difference between experimental and observational research design

Observational vs. Experimental Study: A Comprehensive Guide

Explore the fundamental disparities between experimental and observational studies in this comprehensive guide by Santos Research Center, Corp. Uncover concepts such as control group, random sample, cohort studies, response variable, and explanatory variable that shape the foundation of these methodologies. Discover the significance of randomized controlled trials and case control studies, examining causal relationships and the role of dependent variables and independent variables in research designs.

This enlightening exploration also delves into the meticulous scientific study process, involving survey members, systematic reviews, and statistical analyses. Investigate the careful balance of control group and treatment group dynamics, highlighting how researchers meticulously assign variables and analyze statistical patterns to discern meaningful insights. From dissecting issues like lung cancer to understanding sleep patterns, this guide emphasizes the precision of controlled experiments and controlled trials, where variables are isolated and scrutinized, paving the way for a deeper comprehension of the world through empirical research.

Introduction to Observational and Experimental Studies

These two studies are the cornerstones of scientific inquiry, each offering a distinct approach to unraveling the mysteries of the natural world.

Observational studies allow us to observe, document, and gather data without direct intervention. They provide a means to explore real-world scenarios and trends, making them valuable when manipulating variables is not feasible or ethical. From surveys to meticulous observations, these studies shed light on existing conditions and relationships.

Experimental studies , in contrast, put researchers in the driver's seat. They involve the deliberate manipulation of variables to understand their impact on specific outcomes. By controlling the conditions, experimental studies establish causal relationships, answering questions of causality with precision. This approach is pivotal for hypothesis testing and informed decision-making.

At Santos Research Center, Corp., we recognize the importance of both observational and experimental studies. We employ these methodologies in our diverse research projects to ensure the highest quality of scientific investigation and to answer a wide range of research questions.

Observational Studies: A Closer Look

In our exploration of research methodologies, let's zoom in on observational research studies—an essential facet of scientific inquiry that we at Santos Research Center, Corp., expertly employ in our diverse research projects.

What is an Observational Study?

Observational research studies involve the passive observation of subjects without any intervention or manipulation by researchers. These studies are designed to scrutinize the relationships between variables and test subjects, uncover patterns, and draw conclusions grounded in real-world data.

Researchers refrain from interfering with the natural course of events in controlled experiment. Instead, they meticulously gather data by keenly observing and documenting information about the test subjects and their surroundings. This approach permits the examination of variables that cannot be ethically or feasibly manipulated, making it particularly valuable in certain research scenarios.

Types of Observational Studies

Now, let's delve into the various forms that observational studies can take, each with its distinct characteristics and applications.

Cohort Studies:  A cohort study is a type of observational study that entails tracking one group of individuals over an extended period. Its primary goal is to identify potential causes or risk factors for specific outcomes or treatment group. Cohort studies provide valuable insights into the development of conditions or diseases and the factors that influence them.

Case-Control Studies:  Case-control studies, on the other hand, involve the comparison of individuals with a particular condition or outcome to those without it (the control group). These studies aim to discern potential causal factors or associations that may have contributed to the development of the condition under investigation.

Cross-Sectional Studies:  Cross-sectional studies take a snapshot of a diverse group of individuals at a single point in time. By collecting data from this snapshot, researchers gain insights into the prevalence of a specific condition or the relationships between variables at that precise moment. Cross-sectional studies are often used to assess the health status of the different groups within a population or explore the interplay between various factors.

Advantages and Limitations of Observational Studies

Observational studies, as we've explored, are a vital pillar of scientific research, offering unique insights into real-world phenomena. In this section, we will dissect the advantages and limitations that characterize these studies, shedding light on the intricacies that researchers grapple with when employing this methodology.

Advantages: One of the paramount advantages of observational studies lies in their utilization of real-world data. Unlike controlled experiments that operate in artificial settings, observational studies embrace the complexities of the natural world. This approach enables researchers to capture genuine behaviors, patterns, and occurrences as they unfold. As a result, the data collected reflects the intricacies of real-life scenarios, making it highly relevant and applicable to diverse settings and populations.

Moreover, in a randomized controlled trial, researchers looked to randomly assign participants to a group. Observational studies excel in their capacity to examine long-term trends. By observing one group of subjects over extended periods, research scientists gain the ability to track developments, trends, and shifts in behavior or outcomes. This longitudinal perspective is invaluable when studying phenomena that evolve gradually, such as chronic diseases, societal changes, or environmental shifts. It allows for the detection of subtle nuances that may be missed in shorter-term investigations.

Limitations: However, like any research methodology, observational studies are not without their limitations. One significant challenge of statistical study lies in the potential for biases. Since researchers do not intervene in the subjects' experiences, various biases can creep into the data collection process. These biases may arise from participant self-reporting, observer bias, or selection bias in random sample, among others. Careful design and rigorous data analysis are crucial for mitigating these biases.

Another limitation is the presence of confounding variables. In observational studies, it can be challenging to isolate the effect of a specific variable from the myriad of other factors at play. These confounding variables can obscure the true relationship between the variables of interest, making it difficult to establish causation definitively. Research scientists must employ statistical techniques to control for or adjust these confounding variables.

Additionally, observational studies face constraints in their ability to establish causation. While they can identify associations and correlations between variables, they cannot prove causality or causal relationship. Establishing causation typically requires controlled experiments where researchers can manipulate independent variables systematically. In observational studies, researchers can only infer potential causation based on the observed associations.

Experimental Studies: Delving Deeper

In the intricate landscape of scientific research, we now turn our gaze toward experimental studies—a dynamic and powerful method that Santos Research Center, Corp. skillfully employs in our pursuit of knowledge.

What is an Experimental Study?

While some studies observe and gather data passively, experimental studies take a more proactive approach. Here, researchers actively introduce an intervention or treatment to an experiment group study its effects on one or more variables. This methodology empowers researchers to manipulate independent variables deliberately and examine their direct impact on dependent variables.

Experimental research are distinguished by their exceptional ability to establish cause-and-effect relationships. This invaluable characteristic allows researchers to unlock the mysteries of how one variable influences another, offering profound insights into the scientific questions at hand. Within the controlled environment of an experimental study, researchers can systematically test hypotheses, shedding light on complex phenomena.

Key Features of Experimental Studies

Central to statistical analysis, the rigor and reliability of experimental studies are several key features that ensure the validity of their findings.

Randomized Controlled Trials:  Randomization is a critical element in experimental studies, as it ensures that subjects are assigned to groups in a random assignment. This randomly assigned allocation minimizes the risk of unintentional biases and confounding variables, strengthening the credibility of the study's outcomes.

Control Groups:  Control groups play a pivotal role in experimental studies by serving as a baseline for comparison. They enable researchers to assess the true impact of the intervention being studied. By comparing the outcomes of the intervention group to those of survey members of the control group, researchers can discern whether the intervention caused the observed changes.

Blinding:  Both single-blind and double-blind techniques are employed in experimental studies to prevent biases from influencing the study or controlled trial's outcomes. Single-blind studies keep either the subjects or the researchers unaware of certain aspects of the study, while double-blind studies extend this blindness to both parties, enhancing the objectivity of the study.

These key features work in concert to uphold the integrity and trustworthiness of the results generated through experimental studies.

Advantages and Limitations of Experimental Studies

As with any research methodology, this one comes with its unique set of advantages and limitations.

Advantages:  These studies offer the distinct advantage of establishing causal relationships between two or more variables together. The controlled environment allows researchers to exert authority over variables, ensuring that changes in the dependent variable can be attributed to the independent variable. This meticulous control results in high-quality, reliable data that can significantly contribute to scientific knowledge.

Limitations:  However, experimental ones are not without their challenges. They may raise ethical concerns, particularly when the interventions involve potential risks to subjects. Additionally, their controlled nature can limit their real-world applicability, as the conditions in experiments may not accurately mirror those in the natural world. Moreover, executing an experimental study in randomized controlled, often demands substantial resources, with other variables including time, funding, and personnel.

Observational vs Experimental: A Side-by-Side Comparison

Having previously examined observational and experimental studies individually, we now embark on a side-by-side comparison to illuminate the key distinctions and commonalities between these foundational research approaches.

Key Differences and Notable Similarities

Methodologies

• Observational Studies : Characterized by passive observation, where researchers collect data without direct intervention, allowing the natural course of events to unfold.
• Experimental Studies : Involve active intervention, where researchers deliberately manipulate variables to discern their impact on specific outcomes, ensuring control over the experimental conditions.
• Observational Studies : Designed to identify patterns, correlations, and associations within existing data, shedding light on relationships within real-world settings.
• Experimental Studies : Geared toward establishing causality by determining the cause-and-effect relationships between variables, often in controlled laboratory environments.
• Observational Studies : Yield real-world data, reflecting the complexities and nuances of natural phenomena.
• Experimental Studies : Generate controlled data, allowing for precise analysis and the establishment of clear causal connections.

Observational studies excel at exploring associations and uncovering patterns within the intricacies of real-world settings, while experimental studies shine as the gold standard for discerning cause-and-effect relationships through meticulous control and manipulation in controlled environments. Understanding these differences and similarities empowers researchers to choose the most appropriate method for their specific research objectives.

When to Use Which: Practical Applications

The decision to employ either observational or experimental studies hinges on the research objectives at hand and the available resources. Observational studies prove invaluable when variable manipulation is impractical or ethically challenging, making them ideal for delving into long-term trends and uncovering intricate associations between certain variables (response variable or explanatory variable). On the other hand, experimental studies emerge as indispensable tools when the aim is to definitively establish causation and methodically control variables.

At Santos Research Center, Corp., our approach to both scientific study and methodology is characterized by meticulous consideration of the specific research goals. We recognize that the quality of outcomes hinges on selecting the most appropriate method of research study. Our unwavering commitment to employing both observational and experimental research studies further underscores our dedication to advancing scientific knowledge across diverse domains.

Conclusion: The Synergy of Experimental and Observational Studies in Research

In conclusion, both observational and experimental studies are integral to scientific research, offering complementary approaches with unique strengths and limitations. At Santos Research Center, Corp., we leverage these methodologies to contribute meaningfully to the scientific community.

Explore our projects and initiatives at Santos Research Center, Corp. by visiting our website or contacting us at (813) 249-9100, where our unwavering commitment to rigorous research practices and advancing scientific knowledge awaits.

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Statistics By Jim

Making statistics intuitive

Observational Study vs Experiment with Examples

By Jim Frost 1 Comment

Comparing Observational Studies vs Experiments

Observational studies and experiments are two standard research methods for understanding the world. Both research designs collect data and use statistical analysis to understand relationships between variables. Beyond that commonality, they are vastly different and have dissimilar sets of pros and cons.

Experiments are controlled investigations where researchers actively manipulate one or more variables to observe the effect on another variable, all within a carefully controlled environment. Researchers must be able to control the treatment condition each subject experiences. Experiments typically use randomization to equalize the experimental groups at the start of the study to control potential confounders.

In this post, we’ll compare an observational study vs experiment, highlighting their definitions, strengths, and when to use them effectively. I work through an example showing how a study can use either approach to answer the same research question.

Learn more about Experimental Design: Definition and Types and Confounding Variable Bias .

Strengths of Observational Studies

Real-World Insights : Observational studies reflect real-world scenarios, providing valuable insights into how things naturally occur. Well-designed observational studies have high external validity , specifically ecological validity .

Does Not Require Randomization : Observational studies shine when researchers can’t manipulate treatment conditions or ethical constraints prevent randomization. For example, studying the long-term effects of smoking requires an observational approach because we can’t ethically assign people to smoke or abstain from smoking.

Cost-Effective : Observational studies are generally less expensive and time-consuming than experiments.

Longitudinal Research : They are well-suited for long-term studies or those tracking trends over time.

Strengths of Experiments

Causality : Experiments are the gold standard for establishing causality. By controlling variables and randomly assigning treatment conditions to participants, researchers can confidently attribute changes to the manipulated factor . Well-designed experiments have high internal validity . Learn more about Correlation vs. Causation: Understanding the Differences .

Controlled Environment : Experiments offer a controlled environment, reducing the influence of confounding variables and enhancing the reliability of results.

Replicability : Well-designed experiments are often easier to replicate, increasing researchers’ ability to compare and confirm results.

Randomization : Random assignment in experiments minimizes bias, ensuring all groups are comparable. Learn more about Random Assignment in Experiments .

When to Choose Observational Studies vs Experiments

Observational studies vs experiments are two vital tools in the statistician ’s arsenal, each offering unique advantages.

Experiments excel in establishing causality, controlling variables, and minimizing the impact of confounders. However, they are more expensive and randomly assigning subjects to the treatment groups is impossible in some settings. Learn more about Randomized Controlled Trials .

Meanwhile, observational studies provide real-world insights, are less expensive, and do not require randomization but are more susceptible to the effects of confounders. Identifying causal relationships is problematic in these studies. Learn more about Observational Studies: Definition & Examples  and Correlational Studies .

Observational studies can be prospective or retrospective studies . On the other hand, randomized experiments must be prospective studies .

The choice between an observational study vs experiment hinges on your research objectives, the context in which you’re working, available time and resources, and your ability to assign subjects to the experimental groups and control other variables.

If you’re looking for a middle ground choice between observational studies vs experiments, consider using a quasi-experimental design. These methods don’t require you to randomly assign participants to the experimental groups and still allow you to draw better causal conclusions about an intervention than an observational study. Learn more about Quasi-Experimental Design Overview & Examples .

Understanding their strengths and differences will help you make the right choice for your statistical endeavors.

Observational Study vs Experiment Example

Suppose you want to assess the health benefits of consuming a daily multivitamin. Let’s explore how an observational study vs experiment would evaluate this research question and their pros and cons.

An observational study will recruit subjects and have them record their vitamin consumption, various health outcomes, and, ideally, record confounding variables. The participants choose whether or not to take vitamins during the study based on their existing habits. Some medical measurements might occur in a lab setting, but researchers are not administering treatments (vitamins). Then, using statistical models, researchers can evaluate the relationship between vitamin consumption and health outcomes while controlling for potential confounders they measured.

An experiment will recruit subjects and then randomly assign them to the treatment group that takes daily vitamins or the control group taking a placebo . Randomization controls all confounders whether the researchers know of them or not. Finally, the researchers compare the treatment to the control group. Learn more about Control Groups in Experiments .

Most vitamin studies are observational because the randomization process would be challenging to implement, and it raises ethical concerns in this context. The random assignment process would override the participants’ preferences for taking vitamins by randomly forcing subjects to consume vitamins or placebos for decades . That’s how long it takes for the differences in health outcomes to manifest. Consequently, enforcing the rigid protocol for so long would be difficult and unethical.

For an observational study, a critical downside is that the pre-existing differences between those who do and do not take vitamins daily comprise a pretty long list of health-related habits and medical measures. Any of them can potentially explain the difference in outcomes instead of the vitamin consumption!

As you can see, using an observational study vs experiment involves many tradeoffs! Let’s close with a table that summarizes the differences.

Differences between an Observational Study and Experiment

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October 22, 2023 at 11:17 pm

Well stated: ” Both research designs collect data and use statistical analysis to understand relationships between variables” I was not familiar with the terms research designs. 😀

PS, I am already receiving all your wonderful mailing. I binge-read them every few weeks. I am planning on getting your other two books when I can. Thanks, and Cheers!

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Experiment vs Observational Study: Similarities & Differences

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

Learn about our Editorial Process

An experiment involves the deliberate manipulation of variables to observe their effect, while an observational study involves collecting data without interfering with the subjects or variables under study.

This article will explore both, but let’s start with some quick explanations:

• Experimental Study : An experiment is a research design wherein an investigator manipulates one or more variables to establish a cause-effect relationship (Tan, 2022). For example, a pharmaceutical company may conduct an experiment to find out if a new medicine for diabetes is effective by administering it to a selected group (experimental group), while not administering it to another group (control group).
• Observational Study : An observational study is a type of research wherein the researcher observes characteristics and measures variables of interest in a subset of a population, but does not manipulate or intervene (Atkinson et al., 2021). An example may be a sociologist who conducts a cross-sectional survey of the population to determine health disparities across different income groups. 

Experiment vs Observational Study

1. experiment.

An experiment is a research method characterized by a high degree of experimental control exerted by the researcher. In the context of academia, it allows for the testing of causal hypotheses (Privitera, 2022).

When conducting an experiment, the researcher first formulates a hypothesis , which is a predictive statement about the potential relationship between at least two variables.

For instance, a psychologist may want to test the hypothesis that participation in physical exercise ( independent variable ) improves the cognitive abilities (dependent variable) of the elderly.

In an experiment, the researcher manipulates the independent variable(s) and then observes the effects on the dependent variable(s). This method of research involves two or more comparison groups—an experimental group that is subjected to the variable being tested and a control group that is not (Sampselle, 2012).

For instance, in the physical exercise study noted above, the psychologist would administer a physical exercise regime to an experimental group of elderly people, while a control group would continue with their usual lifestyle activities .

One of the unique features of an experiment is random assignment . Participants are randomly allocated to either the experimental or control groups to ensure that every participant has an equal chance of being in either group. This reduces the risk of confounding variables and increases the likelihood that the results are attributable to the independent variable rather than another factor (Eich, 2014).

For instance, in the physical exercise example, the psychologist would randomly assign participants to the experimental or control group to reduce the potential impact of external variables such as diet or sleep patterns.

1. Impacts of Films on Happiness: A psychologist might create an experimental study where she shows participants either a happy, sad, or neutral film (independent variable) then measures their mood afterward (dependent variable). Participants would be randomly assigned to one of the three film conditions.

2. Impacts of Exercise on Weight Loss: In a fitness study, a trainer could investigate the impact of a high-intensity interval training (HIIT) program on weight loss. Half of the participants in the study are randomly selected to follow the HIIT program (experimental group), while the others follow a standard exercise routine (control group).

3. Impacts of Veganism on Cholesterol Levels: A nutritional experimenter could study the effects of a particular diet, such as veganism, on cholesterol levels. The chosen population gets assigned either to adopt a vegan diet (experimental group) or stick to their usual diet (control group) for a specific period, after which cholesterol levels are measured.

Read More: Examples of Random Assignment

Strengths and Weaknesses

Read More: Experimental Research Examples

2. Observational Study

Observational research is a non-experimental research method in which the researcher merely observes the subjects and notes behaviors or responses that occur (Ary et al., 2018).

This approach is unintrusive in that there is no manipulation or control exerted by the researcher. For instance, a researcher could study the relationships between traffic congestion and road rage by just observing and recording behaviors at a set of busy traffic lights, without applying any control or altering any variables.

In observational studies, the researcher distinguishes variables and measures their values as they naturally occur. The goal is to capture naturally occurring behaviors , conditions, or events (Ary et al., 2018).

For example, a sociologist might sit in a cafe to observe and record interactions between staff and customers in order to examine social and occupational roles .

There is a significant advantage of observational research in that it provides a high level of ecological validity – the extent to which the data collected reflects real-world situations – as the behaviors and responses are observed in a natural setting without experimenter interference (Holleman et al., 2020)

However, the inability to control various factors that might influence the observations may expose these studies to potential confounding bias , a consideration researchers must take into account (Schober & Vetter, 2020).

1. Behavior of Animals in the Wild: Zoologists often use observational studies to understand the behaviors and interactions of animals in their natural habitats. For instance, a researcher could document the social structure and mating behaviors of a wolf pack over a period of time.

2. Impact of Office Layout on Productivity: A researcher in organizational psychology might observe how different office layouts affect staff productivity and collaboration. This involves the observation and recording of staff interactions and work output without altering the office setting.

3. Foot Traffic and Retail Sales: A market researcher might conduct an observational study on how foot traffic (the number of people passing by a store) impacts retail sales. This could involve observing and documenting the number of walk-ins, time spent in-store, and purchase behaviors.

Read More: Observational Research Examples

Experimental and Observational Study Similarities and Differences

Experimental and observational research both have their place – one is right for one situation, another for the next.

Experimental research is best employed when the aim of the study is to establish cause-and-effect relationships between variables – that is, when there is a need to determine the impact of specific changes on the outcome (Walker & Myrick, 2016).

One of the standout features of experimental research is the control it gives to the researcher, who dictates how variables should be changed and assigns participants to different conditions (Privitera, 2022). This makes it an excellent choice for medical or pharmaceutical studies, behavioral interventions, and any research where hypotheses concerning influence and change need to be tested.

For example, a company might use experimental research to understand the effects of staff training on job satisfaction and productivity.

Observational research , on the other hand, serves best when it’s vital to capture the phenomena in their natural state, without intervention, or when ethical or practical considerations prevent the researcher from manipulating the variables of interest (Creswell & Poth, 2018).

It is the method of choice when the interest of the research lies in describing what is, rather than altering a situation to see what could be (Atkinson et al., 2021).

This approach might be utilized in studies that aim to describe patterns of social interaction, daily routines, user experiences, and so on. A real-world example of observational research could be a study examining the interactions and learning behaviors of students in a classroom setting.

I’ve demonstrated their similarities and differences a little more in the table below:

Experimental and observational research each have their place, depending upon the study. Importantly, when selecting your approach, you need to reflect upon your research goals and objectives, and select from the vast range of research methodologies , which you can read up on in my next article, the 15 types of research designs .

Ary, D., Jacobs, L. C., Irvine, C. K. S., & Walker, D. (2018). Introduction to research in education . London: Cengage Learning.

Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J. W., & Williams, R. A. (2021). SAGE research methods foundations . New York: SAGE Publications Ltd.

Creswell, J.W., and Poth, C.N. (2018). Qualitative Inquiry and Research Design: Choosing among Five Approaches . New York: Sage Publications.

Eich, E. (2014). Business Research Methods: A Radically Open Approach . Frontiers Media SA.

Holleman, G. A., Hooge, I. T., Kemner, C., & Hessels, R. S. (2020). The ‘real-world approach’and its problems: A critique of the term ecological validity. Frontiers in Psychology , 11 , 721. doi: https://doi.org/10.3389/fpsyg.2020.00721  

Privitera, G. J. (2022). Research methods for the behavioral sciences . Sage Publications.

Sampselle, C. M. (2012). The Science and Art of Nursing Research . South University Online Press.

Schober, P., & Vetter, T. R. (2020). Confounding in observational research. Anesthesia & Analgesia , 130 (3), 635.

Tan, W. C. K. (2022). Research methods: A practical guide for students and researchers . World Scientific.

Walker, D., and Myrick, F. (2016). Grounded Theory: An Exploration of Process and Procedure . New York: Qualitative Health Research.

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Frequently asked questions

What is the difference between an observational study and an experiment.

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 .

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.

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.

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.

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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Observational Study vs Experimental Study: Which One is Right for Your Research?

When conducting research, it is essential to carefully consider the study design, whether to opt for an observational or an experimental approach. Observational studies and experimental studies are two main categories of research studies, each with its own strengths and limitations.

In observational studies, researchers observe the effect of a risk factor or intervention without trying to change exposure. This type of study design is useful when it is unethical or impractical to conduct an experimental study . There are different types of observational studies , including cohort studies and case control studies . In cohort studies , researchers compare what happens to members of a group exposed to a variable to those who are not exposed. In case control studies , researchers compare individuals with and without a health problem.

On the other hand, experimental studies involve introducing an intervention and studying its effects. Randomized controlled trials (RCTs) are a type of experimental study where eligible people are randomly assigned to different groups to receive either the intervention or a control. RCTs are considered the gold standard for producing reliable evidence and establishing cause-effect relationships.

In the realm of scientific research, choosing the right methodology can make or break the validity and impact of a study. Two primary approaches dominate the field: observational and experimental studies. While observational studies excel in exploring relationships and correlations, they often grapple with limitations such as confounding variables and a lack of causal inference. 

On the other hand, experimental studies stand out for their ability to establish cause-and-effect relationships, thanks to the researcher’s ability to control variables. However, they are not without their drawbacks, including ethical considerations and sometimes limited real-world applicability.

Deciding when to use an experimental vs. observational study design hinges on your research objectives. Are you aiming for generalizability, or is pinpointing causality your main goal? The types of data collection methods you employ, whether quantitative or qualitative, will also depend on the design of your study. 

For instance, survey design is often more prevalent in observational research, while experimental studies may involve more controlled data collection techniques. Both approaches have their pros and cons, and understanding these can guide you in achieving robust data analysis and fulfilling your research objectives.

So, whether you’re comparing the validity in observational and experimental research or pondering the ethics involved in each, it’s crucial to weigh the advantages and limitations of your chosen method. 

This blog post aims to delve deeper into these aspects, offering a comprehensive guide to help you navigate the intricate landscape of research methods.When it comes to observational studies, they are open to dispute and can contain biases. However, they offer the advantage of studying phenomena as they naturally occur in real-life settings. 

Experimental studies, on the other hand, are more controlled and can provide stronger evidence of causal relationships. However, they are often more expensive and may require a smaller sample size compared to observational studies.

Ultimately, the choice between observational and experimental study design depends on the research question and available resources. It is important to carefully assess the objectives of the study, ethical considerations, and the level of control necessary to achieve reliable results.

Image Description1: Types of observational studies, types of experimental studies,differences between observational and experimental studies, pros and cons of observational studies

• Observational studies involve observing the effect of a risk factor or intervention without trying to change exposure.
• Cohort studies and case control studies are types of observational studies .
• Experimental studies involve introducing an intervention and studying its effects.
• Randomized controlled trials (RCTs) are a type of experimental study and considered the gold standard for producing reliable evidence.
• Observational studies are useful when it is unethical or impractical to conduct an RCT.

Observational Study vs Experimental Study

Understanding observational studies.

Observational studies involve observing the effect of a risk factor or intervention without trying to change exposure, providing valuable insights into real-world scenarios. These studies play a crucial role in understanding the relationship between variables and identifying potential associations. Two common types of observational studies are cohort studies and case control studies.

“Observational studies provide an opportunity to examine how certain factors may influence outcomes in a natural setting. They allow researchers to explore a wide range of variables, including genetic, environmental, and lifestyle factors, providing a comprehensive understanding of complex phenomena.”

In cohort studies, researchers follow a group of individuals over time, comparing those exposed to a particular variable with those who are not. This allows for the examination of the development of diseases or outcomes between the two groups. On the other hand, case control studies involve comparing individuals with a specific health problem (cases) to those without the health problem (controls). By examining the exposure history of both groups, researchers can identify potential risk factors.

While observational studies offer numerous advantages, such as their ability to explore a wide range of variables and provide insights into long-term outcomes, they also have limitations. One significant drawback is the potential for bias, as researchers cannot control the exposure or intervention being studied. Additionally, observational studies may lack the ability to establish cause-effect relationships and are subject to confounding variables. Ethical considerations regarding informed consent, privacy, and data collection also play a vital role in observational studies.

Overall, understanding observational studies is essential for researchers and professionals in various fields. They provide valuable data that can inform decision-making and contribute to the advancement of knowledge in many areas. It is crucial to acknowledge the strengths and limitations of observational studies when interpreting their findings and to consider them in conjunction with other study designs to gain a comprehensive understanding of the research question at hand.

Image Description: Ethics in observational and experimental studies, when to use experimental vs observational study, observational vs experimental study design

Exploring Experimental Studies

Experimental studies involve introducing an intervention and carefully examining its impact, allowing researchers to establish cause-effect relationships. This study design offers several advantages that make it a powerful tool in scientific research. One major advantage is the level of control that researchers have over the variables in the study. By manipulating the independent variable and controlling for confounding factors, researchers can isolate the effects of the intervention and draw reliable conclusions. This control over variables is crucial in establishing causal relationships between the intervention and the observed outcomes.

Another strength of experimental studies is the ability to randomize participants into different groups. This random assignment helps to minimize bias and ensures that the groups being compared are similar in terms of all potential confounding variables. Randomized controlled trials (RCTs), a type of experimental study, are often considered the gold standard for producing reliable evidence. These studies randomly assign eligible individuals to either the intervention group or a control group, allowing for a more rigorous examination of the intervention’s effects.

When it comes to data collection , experimental studies often employ standardized protocols and procedures. This consistency helps to ensure the reliability and validity of the data collected. By following a predetermined plan, researchers can gather detailed and accurate information on the intervention and its outcomes. This rigorous data collection process contributes to the overall strength and credibility of experimental studies.

Ethics in Observational and Experimental Studies

Ethical considerations play a significant role in both observational and experimental studies. In experimental studies, researchers must ensure that the intervention being introduced is safe and does not pose any harm to the participants. Additionally, informed consent is a critical ethical requirement, as participants must be fully informed about the nature of the study and any potential risks or benefits involved.

In observational studies, ethical concerns arise when researchers are observing subjects without intervening or manipulating any variables. It is essential to consider privacy and confidentiality, ensuring that the participants’ information is protected and used only for research purposes. Ethical guidelines and review boards help to ensure that both observational and experimental studies adhere to ethical standards and prioritize the welfare of the participants.

Image Description: Observational vs experimental data collection

Experimental studies offer several advantages, including the ability to establish cause-effect relationships, rigorous data collection protocols, and the use of random assignment to minimize bias. Researchers have control over the variables being studied and can carefully manipulate the intervention to examine its impact. These strengths contribute to the reliability and validity of the findings obtained from experimental studies.

Types of Observational Studies

Observational studies can be classified into various types, including cohort studies and case control studies, each with its own strengths and limitations. Cohort studies involve comparing what happens to members of a group exposed to a particular variable to those who are not exposed. This type of study allows researchers to study the natural progression of a disease or the impact of an intervention over time.

Case control studies, on the other hand, involve comparing individuals with a specific health problem (cases) to those without the problem (controls). This type of study is useful in investigating rare diseases or conditions with long latency periods. By identifying individuals who already have the health problem, researchers can then look back and determine whether certain factors were more prevalent in the cases compared to the controls.

While observational studies are valuable in providing insights into the association between risk factors or interventions and health outcomes, they do have drawbacks. Generalizability, or the ability to apply the findings to a larger population, can be limited due to the specific characteristics of the study sample. Additionally, observational studies may be prone to biases, such as selection bias or confounding variables, which can affect the accuracy and validity of the results.

Despite these limitations, observational studies play a crucial role in generating hypotheses and informing public health interventions. They provide valuable real-world data and can often be conducted ethically when it is not feasible or ethical to perform experimental studies. However, when establishing cause-effect relationships and minimizing biases are essential, experimental studies, such as randomized controlled trials (RCTs), are the preferred study design.

Image Description: Generalizability in observational vs experimental studies

Observational studies serve as a foundation for our understanding of health and disease, providing valuable data on the association between risk factors and outcomes. However, caution must be exercised in interpreting the findings, considering the limitations inherent in these study designs.

The Importance of Randomized Controlled Trials (RCTs)

Randomized controlled trials (RCTs) are considered the gold standard in research, providing rigorous evidence through random assignment of participants into different groups. By randomizing participants, RCTs ensure that any observed differences in outcomes between the intervention and control groups can be attributed to the effects of the intervention itself, rather than other factors. 

This design helps eliminate bias and increases the validity of the study findings.

One of the key strengths of RCTs is their ability to establish cause-effect relationships. By comparing outcomes between the intervention and control groups, researchers can determine whether the intervention actually caused the observed effects. This is crucial when evaluating the effectiveness of new treatments or interventions before they are widely implemented.

Another advantage of RCTs is their ability to control for confounding variables. 

Through random assignment, RCTs distribute these variables equally among the intervention and control groups, reducing the likelihood of confounding factors affecting the study results. This allows researchers to isolate the effects of the intervention, making the findings more reliable and applicable.

Moreover, RCTs are valuable in assessing the safety and efficacy of interventions. 

They provide objective data on the benefits and risks associated with the intervention, enabling informed decision-making in healthcare and policy development. RCTs are particularly important in evaluating the effectiveness of new drugs, medical devices, and treatments, ensuring that they are safe and effective before reaching the market.

However, it is important to acknowledge the limitations of observational research when compared to RCTs. 

Observational studies, such as cohort studies and case control studies, can be useful when RCTs are unethical or impractical to conduct. They can provide valuable insights into the natural course of diseases, risk factors, and long-term outcomes. However, they are open to biases and confounding factors, making it challenging to establish causality.

In conclusion , randomized controlled trials (RCTs) play a vital role in research, providing robust evidence through random assignment of participants and controlled experimental conditions. 

They are essential for establishing cause-effect relationships and evaluating the safety and efficacy of interventions. While observational studies have their place in research, RCTs are widely regarded as the gold standard for producing reliable evidence. Choosing the appropriate study design based on the research question and available resources is crucial in conducting valid and impactful research.

Image Description: Comparing validity in observational and experimental research

When to Use Observational Studies

Observational studies are particularly useful when ethical or practical constraints make it difficult to conduct experimental studies. These studies involve observing the effect of a risk factor or intervention without trying to change exposure. When it is unethical to intervene or manipulate variables, observational studies provide a valuable approach to gather data and investigate relationships between variables.

One scenario where observational studies are appropriate is when studying the long-term effects of a risk factor. For example, if researchers want to explore the link between smoking and lung cancer, it would be unethical to assign individuals to smoke for an extended period. Instead, they can observe a large group of smokers and non-smokers over time, collecting data on their health outcomes to assess the potential association.

Additionally, observational studies are beneficial when studying rare diseases or events. Since rare conditions have a low occurrence rate, it might be impractical to conduct a controlled experiment specifically targeting those conditions. Observational studies provide an opportunity to investigate such rare occurrences in a more natural setting, enabling researchers to gain insights into potential risk factors or causes.

Image Description: Observational study drawbacks, experimental study strengths

While observational studies have their strengths, they also present limitations. There is always a risk of biases and confounding variables influencing the observed associations. Causality cannot be definitively established in observational studies, as other factors may be contributing to the observed relationship. Additionally, data collected in observational studies often relies on self-reporting, which can introduce inaccuracies.

In summary, when ethical or practical constraints limit the feasibility of conducting experimental studies, observational studies offer a valuable alternative. They allow researchers to gather data, observe associations, and explore relationships between variables in a real-world setting. However, it is crucial to be aware of the limitations of observational studies and carefully interpret the results to avoid drawing incorrect conclusions.

When to Use Experimental Studies

Experimental studies are particularly valuable when establishing cause-effect relationships and controlling variables is crucial for achieving research objectives . These studies involve introducing an intervention and carefully studying its effects under controlled conditions. By randomly assigning participants to different groups, researchers can minimize bias and draw conclusions about the impact of the intervention.

One of the primary advantages of experimental studies is their ability to provide strong evidence for causality. By manipulating the independent variable and measuring the dependent variable, researchers can establish a cause-and-effect relationship between the two. This is especially important when evaluating the effectiveness of new treatments or interventions.

Data collection in experimental studies is typically more structured and standardized compared to observational studies. Researchers can control variables, ensuring that any observed effects are directly attributed to the intervention being studied. This level of control allows for more accurate and reliable results.

However, it’s important to note that experimental studies may not always be feasible or ethical. Some research questions may involve interventions that are impractical or unethical to implement in a controlled setting. In such cases, observational studies can provide valuable insights. Additionally, experimental studies can be more time-consuming and expensive, requiring careful planning and sufficient resources.

Example of Experimental Study:

Research Objective: To investigate the effectiveness of a new drug in reducing symptoms of a specific medical condition.

This example highlights how an experimental study can provide valuable insights into the effectiveness of a new treatment. By controlling variables and manipulating the independent variable, researchers are able to establish a clear cause-effect relationship and draw conclusions about the impact of the intervention.


Designing Observational Studies

Designing observational studies requires careful planning, including determining research objectives , selecting appropriate survey designs, and choosing suitable data analysis methods. Observational studies are characterized by their ability to observe and collect data without directly manipulating variables or interventions. This study design is commonly used when it is unethical or impractical to conduct experimental studies, such as in cases where it is not possible to assign participants to different groups or manipulate certain variables.

When designing an observational study , researchers must first establish clear research objectives . This involves identifying the specific research question or hypothesis to be addressed and defining the scope and objectives of the study. By clearly outlining the goals of the research, researchers can ensure that their study design aligns with their objectives and addresses relevant variables or factors.

Image Description: Quantitative research, qualitative research, design of study, survey design, research objectives, data analysis

In terms of survey design , observational studies rely on the collection of data through observations or surveys. Researchers must carefully choose the appropriate survey design based on the nature of their research question and the target population. Common survey designs used in observational studies include cross-sectional studies, cohort studies, and case-control studies. Each design has its strengths and limitations, and researchers must select the most suitable design for their specific research objectives.

Data analysis is a crucial component of observational studies. Researchers must choose appropriate methods to analyze the collected data to draw meaningful conclusions. This may involve statistical analysis techniques, qualitative analysis methods, or a combination of both, depending on the nature of the data and research question. Data analysis methods must be selected in a way that allows for accurate interpretation and provides valuable insights into the observed phenomena.

Designing Experimental Studies

Designing experimental studies involves meticulous planning to ensure research objectives are met, data analysis is accurate, and variables are effectively controlled. In experimental studies, the primary goal is to establish cause-effect relationships by introducing an intervention and studying its effects. To achieve this, researchers must carefully consider several key aspects of study design.

Data Analysis: One of the crucial steps in designing an experimental study is determining the appropriate data analysis methods. Quantitative research methods, such as statistical analysis, are commonly used in experimental studies to analyze numerical data and identify patterns or associations. On the other hand, qualitative research methods, such as content analysis or thematic analysis, can provide valuable insights into participants’ perspectives and experiences. The choice of data analysis approach depends on the research objectives and the type of data collected.

Control Variables: Another important consideration is controlling variables. Experimental studies aim to isolate the effects of the intervention by controlling for other factors that could influence the outcome. This involves identifying and selecting relevant variables that may confound the results and implementing strategies to minimize their impact. By controlling variables, researchers can increase the internal validity of the study and strengthen the causal inference.

Sample Size and Sampling Strategy: Determining an appropriate sample size is crucial to ensure the study’s statistical power and generalizability of the findings. The sample size calculation should take into account the desired effect size, statistical significance level, and the anticipated variability of the outcome measure. Additionally, researchers need to carefully consider the sampling strategy to select a representative sample that accurately reflects the target population. Randomization techniques, such as simple random sampling or stratified random sampling, can help minimize bias and increase the external validity of the study.

Designing experimental studies requires careful planning and consideration of various factors to ensure the research objectives are achieved. By implementing robust study design and rigorous data analysis methods, researchers can produce reliable evidence and establish cause-effect relationships. However, it is important to note that experimental studies can be resource-intensive and may require specific expertise, making it essential to assess the available resources and feasibility before embarking on such research.

Choosing between observational studies and experimental studies depends on various factors, including research objectives, data collection methods, and the desired outcomes. Observational studies involve observing the effect of a risk factor or intervention without trying to change exposure. They are useful when it is unethical or impractical to conduct an experimental study, such as studying the effects of smoking on lung cancer. However, observational studies are open to dispute and can contain biases, such as selection bias or information bias.

On the other hand, experimental studies involve introducing an intervention and studying its effects. Randomized controlled trials (RCTs) are a type of experimental study that are considered the gold standard for producing reliable evidence. In RCTs, eligible participants are randomly assigned to different groups to receive either the intervention or a control. Experimental studies are more controlled and can establish cause-effect relationships, such as testing the effectiveness of a new drug. However, they are often expensive and may require a smaller sample size compared to observational studies.

It is crucial to select the most appropriate study design based on the research question and the available resources. If the research aims to investigate the association between two variables, and it is not feasible to change the exposure of participants, an observational study would be the preferred choice. However, if the research aims to establish causality and control the exposure, an experimental study, such as an RCT, would be more suitable.

In conclusion , both observational studies and experimental studies have their strengths and limitations. The choice between them depends on the specific research objectives, the ability to control the exposure or intervention, and the resources available. By understanding the differences between these study designs and considering these factors, researchers can make informed decisions to ensure the validity and reliability of their findings.

Q: What are the main differences between observational studies and experimental studies?

A: Observational studies involve observing the effect of a risk factor or intervention without trying to change exposure, while experimental studies involve introducing an intervention and studying its effects.

Q: What types of observational studies exist?

A: Cohort studies and case control studies are types of observational studies.

Q: How do cohort studies and case control studies differ?

A: In cohort studies, researchers compare what happens to members of a group exposed to a variable to those who are not exposed. In case control studies, researchers compare individuals with and without a health problem.

Q: What are randomized controlled trials (RCTs)?

A: Randomized controlled trials (RCTs) are a type of experimental study where eligible people are randomly assigned to different groups to receive either the intervention or a control. RCTs are considered the gold standard for producing reliable evidence.

Q: When are observational studies useful?

A: Observational studies are useful when it is unethical or impractical to conduct a randomized controlled trial (RCT).

Q: What are the advantages of experimental studies?

A: Experimental studies are more controlled and can establish cause-effect relationships. They are considered the gold standard for producing reliable evidence.

Q: What are the limitations of observational studies?

A: Observational studies are open to dispute and can contain biases. They also have limitations in terms of generalizability and establishing cause-effect relationships.

Q: Are observational studies more cost-effective compared to experimental studies?

A: Observational studies are generally more cost-effective compared to experimental studies, as they may require a smaller sample size.

Q: How do I choose the appropriate study design for my research?

A: The choice of study design depends on the specific research question and the available resources. Observational studies are preferred when ethical or practical constraints prevent the use of experimental studies, while experimental studies are suitable for establishing cause-effect relationships.

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Experiment vs Observational Study: A Deeper Look

When we read about research studies and reports, many are times that we fail to pay attention to the design of the study. For you to know the quality of the research findings, it is paramount to start by understanding some basics of research/study design.

The primary goal of doing a study is to evaluate the relationship between several variables. For example, does eating fast food result in teenagers being overweight? Or does going to college increase the chances of getting a job? Most studies fall into two main categories, observational and experimental studies, but what is the difference? Other widely accepted research types are cohort studies, randomized controls, and case-control studies, but these three are part of either experimental or observational study. Keep reading to understand the difference between observational study and experiment.

What Is An Observational Study?

To understand observational study vs experiment, let us start by looking at each of them.

So, what is an observational study ? This is a form of research where the measurement is done on the selected sample without running a control experiment. Therefore, the researcher observes the impact of a specific risk factor, such as treatment or intervention, without focusing on who is not exposed. It is simply a matter of observing what is happening.

When an observational report is released, it indicates that there might be a relationship between several variables, but this cannot be relied on. It is simply too weak or biased. We will demonstrate this with an example.

A study asking people how they liked a new film that was released a few months ago is a good example of an observational study. The researcher in the study does not have any control over the participants. Therefore, even if the study point to some relationship between the main variables, it is considered too weak. For example, the study did not factor in the possibility of viewers watching other films.

The main difference between an observational study and an experiment is that the latter is randomized . Again, unlike the observational study statistics, which are considered biased and weak, evidence from experimental research is stronger.

Advantages of Observational Studies

If you are thinking of carrying a research and have been wondering whether to go for randomized experiment vs observational study, here are some key advantages of the latter.

• Because the observational study does not require the use of control, it is inexpensive to undertake. Suppose you take the example of a study looking at the impact of introducing a new learning method into a school. In that case, all you need is to ask any interested students to participate in a survey with questions, such as “yes” and “no.”
• Doing observational research can also be pretty simple because you do not have to keep looking into multiple variables, and trying to control some groups.
• Sometimes the observational method is the only way to study some things, such as exposure to specific threats. For example, it might not be ethical to expose people to harmful variables, such as radiation. However, it is possible to study the exposed population living in affected areas using observational studies.

While the advantages of observational research might appear attractive, you need to weigh them against the cons. To run conclusive observational research, you might require a lot of time. Sometimes, this might run for years or decades.

The results from observational studies are also open to a lot of criticism because of confounding biases. For example, a cohort study might conclude that most people who love to meditate regularly suffer less from heart issues. However, this alone might not be the only cause of low cases of heart problems. The people who medicate might also be following healthy diets and doing a lot of exercises to stay healthy.

Types of Observational Studies

Observational studies further branches into several categories, including cohort study, cross-sectional, and case-control. Here is a breakdown of these different types of studies:

• Cohort Study

For study purposes, a “cohort” is a team or group of people who are somehow linked. Example, people born within a specific period might be referred to as a “birth cohort.”

The concept of cohort study edges close to that of experimental research. Here, the researcher records whether every participant in the cohort is affected by the selected variables. In a medical setting, the researcher might want to know whether the cohort population in the study got exposed to a certain variable and if they developed the medical condition of interest. This is the most preferred method of study when urgent response, especially to a public health concern, such as a disease outbreak is reported.

It is important to appreciate that this is different from experimental research because the investigator simply observes but does not determine the exposure status of the participants.

• Case Control Study

In this type of study, the researcher enrolls people with a health issue and another without the problem. Then, the two groups are compared based on exposure. The control group is used to generate an estimate of the expected exposure in the population.

• Cross-Sectional Research

This is the third type of observational type of study, and it involves taking a sample from a population that is exposed to health risk and measuring them to establish the extent of the outcome. This study is very common in health settings when researchers want to know the prevalence of a health condition at any specific moment. For example, in a cross-sectional study, some of the selected persons might have lived with high blood pressure for years, while others might have started seeing the signs recently.

Experimental Studies

Now that you know the observational study definition, we will now compare it with experiment research. So, what is experimental research?

In experimental design, the researcher randomly assigns a selected part of the population some treatment to make a cause and effect conclusion. The random selection of samples is largely what makes the experiment different from the observational study design.

The researcher controls the environment, such as exposure levels, and then checks the response produced by the population. In science, the evidence generated by experimental studies is stronger and less contested compared to that produced by observational studies.

Sometimes, you might find experimental study design being referred to as a scientific study. Always remember that when using experimental studies, you need two groups, the main experiment group (part of the population exposed to a variable) and the control (another group that does not get exposed/ treatment by the researcher).

Benefits of Using Experimental Study Design

Here are the main advantages to expect for using experimental study vs observational experiment.

• Most experimental studies are shorter and smaller compared to observational studies.
• The study, especially the selected sample and control group, is monitored closely to ensure the results are accurate.
• Experimental study is the most preferred method of study when targeting uncontested results.

When using experimental studies, it is important to appreciate that it can be pretty expensive because you are essentially following two groups, the experiment sample and control. The cost also arises from the factor that you might need to control the exposure levels and closely follow the progress before drawing a conclusion.

Observational Study vs Experiment: Examples

Now that we have looked at how each design, experimental and observational, work, we will now turn to examples and identify their application.

To improve the quality of life, many people are trying to quit smoking by following different strategies, but it is true that quitting is not easy. So the methods that are used by smokers include:

• Using drugs to reduce addiction to nicotine.
• Using therapy to train smokers how to stop smoking.
• Combining therapy and drugs.
• Cold turkey (neither of the above).

The variable in the study is (I, II, III, IV), and the outcome or response is success or failure to quit the problem of smoking. If you select to use an observational method, the values of the variables (I, ii, iii, iv) would happen naturally, meaning that you would not control them. In an experimental study, values would be assigned by the researcher, implying that you would tell the participants the methods to use. Here is a demonstration:

• Observational Study: Here, you would imagine a population of people trying to quit smoking. Then, use a survey, such as online or telephone interviews, to reach the smokers trying to stop the habit. After a year later, you reach the same persons again, to enquire whether they were successful. Note that you do not run any control over the population.
• Experimental study: In this case, a representative sample of smokers trying to stop the habit is selected through a survey. Say you reach about 1000. Now, the number is divided into four groups of 250 persons, and each group is allocated one of the four methods above (i, ii, iii, or iv).

The results from the experimental study might be as shown below:

From the results of the experimental study, we can say that combining therapy and drugs method helped most smokers to quit the habit successfully. Therefore, a policy can be developed to adopt the most successful method for helping smokers quit the problem.

It is important to note that both studies commence with a random sample. The difference between an observational study and an experiment is that the sample is divided in the latter while it is not in the former. In the case of the experimental study, the researcher is controlling the main variables and then checking the relationship.

A researcher picked a random sample of learners in a class and asked them about their study habits at home. The data showed that students who used at least 30 minutes to study after school scored better grades than those who never studied at all.

This type of study can be classified as observational because the researcher simply asked the respondents about their study habits after school. Because there was no group given a particular treatment, the study cannot qualify as experimental.

In another study, the researcher randomly picked two groups of students in school to determine the effectiveness of a new study method. Group one was asked to follow the new method for a period of three months, while the other was asked to simply study the way they were used. Then, the researcher checked the scores between the two groups to determine if the new method is better.

So, is this an experimental or observational study? This type of study can be categorized as experimental because the researcher randomly picked two groups of respondents. Then, one group was given some treatment, and the other one was not.

In one of the studies, the researcher took a random sample of people and looked at their eating habits. Then, every member was classified as either healthy or at risk of developing obesity. The researcher also drew recommendations to help people at risk of developing overweight issues to avoid the problem.

This type of study is observational because the researcher took a random sample but did no accord any group a special treatment. The study simply observed the people’s eating habits and classified them.

In one of the studies done in Japan, the researcher wanted to know the levels of radioactive materials in people’s tissues after the bombing of Hiroshima and Nagasaki in 1945. Therefore, he took a random sample of 1000 people in the region and asked them to get checked to determine the levels of radiation in their tissues.

After the study, the researcher concluded that the level of radiation in people’s tissues is still very high and might be associated with different types of diseases being reported in the region. Can you determine what type of study design this is?

The research is an example observational study because it did not have any control. The researcher only observed the levels but did not have any type of control group. Again, there was no special treatment to one of the study populations.

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If you are a researcher, it is very important to be able to define observational study and experiment research before commencing your work. This can help you to determine the different parameters and how to go about the study. As we have demonstrated, observational studies mainly involve gathering the findings from the field without trying to control the variables. Although this study’s results can be contested, it is the most recommended method when using other studies such as experimental design, is unfeasible or unethical.

Experimental studies giving the researcher greater control over the study population by controlling the variables. Although more expensive, it takes a relatively shorter time, and results are less biased.

Now, go ahead and design your study. Always remember that you can seek help from either your lecturer or an expert when designing the study. Once you understand the concept of observational study vs experiment well, research can become so enjoyable and fun.

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Experimental research vs. observational studies.

There are two main types of medical research studies, Observational and Experimental. The key difference between observational study design and experimental design is how participants are assigned to a particular group . In experimental studies the researcher assigns the participants to a particular group (usually by using randomization ). Unlike experimental research, in observational studies group assignment is controlled by “natural conditions.” In observational studies these natural conditions can include the study participant intentionally or unintentionally “assigning” themselves to a particular treatment or exposure. This group assignment can be based on personal preference, genetics, environment, social determinants and/or other factors. Researchers are essentially trying to be a “fly on the wall” without directly intervening in group assignment. They just want to OBSERVE what would have happened to the study participants naturally.

In an experimental research study the researcher may split participants into group A that receive a drug and group B that receives a placebo. In an observational study of this same drug the researchers are basically just observing people who would have taken the drug naturally. In this scenario you might observe patients who decided on their own volition to take the drug and compare them to a similar group of patients who are not currently taking the drug.

The experimental method is the “Gold Standard” for studying therapeutic interventions (treatments) or prophylactic interventions (prevention). The most common type of experimental research in medicine is the randomized controlled trial. Observational research is typically used to determine associations between health outcomes and exposures/risk factors. This can include studies on diagnosis, prognosis, etiology or harm. The most common types of observational research include cohort studies, case-control studies, case series, and cross-sectional studies.

Based on the higher amount of control the researchers have during experiments there is a higher level of validity and less bias than in observational studies. Well-designed experimental studies allow for us to determine causality (AKA this drug or exposure directly caused the change in outcomes). Alternatively, observational studies can only determine correlations between groups (AKA patients with a history of this exposure tend to have this outcome more often but we aren’t sure if the exposure caused the outcome). Observational studies have a much higher chance of confounding , because randomization cannot take place in these studies. Additionally, observational studies cannot be blinded .

However, there are some drawbacks to experimental research. These studies tend to require more resources (money) to perform. This also means it is not feasible to use experiments in order to study very rare outcomes since you would need a huge study population and that would be too expensive. In certain situations ethics also prevent experimental studies from being able to study exposures that are expected to cause harm. For example, if you wanted to study the effects of cigarettes you could not ethically assign patients to a group that required them to start smoking. By comparison some types of observational research are relatively cheap and fast. For example, if you wanted to study the effects of cigarettes you could likely find a data set that has already been collected by somebody else and just look at the group retrospectively. Since patients are getting exposures based on natural conditions you can also study certain exposures that are expected to cause harm without ethical concerns. If you are just observing somebody who decided to smoke on their own that isn’t an ethical concern like forcing a participant to smoke would be.

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Difference Between Observational Study And Experiment (Explained)

Welcome to our article on the difference between observational studies and experiments, two commonly used research methodologies in gathering information and studying the relationship between variables. Understanding these methodologies and their distinctions is crucial for researchers and anyone interested in research design and methodology.

Observational studies and experiments have different approaches to data collection and analysis. While observational studies focus on observing and analyzing existing relationships, experiments involve introducing interventions and studying their effects. Let’s dive deeper into each methodology and explore their strengths, weaknesses, and application in different research contexts.

Key Takeaways:

• An observational study involves observing and analyzing relationships without manipulating variables, while an experiment involves introducing interventions to establish cause-and-effect relationships.
• Observational studies provide insights into real-life circumstances and have lower costs but may lack the ability to establish causality.
• Experiments offer stronger evidence, greater control over variables, but can be more expensive and have limitations in terms of participant recruitment and study duration.
• Choosing the right methodology depends on the research question, available resources, and ethical considerations.
• Understanding the differences between observational studies and experiments can help researchers make informed decisions in designing their studies and interpreting their findings.

Table of Contents

Observational Study

An observational study is a research method that involves the observation and measurement of individuals or groups without manipulating the variables or imposing any kind of treatment or control group. It is a non-experimental approach that aims to gather data from real-life circumstances. The primary focus of an observational study is to observe and analyze existing relationships between variables.

In observational research , data collection methods can vary depending on the research question and context. Researchers may use techniques such as surveys, interviews, or direct observations to gather information. This flexibility allows for a comprehensive understanding of the phenomenon under study.

Observational studies provide valuable insights into the relationship between variables in real-life settings. They allow researchers to understand how variables interact and influence each other without intervention or manipulation.

However, it’s important to note that observational studies do not establish a cause-and-effect relationship between variables. While they provide valuable information, the findings should be interpreted with caution. Observational studies are generally less expensive and can take several years to complete due to the complexities of data collection and analysis.

Benefits and Limitations of Observational Studies

Observational studies offer several benefits in gathering data and studying real-life circumstances. Some key advantages include:

• Studying phenomena in natural settings: Observational studies provide the opportunity to study variables in their natural context, allowing for a better understanding of their real-world implications.
• Flexibility in data collection methods: Researchers can choose from a variety of data collection methods to suit the research question and context. This flexibility enables a comprehensive approach to data gathering.
• Studying rare events or conditions: Observational studies can be particularly useful in studying rare events or conditions that may be difficult to replicate in experimental settings.

However, observational studies also have limitations that should be considered. Some of these limitations include:

• Potential confounding biases: Observational studies may be subject to confounding biases, as researchers have limited control over variables and potential confounding factors.
• Inability to establish causality: Unlike experimental studies, observational studies cannot establish a cause-and-effect relationship between variables. The findings should be interpreted as associations or correlations.
• Reliance on existing data: Observational studies often rely on existing data sources, which may have limitations in terms of accuracy or relevance to the research question.

Table: Pros and Cons of Observational Studies

Experimental Study

An experimental study is a research methodology that involves randomly assigning participants to different groups and introducing an intervention to study its effects. This approach allows researchers to establish a cause-and-effect relationship between variables, providing stronger evidence compared to observational studies. Experimental studies are closely monitored and involve a more controlled environment.

One of the key features of experimental studies is the manipulation of variables. Researchers have control over the independent variable, which is the factor being manipulated, and can observe its impact on the dependent variable, which is the outcome being measured. By controlling other variables and randomizing the assignment of participants to groups, experimental studies aim to isolate the effects of the intervention.

Experimental studies are typically smaller in scale and shorter in duration compared to observational studies. Due to the controlled nature of the research design , experimental studies require careful planning and implementation. Researchers need to consider factors such as participant recruitment, ethical considerations, and the generalizability of findings to real-life settings.

Benefits and Limitations of Experimental Studies

Experimental studies offer several benefits. Firstly, they allow researchers to establish cause-and-effect relationships between variables, contributing to a deeper understanding of the research question. Secondly, experimental studies provide greater control over variables, minimizing the influence of confounding factors. Thirdly, the ability to manipulate interventions enables researchers to test specific hypotheses and theories.

Despite these benefits, experimental studies also have limitations. They can be expensive and require significant resources to execute. Participant recruitment may be challenging, as individuals may be hesitant to participate in studies that involve interventions or randomization. Ethical considerations must be carefully addressed to ensure the well-being and rights of participants. Additionally, while experimental studies provide valuable insights, the findings may not always generalize to real-life situations due to the controlled nature of the research.

In conclusion, experimental studies are a powerful research methodology for establishing cause-and-effect relationships. They offer control over variables and the ability to manipulate interventions, providing strong evidence for research questions. However, they require careful planning, resources, and ethical considerations. Researchers should weigh the benefits and limitations of experimental studies to determine if this methodology aligns with their research objectives.

Research Methodology Comparison

When conducting a study, researchers have the option to choose between an observational study or an experimental study. The main difference between these two methodologies lies in the level of control over variables and the ability to establish cause-and-effect relationships.

In an observational study, researchers observe and analyze existing relationships between variables without manipulating them. This method is particularly useful when studying phenomena in real-life contexts and when it is not possible or ethical to manipulate variables. Observational studies provide insights into the relationship between variables but do not establish a cause-and-effect relationship. They are also less expensive and can take several years to complete.

On the other hand, experimental studies involve randomly assigning participants to different groups and introducing an intervention to study its effects. This method allows researchers to establish a cause-and-effect relationship between variables. Although experimental studies are more controlled, they can be more expensive and require careful participant recruitment. They also tend to have shorter durations compared to observational studies.

When choosing the appropriate research methodology , it is important to consider the research question, available resources, and ethical considerations. Observational studies are suitable for exploring relationships in real-life contexts, while experimental studies are ideal for establishing cause-and-effect relationships and when control over variables is necessary.

Table: Research Methodology Comparison

In summary, observational studies focus on observing and analyzing existing relationships, while experimental studies involve manipulating variables to establish causal relationships. Both methodologies have their strengths and weaknesses, and the choice should be based on the specific research objectives and constraints.

Observational studies offer several benefits in gathering information and studying the relationship between variables. One of the key advantages is their ability to study phenomena in real-life settings, providing insights into how variables naturally interact in everyday situations. This allows researchers to understand the complexity of human behavior and factors that may influence outcomes. Additionally, observational studies provide the flexibility to use various data collection methods, such as surveys, interviews, or direct observation, depending on the research question and context.

Another benefit of observational studies is their capability to study rare events or conditions that may be difficult to replicate in experimental settings. By observing real-life scenarios, researchers can gather data on these unique occurrences and investigate their causes or effects. This can contribute to a deeper understanding of specific phenomena and help identify potential interventions or treatments.

However, observational studies also have limitations that researchers need to consider. One limitation is the potential for confounding biases, where uncontrolled variables may influence the relationship between the variables of interest. Without random assignment or control over the variables, it can be challenging to establish a cause-and-effect relationship. Additionally, observational studies rely on existing data, which may introduce inherent biases or limitations in terms of data quality or availability. These factors can impact the validity and generalizability of the findings.

Observational studies provide valuable insights into real-life scenarios and relationships between variables. They allow researchers to observe and understand behaviors and events as they naturally occur, providing a rich context for analysis. However, it is essential to consider the limitations of observational studies, such as potential biases and the inability to establish causal relationships. By carefully weighing their benefits and limitations , researchers can make informed decisions about when and how to use observational studies in their research designs.

Experimental studies offer several benefits that make them a valuable research methodology. One of the key advantages is their ability to establish cause-and-effect relationships between variables. By manipulating the independent variable and measuring its impact on the dependent variable, researchers can determine whether the intervention has a direct effect. This allows for a more rigorous examination of research questions and provides stronger evidence compared to observational studies.

Another advantage of experimental studies is the greater control over variables. Researchers can carefully design the study to control for confounding factors and minimize bias. By randomly assigning participants to different groups, they can ensure that the groups are similar in all aspects except for the intervention being tested. This control helps to isolate the effects of the intervention and increase the internal validity of the study.

Experimental studies provide stronger evidence compared to observational studies due to the control over variables and the random assignment of participants.

However, there are limitations to experimental studies that researchers should consider. One limitation is the cost and time required to conduct an experiment . Experimental studies often involve recruiting and managing participants, implementing the intervention, collecting data, and analyzing the results. These activities can be resource-intensive and may require a significant investment in time and funding.

Furthermore, experimental studies may have limitations in terms of participant recruitment and study duration. It can be challenging to recruit a sufficient number of participants who meet the inclusion criteria and are willing to participate in the study. Additionally, some research questions may require long-term follow-up, which can be challenging to sustain over an extended period.

Table: Benefits and Limitations of Experimental Studies

Choosing the Right Methodology

When embarking on a research project, selecting the appropriate methodology is crucial to ensure accurate and meaningful findings. The choice between an observational study and an experiment depends on various factors such as the research question, available resources, and ethical considerations.

An observational study is appropriate when the goal is to explore relationships in real-life contexts where manipulating variables is not feasible or ethical. This methodology involves observing and analyzing existing relationships without any intervention or control group. Observational studies provide valuable insights into how variables might be related but do not establish cause-and-effect relationships. Researchers can use various data collection methods, such as surveys, interviews, or existing records, to gather information.

An experiment , on the other hand, is ideal for establishing cause-and-effect relationships and requires control over variables. In experimental studies, researchers manipulate the independent variable and observe its effects on the dependent variable. This methodology involves randomly assigning participants to different groups, where one group receives the intervention or treatment while the other serves as a control. Experimental studies provide more rigor and stronger evidence compared to observational studies; however, they can be costlier and more time-consuming.

Before choosing a methodology, researchers should carefully consider their research objectives and the specific limitations and strengths associated with each approach. It is essential to prioritize the integrity of the study design and the reliability of the results by selecting the most appropriate methodology for the research question at hand.

Table: Comparison of Observational Study and Experiment

In conclusion, understanding the difference between observational studies and experiments is crucial for researchers in selecting the appropriate methodology for their studies. Observational studies focus on observing and analyzing existing relationships, while experiments involve manipulating variables to establish cause-and-effect relationships. Each methodology comes with its advantages and limitations.

Observational studies provide valuable insights into real-life circumstances and are less expensive to conduct. However, they cannot establish causality and may be subject to potential biases. On the other hand, experiments provide stronger evidence and control over variables but can be more costly and time-consuming.

When deciding which methodology to choose, researchers should consider their research question, available resources, and ethical considerations. Observational studies are suitable for exploring relationships in real-life contexts, while experiments are ideal for establishing cause-and-effect relationships. By carefully considering the benefits and limitations of each methodology, researchers can design robust studies and interpret their findings effectively.

What is the difference between an observational study and an experiment?

An observational study involves observing and analyzing the effect of a risk factor, treatment, or intervention without manipulating who is or isn’t exposed to it. An experiment, on the other hand, involves introducing an intervention and studying its effects by manipulating variables.

What is an observational study?

An observational study is a non- experimental research method where researchers observe and measure members of a sample without manipulating variables or imposing any treatment or control group.

What is an experiment?

An experiment is a research method where participants are randomly assigned to different groups and an intervention is introduced to study its effects. This allows researchers to establish a cause-and-effect relationship between variables.

What are the benefits of observational studies?

Observational studies provide insights into real-life circumstances, have flexibility in data collection methods, and can study rare events or conditions.

What are the limitations of observational studies?

Observational studies may be subject to confounding biases, cannot establish causality, and rely on existing data. They may also be subject to interpretation and biases due to the lack of control over variables.

What are the benefits of experimental studies?

Experimental studies allow researchers to establish cause-and-effect relationships, have greater control over variables, and can manipulate interventions.

What are the limitations of experimental studies?

Experimental studies can be expensive and time-consuming, may have limitations in participant recruitment and ethical considerations, and their findings may not always be generalizable to real-life settings.

How do I choose the right methodology?

The choice of methodology depends on the research question, available resources, and ethical considerations. Observational studies are suitable for exploring relationships in real-life contexts, while experiments are ideal for establishing cause-and-effect relationships and when control over variables is necessary.

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Experimental vs Quasi-Experimental Design: Which to Choose?

Here’s a table that summarizes the similarities and differences between an experimental and a quasi-experimental study design:

What is a quasi-experimental design?

A quasi-experimental design is a non-randomized study design used to evaluate the effect of an intervention. The intervention can be a training program, a policy change or a medical treatment.

Unlike a true experiment, in a quasi-experimental study the choice of who gets the intervention and who doesn’t is not randomized. Instead, the intervention can be assigned to participants according to their choosing or that of the researcher, or by using any method other than randomness.

Having a control group is not required, but if present, it provides a higher level of evidence for the relationship between the intervention and the outcome.

(for more information, I recommend my other article: Understand Quasi-Experimental Design Through an Example ) .

Examples of quasi-experimental designs include:

• One-Group Posttest Only Design
• Static-Group Comparison Design
• One-Group Pretest-Posttest Design
• Separate-Sample Pretest-Posttest Design

What is an experimental design?

An experimental design is a randomized study design used to evaluate the effect of an intervention. In its simplest form, the participants will be randomly divided into 2 groups:

• A treatment group: where participants receive the new intervention which effect we want to study.
• A control or comparison group: where participants do not receive any intervention at all (or receive some standard intervention).

Randomization ensures that each participant has the same chance of receiving the intervention. Its objective is to equalize the 2 groups, and therefore, any observed difference in the study outcome afterwards will only be attributed to the intervention – i.e. it removes confounding.

(for more information, I recommend my other article: Purpose and Limitations of Random Assignment ).

Examples of experimental designs include:

• Posttest-Only Control Group Design
• Pretest-Posttest Control Group Design
• Solomon Four-Group Design
• Matched Pairs Design
• Randomized Block Design

When to choose an experimental design over a quasi-experimental design?

Although many statistical techniques can be used to deal with confounding in a quasi-experimental study, in practice, randomization is still the best tool we have to study causal relationships.

Another problem with quasi-experiments is the natural progression of the disease or the condition under study — When studying the effect of an intervention over time, one should consider natural changes because these can be mistaken with changes in outcome that are caused by the intervention. Having a well-chosen control group helps dealing with this issue.

So, if losing the element of randomness seems like an unwise step down in the hierarchy of evidence, why would we ever want to do it?

This is what we’re going to discuss next.

When to choose a quasi-experimental design over a true experiment?

The issue with randomness is that it cannot be always achievable.

So here are some cases where using a quasi-experimental design makes more sense than using an experimental one:

• If being in one group is believed to be harmful for the participants , either because the intervention is harmful (ex. randomizing people to smoking), or the intervention has a questionable efficacy, or on the contrary it is believed to be so beneficial that it would be malevolent to put people in the control group (ex. randomizing people to receiving an operation).
• In cases where interventions act on a group of people in a given location , it becomes difficult to adequately randomize subjects (ex. an intervention that reduces pollution in a given area).
• When working with small sample sizes , as randomized controlled trials require a large sample size to account for heterogeneity among subjects (i.e. to evenly distribute confounding variables between the intervention and control groups).

Further reading

• Statistical Software Popularity in 40,582 Research Papers
• Checking the Popularity of 125 Statistical Tests and Models
• Objectives of Epidemiology (With Examples)
• 12 Famous Epidemiologists and Why

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Methods in epidemiology: observational study designs

Affiliation.

• 1 Department of Pharmacy Practice, Raabe College of Pharmacy, Ohio Northern University, Ada, Ohio 45810, USA.
• PMID: 20874034
• DOI: 10.1592/phco.30.10.973

This article is the first of a three-part series intended to enhance clinical pharmacists' understanding of methods frequently used in epidemiologic research and their applications. The basic tenets of epidemiology and uses for data derived from epidemiologic studies are given, along with a high-level overview of the differences between experimental and observational study designs. The defining characteristics of each of the observational study designs (case report or case series, ecologic, cross-sectional, cohort, case-control, nested case-control, and case-cohort) and the resultant strengths and limitations of the study designs are presented. Applications for observational studies in pharmacoepidemiology (including the case-crossover and case-time-control study designs) are discussed. Finally, points to consider when evaluating data from observational studies are addressed.

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Home » Correlational Research Vs Experimental Research

Correlational Research Vs Experimental Research

Table of Contents

Correlational research and experimental research are two different research approaches used in social sciences and other fields of research.

Correlational Research

Correlational Research is a research approach that examines the relationship between two or more variables. It involves measuring the degree of association or correlation between the variables without manipulating them. The goal of correlational research is to identify whether there is a relationship between the variables and the strength of that relationship. Correlational research is typically conducted through surveys, observational studies, or secondary data analysis.

Experimental Research

Experimental Research , on the other hand, is a research approach that involves the manipulation of one or more variables to observe the effect on another variable. The goal of experimental research is to establish a cause-and-effect relationship between the variables. Experimental research is typically conducted in a controlled environment and involves random assignment of participants to different groups to ensure that the groups are equivalent. The data is collected through measurements and observations, and statistical analysis is used to test the hypotheses.

Difference Between Correlational Research and Experimental Research

Here’s a comparison table that highlights the differences between correlational research and experimental research:

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Observational vs quasi-experimental design?

I am having some difficulty understanding the difference between and identifying an observational vs quasi-experimental design. From my understanding, an observational study is one in which the researcher does not influence the system and only records what they observe (duh). In an experimental study, the researcher manipulates the experimental units such they have different treatments and measures some resultant metric(s), usually to compare them. My understanding of quasi experimental studies is that the researcher uses groups that are already different from one another rather than apply the treatment to the EUs themselves.

For example, let's say a researcher is making observations on bird diversity in different land use types (eg forest vs agriculture). Odds are he or she isn't going to make a forest and farm and put birds in them to see which survive. They are going to go to several farms and several forests and observe the birds that live in each.

Now, it seems that this is a quasi-experimental design based on my above definitions, but does that mean that every comparison study is going to be either experimental or quasi experimental? I can't think of particularly many studies that would fall under the observational category, in that case (correlational, descriptive).

• experiment-design
• observational-study

5 Answers 5

First, as far as you have described the research design, the study is not a quasi-experiment.

I prefer the term natural experiment to quasi-experiment , because I think it more clearly communicates the fact that treatment needs to have been randomly assigned (or as-if randomly assigned). I use the term natural experiments below, but I consider the two equivalent in meaning.

You are correct that experiments are confined to those situations where a researcher actually manipulates treatment assignment.

Observational studies comprise anything that was not an experiment. Natural experiments are a subset of observational studies, but in a natural experiment units were assigned to treatment in a random process (or as-if random, or almost random).

You might look for a natural experiment (or quasi-experiment) if you were seeking to identify the causal effect of a treatment on a set of outcomes. Then you would look for a situation where assignment to that treatment was assigned randomly (or as-if randomly) by nature or a government program, for example. For example, if you wanted to study the impact of forest fires on bird diversity, you might find a place where the government has defined that it will fight fires when they come with X miles of residential areas. After forest fires, you could compare (i) bird diversity in areas affected by the forest fire just a little further than X miles away from residential areas (treatment group) to (ii) bird diversity in areas just a little less than X miles away from residential areas (control group). Because birds would not choose where to live prior to fire based on the government's designation of the distance X, we can expect that before the fire on either side of the X-mile cutoff, birds would be identical on average. There assignment to treatment (being "treated" by the forest fire) is as-if random on either side of the X-mile cutoff. This design is called a regression-discontinuity design [1] or a geographic regression discontinuity design [2].

Also, see more discussion of the difference here: Panel study is a quasi-experimental study? Quasi-experimental is the same as correlational?

• https://en.wikipedia.org/wiki/Regression_discontinuity_design
• "Geographic boundaries as regression discontinuities." LJ Keele, R Titiunik. Political Analysis , 2014

I can try to give an example from my own field, econometrics:

Economists are interested in "returns to schooling", i.e., how much more do you earn per additional year of schooling obtained.

An experiment is not an option, as, for good and obvious reasons, you cannot force people to continue or stop their educational career just because of the empirical analysis.

Observational data is generally tricky to interpret if you are interested in the causal effect of another year of schooling, because there are "confounders" that imply that the (generally positive) correlation between schooling and earnings is not (fully) causal. For example, more able, motivated and careful individuals can be thought to choose to obtain more schooling, and such individuals would have at least partly done well in the labor market without additional schooling.

Now, sometimes nature or law is kind enough to hand you a "quasi-experiment". In the above example, researchers have for example exploited changes in compulsory schooling laws. If, effective say 1955, all students in a country are obliged to attend secondary school for, say, 10 rather than 8 years, there will be at least some students who obtain more schooling only because of the new law, and not because they choose so.

Instrumental variable approaches may then be a credible way to exploit this so-called exogenous variation to say something about the causal effect of schooling.

The quasi experimental design is the one that uses an "experimental research procedure" but not all extraneous variables are controlled. Quasi experimental designs lack random assignment of participants to groups. Only in strong experimental designs is this achieved.

Causal inferences can only be done from quasi-experimental designs if (1) cause and effect covary, (2) cause must precede effect and (3) rival hypothesis must be implausible (so the relationship between variables must not be due a confounding extraneous variable).

Now, the third condition is hard to achieve since there is no randomization.

So we can see Quasi-experimental designs to be a better option than weak experimental designs and not as good as strong experimental designs.

For your bird-watching scenario, using a quasi-experimental design will not have a conclusive result and the relationship between the type of bird and the land use parameters might be affected by other variables like weather, migration seasons, temperature, humidity, wind orientation, etc. However this might be good enough for your study if you are not able to apply a strong experimental design.

On the other hand, the observational study draws inferences from a sample to a population where the independent variable is not under the control of the researcher. The observational study is then more into the data collection process, where you as a researcher must collect what you can, to draw inferences from there. The inference in this case (statistically speaking) could be managed by the amount and quality of attributes recorded. Naturalistic observation is conducted in real world observations and subject to noise and error. Since the observational study might be conducted in a single farm or a couple selected from the near-by surroundings, this will not be using a randomized sample which might also be prone to statistical error for causal relationships. The only way the observational study will be good enough to demonstrate cause and effect, will be when it is ran under laboratory conditions, say your birds are in a controlled environment, where several domes are created that represents each land/farm type and then you basically observe behavior or whatever you are measuring. The laboratory observation is closely similar to a quasi-experimental design since you are having control of a variable (the setting).

hope this helps.

I would like to answer your question from the Epidemiology point of view.

Basically,there are three kind of studies in Epidemiology, observational study, Experimental and theoretical study.

For observational studies, as a researcher you will not give any interventions to any groups you will study. You just collect data cross-sectionally, retrospectively or prospectively.

For experimental design, as a researcher you will allocate your intervention to some groups and other groups will not receive your intervention.

There are randomized experiments (such as clinical trials) and non-randomized experiment.

For randomized experiments patient belongs to which group is determined by randomization procedures).

For non-randomized experiment, which is also called quasi-experiment , there are no randomization procedures to allocate patients to different groups, it might just be done by convenience.

The point of experiments is to determine causality, which typically requires establishing that: 1) one thing happened before the other, 2) that the putative cause had some explanation mechanism for affecting the outcome, and 3) that there are no competing explanations or alternate causes. Also helps if the relationship is reliable--that the lights go on every time you hit the switch. Experiments are designed to establish these relationships, by controlling conditions to establish chronological sequence and control for possible alternate causes. Effective experimental design also includes a control: A population that is not given the experimental treatment.

In many cases, it's not possible/safe/legal to establish a control group before the experiment. In which case, it's a quasi-experiment. If the assumption that the affects of the treatment were random is true (ie, people weren't somehow selected for the treatment, by age/socio-economic status/race etc), then it's a control. The assumption is random assignment, but sometimes that assumption has to be relaxed (or controlled for); if not controlled for, it weakens the strength of your causal inferences.

Control groups also rely on the assumption that the two groups prior to the treatment were identical. This measurement is typically called the a 'pre-test' measurement. Then after the experimental treatment is applied, a 'post-test measure is made. The pre-test measure should be the same for both the treatment (experimental) and control groups. And then, if the experimental treatment did anything, the post-test value for the treatment and control groups should be different. In summary, both experiments (natural and otherwise) should have a pre-test, post-test and control group.

Actual observational studies belong to a completely different style of science: inductive rather than deductive, and can generally be identified with qualitative traditions. The essential difference is that numerical data sufficient for a statistical analysis isn't available. In a comparative case study, the sample might be as small as two. While their are a variety of qualitative techniques I'm not remotely qualified to talk about, I'll make a very broad generalization and say: they compare fewer, less well defined things, because part of the role of qualitative research is to define what things are: to create constructs an definitions and theorize (based on observations) what the possible causal relationships between things might be.

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• Experimental Vs Non-Experimental Research: 15 Key Differences

There is a general misconception around research that once the research is non-experimental, then it is non-scientific, making it more important to understand what experimental and experimental research entails. Experimental research is the most common type of research, which a lot of people refer to as scientific research. 

Non experimental research, on the other hand, is easily used to classify research that is not experimental. It clearly differs from experimental research, and as such has different use cases. 

In this article, we will be explaining these differences in detail so as to ensure proper identification during the research process.

What is Experimental Research?  

Experimental research is the type of research that uses a scientific approach towards manipulating one or more control variables of the research subject(s) and measuring the effect of this manipulation on the subject. It is known for the fact that it allows the manipulation of control variables. 

This research method is widely used in various physical and social science fields, even though it may be quite difficult to execute. Within the information field, they are much more common in information systems research than in library and information management research.

Experimental research is usually undertaken when the goal of the research is to trace cause-and-effect relationships between defined variables. However, the type of experimental research chosen has a significant influence on the results of the experiment.

Therefore bringing us to the different types of experimental research. There are 3 main types of experimental research, namely; pre-experimental, quasi-experimental, and true experimental research.

Pre-experimental Research

Pre-experimental research is the simplest form of research, and is carried out by observing a group or groups of dependent variables after the treatment of an independent variable which is presumed to cause change on the group(s). It is further divided into three types.

• One-shot case study research 
• One-group pretest-posttest research 
• Static-group comparison

Quasi-experimental Research

The Quasi type of experimental research is similar to true experimental research, but uses carefully selected rather than randomized subjects. The following are examples of quasi-experimental research:

• Time series 
• No equivalent control group design
• Counterbalanced design.

True Experimental Research

True experimental research is the most accurate type,  and may simply be called experimental research. It manipulates a control group towards a group of randomly selected subjects and records the effect of this manipulation.

True experimental research can be further classified into the following groups:

• The posttest-only control group 
• The pretest-posttest control group 
• Solomon four-group 

Pros of True Experimental Research

• Researchers can have control over variables.
• It can be combined with other research methods.
• The research process is usually well structured.
• It provides specific conclusions.
• The results of experimental research can be easily duplicated.

Cons of True Experimental Research

• It is highly prone to human error.
• Exerting control over extraneous variables may lead to the personal bias of the researcher.
• It is time-consuming.
• It is expensive. 
• Manipulating control variables may have ethical implications.
• It produces artificial results.

What is Non-Experimental Research?  

Non-experimental research is the type of research that does not involve the manipulation of control or independent variable. In non-experimental research, researchers measure variables as they naturally occur without any further manipulation.

This type of research is used when the researcher has no specific research question about a causal relationship between 2 different variables, and manipulation of the independent variable is impossible. They are also used when:

• subjects cannot be randomly assigned to conditions.
• the research subject is about a causal relationship but the independent variable cannot be manipulated.
• the research is broad and exploratory
• the research pertains to a non-causal relationship between variables.
• limited information can be accessed about the research subject.

There are 3 main types of non-experimental research , namely; cross-sectional research, correlation research, and observational research.

Cross-sectional Research

Cross-sectional research involves the comparison of two or more pre-existing groups of people under the same criteria. This approach is classified as non-experimental because the groups are not randomly selected and the independent variable is not manipulated.

For example, an academic institution may want to reward its first-class students with a scholarship for their academic excellence. Therefore, each faculty places students in the eligible and ineligible group according to their class of degree.

In this case, the student’s class of degree cannot be manipulated to qualify him or her for a scholarship because it is an unethical thing to do. Therefore, the placement is cross-sectional.

Correlational Research

Correlational type of research compares the statistical relationship between two variables .Correlational research is classified as non-experimental because it does not manipulate the independent variables.

For example, a researcher may wish to investigate the relationship between the class of family students come from and their grades in school. A questionnaire may be given to students to know the average income of their family, then compare it with CGPAs. 

The researcher will discover whether these two factors are positively correlated, negatively corrected, or have zero correlation at the end of the research.

Observational Research

Observational research focuses on observing the behavior of a research subject in a natural or laboratory setting. It is classified as non-experimental because it does not involve the manipulation of independent variables.

A good example of observational research is an investigation of the crowd effect or psychology in a particular group of people. Imagine a situation where there are 2 ATMs at a place, and only one of the ATMs is filled with a queue, while the other is abandoned.

The crowd effect infers that the majority of newcomers will also abandon the other ATM.

You will notice that each of these non-experimental research is descriptive in nature. It then suffices to say that descriptive research is an example of non-experimental research.

Pros of Observational Research

• The research process is very close to a real-life situation.
• It does not allow for the manipulation of variables due to ethical reasons.
• Human characteristics are not subject to experimental manipulation.

Cons of Observational Research

• The groups may be dissimilar and nonhomogeneous because they are not randomly selected, affecting the authenticity and generalizability of the study results.
• The results obtained cannot be absolutely clear and error-free.

What Are The Differences Between Experimental and Non-Experimental Research?    

• Definitions

Experimental research is the type of research that uses a scientific approach towards manipulating one or more control variables and measuring their defect on the dependent variables, while non-experimental research is the type of research that does not involve the manipulation of control variables.

The main distinction in these 2 types of research is their attitude towards the manipulation of control variables. Experimental allows for the manipulation of control variables while non-experimental research doesn’t.

 Examples of experimental research are laboratory experiments that involve mixing different chemical elements together to see the effect of one element on the other while non-experimental research examples are investigations into the characteristics of different chemical elements.

Consider a researcher carrying out a laboratory test to determine the effect of adding Nitrogen gas to Hydrogen gas. It may be discovered that using the Haber process, one can create Nitrogen gas.

Non-experimental research may further be carried out on Ammonia, to determine its characteristics, behaviour, and nature.

There are 3 types of experimental research, namely; experimental research, quasi-experimental research, and true experimental research. Although also 3 in number, non-experimental research can be classified into cross-sectional research, correlational research, and observational research.

The different types of experimental research are further divided into different parts, while non-experimental research types are not further divided. Clearly, these divisions are not the same in experimental and non-experimental research.

• Characteristics

Experimental research is usually quantitative, controlled, and multivariable. Non-experimental research can be both quantitative and qualitative , has an uncontrolled variable, and also a cross-sectional research problem.

The characteristics of experimental research are the direct opposite of that of non-experimental research. The most distinct characteristic element is the ability to control or manipulate independent variables in experimental research and not in non-experimental research. 

In experimental research, a level of control is usually exerted on extraneous variables, therefore tampering with the natural research setting. Experimental research settings are usually more natural with no tampering with the extraneous variables.

• Data Collection/Tools

  The data used during experimental research is collected through observational study, simulations, and surveys while non-experimental data is collected through observations, surveys, and case studies. The main distinction between these data collection tools is case studies and simulations.

Even at that, similar tools are used differently. For example, an observational study may be used during a laboratory experiment that tests how the effect of a control variable manifests over a period of time in experimental research. 

However, when used in non-experimental research, data is collected based on the researcher’s discretion and not through a clear scientific reaction. In this case, we see a difference in the level of objectivity. 

The goal of experimental research is to measure the causes and effects of variables present in research, while non-experimental research provides very little to no information about causal agents.

Experimental research answers the question of why something is happening. This is quite different in non-experimental research, as they are more descriptive in nature with the end goal being to describe what .

 Experimental research is mostly used to make scientific innovations and find major solutions to problems while non-experimental research is used to define subject characteristics, measure data trends, compare situations and validate existing conditions.

For example, if experimental research results in an innovative discovery or solution, non-experimental research will be conducted to validate this discovery. This research is done for a period of time in order to properly study the subject of research.

Experimental research process is usually well structured and as such produces results with very little to no errors, while non-experimental research helps to create real-life related experiments. There are a lot more advantages of experimental and non-experimental research , with the absence of each of these advantages in the other leaving it at a disadvantage.

For example, the lack of a random selection process in non-experimental research leads to the inability to arrive at a generalizable result. Similarly, the ability to manipulate control variables in experimental research may lead to the personal bias of the researcher.

• Disadvantage

 Experimental research is highly prone to human error while the major disadvantage of non-experimental research is that the results obtained cannot be absolutely clear and error-free. In the long run, the error obtained due to human error may affect the results of the experimental research.

Some other disadvantages of experimental research include the following; extraneous variables cannot always be controlled, human responses can be difficult to measure, and participants may also cause bias.

  In experimental research, researchers can control and manipulate control variables, while in non-experimental research, researchers cannot manipulate these variables. This cannot be done due to ethical reasons. 

For example, when promoting employees due to how well they did in their annual performance review, it will be unethical to manipulate the results of the performance review (independent variable). That way, we can get impartial results of those who deserve a promotion and those who don’t.

Experimental researchers may also decide to eliminate extraneous variables so as to have enough control over the research process. Once again, this is something that cannot be done in non-experimental research because it relates more to real-life situations.

Experimental research is carried out in an unnatural setting because most of the factors that influence the setting are controlled while the non-experimental research setting remains natural and uncontrolled. One of the things usually tampered with during research is extraneous variables.

In a bid to get a perfect and well-structured research process and results, researchers sometimes eliminate extraneous variables. Although sometimes seen as insignificant, the elimination of these variables may affect the research results.

Consider the optimization problem whose aim is to minimize the cost of production of a car, with the constraints being the number of workers and the number of hours they spend working per day. 

In this problem, extraneous variables like machine failure rates or accidents are eliminated. In the long run, these things may occur and may invalidate the result.

• Cause-Effect Relationship

The relationship between cause and effect is established in experimental research while it cannot be established in non-experimental research. Rather than establish a cause-effect relationship, non-experimental research focuses on providing descriptive results.

Although it acknowledges the causal variable and its effect on the dependent variables, it does not measure how or the extent to which these dependent variables change. It, however, observes these changes, compares the changes in 2 variables, and describes them.

Experimental research does not compare variables while non-experimental research does. It compares 2 variables and describes the relationship between them.

The relationship between these variables can be positively correlated, negatively correlated or not correlated at all. For example, consider a case whereby the subject of research is a drum, and the control or independent variable is the drumstick.

Experimental research will measure the effect of hitting the drumstick on the drum, where the result of this research will be sound. That is, when you hit a drumstick on a drum, it makes a sound.

Non-experimental research, on the other hand, will investigate the correlation between how hard the drum is hit and the loudness of the sound that comes out. That is, if the sound will be higher with a harder bang, lower with a harder bang, or will remain the same no matter how hard we hit the drum.

• Quantitativeness

Experimental research is a quantitative research method while non-experimental research can be both quantitative and qualitative depending on the time and the situation where it is been used. An example of a non-experimental quantitative research method is correlational research .

Researchers use it to correlate two or more variables using mathematical analysis methods. The original patterns, relationships, and trends between variables are observed, then the impact of one of these variables on the other is recorded along with how it changes the relationship between the two variables.

Observational research is an example of non-experimental research, which is classified as a qualitative research method.

• Cross-section

Experimental research is usually single-sectional while non-experimental research is cross-sectional. That is, when evaluating the research subjects in experimental research, each group is evaluated as an entity.

For example, let us consider a medical research process investigating the prevalence of breast cancer in a certain community. In this community, we will find people of different ages, ethnicities, and social backgrounds. 

If a significant amount of women from a particular age are found to be more prone to have the disease, the researcher can conduct further studies to understand the reason behind it. A further study into this will be experimental and the subject won’t be a cross-sectional group. 

A lot of researchers consider the distinction between experimental and non-experimental research to be an extremely important one. This is partly due to the fact that experimental research can accommodate the manipulation of independent variables, which is something non-experimental research can not.

Therefore, as a researcher who is interested in using any one of experimental and non-experimental research, it is important to understand the distinction between these two. This helps in deciding which method is better for carrying out particular research. 

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• Key Differences

Know the Differences & Comparisons

Difference Between Survey and Experiment

While surveys collected data, provided by the informants, experiments test various premises by trial and error method. This article attempts to shed light on the difference between survey and experiment, have a look.

Content: Survey Vs Experiment

Comparison chart.

Definition of Survey

By the term survey, we mean a method of securing information relating to the variable under study from all or a specified number of respondents of the universe. It may be a sample survey or a census survey. This method relies on the questioning of the informants on a specific subject. Survey follows structured form of data collection, in which a formal questionnaire is prepared, and the questions are asked in a predefined order.

Informants are asked questions concerning their behaviour, attitude, motivation, demographic, lifestyle characteristics, etc. through observation, direct communication with them over telephone/mail or personal interview. Questions are asked verbally to the respondents, i.e. in writing or by way of computer. The answer of the respondents is obtained in the same form.

Definition of Experiment

The term experiment means a systematic and logical scientific procedure in which one or more independent variables under test are manipulated, and any change on one or more dependent variable is measured while controlling for the effect of the extraneous variable. Here extraneous variable is an independent variable which is not associated with the objective of study but may affect the response of test units.

In an experiment, the investigator attempts to observe the outcome of the experiment conducted by him intentionally, to test the hypothesis or to discover something or to demonstrate a known fact. An experiment aims at drawing conclusions concerning the factor on the study group and making inferences from sample to larger population of interest.

Key Differences Between Survey and Experiment

The differences between survey and experiment can be drawn clearly on the following grounds:

• A technique of gathering information regarding a variable under study, from the respondents of the population, is called survey. A scientific procedure wherein the factor under study is isolated to test hypothesis is called an experiment.
• Surveys are performed when the research is of descriptive nature, whereas in the case of experiments are conducted in experimental research.
• The survey samples are large as the response rate is low, especially when the survey is conducted through mailed questionnaire. On the other hand, samples required in the case of experiments is relatively small.
• Surveys are considered suitable for social and behavioural science. As against this, experiments are an important characteristic of physical and natural sciences.
• Field research refers to the research conducted outside the laboratory or workplace. Surveys are the best example of field research. On the contrary, Experiment is an example of laboratory research. A laboratory research is nothing but research carried on inside the room equipped with scientific tools and equipment.
• In surveys, the data collection methods employed can either be observation, interview, questionnaire, or case study. As opposed to experiment, the data is obtained through several readings of the experiment.

While survey studies the possible relationship between data and unknown variable, experiments determine the relationship. Further, Correlation analysis is vital in surveys, as in social and business surveys, the interest of the researcher rests in understanding and controlling relationships between variables. Unlike experiments, where casual analysis is significant.

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sanjay kumar yadav says

November 17, 2016 at 1:08 am

Ishika says

September 9, 2017 at 9:30 pm

The article was quite helpful… Thank you.

May 21, 2018 at 3:26 pm

Can you develop your Application for Android

Surbhi S says

May 21, 2018 at 4:21 pm

Yeah, we will develop android app soon.

October 31, 2018 at 12:32 am

If I was doing an experiment with Poverty and Education level, which do you think would be more appropriate for me?

Thanks, Chris

Ndaware M.M says

January 7, 2021 at 2:29 am

So interested,

Victoria Addington says

May 18, 2023 at 5:31 pm

Thank you for explaining the topic

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Survey vs Experiment: Know How Two Research Methods Differ from Each Other

Research methods are procedures that span the steps from nonspecific assumptions to detailed approaches of data collection, analysis, and interpretation. These are essentially well-planned, value-neutral and scientific. 

Generally, the research method includes experimental study, focus groups, survey method, numerical schemes, theoretical procedures, etc. However, each study domain demands a specific type of research method. 

For instance , for research that requires investigation of characteristics, opinions or behaviours of a group of people, survey method can be used. 

Whereas, research that demands explanation based on observations, collected facts, and measurements, the experiment research method is used. 

Know more about experiment and survey method  

• Experiment method 

Derived from Latin word ‘experior’ (meaning – attempt), experiment is a systematic approach that tests the hypothesis by performing a procedure under highly controlled conditions. This approach is based on a comparison between two or more variables and is ideal for studying the primary data. Experiment involves manipulating a certain independent variable and determining its effect on a dependent variable. 

For example, you can measure the impact of how water intake on people’s metabolism by letting the experimental group drink 6 glasses of  water per day while letting the controlled group to drink only 3 glasses of water. Their metabolism rates can then be compared after a couple of weeks, and statistical tests such as T-test can be used to validate the results. 

Typically, an experimental research method consists of three types of designs: pre-experimental, true- experimental, and quasi-experimental design.   

• Pre-experimental design – In this approach, a group(s), is kept under observation after factors for cause & effect are considered.  
• True-experimental design – Being the most accurate design, this method is used to establish a cause-effect relationship within a group(s). 
• Quasi-experimental design – Here, the independent variable will be manipulated, but the members of a population are not randomly assigned.

Experimental research design includes key characteristics such as: 

• Manipulating the independent variable
• Determining the factors that cause effects
• Comparison of two or more groups
• Deciding the extent and nature of the treatment

Experiment research method offers several advantages such as – accurate results, control over variables, determination of cause & effect of a study hypothesis, and can be used in collaboration with other research designs. 

• Survey method 

Derived from Latin word ‘supervidere’ (meaning – to see), survey method, best suited for    descriptive research, studies the opinion, behaviours, attributes and feelings of an individual or a group of people. This process collection of numbered data and statistically analysing responses to the questions in order to test the hypothesis about the nature of relationships within a group. 

For instance , if you are intended to study the happiness levels among employees’ working in a specific organisation. Here the data will be collected through questionnaires, phone calls, Emails, etc. Upon collecting the data regarding the individuals’ perceived emotional states, statistical tests such as getting the weighted mean can be utilised to assess the responses. 

Based on the design, survey research method can be divided into three types of studies: cross-sectional, longitudinal and correlational study. 

• Cross-sectional study – Defined as observational research type, this study evaluates data of variables gathered at a given point of time across a sample population.
• Longitudinal study – This method uses repeated or continuous measures to follow certain individuals over an extended period of time ( more often years or decades).
• Correlational study – This non-experimental design studies two different variables and runs a statistical analysis to determine the relation between the variables without the interference of external variables. 

The significant features of the survey research method include: 

• Involvement in the process of sampling from a population 
• Developing instrument for data collection process
• Collecting data via interviews or questionnaires
• Acquiring greater response rate

Survey method offers several benefits of which include – primary data collected is easy to analyse, data can be collected at a faster rate and easily, offers precise information, and is flexible. 

Key differences between experiment and survey method 

Choosing the right research method is vital for any research. Hence make sure you understand the requirements of your study and choose the research method accordingly. 

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