Variable
✔ if this is the independent variable
✔ if this is the dependent variable
✔ if this is a control variable
Acid concentration
Volume of acid
Temperature change
Mass of magnesium
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1) The type of liquid (c)
2) The strength of the electromagnet
3a) The type of pen
3b) The time taken for each pen to run out
3c) The experiment is unreliable because so many variables were left uncontrolled. April and Harry should have controlled the amount of writing produced by each person, even the size of writing would have impacted how quickly each pen ran out. The amount of ink in each pen when they started should also have been controlled.
Variable | ✔ if this is the independent variable | ✔ if this is the dependent variable | ✔ if this is a control variable |
Acid concentration | ✔ | ||
Volume of acid | ✔ | ||
Temperature change | ✔ | ||
Mass of magnesium | ✔ |
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In the realm of research, particularly in mathematics and the sciences understanding the concept of the variables is fundamental. The Variables are integral to the formulation of hypotheses the design of the experiments and interpretation of data. They serve as the building blocks for the mathematical models and statistical analyses making it possible to describe, analyze and predict phenomena.
This article aims to provide a comprehensive overview of the variables in the research explaining their significance, types and roles. By the end of this article, students and researchers will have a clearer understanding of how to identify, use and interpret variables in their research projects.
Table of Content
Types of variables, independent variables, dependent variables, control variables, extraneous variables, moderator variables, mediator variables.
Variables are elements that can change or vary within the experiment or study. They can represent different types of data such as numerical values, categories or even qualitative attributes. In mathematical terms, variables are symbols that can assume different values.
Various types of variables are:
Definition: Variables that are manipulated or controlled in an experiment to observe their effect on other variables.
Example: In a study examining the effect of study time on the test scores the amount of the study time is the independent variable.
Definition: Variables that are measured or observed in response to the changes in the independent variable.
Example: In the same study the test scores are the dependent variable.
Definition: Variables that are kept constant to ensure that the results are due to the manipulation of the independent variable.
Example: The study environment could be a control variable in the study on the study time and test scores.
Definition: Variables that are not intentionally studied but could affect the outcome of the experiment.
Example: The amount of the sleep students get before the test could be an extraneous variable.
Definition: V ariables that influence the strength or direction of the relationship between independent and dependent variables.
Example: The difficulty of the test could be a moderator variable affecting the relationship between the study time and test scores.
Definition: Variables that explain the process through which the independent variable affects the dependent variable.
Example: The level of the understanding of the material could be a mediator variable in the study on study time and test scores.
Variables are crucial in research for the several reasons:
Let’s consider a study investigating the relationship between the number of the hours spent practicing a mathematical problem and the performance on the test.
Formulating the Hypothesis: “Increasing the number of hours spent practicing mathematical problems will improve the test performance.”
Visualizing data helps in understanding the relationships between the variables. Here are some common methods:
Question 1: In a study examining the effect of the sleep on the academic performance identify the independent, dependent and control variables.
Independent Variable: Amount of sleep. Dependent Variable: Academic performance (grades). Control Variable: Study environment, type of the academic tasks.
Question 2: Explain how an extraneous variable can affect the outcome of an experiment.
An extraneous variable such as the amount of the caffeine consumed could affect the academic performance of the students in a study examining the effect of sleep on the academic performance. If not controlled it could confound the results by the influencing the dependent variable independently of the independent variable.
Question 3: Describe how you would control for extraneous variables in a study.
To control for the extraneous variables researchers can use the random assignment ensure consistent conditions or include the extraneous variables in the statistical analysis to the account for their potential impact.
Q1: How can you identify the independent variable in a given research study?
Q2: What steps can you take to ensure that control variables are effectively managed in an experiment?
Q3: How does the presence of extraneous variables impact the validity of research findings?
Q4: In what ways can a moderator variable affect the relationship between independent and dependent variables?
Q5: What are some common methods for visualizing the relationship between independent and dependent variables?
Q6: How can you determine if a variable should be classified as a mediator in your research?
Q7: What are the key differences between categorical and continuous variables, and how do they influence data analysis?
Q8: How do you formulate a hypothesis involving multiple variables in a complex study?
Q9: What strategies can be employed to reduce the impact of extraneous variables in field research?
Q10: How can statistical methods be used to account for control variables in the analysis of research data?
Variables are the cornerstone of the research in mathematics and other sciences. They allow researchers to the formulate hypotheses, design experiments analyze data and draw meaningful conclusions. By understanding and effectively managing different types of the variables researchers can enhance the validity and reliability of their studies.
Concept of variable and Raw data Dependent and Independent variable
What is an independent variable.
An independent variable is the variable that is manipulated in an experiment to the observe its effect on the dependent variable.
A dependent variable is the variable that is measured or observed in the response to the changes in the independent variable.
Control variables are important because they help ensure that the results of an experiment are due to the manipulation of the independent variable and not other factors.
Moderator variables influence the strength or direction of the relationship between the independent and dependent variables while mediator variables explain the process through which the independent variable affects the dependent variable.
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Title: compact embedding from variable-order sobolev space to $l^{q(x)}(ω)$ and its application to choquard equation with variable order and variable critical exponent.
Abstract: In this paper, we prove the compact embedding from the variable-order Sobolev space $W^{s(x,y),p(x,y)}_0 (\Omega)$ to the Nakano space $L^{q(x)}(\Omega)$ with a critical exponent $q(x)$ satisfying some conditions. It is noteworthy that the embedding can be compact even when $q(x)$ reaches the critical Sobolev exponent $p_s^*(x)$. As an application, we obtain a nontrivial solution of the Choquard equation \begin{equation*} \displaystyle (-\Delta)_{p(\cdot,\cdot)}^{s(\cdot,\cdot)}u+|u|^{p(x,x)-2}u=\left(\int_{\Omega}\frac{|u(y)|^{r(y)}}{|x-y|^{\frac{\alpha(x)+\alpha(y)}{2}}}dy\right) |u(x)|^{r(x)-2}u(x)\quad\text{in $\Omega$} \end{equation*} with variable upper critical exponent in the sense of Hardy-Littlewood-Sobolev inequality under an appropriate boundary condition.
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classes: | 35J92, 35A15, 35B33, 35R11, 35A01 |
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The size and complexity of datasets resulting from comparative research experiments in the agricultural domain is constantly increasing. Often the number of variables measured in an experiment exceeds the number of experimental units composing the experiment. When there is a necessity to model the covariance relationships that exist between variables in these experiments, estimation difficulties can arise due to the resulting covariance structure being of reduced rank. A statistical method, based in a linear mixed model framework, is presented for the analysis of designed experiments where datasets are characterised by a greater number of variables than experimental units, and for which the modelling of complex covariance structures between variables is desired. Aided by a clustering algorithm, the method enables the estimation of covariance through the introduction of covariance clusters as random effects into the modelling framework, providing an extension of the traditional variance components model for building covariance structures. The method was applied to a multi-phase mass spectrometry-based proteomics experiment, with the aim of exploring changes in the proteome of barley grain over time during the malting process. The modelling approach provides a new linear mixed model-based method for the estimation of covariance structures between variables measured from designed experiments, when there are a small number of experimental units, or observations, informing covariance parameter estimates.
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Although corruption persists in the process of marketization in transitional economies, there is still a lack of sufficient evidence on the exact causal effects and mechanisms of demarketization on corruption. As an external shock, the Global Financial Crisis presents an opportunity for a quasi-experiment that allows for the differentiation of declining levels of marketization between China’s developed coastal areas and other areas. This article aims to empirically examine the relationship between demarketization and provincial corruption levels in China, combining an instrumental variable method with a synthetic control approach. The study reveals that foreign direct investment outflow leads to a substantial decline in corruption levels by decreasing rent-seeking opportunities. These findings provide robust evidence to support the negative causal relationship between demarketization and corruption in transitional economies, where marketization alone cannot effectively curb corruption in the presence of a dual-track economy.
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A dual-track economy is defined as the coexistence of both a free market and a state-controlled market [ 34 ], p.142).
According to China Statistical Yearbook, the total GDP of the eight developed coastal provinces accounted for approximately 53.2% of China’s GDP in 2008. Macao, Hongkong, and Taiwan are excluded from our investigation as they enjoyed highly local autonomy with political and economic authority beyond any province in mainland China.
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This research was supported by the Fundamental Research Funds for the Central Universities(2021ECNU-HWCBFBLW005 & 2022QKT005)and the National Social Science Foundation of China (22CZZ045).
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Center for Modern Chinese City Studies & School of Public Management, East China Normal University, No.3663, North Zhongshan Rd, Shanghai, 200062, China
Center for Chinese Public Administration Research & School of Government, Sun Yat-sen University, No. 135, Xingang Xi Road, Guangzhou, 510275, China
Sunny L. Yang
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Ye, Z., Yang, S.L. Demarketization and Corruption in China: Evidence from a Quasi-Experiment. J OF CHIN POLIT SCI (2024). https://doi.org/10.1007/s11366-024-09895-1
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The two key variables in science are the independent and dependent variable, but there are other types of variables that are important. In a science experiment, a variable is any factor, attribute, or value that describes an object or situation and is subject to change. An experiment uses the scientific method to test a hypothesis and establish whether or not there is a cause and effect ...
A science project involving plants has controlled variables in the amount of water given to each plant and the amount and kind of soil in which the plant is living. Place one plant in direct sunlight and the other in a shaded area or indoors to conduct the science experiment. Record daily results in the height of the plant.
These variables are crucial for defining the relationships between factors within an experiment or study and determining the cause-and-effect relationships that underpin scientific knowledge. Independent Variables: An independent variable is a factor or characteristic that the researcher manipulates or controls in an experiment or study. It is ...
1. Precision and Accuracy: By identifying and defining variables, researchers can design experiments with precision and accuracy. This clarity helps ensure that the measurements and observations made during the experiment are relevant to the research question, reducing the likelihood of errors or misinterpretations. 2.
Here are several examples of independent and dependent variables in experiments: In a study to determine whether how long a student sleeps affects test scores, the independent variable is the length of time spent sleeping while the dependent variable is the test score. You want to know which brand of fertilizer is best for your plants.
In an experiment, all of the things that can change are called variables. There are three types of variables in a good experiment: independent variables, dependent variables, and controlled variables. What is an independent variable? The independent variable is the one thing that the scientist changes. Scientists change only one thing at a time ...
A well-designed experiment with two independent variables can tell you whether the variables interact (modify each other's effects). However, experiments with more than one independent variable have to follow specific design guidelines, and the results must be analyzed using a special class of statistical tests to disentangle the effects of the ...
Example (salt tolerance experiment) Independent variables (aka treatment variables) Variables you manipulate in order to affect the outcome of an experiment. The amount of salt added to each plant's water. Dependent variables (aka response variables) Variables that represent the outcome of the experiment.
Table of contents. Step 1: Define your variables. Step 2: Write your hypothesis. Step 3: Design your experimental treatments. Step 4: Assign your subjects to treatment groups. Step 5: Measure your dependent variable. Other interesting articles. Frequently asked questions about experiments.
The second experiment does not require any more data collection, but it does require looking at the data from experiment #1 in a different way. For experiment #2, graph the data with the voltage on the y-axis and time on the x-axis for each type (low, medium, high) of current drain device. Variables Experiment #1:
Types of Variables. Independent Variable: The independent variable is the one condition that you change in an experiment. Example: In an experiment measuring the effect of temperature on solubility, the independent variable is temperature. Dependent Variable: The dependent variable is the variable that you measure or observe.
This video explains independent, dependent, and controlled variables, with a special emphasis on controlling variables in experimental design. Helpful in u...
FOCUS ON THE VARIABLES. Students can sometimes get lost in the steps of an experiment and forget what brought the results about. For this reason, I make sure that my students can communicate to each other what the variables were and, more importantly, why each variable exists.For example, in the plant growth experiment, the goal is for my students to be able to explain that:
All types of variables can affect your science experiment. Get information about independent, dependent, control, intervening, and extraneous variables.
The " variables " are any factor, trait, or condition that can be changed in the experiment and that can have an effect on the outcome of the experiment. An experiment can have three kinds of variables: i ndependent, dependent, and controlled. The independent variable is one single factor that is changed by the scientist followed by ...
What are Variables? In science, a variable is any factor, trait, or condition that can exist in differing amounts or types. Scientists try to figure out how the natural world works.To do this they use experiments to search for cause and effect relationships. Cause and effect relationships explain why things happen and allow you to reliably ...
An experiment designed to determinate the effect of a fertilizer on plant growth has the following variables:Independent VariablesFertilizerDependent VariablesPlant height, plant weight, number of leavesExtraneous VariablesPlant type, sunlight, water, temperature, air quality, windSituational VariablesSunlight, water, temperature, air quality ...
Definition of Independent and Dependent Variables. The independent variable and dependent variable are used in a very specific type of scientific study called the experiment.. Although there are many variations of the experiment, generally speaking, it involves either the presence or absence of the independent variable and the observation of what happens to the dependent variable.
There are two key variables in every experiment: the independent variable and the dependent variable. Independent variable: What the scientist changes or what changes on its own. Dependent variable: What is being studied/measured. The independent variable (sometimes known as the manipulated variable) is the variable whose change isn't affected ...
A variable is something that can change or vary for an experiment to be a success. There are three types- an independent variable (sometimes called a manipulated variable), a dependent variable (sometimes referred to as the responding variable), and the controlled variable. Each has an important role to play in experiments.
Control variable - these are the elements that are kept the same during a scientific experiment. There can be multiple control variables. There can be multiple control variables. Any change to a controlled variable would invalidate the results, so it's really important that they are kept the same throughout.
Independent and Dependent Variables, Explained With Examples. Written by MasterClass. Last updated: Mar 21, 2022 • 4 min read. In experiments that test cause and effect, two types of variables come into play. One is an independent variable and the other is a dependent variable, and together they play an integral role in research design.
Designing Experiments: Variables determine the structure and design of the experiments guiding the procedures for the data collection and analysis. Analyzing Data: Understanding the relationships between the variables is key to the analyzing data and drawing meaningful conclusions from the research.
arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is ...
The size and complexity of datasets resulting from comparative research experiments in the agricultural domain is constantly increasing. Often the number of variables measured in an experiment exceeds the number of experimental units composing the experiment. When there is a necessity to model the covariance relationships that exist between variables in these experiments, estimation ...
Although corruption persists in the process of marketization in transitional economies, there is still a lack of sufficient evidence on the exact causal effects and mechanisms of demarketization on corruption. As an external shock, the Global Financial Crisis presents an opportunity for a quasi-experiment that allows for the differentiation of declining levels of marketization between China ...