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The simplest way to understand a variable is as any characteristic or attribute that can experience change or vary over time or context – hence the name “variable”. For example, the dosage of a particular medicine could be classified as a variable, as the amount can vary (i.e., a higher dose or a lower dose). Similarly, gender, age or ethnicity could be considered demographic variables, because each person varies in these respects.
Within research, especially scientific research, variables form the foundation of studies, as researchers are often interested in how one variable impacts another, and the relationships between different variables. For example:
As you can see, variables are often used to explain relationships between different elements and phenomena. In scientific studies, especially experimental studies, the objective is often to understand the causal relationships between variables. In other words, the role of cause and effect between variables. This is achieved by manipulating certain variables while controlling others – and then observing the outcome. But, we’ll get into that a little later…
Variables can be a little intimidating for new researchers because there are a wide variety of variables, and oftentimes, there are multiple labels for the same thing. To lay a firm foundation, we’ll first look at the three main types of variables, namely:
Simply put, the independent variable is the “ cause ” in the relationship between two (or more) variables. In other words, when the independent variable changes, it has an impact on another variable.
For example:
It’s useful to know that independent variables can go by a few different names, including, explanatory variables (because they explain an event or outcome) and predictor variables (because they predict the value of another variable). Terminology aside though, the most important takeaway is that independent variables are assumed to be the “cause” in any cause-effect relationship. As you can imagine, these types of variables are of major interest to researchers, as many studies seek to understand the causal factors behind a phenomenon.
While the independent variable is the “ cause ”, the dependent variable is the “ effect ” – or rather, the affected variable . In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable.
Keeping with the previous example, let’s look at some dependent variables in action:
In scientific studies, researchers will typically pay very close attention to the dependent variable (or variables), carefully measuring any changes in response to hypothesised independent variables. This can be tricky in practice, as it’s not always easy to reliably measure specific phenomena or outcomes – or to be certain that the actual cause of the change is in fact the independent variable.
As the adage goes, correlation is not causation . In other words, just because two variables have a relationship doesn’t mean that it’s a causal relationship – they may just happen to vary together. For example, you could find a correlation between the number of people who own a certain brand of car and the number of people who have a certain type of job. Just because the number of people who own that brand of car and the number of people who have that type of job is correlated, it doesn’t mean that owning that brand of car causes someone to have that type of job or vice versa. The correlation could, for example, be caused by another factor such as income level or age group, which would affect both car ownership and job type.
To confidently establish a causal relationship between an independent variable and a dependent variable (i.e., X causes Y), you’ll typically need an experimental design , where you have complete control over the environmen t and the variables of interest. But even so, this doesn’t always translate into the “real world”. Simply put, what happens in the lab sometimes stays in the lab!
As an alternative to pure experimental research, correlational or “ quasi-experimental ” research (where the researcher cannot manipulate or change variables) can be done on a much larger scale more easily, allowing one to understand specific relationships in the real world. These types of studies also assume some causality between independent and dependent variables, but it’s not always clear. So, if you go this route, you need to be cautious in terms of how you describe the impact and causality between variables and be sure to acknowledge any limitations in your own research.
In an experimental design, a control variable (or controlled variable) is a variable that is intentionally held constant to ensure it doesn’t have an influence on any other variables. As a result, this variable remains unchanged throughout the course of the study. In other words, it’s a variable that’s not allowed to vary – tough life 🙂
As we mentioned earlier, one of the major challenges in identifying and measuring causal relationships is that it’s difficult to isolate the impact of variables other than the independent variable. Simply put, there’s always a risk that there are factors beyond the ones you’re specifically looking at that might be impacting the results of your study. So, to minimise the risk of this, researchers will attempt (as best possible) to hold other variables constant . These factors are then considered control variables.
Some examples of variables that you may need to control include:
Which specific variables need to be controlled for will vary tremendously depending on the research project at hand, so there’s no generic list of control variables to consult. As a researcher, you’ll need to think carefully about all the factors that could vary within your research context and then consider how you’ll go about controlling them. A good starting point is to look at previous studies similar to yours and pay close attention to which variables they controlled for.
Of course, you won’t always be able to control every possible variable, and so, in many cases, you’ll just have to acknowledge their potential impact and account for them in the conclusions you draw. Every study has its limitations , so don’t get fixated or discouraged by troublesome variables. Nevertheless, always think carefully about the factors beyond what you’re focusing on – don’t make assumptions!
As we mentioned, independent, dependent and control variables are the most common variables you’ll come across in your research, but they’re certainly not the only ones you need to be aware of. Next, we’ll look at a few “secondary” variables that you need to keep in mind as you design your research.
Let’s jump into it…
A moderating variable is a variable that influences the strength or direction of the relationship between an independent variable and a dependent variable. In other words, moderating variables affect how much (or how little) the IV affects the DV, or whether the IV has a positive or negative relationship with the DV (i.e., moves in the same or opposite direction).
For example, in a study about the effects of sleep deprivation on academic performance, gender could be used as a moderating variable to see if there are any differences in how men and women respond to a lack of sleep. In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep.
It’s important to note that while moderators can have an influence on outcomes , they don’t necessarily cause them ; rather they modify or “moderate” existing relationships between other variables. This means that it’s possible for two different groups with similar characteristics, but different levels of moderation, to experience very different results from the same experiment or study design.
Mediating variables are often used to explain the relationship between the independent and dependent variable (s). For example, if you were researching the effects of age on job satisfaction, then education level could be considered a mediating variable, as it may explain why older people have higher job satisfaction than younger people – they may have more experience or better qualifications, which lead to greater job satisfaction.
Mediating variables also help researchers understand how different factors interact with each other to influence outcomes. For instance, if you wanted to study the effect of stress on academic performance, then coping strategies might act as a mediating factor by influencing both stress levels and academic performance simultaneously. For example, students who use effective coping strategies might be less stressed but also perform better academically due to their improved mental state.
In addition, mediating variables can provide insight into causal relationships between two variables by helping researchers determine whether changes in one factor directly cause changes in another – or whether there is an indirect relationship between them mediated by some third factor(s). For instance, if you wanted to investigate the impact of parental involvement on student achievement, you would need to consider family dynamics as a potential mediator, since it could influence both parental involvement and student achievement simultaneously.
A confounding variable (also known as a third variable or lurking variable ) is an extraneous factor that can influence the relationship between two variables being studied. Specifically, for a variable to be considered a confounding variable, it needs to meet two criteria:
Some common examples of confounding variables include demographic factors such as gender, ethnicity, socioeconomic status, age, education level, and health status. In addition to these, there are also environmental factors to consider. For example, air pollution could confound the impact of the variables of interest in a study investigating health outcomes.
Naturally, it’s important to identify as many confounding variables as possible when conducting your research, as they can heavily distort the results and lead you to draw incorrect conclusions . So, always think carefully about what factors may have a confounding effect on your variables of interest and try to manage these as best you can.
Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study. They’re also known as hidden or underlying variables , and what makes them rather tricky is that they can’t be directly observed or measured . Instead, latent variables must be inferred from other observable data points such as responses to surveys or experiments.
For example, in a study of mental health, the variable “resilience” could be considered a latent variable. It can’t be directly measured , but it can be inferred from measures of mental health symptoms, stress, and coping mechanisms. The same applies to a lot of concepts we encounter every day – for example:
One way in which we overcome the challenge of measuring the immeasurable is latent variable models (LVMs). An LVM is a type of statistical model that describes a relationship between observed variables and one or more unobserved (latent) variables. These models allow researchers to uncover patterns in their data which may not have been visible before, thanks to their complexity and interrelatedness with other variables. Those patterns can then inform hypotheses about cause-and-effect relationships among those same variables which were previously unknown prior to running the LVM. Powerful stuff, we say!
In the world of scientific research, there’s no shortage of variable types, some of which have multiple names and some of which overlap with each other. In this post, we’ve covered some of the popular ones, but remember that this is not an exhaustive list .
To recap, we’ve explored:
If you’re still feeling a bit lost and need a helping hand with your research project, check out our 1-on-1 coaching service , where we guide you through each step of the research journey. Also, be sure to check out our free dissertation writing course and our collection of free, fully-editable chapter templates .
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Methodology
Published on February 3, 2022 by Pritha Bhandari . Revised on June 22, 2023.
In research, variables are any characteristics that can take on different values, such as height, age, temperature, or test scores.
Researchers often manipulate or measure independent and dependent variables in studies to test cause-and-effect relationships.
Your independent variable is the temperature of the room. You vary the room temperature by making it cooler for half the participants, and warmer for the other half.
What is an independent variable, types of independent variables, what is a dependent variable, identifying independent vs. dependent variables, independent and dependent variables in research, visualizing independent and dependent variables, other interesting articles, frequently asked questions about independent and dependent variables.
An independent variable is the variable you manipulate 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:
These terms are especially used in statistics , where you estimate the extent to which an independent variable change can explain or predict changes in the dependent variable.
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There are two main types of independent variables.
In experiments, you manipulate independent variables directly to see how they affect your dependent variable. The independent variable is usually applied at different levels to see how the outcomes differ.
You can apply just two levels in order to find out if an independent variable has an effect at all.
You can also apply multiple levels to find out how the independent variable affects the dependent variable.
You have three independent variable levels, and each group gets a different level of treatment.
You randomly assign your patients to one of the three groups:
A true experiment requires you to randomly assign different levels of an independent variable to your participants.
Random assignment helps you control participant characteristics, so that they don’t affect your experimental results. This helps you to have confidence that your dependent variable results come solely from the independent variable manipulation.
Subject variables are characteristics that vary across participants, and they can’t be manipulated by researchers. For example, gender identity, ethnicity, race, income, and education are all important subject variables that social researchers treat as independent variables.
It’s not possible to randomly assign these to participants, since these are characteristics of already existing groups. Instead, you can create a research design where you compare the outcomes of groups of participants with characteristics. This is a quasi-experimental design because there’s no random assignment. Note that any research methods that use non-random assignment are at risk for research biases like selection bias and sampling bias .
Your independent variable is a subject variable, namely the gender identity of the participants. You have three groups: men, women and other.
Your dependent variable is the brain activity response to hearing infant cries. You record brain activity with fMRI scans when participants hear infant cries without their awareness.
A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome you’re interested in measuring, and it “depends” on your independent variable.
In statistics , dependent variables are also called:
The dependent variable is what you record after you’ve manipulated the independent variable. You use this measurement data to check whether and to what extent your independent variable influences the dependent variable by conducting statistical analyses.
Based on your findings, you can estimate the degree to which your independent variable variation drives changes in your dependent variable. You can also predict how much your dependent variable will change as a result of variation in the independent variable.
Distinguishing between independent and dependent variables can be tricky when designing a complex study or reading an academic research paper .
A dependent variable from one study can be the independent variable in another study, so it’s important to pay attention to research design .
Here are some tips for identifying each variable type.
Use this list of questions to check whether you’re dealing with an independent variable:
Check whether you’re dealing with a dependent variable:
Independent and dependent variables are generally used in experimental and quasi-experimental research.
Here are some examples of research questions and corresponding independent and dependent variables.
Research question | Independent variable | Dependent variable(s) |
---|---|---|
Do tomatoes grow fastest under fluorescent, incandescent, or natural light? | ||
What is the effect of intermittent fasting on blood sugar levels? | ||
Is medical marijuana effective for pain reduction in people with chronic pain? | ||
To what extent does remote working increase job satisfaction? |
For experimental data, you analyze your results by generating descriptive statistics and visualizing your findings. Then, you select an appropriate statistical test to test your hypothesis .
The type of test is determined by:
You’ll often use t tests or ANOVAs to analyze your data and answer your research questions.
In quantitative research , it’s good practice to use charts or graphs to visualize the results of studies. Generally, the independent variable goes on the x -axis (horizontal) and the dependent variable on the y -axis (vertical).
The type of visualization you use depends on the variable types in your research questions:
To inspect your data, you place your independent variable of treatment level on the x -axis and the dependent variable of blood pressure on the y -axis.
You plot bars for each treatment group before and after the treatment to show the difference in blood pressure.
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Research bias
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.
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:
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.
You want to find out how blood sugar levels are affected by drinking diet soda and regular soda, so you conduct an experiment .
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!
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.
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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 relationship between two variables: the independent and dependent variables. But, there are other important types of variables, too, including controlled and confounding variables. Here’s what you need to know, with examples.
An experiment examines whether or not there is a relationship between the independent and dependent variables. The independent variable is the one factor a researcher intentionally changes or manipulates. The dependent variable is the factor that is measured, to see how it responds to the independent variable.
For example , consider an experiment looking to see whether taking caffeine affects how many words you remember from a list. The independent variable is the amount of caffeine you take, while the dependent variable is how many words you remember.
But, there are lot more potential variables you control (and usually measure and record) so you get the truest results from the experiment. The controlled variables are factors you hold steady so they don’t affect the results. In this experiment, examples include the amount and source of the caffeine (coffee? tea? caffeine tablets?), the time between taking the caffeine and recalling the words, the number and order of words on the list, the temperature of the room, and anything else you think might matter. Observing and recording controlled variables might not seem very important, but if someone goes to repeat your experiment and gets different results, it might turn out that a controlled variable has a bigger effect than you suspected!
A confounding variable is a variable that has a hidden effect on the results. Sometimes, once you identify a confounding variable, you can turn it into a controlled variable in a later experiment. In the coffee experiment, examples of confounding variables include a subject’s sensitivity to caffeine and the time of day that you conduct the experiment. Age and initial hydration levels are additional factors that may confound the results.
Other types of variables get their names from special properties:
Educational resources and simple solutions for your research journey
A variable is an important element of research. It is a characteristic, number, or quantity of any category that can be measured or counted and whose value may change with time or other parameters.
Variables are defined in different ways in different fields. For instance, in mathematics, a variable is an alphabetic character that expresses a numerical value. In algebra, a variable represents an unknown entity, mostly denoted by a, b, c, x, y, z, etc. In statistics, variables represent real-world conditions or factors. Despite the differences in definitions, in all fields, variables represent the entity that changes and help us understand how one factor may or may not influence another factor.
Variables in research and statistics are of different types—independent, dependent, quantitative (discrete or continuous), qualitative (nominal/categorical, ordinal), intervening, moderating, extraneous, confounding, control, and composite. In this article we compare the first two types— independent vs dependent variables .
Table of Contents
Researchers conduct experiments to understand the cause-and-effect relationships between various entities. In such experiments, the entities whose values change are called variables. These variables describe the relationships among various factors and help in drawing conclusions in experiments. They help in understanding how some factors influence others. Some examples of variables include age, gender, race, income, weight, etc.
As mentioned earlier, different types of variables are used in research. Of these, we will compare the most common types— independent vs dependent variables . The independent variable is the cause and the dependent variable is the effect, that is, independent variables influence dependent variables. In research, a dependent variable is the outcome of interest of the study and the independent variable is the factor that may influence the outcome. Let’s explain this with an independent and dependent variable example : In a study to analyze the effect of antibiotic use on microbial resistance, antibiotic use is the independent variable and microbial resistance is the dependent variable because antibiotic use affects microbial resistance.( 1)
Here is a list of the important characteristics of independent variables .( 2,3)
Independent variables in research are of the following two types:( 4)
Quantitative independent variables differ in amounts or scales. They are numeric and answer questions like “how many” or “how often.”
Here are a few quantitative independent variables examples :
Qualitative independent variables are non-numerical variables.
A few qualitative independent variables examples are listed below:
A quantitative variable is represented by actual amounts and a qualitative variable by categories or groups.
Here are a few characteristics of dependent variables: ( 3)
Here are a few dependent variable examples :
Dependent variables are of two types:( 5)
These variables can take on any value within a given range and are measured on a continuous scale, for example, weight, height, temperature, time, distance, etc.
These variables are divided into distinct categories. They are not measured on a continuous scale so only a limited number of values are possible, for example, gender, race, etc.
The following table compares independent vs dependent variables .
How to identify | Manipulated or controlled | Observed or measured |
Purpose | Cause or predictor variable | Outcome or response variable |
Relationship | Independent of other variables | Influenced by the independent variable |
Control | Manipulated or assigned by researcher | Measured or observed during experiments |
Listed below are a few examples of research questions from various disciplines and their corresponding independent and dependent variables.( 6)
Genetics | What is the relationship between genetics and susceptibility to diseases? | genetic factors | susceptibility to diseases |
History | How do historical events influence national identity? | historical events | national identity |
Political science | What is the effect of political campaign advertisements on voter behavior? | political campaign advertisements | voter behavior |
Sociology | How does social media influence cultural awareness? | social media exposure | cultural awareness |
Economics | What is the impact of economic policies on unemployment rates? | economic policies | unemployment rates |
Literature | How does literary criticism affect book sales? | literary criticism | book sales |
Geology | How do a region’s geological features influence the magnitude of earthquakes? | geological features | earthquake magnitudes |
Environment | How do changes in climate affect wildlife migration patterns? | climate changes | wildlife migration patterns |
Gender studies | What is the effect of gender bias in the workplace on job satisfaction? | gender bias | job satisfaction |
Film studies | What is the relationship between cinematographic techniques and viewer engagement? | cinematographic techniques | viewer engagement |
Archaeology | How does archaeological tourism affect local communities? | archaeological techniques | local community development |
Experiments usually have at least two variables—independent and dependent. The independent variable is the entity that is being tested and the dependent variable is the result. Classifying independent and dependent variables as discrete and continuous can help in determining the type of analysis that is appropriate in any given research experiment, as shown in the table below. ( 7)
Chi-Square | t-test | ||
Logistic regression | ANOVA | ||
Phi | Regression | ||
Cramer’s V | Point-biserial correlation | ||
Logistic regression | Regression | ||
Point-biserial correlation | Correlation |
Here are some more research questions and their corresponding independent and dependent variables. ( 6)
What is the impact of online learning platforms on academic performance? | type of learning | academic performance |
What is the association between exercise frequency and mental health? | exercise frequency | mental health |
How does smartphone use affect productivity? | smartphone use | productivity levels |
Does family structure influence adolescent behavior? | family structure | adolescent behavior |
What is the impact of nonverbal communication on job interviews? | nonverbal communication | job interviews |
In addition to all the characteristics of independent and dependent variables listed previously, here are few simple steps to identify the variable types in a research question.( 8)
Let’s try out these steps with an example.
A researcher wants to conduct a study to see if his new weight loss medication performs better than two bestseller alternatives. He wants to randomly select 20 subjects from Richmond, Virginia, aged 20 to 30 years and weighing above 60 pounds. Each subject will be randomly assigned to three treatment groups.
To identify the independent and dependent variables, we convert this paragraph into a question, as follows: Does the new medication perform better than the alternatives? Here, the medications are the independent variable and their performances or effect on the individuals are the dependent variable.
Data visualization is the graphical representation of information by using charts, graphs, and maps. Visualizations help in making data more understandable by making it easier to compare elements, identify trends and relationships (among variables), among other functions.
Bar graphs, pie charts, and scatter plots are the best methods to graphically represent variables. While pie charts and bar graphs are suitable for depicting categorical data, scatter plots are appropriate for quantitative data. The independent variable is usually placed on the X-axis and the dependent variable on the Y-axis.
Figure 1 is a scatter plot that depicts the relationship between the number of household members and their monthly grocery expenses. 9 The number of household members is the independent variable and the expenses the dependent variable. The graph shows that as the number of members increases the expenditure also increases.
Let’s summarize the key takeaways about independent vs dependent variables from this article:
The following table lists the different types of variables used in research.( 10)
Categorical | Measures a construct that has different categories | gender, race, religious affiliation, political affiliation |
Quantitative | Measures constructs that vary by degree of the amount | weight, height, age, intelligence scores |
Independent (IV) | Measures constructs considered to be the cause | Higher education (IV) leads to higher income (DV) |
Dependent (DV) | Measures constructs that are considered the effect | Exercise (IV) will reduce anxiety levels (DV) |
Intervening or mediating (MV) | Measures constructs that intervene or stand in between the cause and effect | Incarcerated individuals are more likely to have psychiatric disorder (MV), which leads to disability in social roles |
Confounding (CV) | “Rival explanations” that explain the cause-and-effect relationship | Age (CV) explains the relationship between increased shoe size and increase in intelligence in children |
Control variable | Extraneous variables whose influence can be controlled or eliminated | Demographic data such as gender, socioeconomic status, age |
2. Why is it important to differentiate between independent vs dependent variables ?
Differentiating between independent vs dependent variables is important to ensure the correct application in your own research and also the correct understanding of other studies. An incorrectly framed research question can lead to confusion and inaccurate results. An easy way to differentiate is to identify the cause and effect.
3. How are independent and dependent variables used in non-experimental research?
So far in this article we talked about variables in relation to experimental research, wherein variables are manipulated or measured to test a hypothesis, that is, to observe the effect on dependent variables. Let’s examine non-experimental research and how variable are used. 11 In non-experimental research, variables are not manipulated but are observed in their natural state. Researchers do not have control over the variables and cannot manipulate them based on their research requirements. For example, a study examining the relationship between income and education level would not manipulate either variable. Instead, the researcher would observe and measure the levels of each variable in the sample population. The level of control researchers have is the major difference between experimental and non-experimental research. Another difference is the causal relationship between the variables. In non-experimental research, it is not possible to establish a causal relationship because other variables may be influencing the outcome.
4. Are there any advantages and disadvantages of using independent vs dependent variables ?
Here are a few advantages and disadvantages of both independent and dependent variables.( 12)
Advantages:
Disadvantages:
We hope this article has provided you with an insight into the use and importance of independent vs dependent variables , which can help you effectively use variables in your next research study.
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1 Dept. of Clinical Psychopharmacology and Neurotoxicology, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka, India.
Students without prior research experience may not know how to conceptualize and design a study. This article explains how an understanding of the classification and operationalization of variables is the key to the process. Variables describe aspects of the sample that is under study; they are so called because they vary in value from subject to subject in the sample. Variables may be independent or dependent. Independent variables influence the value of other variables; dependent variables are influenced in value by other variables. A hypothesis states an expected relationship between variables. A significant relationship between an independent and dependent variable does not prove cause and effect; the relationship may partly or wholly be explained by one or more confounding variables. Variables need to be operationalized; that is, defined in a way that permits their accurate measurement. These and other concepts are explained with the help of clinically relevant examples.
This article explains the following concepts: Independent variables, dependent variables, confounding variables, operationalization of variables, and construction of hypotheses.
In any body of research, the subject of study requires to be described and understood. For example, if we wish to study predictors of response to antidepressant drugs (ADs) in patients with major depressive disorder (MDD), we might select patient age, sex, age at onset of MDD, number of previous episodes of depression, duration of current depressive episode, presence of psychotic symptoms, past history of response to ADs, and other patient and illness characteristics as potential predictors. These characteristics or descriptors are called variables. Whether or not the patient responds to AD treatment is also a variable. A solid understanding of variables is the cornerstone in the conceptualization and preparation of a research protocol, and in the framing of study hypotheses. This subject is presented in two parts. This article, Part 1, explains what independent and dependent variables are, how an understanding of these is important in framing hypotheses, and what operationalization of a variable entails.
Variables are defined as characteristics of the sample that are examined, measured, described, and interpreted. Variables are so called because they vary in value from subject to subject in the study. As an example, if we wish to examine the relationship between age and height in a sample of children, age and height are the variables of interest; their values vary from child to child. In the earlier example, patients vary in age, sex, duration of current depressive episode, and response to ADs. Variables are classified as dependent and independent variables and are usually analyzed as categorical or continuous variables.
Independent variables are defined as those the values of which influence other variables. For example, age, sex, current smoking, LDL cholesterol level, and blood pressure are independent variables because their values (e.g., greater age, positive for current smoking, and higher LDL cholesterol level) influence the risk of myocardial infarction. Dependent variables are defined as those the values of which are influenced by other variables. For example, the risk of myocardial infarction is a dependent variable the value of which is influenced by variables such as age, sex, current smoking, LDL cholesterol level, and blood pressure. The risk is higher in older persons, in men, in current smokers, and so on.
There may be a cause–effect relationship between independent and dependent variables. For example, consider a clinical trial with treatment (iron supplement vs placebo) as the independent variable and hemoglobin level as the dependent variable. In children with anemia, an iron supplement will raise the hemoglobin level to a greater extent than will placebo; this is a cause–effect relationship because iron is necessary for the synthesis of hemoglobin. However, consider the variables teeth and weight . An alien from outer space who has no knowledge of human physiology may study human children below the age of 5 years and find that, as the number of teeth increases, weight increases. Should the alien conclude that there is a cause–effect relationship here, and that growing teeth causes weight gain? No, because a third variable, age, is a confounding variable 1 – 3 that is responsible for both increase in the number of teeth and increase in weight. In general, therefore, it is more proper to state that independent variables are associated with variations in the values of the dependent variables rather than state that independent variables cause variations in the values of the dependent variables. For causality to be asserted, other criteria must be fulfilled; this is out of the scope of the present article, and interested readers may refer to Schunemann et al. 4
As a side note, here, whether a particular variable is independent or dependent will depend on the question that is being asked. For example, in a study of factors influencing patient satisfaction with outpatient department (OPD) services, patient satisfaction is the dependent variable. But, in a study of factors influencing OPD attendance at a hospital, OPD attendance is the dependent variable, and patient satisfaction is merely one of many possible independent variables that can influence OPD attendance.
Students must have a clear idea about what they want to study in order to conceptualize and frame a research protocol. The first matters that they need to address are “What are my research questions?” and “What are my hypotheses?” Both questions can be answered only after choosing the dependent variables and then the independent variables for study.
In the case of a student who is interested in studying predictors of AD outcomes in patients with MDD, treatment response is the dependent variable and patient and clinical characteristics are possible independent variables. So, the selection of dependent and independent variables helps defines the objectives of the study:
Note that in a formal research protocol, the student will need to state all the independent variables and not merely list examples. The student may also choose to include additional independent variables, such as baseline biochemical, psychophysiological, and neuroradiological measures.
A hypothesis is a clear statement of what the researcher expects to find in the study. As an example, a researcher may hypothesize that longer duration of current depression is associated with poorer response to ADs. In this hypothesis, the duration of the current episode of depression is the independent variable and treatment response is the dependent variable. It should be obvious, now, that a hypothesis can also be defined as the statement of an expected relationship between an independent and a dependent variable . Or, expressed visually, (independent variable) (arrow) (dependent variable) = hypothesis.
It would be a waste of time and energy to do a study to examine only one question: whether duration of current depression predicts treatment response. So, it is usual for research protocols to include many independent variables and many dependent variables in the generation of many hypotheses, as shown in Table 1 . Pairing each variable in the “independent variable” column with each variable in the “dependent variable” column would result in the generation of these hypotheses. Table 2 shows how this is done for age. Sets of hypotheses can likewise be constructed for the remaining independent and dependent variables in Table 1 . Importantly, the student must select one of these hypotheses as the primary hypothesis; the remaining hypotheses, no matter how many they are, would be secondary hypotheses. It is necessary to have only one hypothesis as the primary hypothesis in order to calculate the sample size necessary for an adequately powered study and to reduce the risk of false positive findings in the analysis. 5 In rare situations, two hypotheses may be considered equally important and may be stated as coprimary hypotheses.
Independent Variables and Dependent Variables in a Study on Sociodemographic and Clinical Prediction of Response of Major Depressive Disorder to Antidepressant Drug Treatment
• Age • Sex • Age at onset of major depressive disorder • Number of past episodes of depression • Past history of response to antidepressant drugs • Duration of current depressive episode • Baseline severity of depression • Baseline suicidality • Baseline melancholia • Baseline psychotic symptoms • Baseline soft neurological signs • Severity of depression • Global severity of illness • Subjective well-being • Quality of life • Everyday functioning |
Combinations of Age with Dependent Variables in the Generation of Hypotheses
1. Older age is associated with less attenuation in the severity of depression. 2. Older age is associated with less attenuation in the global severity of illness. 3. Older age is associated with less improvement in subjective well-being. 4. Older age is associated with less improvement in quality of life. 5. Older age is associated with less improvement in everyday functioning. |
In Table 1 , suicidality is listed as an independent variable and severity of depression, as a dependent variable. These variables need to be operationalized; that is, stated in a way that explains how they will be measured. Table 3 presents three ways in which suicidality can be measured and four ways in which (reduction in) the severity of depression can be measured. Now, each way of measurement in the “independent variable” column can be paired with a way of measurement in the “dependent variable” column, making a total of 12 possible hypotheses. In like manner, the many variables listed in Table 1 can each be operationalized in several different ways, resulting in the generation of a very large number of hypotheses. As already stated, the student must select only one hypothesis as the primary hypothesis.
Possible Ways of Operationalization of Suicidality and Depression
Independent Variable: Suicidality | Dependent Variable: Severity of Depression |
• Item score on the HAM-D • Item score on the MADRS • Beck scale for Suicide ideation total score | • MADRS total score • HAM-D total score • HAM-D response rate • HAM-D remission rate |
HAM-D: Hamilton Depression Rating Scale, MADRS: Montgomery–Asberg Depression Rating Scale.
Much thought should be given to the operationalization of variables because variables that are carelessly operationalized will be poorly measured; the data collected will then be of poor quality, and the study will yield unreliable results. For example, socioeconomic status may be operationalized as lower, middle, or upper class, depending on the patient’s monthly income, on the total monthly income of the family, or using a validated socioeconomic status assessment scale that takes into consideration income, education, occupation, and place of residence. The student must choose the method that would best suit the needs of the study, and the method that has the greatest scientific acceptability. However, it is also permissible to operationalize the same variable in many different ways and to include all these different operationalizations in the study, as shown in Table 3 . This is because conceptualizing variables in different ways can help understand the subject of the study in different ways.
Operationalization of variables requires a consideration of the reliability and validity of the method of operationalization; discussions on reliability and validity are out of the scope of this article. Operationalization of variables also requires specification of the scale of measurement: nominal, ordinal, interval, or ratio; this is also out of the scope of the present article. Finally, operationalization of variables can also specify details of the measurement procedure. As an example, in a study on the use of metformin to reduce olanzapine-associated weight gain, we may state that we will obtain the weight of the patient but fail to explain how we will do it. Better would be to state that the same weighing scale will be used. Still better would be to state that we will use a weighing instrument that works on the principle of moving weights on a levered arm, and that the same instrument will be used for all patients. And best would be to add that we will weigh patients, dressed in standard hospital gowns, after they have voided their bladder but before they have eaten breakfast. When the way in which a variable will be measured is defined, measurement of that variable becomes more objective and uniform
The next article, Part 2, will address what categorical and continuous variables are, why continuous variables should not be converted into categorical variables and when this rule can be broken, and what confounding variables are.
Declaration of Conflicting Interests: The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author received no financial support for the research, authorship, and/or publication of this article.
Have you ever pondered the invisible threads connecting different aspects of our lives? Enter the realm of research topics with independent and dependent variables, where we unravel everyday mysteries through straightforward investigations. Our aim is to demystify complex concepts, making the world of research accessible and intriguing. Let’s delve into these topics, exploring questions that resonate with our daily experiences.
Consider the exploration of the relationship between exercise and academic performance. In this study, “Does Exercise Affect Academic Performance?” serves as the guiding question. The amount of exercise becomes the independent variable, while academic performance takes on the role of the dependent variable. This inquiry seeks to illuminate the potential links between physical activity and academic success, offering valuable insights into the interconnectedness of a healthy body and a thriving mind.
Now, shift focus to another intriguing investigation: “How Does Screen Time Affect Sleep Patterns in Teens?” Here, daily screen time becomes the independent variable, and sleep patterns act as the dependent variable. The study delves into the impact of screen usage on the sleep quality of teenagers, providing a window into the intricate dynamics between technology and well-being.
In this collection of good research topics , we cover each topic. It is a gateway to understanding the intricacies of our surroundings. Through these investigations, we aim to showcase how research empowers us to grasp the simple yet profound relationships that shape our daily lives.
Table of Contents
Research topics with independent and dependent variables involve the exploration of relationships between different factors or elements in a study. In scientific research, these variables play crucial roles in understanding cause-and-effect relationships or correlations. Let’s break down the key components:
Independent and dependent variables can take various forms depending on the nature of the study and the specific factors being investigated. Here are different types of independent and dependent variables:
Choosing the best research topics with independent and dependent variables involves careful consideration and planning. Here are seven easy steps to help you select a meaningful and feasible research topic:
Start by considering topics that genuinely interest you. Your enthusiasm for the subject will keep you motivated throughout the research process.
Clearly articulate the research question you want to address. This question should be specific and focused and should involve variables that you can measure or manipulate.
Evaluate the feasibility of your chosen topic. Ensure that you have access to the necessary resources, data, and equipment to conduct the research. Consider the time and budget constraints as well.
Conduct a literature review to understand what research has already been done in the chosen area. This will help you identify gaps in existing knowledge and refine your research question.
Clearly define the independent and dependent variables in your research question. Ensure that these variables are measurable and there is a logical and theoretical basis for examining their relationship.
Think about the type of research design that suits your question. Will it be an experiment, observational study, survey, or a combination? The design should align with your research question and the nature of the variables.
Share your potential research topic with peers, mentors, or advisors. Their feedback can provide valuable insights and help you refine your research question, ensuring that it is relevant and feasible.
Here are the most interesting research topics with independent and dependent variables examples:
These are the Research Topics With Independent And Dependent Variables pdf:
Want to know What is a research title with independent and dependent variables? Here are 10 examples of research titles along with the identified independent and dependent variables presented below:
1. Impact of Sleep Duration on Academic Performance | Sleep Duration | Academic Performance |
2. Influence of Social Media Usage on Well-being | Social Media Usage | Well-being |
3. Effect of Exercise Intensity on Weight Loss | Exercise Intensity | Weight Loss |
4. Relationship between Smartphone Use and Anxiety | Smartphone Use | Anxiety |
5. Role of Parental Involvement in Student Success | Parental Involvement | Student Success |
6. Impact of Temperature on Plant Growth | Temperature | Plant Growth |
7. Effect of Music Tempo on Productivity | Music Tempo | Productivity |
8. Influence of Advertising on Consumer Purchases | Advertising | Consumer Purchases |
9. Relationship between Study Habits and GPA | Study Habits | GPA |
10. Effect of Training Program on Employee Turnover | Training Program | Employee Turnover |
In these Research Topics With Independent And Dependent Variables examples, we explored how different things are connected. Like, does more sleep mean better grades? Or how does using our phones a lot affect how we feel? These are research topics, and we looked at the things we can change (independent variables) and what happens as a result (dependent variables).
Each topic, like how exercise links to grades or if music speed changes how well we work, helps us understand life better. The big idea is to pick things we want to study, figure out what we can change or measure, and see what happens. This helps us learn new things and answers questions we might have about how the world works.
So, whether it’s plants growing with different temperatures or how ads impact what we buy, these examples show that by asking questions and studying variables, we can uncover cool stuff about the world around us. And that’s what research is all about – finding out more and more about the things we’re curious about.
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Dependent Variable The variable that depends on other factors that are measured. These variables are expected to change as a result of an experimental manipulation of the independent variable or variables. It is the presumed effect.
Independent Variable The variable that is stable and unaffected by the other variables you are trying to measure. It refers to the condition of an experiment that is systematically manipulated by the investigator. It is the presumed cause.
Cramer, Duncan and Dennis Howitt. The SAGE Dictionary of Statistics . London: SAGE, 2004; Penslar, Robin Levin and Joan P. Porter. Institutional Review Board Guidebook: Introduction . Washington, DC: United States Department of Health and Human Services, 2010; "What are Dependent and Independent Variables?" Graphic Tutorial.
Don't feel bad if you are confused about what is the dependent variable and what is the independent variable in social and behavioral sciences research . However, it's important that you learn the difference because framing a study using these variables is a common approach to organizing the elements of a social sciences research study in order to discover relevant and meaningful results. Specifically, it is important for these two reasons:
A variable in research simply refers to a person, place, thing, or phenomenon that you are trying to measure in some way. The best way to understand the difference between a dependent and independent variable is that the meaning of each is implied by what the words tell us about the variable you are using. You can do this with a simple exercise from the website, Graphic Tutorial. Take the sentence, "The [independent variable] causes a change in [dependent variable] and it is not possible that [dependent variable] could cause a change in [independent variable]." Insert the names of variables you are using in the sentence in the way that makes the most sense. This will help you identify each type of variable. If you're still not sure, consult with your professor before you begin to write.
Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349;
The process of examining a research problem in the social and behavioral sciences is often framed around methods of analysis that compare, contrast, correlate, average, or integrate relationships between or among variables . Techniques include associations, sampling, random selection, and blind selection. Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent.
The variables should be outlined in the introduction of your paper and explained in more detail in the methods section . There are no rules about the structure and style for writing about independent or dependent variables but, as with any academic writing, clarity and being succinct is most important.
After you have described the research problem and its significance in relation to prior research, explain why you have chosen to examine the problem using a method of analysis that investigates the relationships between or among independent and dependent variables . State what it is about the research problem that lends itself to this type of analysis. For example, if you are investigating the relationship between corporate environmental sustainability efforts [the independent variable] and dependent variables associated with measuring employee satisfaction at work using a survey instrument, you would first identify each variable and then provide background information about the variables. What is meant by "environmental sustainability"? Are you looking at a particular company [e.g., General Motors] or are you investigating an industry [e.g., the meat packing industry]? Why is employee satisfaction in the workplace important? How does a company make their employees aware of sustainability efforts and why would a company even care that its employees know about these efforts?
Identify each variable for the reader and define each . In the introduction, this information can be presented in a paragraph or two when you describe how you are going to study the research problem. In the methods section, you build on the literature review of prior studies about the research problem to describe in detail background about each variable, breaking each down for measurement and analysis. For example, what activities do you examine that reflect a company's commitment to environmental sustainability? Levels of employee satisfaction can be measured by a survey that asks about things like volunteerism or a desire to stay at the company for a long time.
The structure and writing style of describing the variables and their application to analyzing the research problem should be stated and unpacked in such a way that the reader obtains a clear understanding of the relationships between the variables and why they are important. This is also important so that the study can be replicated in the future using the same variables but applied in a different way.
Fan, Shihe. "Independent Variable." In Encyclopedia of Research Design. Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 592-594; "What are Dependent and Independent Variables?" Graphic Tutorial; “Case Example for Independent and Dependent Variables.” ORI Curriculum Examples. U.S. Department of Health and Human Services, Office of Research Integrity; Salkind, Neil J. "Dependent Variable." In Encyclopedia of Research Design , Neil J. Salkind, editor. (Thousand Oaks, CA: SAGE, 2010), pp. 348-349; “Independent Variables and Dependent Variables.” Karl L. Wuensch, Department of Psychology, East Carolina University [posted email exchange]; “Variables.” Elements of Research. Dr. Camille Nebeker, San Diego State University.
These are properties, characteristics or attributes of some object or phenomena that can be assigned with different values or amounts.
These serves as a basis for collecting, organizing, presenting, analyzing and interpreting data in quantitative research.
TYPES OF VARIABLES
Based on the Role Taken.
Value and Scale
Levels of measurement
· BASED ON THE ROLE TAKEN
1. DEPENDENT VARIABLES
- these are variables that change as a result of an intervention or experiment.
- it is also referred as the outcome, effect, or response variables
2. INDEPENDENT VARIABLES
- It what the researcher manipulates to see if it changes the dependent variable.
- It is also called treatment, antecedent, manipulated, or predictor variables.
3. CONTROL VARIABLES
- These are the variables that are held constant on the given situation.
4. INTERVENING VARIABLES
- These are the hypothetical variable which serves as link or bridge the gap between dependent and independent variables.
- Also known as the mediating variable.
5. EXTRANEOUS VARIABLES
- It can affect the dependent variable or the outcomes of the study.
BASED ON VALUE AND SCALE
1. DISCRETE VARIABLES
- It assumes a distinct point on a scale. Thus, It has an exact value and has no fractional unit. (EX: number of puppies, number of children in a household.)
2. CONTINOUS VARIABLES
- It assumes a continuous point on a scale. (EX: height, weight, average daily temperate.)
BASED ON THE LEVELS OF MEASUREMENT
NOMINAL ORDINAL à CATEGORICAL VARIABLES
INTERVAL RATIO à NUMERICAL VARIABLES
NOMINAL VARIABLES - theres no number associated with these variables because they categorize or classified. (Ex; gender, religion, favorite movie genre)
ORDINAL VARIABLES - It bears the characteristics of a nominal variable but it is ranked in certain order. (Ex; social class; upper, middle. Sizes; small, medium, large.)
INTERVAL VARIABLES - denotes the exact difference between two points on a scale.
(Ex; temperature scale, difference between 70 degree and 80 degree)
RATIO VARIABLES - variables that posses the characteristics of the nominal, ordinal and interval variables and these are based on a fixed starting point. (Ex; num of students per classroom, voter turnout, annual family income.)
USES OF VARIABLES IN CHARACTERISTICS RESEARCH
1. CLASSIFYING - we are able to describe the phenomenon or population we are studying.
2. MEASURING-variables allow us to measure the frequency, magnitude and impact among others of particular subject or concept.
3. EXPLAINING - variables allow one to identify the meaning, purpose and use of social phenomenon.
4. ASSESSING RELATIONSHIPS-through hypothesis testing, we can examine which variables are related to which and how these are related to one another.
1. DEFINE THE RESEARCH PROBLEM - where you identify a research topic and transform it into a researchable problem or question that can be investigated.
2. DO THE REVIEW OF RELATED LITERATURE-is to obtain background information about the research topic.
3. FORMULATE HYPOTHESIS - serves as the tentative answer to the posed research question at the
beginning of the inquiry process.
4. PREPARE THE RESEARCH DESIGN -the 'blueprint' of the research that provides the details on how data will be collected, analyzed and interpreted.
5. COLLECT DATA - involves obtaining necessary information to answer the posed research question.
6. ANALYZE AND INTERPRET DATA - data analysis paved the way to make sense of the collected data by transforming them into appropriate tables and graphs.
7. CONCLUDE AND MAKE RECOMMENDATIONS - the researcher should report the findings into a comprehensive research paper and rests on its dissemination for public consumption afterwards.
TYPES OF QUANTITATIVE RESEARCH
1. DESCRIPTIVE RESEARCH - generally concerns the investigation, measurement and description of one or more aspects or characteristics of one or more groups, communities, or phenomenon.
2. CORRELATIONAL RESEARCH - it studies the relationship between two or more characteristics of one or more groups.
3. CAUSAL-COMPARATIVE RESEARCH -compares one or more measurable characteristics of two or
more groups to find the similarities and differences between them.
4. EXPERIMENTAL RESEARCH - A research that intends to actively manipulate conditions or inputs to observe the outcomes.
STRENGTHS OF QUANTI RESEARCH
Uses robust instrumentation which may yield results that can be generalized to a larger
population.
Allows for greater accuracy of data because variables are isolated, manipulated and controlled.
LIMITATIONS OF QUANTI RESEARCH
Employs inflexible research design due to the rigidity and robustness of the instrumentation. Participants have limited participation on the design and structure of the questionnaire.
IMAGES
COMMENTS
The dependent variable measures effects of the independent variable. Those are two variables in an experiment or in research. What are dependent and independent variables? In a scientific experiment, an independent variable is the variable that is manipulated or changed in order to test the impacts on the dependent variable. Meanwhile, the dependent variable is the variable that is being ...
Examples of Independent and Dependent Variables. 1. Gatorade and Improved Athletic Performance. A sports medicine researcher has been hired by Gatorade to test the effects of its sports drink on athletic performance. The company wants to claim that when an athlete drinks Gatorade, their performance will improve.
While the independent variable is the " cause ", the dependent variable is the " effect " - or rather, the affected variable. In other words, the dependent variable is the variable that is assumed to change as a result of a change in the independent variable. Keeping with the previous example, let's look at some dependent variables ...
The independent variable is the cause. Its value is independent of other variables in your study. The dependent variable is the effect. Its value depends on changes in the independent variable. Example: Independent and dependent variables. You design a study to test whether changes in room temperature have an effect on math test scores.
Independent variables are the variables that affect the dependent variables. Dependent variables are what is being affected and measured. Say someone was doing a study about how sleep affects productivity. Independent example: how little sleep or how much sleep the participants get. Dependent example: productivity of participants.
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 ...
The independent variable is the cause and the dependent variable is the effect, that is, independent variables influence dependent variables. In research, a dependent variable is the outcome of interest of the study and the independent variable is the factor that may influence the outcome. Let's explain this with an independent and dependent ...
Pairing each variable in the "independent variable" column with each variable in the "dependent variable" column would result in the generation of these hypotheses. Table 2 shows how this is done for age. Sets of hypotheses can likewise be constructed for the remaining independent and dependent variables in Table 1. Importantly, the ...
4. Research Topics With Independent And Dependent Variables In Psychology and Behavior. Topic: The Role of Personality Traits in Career Success. Independent Variable: Personality Traits. Dependent Variable: Career Success. Topic: Impact of Music on Mood. Independent Variable: Music Genre. Dependent Variable: Mood.
In scientific research, we often want to study the effect of one variable on another one. For example, you might want to test whether students who spend more time studying get better exam scores. The variables in a study of a cause-and-effect relationship are called the independent and dependent variables. The independent variable is the cause.
Research Topic Independent Variable Dependent Variable; All Research Topics: Manipulated by the researcher. Measured by the researcher. All Research Topics: What is being changed.
Designation of the dependent and independent variable involves unpacking the research problem in a way that identifies a general cause and effect and classifying these variables as either independent or dependent. The variables should be outlined in the introduction of your paper and explained in more detail in the methods section. There are no ...
A research topic example in education could explore how the type of technology used in the classroom affects student engagement, with technology being the independent variable and engagement the dependent variable. An example of a research topic in education with independent and dependent variables might be studying the impact of technology in ...
In research, the dependent and independent variables are important concepts to understand. These variables play a crucial role in designing and conducting experiments, and help researchers to establish cause-and-effect relationships between variables. The independent variable is the cause, and the dependent variable is the effect.
Therefore during research. the variables are manipulated by the experimenters. In an experiment. the independent variable is the variable that is varied or manipulated by the researcher. and the dependent variable is the response that is measured. An independent variable is the presumed cause. whereas the dependent variable is the presumed effect.
Give an example of a research topic with dependent,independent.controlled variable and methdology..thanks - 2397579. ... elijahrecto01 elijahrecto01 Independent and Dependent Variable Examples 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 ...
Brainly User. Explanation: The independent variable is the variable the experimenter changes or controls and is assumed to have a direct effect on the dependent variable. ... The dependent variable is the variable being tested and measured in an experiment, and is 'dependent' on the independent variable. Advertisement.
- These are the hypothetical variable which serves as link or bridge the gap between dependent and independent variables. - Also known as the mediating variable. 5. EXTRANEOUS VARIABLES ... DEFINE THE RESEARCH PROBLEM - where you identify a research topic and transform it into a researchable problem or question that can be investigated. 2. DO ...
Meanwhile, the dependent variable, or response variable, is the variable that is observed and that changes in response to the independent variable. The core of many research questions is to understand this cause-and-effect relationship by pitting one or more independent variables against a dependent variable.
Hey mate here is your answer =====≠===== variable and independent variable in research ===== *A variable is defined as anything that has a quantity or quality that varies. *The dependent variable is the variable a researcher is interested in. An independent variable is *a variable believed to affect the dependent variable.
Final answer: To select a research topic with a dependent variable and independent variables, consider studying the effects of exercise on heart rate. Create hypotheses and construct a hypothesized model to represent the relationships between the variables.. Explanation: In order to select a research topic with one dependent variable (DV) and at least three independent variables (IV), you can ...
All research studies need a theoretical framework. 2. A conceptual framework is summarized in a paradigm. ... Variables can be identified as dependent and independent. 9. Dependent variables "assumed cause" of a problem the reason that causes variation in a dependent variable. 10. Independent "assumed effect" of other variables or the problem ...
The influence of independent variables on dependent variables is what we expect. The term "dependent variable" refers to what happens as a result of the independent variable. Researchers regularly alter or measure independent and dependent variables in studies in order to determine the causation of links. The independent variable is the cause.