment is done in order to ensure that participants are not assigned to experimental groups in a way that could bias the study results. A study that compares two groups but does not randomly assign participants to the groups is referred to as quasi-experimental, rather than a true experiment.

Blind and Double-Blind Studies

In a blind experiment, participants don’t know whether they are in the experimental or control group. For example, in a study of a new experimental drug, participants in the control group may be given a pill (known as a placebo ) that has no active ingredients but looks just like the experimental drug. In a double-blind study , neither the participants nor the experimenter knows which group the participant is in (instead, someone else on the research staff is responsible for keeping track of group assignments). Double-blind studies prevent the researcher from inadvertently introducing sources of bias into the data collected.

Example of a Controlled Experiment

If you were interested in studying whether or not violent television programming causes aggressive behavior in children, you could conduct a controlled experiment to investigate. In such a study, the dependent variable would be the children’s behavior, while the independent variable would be exposure to violent programming. To conduct the experiment, you would expose an experimental group of children to a movie containing a lot of violence, such as martial arts or gun fighting. The control group, on the other hand, would watch a movie that contained no violence.

To test the aggressiveness of the children, you would take two measurements : one pre-test measurement made before the movies are shown, and one post-test measurement made after the movies are watched. Pre-test and post-test measurements should be taken of both the control group and the experimental group. You would then use statistical techniques to determine whether the experimental group showed a significantly greater increase in aggression, compared to participants in the control group.

Studies of this sort have been done many times and they usually find that children who watch a violent movie are more aggressive afterward than those who watch a movie containing no violence.

Strengths and Weaknesses

Controlled experiments have both strengths and weaknesses. Among the strengths is the fact that results can establish causation. That is, they can determine cause and effect between variables. In the above example, one could conclude that being exposed to representations of violence causes an increase in aggressive behavior. This kind of experiment can also zero-in on a single independent variable, since all other factors in the experiment are held constant.

On the downside, controlled experiments can be artificial. That is, they are done, for the most part, in a manufactured laboratory setting and therefore tend to eliminate many real-life effects. As a result, analysis of a controlled experiment must include judgments about how much the artificial setting has affected the results. Results from the example given might be different if, say, the children studied had a conversation about the violence they watched with a respected adult authority figure, like a parent or teacher, before their behavior was measured. Because of this, controlled experiments can sometimes have lower external validity (that is, their results might not generalize to real-world settings).

Updated  by Nicki Lisa Cole, Ph.D.

• The Difference Between Control Group and Experimental Group
• Examples of Independent and Dependent Variables
• Difference Between Independent and Dependent Variables
• What Is a Double Blind Experiment?
• Scientific Method Vocabulary Terms
• What Is a Controlled Experiment?
• Understanding Simple vs Controlled Experiments
• Understanding Experimental Groups
• What Is the Difference Between a Control Variable and Control Group?
• Example of a Permutation Test
• What Are Natural Experiments and How Do Economists Use Them?
• What Is an Experiment? Definition and Design
• What Is a Control Group?
• The Milgram Experiment: How Far Will You Go to Obey an Order?
• What Is Deindividuation in Psychology? Definition and Examples
• What Is the Mere Exposure Effect in Psychology?

Identification of lipid metabolism-related gene signature in the bone marrow microenvironment of multiple myelomas through deep analysis of transcriptomic data

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• Volume 24 , article number  136 , ( 2024 )

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• Zhen Wang 1 ,
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• Hui Xu 2 &
• Shijun Li 1 , 3  

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Dysregulated lipid metabolism in the bone marrow microenvironment (BMM) plays a vital role in multiple myeloma (MM) development, progression, and drug resistance. However, the exact mechanism by which lipid metabolism impacts the BMM, promotes tumorigenesis, and triggers drug resistance remains to be fully elucidated.By analyzing the bulk sequencing and single-cell sequencing data of MM patients, we identified lipid metabolism-related genes differential expression significantly associated with MM prognosis, referred to as LMRPgenes. Using a cohort of ten machine learning algorithms and 117 combinations, LMRPgenes predictive models were constructed. Further exploration of the effects of the model risk score (RS) on the survival status, immune status of patients with BMM, and response to immunotherapy was conducted. The study also facilitated the identification of personalized therapeutic strategies targeting specified risk categories within patient cohorts.Analysis of the scRNA-seq data revealed increased lipid metabolism-related gene enrichment scores (LMESs) in erythroblasts and progenitor, malignant, and Tprolif cells but decreased LMESs in lymphocytes. LMESs were also strongly correlated with most of the 50 hallmark pathways within these cell populations. An elevated malignant cell ratio and reduced lymphocytes were observed in the high LMES group. Moreover, the LMRPgenes predictive model, consisting of 14 genes, showed great predictive power. The risk score emerged as an independent indicator of poor outcomes. Inverse relationships between the RS and immune status were noted, and a high RS was associated with impaired immunotherapy responses. Drug sensitivity assays indicated the effectiveness of bortezomib, buparlisib, dinaciclib, staurosporine, rapamycin, and MST-312 in the high-RS group, suggesting their potential for treating patients with high-RS values and poor response to immunotherapy. Ultimately, upon verification via qRT-PCR, we observed a significant upregulation of ACBD6 in NDMM group compared to the control group.Our research enhances the knowledge base regarding the association between lipid metabolism-related genes (LMRGs) and the BMM in MM patients, offering substantive insights into the mechanistic effects of the BMM mediated by LMRGs.

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Introduction

Multiple myeloma (MM) is an incurable hematological malignancy marked by the extensive proliferation and aggregation of monoclonal plasma cells (PCs) within the bone marrow (BM). This tumor type is typically aggressive and has a steadily increasing incidence rate. Currently, it has become the second most prevalent hematological tumor [ 1 ]. The application of proteasome inhibitors (PIs), immunomodulators, monoclonal antibodies (mAbs), and chimeric antigen receptor T cell (CAR-T) immunotherapy has significantly enhanced the survival prospects of MM patients, increasing the 5-year survival rate from 25 to over 50% [ 2 ]. Despite these advances, the phenomenon of drug resistance remains a significant hurdle, and the underlying mechanisms of MM drug resistance remain largely elusive. Multiple factors are believed to contribute to the development of drug resistance, such as genetic mutations in crucial drug targets, clonal evolution of malignant cells, and alterations within the BMM, among which the "natural selection" of the BMM is crucial for MM drug resistance [ 3 , 4 , 5 ].

The BMM, a complex milieu comprising cellular and acellular components, is crucial for MM cell survival and proliferation. It regulates host nutrition, metabolism, and immune status and participates in the pathophysiology of various tumors, including breast cancer, prostate cancer, and MM [ 6 , 7 , 8 , 9 ]. In particular, BM adipocytes (BMAds), which constitute approximately 70% of the BM volume in MM patients, are integral to the BMM architecture. Recent investigations have highlighted the critical role of metabolic reprogramming, driven by alterations in MM lipid metabolism, in supporting energy production, membrane biosynthesis, and lipid-mediated signaling pathways during cancer progression [ 10 , 11 , 12 ]. Additionally, the accumulation of lipid droplets in malignant PCs represents an adaptive mechanism crucial for their survival under adverse conditions [ 13 ].

Thus, understanding and targeting lipid metabolic pathways in MM has emerged as a promising therapeutic strategy for treating this malignancy.

Materials and methods

Data collection and preprocessing.

Sequencing data and corresponding clinical data from the single-cell sequencing dataset (scRNA-seq) (GSE161801) [ 5 ] were obtained from the TISCH database ( http://tisch.comp-genomics.org/home/ ) [ 14 ]. The analysis focused on BM samples from 19 relapsed refractory multiple myeloma (RRMM) patients before treatment. Additionally, the bulk RNA-seq datasets GSE4204 [ 15 ] (with 559 pretreatment samples) and GSE136400 [ 16 ] (with 354 pretreatment samples) were obtained from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). Using R “limma” and “sva” to remove batch effects, a merged dataset of 892 samples and 16,729 genes was prepared for further analysis. Lipid metabolism-related genes (LMRGs) were retrieved from the MsigDB by searching ( https://www.gsea-msigdb.org/gsea/msigdb/human/geneset/REACTOME_METABOLISM_OF_LIPIDS and https://www.gsea-msigdb.org/gsea/msigdb/human/geneset/WP_LIPID_METABOLISM_PATHWAY ), and a total of 760 LMRGs were obtained after merging and deduplication.

Analysis of scRNA-seq data

The scRNA-seq data were analyzed with the R package “Seurat.” Filtration and cell identification were carried out directly using the filtration conditions of the TISCH database. The unified modular approximation and projection (UMAP) algorithm was applied for downscaling visualization and cell clustering annotation on the single-cell data. Then, nine distinct cell clusters were identified: B, CD8 + T, dendritic (DC), erythroblast, malignant, natural killer (NK), monocyte/macrophage, progenitor, and Tprolif cell clusters.

Differentially expressed genes (DEGs) across various cell types were pinpointed employing the “FindAllMarkers” function of the R “Seurat.” The functions highlighted the top 5 DEGs exhibiting high and low expression levels within each cell cluster. Marker genes for each cell type were identified by the R “COSG,” and a heatmap was generated to visualize the top 10 DEGs.

Furthermore, according to the “COSG” analysis results, the top 100 genes from each cell cluster were selected and subsequently enriched using the R “clusterprofiler.” This enrichment included KEGG and Reactome pathway analyses.

Correlation analysis between the expression of lipid metabolism-related genes and hallmark gene sets

Information on 50 hallmark pathways was retrieved from the MsigDB ( http://www.gsea-msigdb.org/gsea/msigdb/index.jsp ). Lipid metabolism-related gene enrichment scores (LMESs) for each cell, along with the enrichment score of each hallmark pathway for each cell, were calculated employing “ssGSEA” from the R package “GSVA.” To identify potential pathways associated with lipid metabolism, we performed a correlation analysis between LMES and the 50 hallmark pathways, with the findings depicted in a heatmap. Subsequently, the samples were divided into high and low LMES groups according to the median expression of LMRGs, followed by an analysis of cell distribution between the two groups.

Lipid metabolism-related DEGs with potential prognostic value

Differential analysis was performed by comparing normal and tumor specimens of single-cell data (logFC > 0.25 and p  < 0.05). This analysis revealed 974 genes with increased expression and 372 genes with decreased expression by adjusting p value. These DEGs, both upregulated and downregulated, intersected with 760 LMRGs, identifying 41 genes whose expression increased and 7 whose expression decreased differentially related to lipid metabolism (LMR-DEGs). To pinpoint LMR-DEGs with potential prognostic significance, Spearman correlation, univariate Cox regression, and survival analyses were conducted using the R “survival” and “survminer” R packages for the 48 LMR-DEGs and the R “ggplot2” R package for visualization. Ultimately, 14 genes (COX p  < 0.05) demonstrating prognostic potential, referred to as LMRPgenes, were selected for inclusion in the prognostic model.

Predictive model construction based on LMRPgenes by multiple machine learning methods

The GSE4204 and GSE136400 datasets were merged as the training set. Ten classic algorithms were integrated to construct a predictive model. These include random forest (RSF), least absolute shrinkage and selection operator (LASSO), gradient boosting machine (GBM), survival support vector machine (Survival-SVM), supervised principal component (SuperPC), ridge regression, partial least squares regression for Cox (plsRcox), CoxBoost, stepwise Cox, and elastic network (Enet) methods. To form 117 machine learning combinations, RSF, LASSO, CoxBoost, and stepwise Cox were combined with other algorithms. These four algorithms are known for their ability to reduce dimensionality and screen variables. The concordance index (C-index) and average C-index were calculated to evaluate the predictive power of the different algorithms and to identify the optimal predictive model. The model derived by RSF emerged as the optimal model. Employing this model, a risk score (RS) was obtained.

The samples were categorized into high- and low-RS groups depending on the RS cutoff value used for survival analysis. Survival analysis was conducted to explore the variation in survival status across different RS groups within the GSE4204, GSE136400, and merged datasets.

Evaluation of predictive models based on LMRPgenes

In this study, the R “pROC” was used to generate receiver operating characteristic (ROC) curves to evaluate the diagnostic performance of the model across three datasets at 1-, 3-, and 5-year intervals for MM. The area under the curve (AUC) was computed to determine the diagnostic efficacy of the model. Moreover, the “plotAUCcurve” function within the R “timeROC” facilitated the plotting of time-dependent AUC variations, enabling an assessment of the model’s predictive accuracy over time. The R package “survival” was used to construct survival curves to examine the potential disparities in overall survival (OS) between the high- and low-RS groups. Additionally, the relationships between age, sex, and the RS were investigated. We performed univariate and multivariate Cox regression analyses on the three datasets to determine whether the RS was an independent prognostic factor for predicting OS in MM patients.

Enrichment analysis of RS-related genes

We performed a correlation analysis between RS and all genes. A heatmap was created to visualize the top 50 genes displaying positive and negative correlations with the RS. Subsequently, a gene set enrichment analysis (GSEA) of these identified genes was conducted employing the R "clusterProfiler." This analysis included GO, KEGG, and Reactome pathway analyses.

Correlation analysis of immune characteristics in BMM with model RS

Recently, there have been significant advancements in immunotherapy and targeted treatments for MM patients. To explore the relationship between the model RS and the immune microenvironment of MM patients, we employed the R "IOBR" to assess the BM immune status of MM patients. We employed eight distinct algorithms[ 17 ], including microenvironment cell populations-counter (MCPcounter), estimating the proportion of immune and cancer cells (EPIC), xCell, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), QuanTIseq, tumor immune estimation resource (TIMER), immunophenoscore (IPS), and ESTIMATE. ESTIMATE was applied to compare the immune, stromal, ESTIMATE, and tumor purity scores between the high- and low-RS groups. Furthermore, by employing the IPS, we calculated the scores for four diverse immunophenotypes: antigen presentation, effector cells, suppressor cells, and checkpoints. The MCPcounter was used to analyze immune cell infiltration between the two groups. Subsequent analyses with other algorithms corroborated these findings. Finally, the correlation between the RS and cytokines, along with their receptors, was evaluated.

Evaluation of immunotherapy efficacy and sensitivity drug prediction

Due to the complexity of the microenvironment in MM, the assessment of immune status mentioned above falls short of adequately reflecting the effectiveness of immunotherapy. Consequently, we used the tumor immune dysfunction and exclusion (TIDE) database to predict immunotherapy outcomes and provide related values (TIDE values).

The TIDE value serves as a barometer for the success of immunotherapy, where a higher value indicates diminished efficacy. Additionally, we employed R "OncoPredict" to predict the efficacy of various nonimmunotherapy anticancer drugs.

By determining the IC50 values for each anticancer drug, we can gauge their potency and discern the difference between the high- and low-RS groups. Notably, an elevated IC50 value signifies reduced sensitivity to the drug.

Tissue Sample Collection

Bone marrow (BM) samples, including 22 NDMM patients, 9 healthy individuals (control group), and 5 MM patients in complete remission (CR group), were collected by BM aspiration at the First Affiliated Hospital of Dalian Medical University. BM samples were transported to the laboratory within two hours for processing. BM slides were analyzed by three specialists who quantified the percentage of plasma cells and determined the mean values. Single nucleated cells were isolated from these samples and preserved at -80°C until further analysis. Ethical clearance for this study was granted by the Research Ethics Review Committee of the First Affiliated Hospital of Dalian Medical University, and informed consent was obtained from all participants.

Quantitative Real-Time Reverse Transcription Polymerase Chain Reaction (qRT-PCR)

For the qRT-PCR analysis, total RNA was extracted from the single nucleated cell samples using a Trizol RNA isolation reagent. The RNA was then reverse-transcribed into cDNA using the PrimeScript RT Reagent Kit (Takara, DRR037A). Quantification of RNA expression was conducted using the SYBR Premix Ex Taq II (Takara) on an ABI 7500 real-time PCR system. The relative expression levels were measured using the comparative Ct method, with gene expression levels normalized to GAPDH. Data were presented as mean ± SD from three replicates. The primer sequences used in amplification were as follows: Forward, 5′-GGCCTGTGATCGAGGACATAA-3′; Reverse, 5′-GCAGCAGCTCTACAATATCCAGAA-3′.

Statistical analysis

All the statistical analyses were conducted with R software (R 4.2.1). The analysis of the scRNA-seq data was facilitated using the R "Seurat." R "survival" and "survminer" were used to perform the Kaplan‒Meier (K–M) survival analysis. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic indicators. The performance of the model was assessed by plotting the ROC curve and calculating the AUC using the "pROC" R package. Spearman's method was utilized to perform correlation analyses. The Kruskal–Wallis test was utilized as a nonparametric method to estimate gene expression differences among three groups. p values less than 0.05 were considered to indicate statistical significance and are annotated as follows: * p  < 0.05; ** p  < 0.01; *** p  < 0.001; **** p  < 0.0001.

Results of single-cell sequencing analysis

In this investigation, we accessed the scRNA-seq dataset (GSE161801) in the TISCH database to obtain pretreatment data from 19 RRMM patients. Through the UMAP algorithm, followed by the annotation of different cell clusters, nine distinct cell clusters were identified: B, CD8 + T, DC, erythroblast, malignant, monocyte/macrophage, NK, progenitor, and Tprolif cell clusters. The UMAP visual representation (Fig.  1 A) revealed that malignant cells constituted a significantly large cluster. Furthermore, the pronounced difference between groups of malignant cells indicates the presence of marked heterogeneity among malignant plasma cells (PCs).

A UMAP plot, annotation of cellular subpopulations; B The top 5 high and low expression genes in each cell cluster within each cell cluster

The "FindAllMarkers" function in the R "Seurat" was used to identify the top five highly and lowly expressed genes in each cell cluster (Fig.  1 B). Additionally, R "COSG" was utilized for the identification of marker genes for each cell type, displaying the top 10 highly expressed differentially expressed genes (DEGs) via a heatmap (Fig.  2 A). The top 100 DEGs identified by "COSG" were subjected to enrichment analysis for KEGG and Reactome pathways, revealing that DEGs in B and DC cells shared similar enrichment pathways. Similarly, DEGs in CD8 + T and NK cells, along with Tprolif and progenitor cells, exhibited comparable enrichment pathways (Fig.  2 B, 2C). KEGG pathway enrichment analysis of malignant PCs highlighted significant enrichment in processes such as protein processing in the endoplasmic reticulum, protein export, various types of N-glycan biosynthesis, and N-glycan biosynthesis. Reactome pathway enrichment analysis further indicated significant enrichment in asparagine N-linked glycosylation, SRP-dependent cotranslational protein targeting to membrane, unfolded protein response, xbp1s chaperone genes, and IRE1alpha-activated chaperones.

A Heatmap showing the top 10 marker genes in each cell cluster; B KEGG pathway enrichment of the top 100 marker genes in each cell cluster; C Reactome pathway enrichment of the top 100 marker genes in each cell cluster

Subsequently, we identified 50 hallmark pathways from the MsigDB database. Using the R "ssGSEA," the lipid metabolism enrichment scores (LMESs) and the enrichment score of each hallmark pathway for each cell were calculated. Analysis revealed that erythroblasts, malignant cells, DCs, progenitors, and Tprolif cells exhibited increased enrichment scores in most hallmark pathways. Notably, malignant cells showed elevated hallmark pathway enrichment scores predominantly in OXIDATIVE PHOSPHORYLATION, MYC TARGETS V1, UNFOLDED PROTEIN RESPONSE, MTORC1 SIGNALING, ADIPOGENESIS, and DNA REPAIR (Fig.  3 A). The LMES for each cell were calculated by employing R "GSVA," depending on the gene expression of LMRGs, and the correlation between LMES and the 50 hallmark pathways was analyzed. The results demonstrated a strong correlation between LMES and hallmark pathways in the erythroblast, malignant, progenitor, and Tprolif cell clusters, specifically in pathways such as the MTORC1 SIGNALING, ADIPOGENESIS, FATTY ACID METABOLISM, OXIDATIVE PHOSPHORYLATION, MYC TARGETS V1, PI3K AKT MTOR SIGNALING, APOPTOSIS, P53 PATHWAY, DNA REPAIR, and GLYCOLYSIS pathways. However, LMES exhibited a weak correlation with most hallmark pathways in CD8 + T, NK, and B cells (Fig.  3 B).

A Heatmap showing the hallmark pathway enrichment score of each cell; B Heatmap showing the correlation between LMES and each hallmark pathway

The LMES values for each cell type are displayed in Fig.  4 A, and a visual UMAP indicating the distribution of LMES across each cell cluster was created (Fig.  4 B), along with the percentage of various cells in each sample (Fig.  4 C). The LMES values of the cell clusters, from highest to lowest, were as follows: erythroblasts, DCs, progenitors, malignant cells, Tprolif cells, monocytes/macrophages, NK cells, CD8 + T cells, and B cells. The percentages of B (1% vs. 8%), CD8 + T (3.3% vs. 27.9%), and NK cells (7.9% vs. 27.1%) were significantly lower in the high LMES group. Conversely, the percentages of malignant (63.6% vs. 22.6%), monocyte/macrophage (15.3% vs. 11.8%), erythroblast (2.6% vs. 0.4%), progenitor (3.4% vs. 1.5%), Tprolif (0.9% vs. 0.3%), and DC cells (1.9% vs. 0.2%) were significantly greater (Fig.  4 C–E). These findings suggest that lipid metabolism may influence immune cell infiltration.

A The distribution of the LMESs in different cell types; B UMAP showing the distribution of LMESs in different cell types and the distribution of high and low score groups in different cell types; C Relative percentages of 9 cell clusters in different sample sources, and the percentages of different cell types in high and low LMES groups; D UMAP shows the distribution of various cell types in high and low LMES groups; E The trend changes in the proportion of various cells in high and low LMES groups

Construction and evaluation of the LMRPgenes predictive model based on multiple machine learning methods

In this study, the GSE4204 (559 pretreatment samples) and GSE136400 (354 pretreatment samples) cohorts were obtained from the GEO database. The R packages “limma” and “sva” were used to eliminate batch effects in the two datasets. Following this process, a merged dataset containing 892 samples and 16,729 genes was compiled. The results of principal component analysis (PCA) before and after batch effect removal are displayed in Fig.  5 A, B .

A , B PCA before and after batch effect removal; C , D Venn plot showing the intersecting genes between up/downregulated DEGs and LMRGs; E Results of univariate Cox regression analysis of LMRPgenes and the correlations between these genes. The circle size represents the p value of the univariate Cox regression analysis

The analysis identified 41 upregulated and 7 downregulated LMR-DEGs from an initial set of 1346 DEGs intersecting with 760 LMRGs (Fig.  5 C, D ). Further analyses, including correlation analysis, univariate Cox regression, and K‒M survival analysis, led to the identification of 14 LMRPgenes: ACBD6, CCNC, SEC24D, NR1H2, FDPS, PRKD3, TECR, SUMF2, SLC44A1, PTGES2, SAR1B, TSPO, SRD5A3, and PRKD2. TSPO, NR1H2, and PTGES2 were found to act as protective factors, whereas the remaining factors were considered risk factors (Fig.  5 E). Additionally, the single-cell expression of these 14 LMRPgenes is depicted in a bubble chart (Fig.  6 ).

The expression of 14 LMRPgenes in single-cell sequencing data

Predictive models based on the 14 LMRPgenes were created using ten machine learning algorithms and 117 combinations. The merged dataset served as the training set, and the GSE4204 and GSE136400 cohorts were used to evaluate the predictive power separately. The concordance index (C-index) and the average C-index for the three datasets were calculated for each algorithm combination to evaluate their predictive capabilities. The RSF-based model achieved an average C-index of 0.919, with a value of 0.94 in the training set, 0.95 for GSE4204, and 0.87 for GSE136400 (Fig.  7 A). Consequently, RSF was employed to construct the LMRPgenes prediction model for MM. Based on RSF variable importance, the genes were ranked in descending order of significance (Fig.  7 B).

A Construction of multiple machine learning models; B Prediction model based on random forest algorithm and variable importance of LMRPgenes; C ROC curves of the training cohort; D Time-dependent ROC curve of the training cohort; E Kaplan–Meier curves of RS in the training cohort

The effectiveness of the model's predictions was evaluated by ROC curve analysis. For the training set, the AUC values of the 1-, 3-, and 5-year intervals were 0.97, 0.963, and 0.949, respectively (Fig.  7 C). The area under the curve (AUC) of the time-dependent ROC curve exceeded 0.9 (Fig.  7 D). For the GSE4204 dataset, the 1-, 3-, and 5-year AUC values were 0.962, 0.976, and 1.0, respectively, with the AUC values approaching 1 over time (Fig.  8 A). In GSE136400, the 3- and 5-year AUC values were 0.89 and 0.918, respectively, with the AUC values exceeding 0.75 and increasing over time (Fig.  8 B). These results underscore the model’s excellent predictive validity across the three datasets.

A , B ROC curves, time-dependent ROC curve, and Kaplan–Meier curves of RS for the cohort GSE4204 and GSE136400; C Relationship between model RS and gender; 8D. Relationship between model RS and OS

Survival status changes were evaluated in these three datasets for different RS groups. The results showed that patients with a high RS exhibited significantly lower OS than those with a low RS (Figs. 7 E, 8 A, B). Moreover, the relationships between the RS, gender, and survival status were evaluated, revealing no significant gender difference between the two groups (Fig.  8 C). However, a significant increase in mortality rate was observed in the high-RS group compared to the low-RS group (83% vs. 20%), with a notable difference in RS values between the alive and dead groups ( p  < 2.22e−16) (Fig.  8 D).

Subsequently, we conducted univariate (Fig.  9 A) and multivariate (Fig.  9 B) Cox regression analyses to determine whether the RS is an independent prognostic factor for predicting the survival of MM patients, along with gender, age, and RS group. The findings indicated that the RS (HR: 1.149, CI 1.127–1.172, p  < 0.001) served as a potent prognostic predictor, indicating the potential ability to predict the outcomes of MM patients.

A , B Univariate and multivariate analyses of the clinical characteristics, RS_group, and RS for the OS

We conducted a correlation analysis between RS and all genes, identifying the top 50 genes that were positively or negatively correlated with RS, as depicted in a heatmap (Fig.  10 A, B). Following the correlation analysis, GSEA, including GO, KEGG, and Reactome pathway analyses, was performed (Fig.  10 C). This analysis revealed that genes closely associated with RS were predominantly enriched in three key areas:

3.1 The biogenesis of ribosomes and ribonucleoprotein complexes, the biogenesis of the ribosomal large subunit, the processing and metabolism of various RNAs, and the translation, modification, and translocation of proteins.

3.2 Expression of mitochondrial genes, mitochondrial translation, the composition of the respiratory chain complex, and ATP biosynthesis.

3.3 Interactions between cytokines and receptors and disorders related to autoimmunity and infections.

A , B Heatmap showing the expression of the top 50 genes positively and negatively related to RS; C GSEA enrichment analysis on genes positively and negatively related to RS, including GO KEGG and Reactome pathways

KEGG analysis revealed a normalized enrichment score (NES) < -2 ( p value < 0.05; FDR < 0.25) for pathways involving viral protein interactions with cytokine receptors, systemic lupus erythematosus, cytokine-receptor interactions, neuroactive ligand‒receptor interactions, rheumatoid arthritis, malaria, cell adhesion molecules, and Staphylococcus infection. This finding suggested that the expression of RS-related genes expression was downregulated in several inflammation-related pathways.

Immunological analysis

We employed the R “IOBR” for analyzing the immune status of the BMM in MM patients using eight algorithms: MCPcounter, EPIC, xCell, CIBERSORT, IPS, quanTIseq, ESTIMATE, and TIMER. ESTIMATE was used to assess the infiltration levels of immune cells within MM samples. The group with a higher RS had significantly lower stromal, immune, and ESTIMATE scores but notably higher tumor purity scores (Fig.  11 B). Additionally, through the immunophenotyping score (IPS), we separately calculated the scores for four distinct immunophenotypes, revealing a negative correlation between the RS and the major histocompatibility complex (MHC) score and a positive correlation with the suppressor cell (SC) score. These findings suggest that a reduction in antigen presentation and an increase in suppressive immunity may facilitate tumor cell immune evasion, thereby diminishing immune-mediated tumor cell clearance and further promoting tumorigenesis.

A Heatmap showing infiltration of immune cells in high- and low-RS groups based on MCPcounter, EPIC, xCell, CIBERSORT, IPS, quanTIseq, ESTIMATE, and TIMER algorithms; B The relationship between the model RS and the immune score related to the microenvironment based on ESTIMATE and IPS algorithms

Using the MCPcounter algorithm, we quantified the infiltration of ten types of immune cells in MM samples. The results revealed that the high-RS group exhibited significant decreases in the infiltration of six immune cell types and two stromal cell types, namely, neutrophils, monocytes, fibroblasts, cytotoxic lymphocytes, endothelial cells, CD8 + T cells, NK cells, and myeloid dendritic cells. CIBERSORT was also utilized to assess the infiltration levels of 22 immune cell types, revealing diminished infiltration of CD8 + T cells, monocytes, macrophages (M1, M2, and M0 types), and eosinophils, except for CD4 + naive and plasma cells. Verifying these results through additional algorithms yielded similar findings, collectively suggesting a decrease in the infiltration of most immune cells within the high-RS group (Fig.  11 A).

This analysis of immune status within the MM dataset indicated a substantial reduction in immune cell infiltration in the high-RS group. Moreover, by correlation analysis, we evaluated the relationships between the RS and the expression of various cytokines (including chemokines, interleukins, and interferons) and their receptors (Fig.  12 A). The analysis revealed a predominantly negative correlation between RS and most cytokines, notably CCL8, PPBP, CXCL16, EPOR, and CXCL12 (Fig.  12 B). The above results showed that BMMs with a high RS are likely to be in a state of immunosuppression, which could contribute to the adverse prognosis observed in this group.

A , B Heatmap showing the relationship between the model RS and cytokines and their receptors; (showing r < −0.25)

Subsequently, we investigated whether the immunosuppression status of MM patients in the high-RS group affects the effectiveness of immunotherapy. The TIDE database was used to predict the relationship between immunotherapy efficacy and the model RS. TIDE analysis indicated that the TIDE score was positively related to the RS (Fig.  13 A), and the high-RS group had a significantly greater TIDE score than did the low-RS group ( p value: 0.0024) (Fig.  13 B), suggesting diminished efficacy of immunotherapy. Moreover, there was a weaker positive immunotherapy response in the high-RS group than in the low-RS group (40% vs. 45%) (Fig.  13 C), and a significant decrease in the RS was noted in the group responding to immunotherapy ( p value: 0.00023) (Fig.  13 D).

A The TIDE analysis reveals a positive correlation between the TIDE score and RS TIDE (the higher RS, the worse the immunotherapy effect); B Box plot showing the TIDE scores difference in high- and low-RS groups; C Percentage of immunotherapy response in high- and low-RS groups; D Difference of model RS in different immunotherapy response groups

Prediction of drug sensitivity to nonimmunotherapeutic treatments

Our study revealed that patients with a high RS are characterized by an immunosuppressive microenvironment and exhibit diminished responses to immunotherapy. To identify more effective treatment options for patients with a high RS, we used the R package “OncoPredict” to determine the IC50 values of various anticancer drugs. The IC50 value serves as an indicator of treatment sensitivity, wherein higher IC50 values signify reduced sensitivity. A comparative analysis was conducted to evaluate the differences in the IC50 values of various anticancer drugs between the high- and low-RS cohorts. The classical proteasome inhibitor bortezomib demonstrated notable therapeutic efficacy in the high-RS group. Additionally, other anticancer drugs, including the panclass I PI3K inhibitor buparlisib, the multicell cycle protein-dependent kinase inhibitor dinaciclib, staurosporine, the proteasome inhibitor MG.132, rapamycin, the telomerase inhibitor MST-312, and the heat shock protein 90 inhibitor luminespib, were observed to have lower IC50 values, indicating significant differences in efficacy between the two RS groups (Fig.  14 ). These findings suggest that these drugs, either individually or in combination, may offer novel therapeutic avenues for MM patients characterized by high-RS and suboptimal immunotherapy outcomes.

The IC50 values of different anticancer drugs in high- and low-RS groups based on R package “oncoPredict.”

Results of RT-PCR

Our analysis focused on the expression levels of the ACBD6 gene across NDMM, CR, and control groups. Notably, ACBD6 was significantly upregulated in NDMM group compared to control group ( p  = 0.001). No significant differences in ACBD6 expression were observed when comparing the CR and control groups, as well as the NDMM and CR groups (Fig.  15 ). Additionally, the gene expression level of ACBD6 was significantly positive correlated with the percentage of malignant PCs in bone marrow(r: 0.7163; p : 0.0002).

A ACBD6 expression levels among different groups; B Correlation analysis between ACBD6 gene expression and the percentage of malignant PCs

Arising from stromal stem cells, BM adipose tissue (BMAT) plays a pivotal role in the progression of multiple myeloma (MM). Two categories of BMAT have been identified based on their anatomic locations: proximal-regulated rBMAT and distal-constitutive cBMAT. The latter tends to aggregate in areas with lower skeletal activity, potentially negatively impacting hematopoiesis. In contrast, rBMAT is intertwined with hematopoietic cells surrounding growth plates and bone trabeculae [ 18 , 19 , 20 , 21 ]. BM adipocytes not only store energy but also release FFAs and produce a range of signaling molecules like leptin, adiponectin, IL-6, TNFα, and IGF-1. These molecules promote myeloma cell proliferation, migration, and resistance to chemotherapy [ 20 , 22 ]. BM adipocytes from MM patients have been shown to foster myeloma growth and shield these cells from the effects of chemotherapy-induced cell apoptosis [ 23 , 24 ].

Panaroni C et al. have revealed that malignant PCs from MM patients trigger lipolysis in BM adipocytes, enhancing gene expression linked to lipolysis during different stages of the disease [ 13 ]. It has been demonstrated that etomoxir inhibits fatty acid oxidation(FAO) by blocking CPT1, the rate-limiting enzyme for FAO, thereby decreasing intracellular ATP levels and leading to cell cycle arrest in the G0/G1 phase of MM cells [ 25 ]. In addition, inhibiting FA synthesis has shown promise in combating tumor growth. A lncRNA derived from the MIR17HG, lnc-17–92, has been found to accelerate tumor growth by upregulating ACACA (an ACC1 encoding gene). Disrupting lnc-17–92 with an antisense oligonucleotide yielded significant tumor suppression in vitro and in vivo [ 26 ]. Overall, the dynamics of FA metabolism—both its synthesis and breakdown—are crucial in the progression, manifestation, and treatment resistance of multiple myeloma.

Increasing evidence suggests that BM stromal cells shield tumor stem cells while fostering an immunosuppressive microenvironment, hindering the eradication of minimal residual disease (MRD) and fostering resistance. CD8 + T cells, pivotal to the adaptive immune response, play a crucial role in anti-tumor immunity. Recent findings revealed a marked reduction in both the quantity and functionality of CD8 + T in BM of MM patients. Moreover, BM CD8 + T function lagged behind that of matched peripheral circulation. In vitro assays unveiled that compromised BM CD8 + T function and diminished mitochondrial mass in MM patients correlated with heightened long-chain FAs uptake, primarily driven by elevated FATP1 expression, with FATP1 inhibition ameliorating CD8 + T cell dysfunction [ 27 ]. Furthermore, CD36 facilitated CD8 + T lipid peroxidation and ferroptosis through the absorption of FAs enriched in BMM of MM, culminating in CD8 + T effector function impairment, while arachidonic acid (AA) emerged as the principal FA in BMM, triggering ferroptosis in CD8 + T [ 28 ]. Collectively, these studies underscore the impact of aberrant lipid metabolism within the BMM on CD8 + T abundance and functionality. Our analysis of the RRMM single-cell dataset showed a significant decrease in CD8 + T abundance in the high LEMS group compared to the low LEMS group. Moreover, CD8 + T infiltration decreased with the RS of the predictive model elevated. Hence, dysregulated lipid metabolism may influence CD8 + T cell infiltration; however, the precise effects require further experimental validation.

Chimeric antigen receptor (CAR) T cell therapy stands out as a novel and promising targeted approach that has demonstrated satisfactory efficacy in patients with RRMM in recent years. However, the occurrence of cytokine release syndrome (CRS) and the clinical outcomes present significant barriers, hampering the widespread clinical application of CAR-T. Platelet-activating factor (PAF), also known as 1-alkyl-2-acetyl-sn-glycero-3-phosphocholine, has garnered attention [ 29 ]. A recent investigation unveiled a distinctive metabolic profile in RRMM patients experiencing CRS and exhibiting poor therapeutic responses. The study observed a significant reduction in the PAF-like molecule intermediate glycerophosphocholine (GPC) alongside a notable elevation in lysophosphatidylcholine (LPC) levels during CRS. This metabolic profile may hold crucial predictive value for the efficacy of CAR-T therapy in RRMM patients. The study suggests that CAR-T therapy may exert its effectiveness by inhibiting the remodeling of PAF synthesis through LPCAT1 inactivation, consequently leading to increased LPC levels in plasma [ 30 ]. Targeting LPCAT1 and PAF remodeling emerges as a promising avenue to bolster MM CAR-T therapy.

LPCAT1, a member of the LPCAT family, plays a pivotal role in LD formation and LPC metabolism. Remarkably, the study revealed a significant upregulation of LPCAT1 expression in MM patients, correlating with poorer OS, a finding validated across two separate cohorts of MM patients [ 30 ]. ACBD6 (Acyl-CoA binding domain containing 6)), a protein involved in regulating various lipid metabolic processes, was highlighted. ACBD6 facilitates acyl donors for LPCAT1, which catalyzes the acylation of monoacyl chain lipids like lysophosphatidic acid (LPA) and LPC. Deficiencies in LPCAT1 enzyme-mediated phosphatidylcholine formation from LPC lead to reduced LD content [ 31 ]. LDs, essential organelles in lipid metabolism, not only regulate intracellular cytoplasmic and lipid metabolism but also play vital roles in cell signaling, intracellular and extracellular transport, and gene transcriptional regulation [ 32 ]. Aberrations in LDs or fatty acids contribute to cancer progression and metastasis. Consequently, several enzymes involved in LD formation or breakdown, such as ACC, fatty acid synthase (FASN), 3-hydroxy-3-methylglutaryl monoacyl-CoA reductase (HMGCR), and stearoyl-CoA desaturase (SCD), have been identified as potential therapeutic targets in cancer treatment.

Moffitt Cancer Center revealed that acidic alterations in the tumor microenvironment, sensed by OGR1, promote LD formation. OGR1 activates downstream signaling through protein phospholipase C and PI3K/AKT, fostering LD formation from amino acid products and promoting tumor progression [ 33 ]. Xu et al. demonstrated that proteasome inhibitors (PIs) induce the accumulation of abnormal LDs in MM, predominantly triglycerides, via activation of the ATF4/SREBP pathway. This LD accumulation serves as a compensatory mechanism to enable protein synthesis, mitigate endoplasmic reticulum stress, and sustain cell survival, ultimately conferring resistance to PIs [ 34 ]. Thus, drugs targeting LD formation and catabolism offer promising avenues for MM therapy.

ACBD6 plays a crucial role as an acyl donor, specifically binding long-chain acyl-coenzyme A to regulate lipid synthesis, LD formation, and protein and lipid esterification [ 31 , 35 ]. However, its role and mechanisms in MM remain underexplored. Our research leveraging both scRNA-seq and bulk-seq data revealed significant differences in ACBD6 expression between normal and tumor tissues in MM, with high ACBD6 expression correlating with notably lower OS ( p  < 0.001). Our analyses of BM samples from 22 NDMM patients, 5 patients in complete remission(CR), and 9 healthy controls indicated that ACBD6 levels were markedly higher in the MM group compared to the control group. Additionally, a strong correlation was found between ACBD6 expression levels and plasma cell counts in the BM for NDMM patients.

ACBD6 is not ubiquitously expressed but is found in specific cell types, such as blood CD34 + progenitor cells and perivascular CD31 + endothelial cells, contributing to hematopoiesis and vascular endothelial development [ 36 ]. It functions as an acyl donor, facilitating LPA acylation, may play a role in MM. LPA is a significant inducer of pro-HB-EGF ectodomain shedding [ 37 ], and HB-EGF is also a biomarker for LPA type 1 receptor (LPA1) activation [ 38 ]. A recent study demonstrated that MM's BM endothelial cells promote angiogenesis in vivo and in vitro through elevated expression of HB-EGF and EGFR. Inhibition of HB-EGF-EGFR signaling was shown to curtail the angiogenic capabilities of endothelial cells and restrict tumor growth in MM mouse models [ 39 ]. Moreover, elevated LPA levels are critical in shielding MM cells from apoptosis induced by PIs in MM patients. LPA's interaction with the LPA2 receptor triggers the downstream MEK1/2-ERK1/2 signaling pathway, enhancing mitochondrial oxidative phosphorylation (OXPHOS), reducing proteasome activity, and improving protein folding in the endoplasmic reticulum, which contributes to MM's resistance to PIs. Utilizing LPA or the LPAR2 receptor as therapeutic targets may enhance the effectiveness of PIs [ 40 ]. Whether the interaction between lipid and protein mentioned above promotes the occurrence and progression of MM, further research is needed to clarify the specific mechanism of action.

Anemia is a prevalent symptom in multiple myeloma (MM), affecting approximately 73% of patients at diagnosis [ 41 ]. While anemia is often attributed to hematopoietic suppression in MM patients, it can occur even with minimal malignant plasma cell infiltration in the bone marrow. The complex pathophysiological mechanisms underlying anemia in MM remain largely unknown.

In our current study, erythroblasts displayed the highest LMES value and strongly correlated with most of the 50 hallmark pathways, which suggests that abnormal lipid metabolism in erythroid precursors may be a reason for anemia in MM. A recent study published in BLOOD highlighted that PHOSPHO1, an enzyme responsible for the hydrolysis of choline phosphate to choline, is significantly upregulated during the final phase of erythropoiesis in both humans and mice. This upregulation accelerates the breakdown of choline phosphate and its precursor, phosphatidylcholine. The inhibition of phosphatidylcholine and phosphocholine metabolism from PHOSPHO1 knockdown lowers glycine and serine levels, forcing cells to depend on glycolysis for amino acid production. This leads to inadequate ATP production and aberrant lipid accumulation, adversely affecting erythropoiesis during the final differentiation stage [ 42 ]. Moreover, ferroptosis plays a crucial role in the physiological processes of embryonic erythropoiesis and erythrocyte senescence [ 43 , 44 ]. It has been demonstrated that activation of the LPA3 receptor can rescue elastin-induced ferroptosis and erythropoiesis defects by inhibiting lipid oxidation and iron accumulation in erythroleukemia K562 cells [ 45 ]. Thus, abnormal lipid metabolism in erythrocytes may impact their maturation and differentiation, offering new insights into the mechanisms underlying MM-associated anemia.

Conclusions

Consequently, understanding abnormal lipid metabolism-related gene in malignant PCs and the BMM of MM patients is crucial. This knowledge will aid in targeting lipid metabolism reprogramming, restoring immune homeostasis, and improving the prognosis of MM patients.

Limitations of Study

This study is subject to several limitations. It relies on data extracted from public databases, which allows for the identification of potential correlations between gene expression patterns and MM. However, these correlations are purely indicative. Establishing concrete causative relationships and delineating specific mechanisms of action require comprehensive experimental validation. This underscores the need for further in-depth experimental studies to substantiate the initial findings presented here.

Data availability

The datasets of this study mainly come from public databases. http://tisch.comp-genomics.org/home/ , GSE161801. https://www.ncbi.nlm.nih.gov/geo/ , GSE4204 and GSE136400.

Abbreviations

Acyl-CoA binding domain containing 6

Autologous stem cell transplantation

Transcription factor 4

The area under the curve

• Bone marrow microenvironment

Bone marrow adipose tissue

Bispecific antibodies

Chimeric antigen receptor T cell

Cell-type identification by estimating relative subsets of RNA transcripts

Concordance index

Differentially expressed genes

Fatty acid elongase6

Elastic network

Estimating the proportion of immune and cancer cells

The fatty acid transporter proteins

Free fatty acids

Fatty acid oxidation

Gradient boosting machine

Gene Expression Omnibus database

Gene ontology

Gene set enrichment analysis

Immunophenoscore

Kyoto encyclopedia of genes and genomes

Least absolute shrinkage and selection operator

Lipid metabolism-related gene enrichment scores

Differential expression genes related with lipid metabolism

• Lipid metabolism-related genes

Lipid metabolism-related genes with differential expression associated with MM prognosis

Lysophosphatidic acid

Lysophosphatidylcholine

Monoclonal antibodies

Microenvironment cell populations-counter

Major histocompatibility complex

• Multiple myeloma

Principal components analysis

Plasma cells

Proline hydroxylase-3

Proteasome inhibitors

Partial least squares regression for cox

Receiver operating characteristic

Relapsed refractory multiple myeloma

Risk scores

Random forest

Suppressor cell

Sterol regulatory element-binding proteins

Supervised principal components

Survival support vector machine

Tumor immune dysfunction and exclusion

Tumor immune estimation resource

The unified modular approximation and projection

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Dan Feng, Zhen Wang, Shengji Cao & Shijun Li

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Shijun Li conceived the research. Dan Feng performed the real data analysis. Zhen Wang performed the functional analysis. All authors wrote the paper. All authors read and approved the final manuscript.

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Feng, D., Wang, Z., Cao, S. et al. Identification of lipid metabolism-related gene signature in the bone marrow microenvironment of multiple myelomas through deep analysis of transcriptomic data. Clin Exp Med 24 , 136 (2024). https://doi.org/10.1007/s10238-024-01398-w

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DOI : https://doi.org/10.1007/s10238-024-01398-w

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Transcriptome-based analysis of the mechanism of action of metabolic disorders induced by waterborne copper stress in coilia nasus.

Simple Summary

1. introduction, 2. materials and methods, 2.1. experimental materials and fish, 2.2. management methods, 2.3. rna extraction, library construction, and sequencing, 2.4. bioinformatics analysis, 2.5. differentially expressed genes (degs), 2.6. gene ontology (go) and kyoto encyclopedia of genes and genomes (kegg) enrichment analysis, 2.7. real-time pcr validation analysis for differentially expressed genes, 3.1. sequence comparison reference statistics, 3.2. sample relationship analysis, 3.3. differentially expressed genes (degs), 3.4. go and kegg pathway enrichment analysis, 3.5. validation of rna-seq results with qrt-rcr, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Huang, D.; Zhang, L.; Mi, H.; Teng, T.; Liang, H.; Ren, M. Transcriptome-Based Analysis of the Mechanism of Action of Metabolic Disorders Induced by Waterborne Copper Stress in Coilia nasus . Biology 2024 , 13 , 476. https://doi.org/10.3390/biology13070476

Huang D, Zhang L, Mi H, Teng T, Liang H, Ren M. Transcriptome-Based Analysis of the Mechanism of Action of Metabolic Disorders Induced by Waterborne Copper Stress in Coilia nasus . Biology . 2024; 13(7):476. https://doi.org/10.3390/biology13070476

Huang, Dongyu, Lu Zhang, Haifeng Mi, Tao Teng, Hualiang Liang, and Mingchun Ren. 2024. "Transcriptome-Based Analysis of the Mechanism of Action of Metabolic Disorders Induced by Waterborne Copper Stress in Coilia nasus " Biology 13, no. 7: 476. https://doi.org/10.3390/biology13070476

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1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-Treated Adult Zebrafish as a Model for Parkinsons Disease

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Dopamine (DA) is a neuroprotective catecholamine and neurotransmitter that works to regulate cognitive functions (Channer et al., 2023). Patients affected by Parkinsons Disease (PD) experience a loss of dopaminergic neurons, lower levels of DA, and downregulated neural DA production. This leads to cognitive and physical decline that is the hallmark of PD. Currently, no cure exists for this prevalent neurodegenerative disease. Danio rerio, or zebrafish, have become an increasingly popular disease model used in PD pharmaceutical development. This model still requires extensive development to better characterize which PD features are adequately represented. Furthermore, the great majority of PD zebrafish models have been performed by treating embryos, which may not be relevant towards age-related human PD development. As an improvement, mature D. rerio (18 months) were treated with the neurotoxic prodrug 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) through intraperitoneal injection to induce Parkinsonianism. Behavioral analysis confirmed the disparity in movement between saline-injected control and the MPTP experimental group, in which swim distance and speed were significantly lowered seven days after MPTP injection. Simultaneously, cognitive decline was apparent in MPTP zebrafish compared to control, demonstrated by decreased alternation in a y-maze. RT-qPCR confirmed trends consistent with downregulation in Parkinsonian genetic markers, specifically DA transporter (DAT), MAO-B, PINK1. In summary, mature zebrafish injected with MPTP present with similar movement and cognitive decline as compared to human disease. Despite its benefits, this model does not appear to recapitulate full pathophysiology of the disease with the full profile of expected gene downregulation. Because of this, it is important that researchers looking for pharmacological interventions for PD only use this zebrafish model when targeting the human-relevant PD symptoms and causes that are represented.

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The authors have declared no competing interest.

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