2. variables
3. variables
4. variables
5. variables 6. variables 7. variables 8. variables | What (exactly) is a variable?
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:
- How someone’s age impacts their sleep quality
- How different teaching methods impact learning outcomes
- How diet impacts weight (gain or loss)
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…
The “Big 3” Variables
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:
- Independent variables (IV)
- Dependant variables (DV)
- Control variables
What is an independent variable?
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:
- Increasing the dosage of a medication (Variable A) could result in better (or worse) health outcomes for a patient (Variable B)
- Changing a teaching method (Variable A) could impact the test scores that students earn in a standardised test (Variable B)
- Varying one’s diet (Variable A) could result in weight loss or gain (Variable B).
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.
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What is a dependent variable?
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:
- Health outcomes (DV) could be impacted by dosage changes of a medication (IV)
- Students’ scores (DV) could be impacted by teaching methods (IV)
- Weight gain or loss (DV) could be impacted by diet (IV)
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.
What is a control variable?
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:
- Temperature
- Time of day
- Noise or distractions
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!
Other types of variables
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.
- Moderating variables
- Mediating variables
- Confounding variables
- Latent variables
Let’s jump into it…
What is a moderating variable?
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.
What is a mediating variable?
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.
What is a confounding variable?
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:
- It must be correlated with the independent variable (this can be causal or not)
- It must have a causal impact on the dependent variable (i.e., influence the DV)
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.
What is a latent variable?
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:
- Emotional intelligence
- Quality of life
- Business confidence
- Ease of use
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!
Let’s recap
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:
- Independent variables (the “cause”)
- Dependent variables (the “effect”)
- Control variables (the variable that’s not allowed to vary)
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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Statistics By Jim
Making statistics intuitive
What is a Variable?
By Jim Frost Leave a Comment
The definition of a variable changes depending on the context. Typically, a letter represents them, and it stands in for a numerical value. In algebra, a variable represents an unknown value that you need to find. For mathematical functions and equations, you input their values to calculate the output. In an equation, a coefficient is a fixed value by which you multiply the variable.
In statistics , a variable is a characteristic of interest that you measure, record, and analyze. Statisticians understand them by defining the type of information they record and their role in an experiment or study.
In this post, learn about the different kinds of variables in statistics and their functions in experiments.
Variables Record Different Types of Information
Statisticians have devised various methods for categorizing variables to help you understand their differences. Below are several key ways to group them by the information they record.
Quantitative vs. Qualitative
Quantitative variables record amounts and quantities. For example, you used 15.7 gallons on your latest road trip. You walked 11,353 steps yesterday. The plant grew 5.6 cm in a week. Each of these examples quantifies a characteristic.
Qualitative or categorical variables define groups in your data. Frequently, you use descriptive language for these groups. For example, marital status, college major, type of fiction (drama, comedy, science fiction, etc.), and architectural style are all categorical and form groups in your data.
In an experiment, the treatment condition is a categorical variable that forms the experimental groups. In a plant fertilizer experiment, treatment condition divides the specimens into the control group and other groups based on fertilizer type.
Learn more about Quantitative vs. Qualitative Data .
Discrete vs. Continuous
When you have a quantitative variable, it can be discrete or continuous.
In broad terms, the difference between the two is the following:
- You count discrete data.
- You measure continuous data .
Discrete variables can only take on specific values that you cannot subdivide. Frequently, discrete data are values that you count and, consequently, are nonnegative integers. For example, you can count the number of people in your household and the number of steps per day.
Continuous variables can assume any value and you can meaningfully divide them into smaller parts, such as fractional and decimal values. Theoretically, continuous data have infinite values between any two values. Typically, you measure them using a scale.
For example, you have continuous data when measuring weight, height, length, time, and temperature.
Related post : Discrete vs. Continuous Data
Statisticians have devised various methods for categorizing data by the types of information they contain. To learn about another approach for organizing data types, read my post about Nominal, Ordinal, Interval, and Ratio Scales .
Random Variables
In statistics, most of the data you analyze are random variables, which are functions describing all values that occur during a series of random events or experiments. They can represent categorical, discrete, and continuous data. Examples include the following:
- Flipping coins or rolling dice and recording the results.
- Drawing a random sample and measuring heights.
- Performing a fertilizer experiment and recording plant growth.
In the preceding examples, an event provides a single value. However, a random variable comprises the entire set of possible values in your sample space.
For random variables, statisticians frequently assess the distribution of possible values, including the central tendency, spread, and skewness . Additionally, probability distribution functions describe the likelihood of obtaining particular values. All these properties provide vital information about the attribute you’re studying.
Related posts : Measures of Central Tendency , Measures of Variability , and Understanding Probability Distributions
Variables Play Different Roles in an Experiment
Finally, thinking about a variable’s role in an experiment or statistical study can help you better understand it.
Dependent Variables
In an experiment, you measure an outcome variable of interest. If you’re studying plant growth, infection rates, or bone density, that will be the outcome you measure. We call these dependent variables because their values depend on other variables in the study that I discuss below.
Independent Variables
In true experiments, researchers control the experimental conditions by assigning each subject to a treatment or control group. In other words, they can set the value of the variable they think will cause changes in the outcome. For example, in a plant growth study, the researchers control whether each plant receives fertilizer or not. When determining if a new vaccine reduces infection rates, they assign participants to either the vaccine or placebo group. Statisticians refer to this type of variable as an independent variable.
Learn more about Independent and Dependent Variables .
Control Variables
Control variables are not the primary focus of the research, but they are properties that researchers need to monitor because they can influence the outcome. Failure to incorporate them into a study can bias the findings. To prevent this bias, scientists can either hold these characteristics constant during the study or let them vary and include them in their models to control them statistically.
Suppose you’re performing a plant growth experiment, and you’re using several types of fertilizer and a control group with no fertilizer. The researchers might measure additional attributes that also affect plant growth. For example, they can record the temperature, moisture, and light conditions.
Learn more about Control Variables .
For more information about graphing and analyzing data for different types of variables, read the following posts:
- Data Types and How to Graph Them
- Hypothesis Testing by Data Types
- Choosing the Correct Type of Regression Analysis
Stevens, S.S., On the Theory of Scales of Measurement, Science, 1946
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Independent and Dependent Variables
Saul McLeod, PhD
Editor-in-Chief for Simply Psychology
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
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Associate Editor for Simply Psychology
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Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
On This Page:
In research, a variable is any characteristic, number, or quantity that can be measured or counted in experimental investigations . One is called the dependent variable, and the other is the independent variable.
In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome. Essentially, the independent variable is the presumed cause, and the dependent variable is the observed effect.
Variables provide the foundation for examining relationships, drawing conclusions, and making predictions in research studies.
Independent Variable
In psychology, the independent variable is the variable the experimenter manipulates or changes and is assumed to directly affect the dependent variable.
It’s considered the cause or factor that drives change, allowing psychologists to observe how it influences behavior, emotions, or other dependent variables in an experimental setting. Essentially, it’s the presumed cause in cause-and-effect relationships being studied.
For example, allocating participants to drug or placebo conditions (independent variable) to measure any changes in the intensity of their anxiety (dependent variable).
In a well-designed experimental study , the independent variable is the only important difference between the experimental (e.g., treatment) and control (e.g., placebo) groups.
By changing the independent variable and holding other factors constant, psychologists aim to determine if it causes a change in another variable, called the dependent variable.
For example, in a study investigating the effects of sleep on memory, the amount of sleep (e.g., 4 hours, 8 hours, 12 hours) would be the independent variable, as the researcher might manipulate or categorize it to see its impact on memory recall, which would be the dependent variable.
Dependent Variable
In psychology, the dependent variable is the variable being tested and measured in an experiment and is “dependent” on the independent variable.
In psychology, a dependent variable represents the outcome or results and can change based on the manipulations of the independent variable. Essentially, it’s the presumed effect in a cause-and-effect relationship being studied.
An example of a dependent variable is depression symptoms, which depend on the independent variable (type of therapy).
In an experiment, the researcher looks for the possible effect on the dependent variable that might be caused by changing the independent variable.
For instance, in a study examining the effects of a new study technique on exam performance, the technique would be the independent variable (as it is being introduced or manipulated), while the exam scores would be the dependent variable (as they represent the outcome of interest that’s being measured).
Examples in Research Studies
For example, we might change the type of information (e.g., organized or random) given to participants to see how this might affect the amount of information remembered.
In this example, the type of information is the independent variable (because it changes), and the amount of information remembered is the dependent variable (because this is being measured).
For the following hypotheses, name the IV and the DV.
1. Lack of sleep significantly affects learning in 10-year-old boys.
IV……………………………………………………
DV…………………………………………………..
2. Social class has a significant effect on IQ scores.
DV……………………………………………….…
3. Stressful experiences significantly increase the likelihood of headaches.
4. Time of day has a significant effect on alertness.
Operationalizing Variables
To ensure cause and effect are established, it is important that we identify exactly how the independent and dependent variables will be measured; this is known as operationalizing the variables.
Operational variables (or operationalizing definitions) refer to how you will define and measure a specific variable as it is used in your study. This enables another psychologist to replicate your research and is essential in establishing reliability (achieving consistency in the results).
For example, if we are concerned with the effect of media violence on aggression, then we need to be very clear about what we mean by the different terms. In this case, we must state what we mean by the terms “media violence” and “aggression” as we will study them.
Therefore, you could state that “media violence” is operationally defined (in your experiment) as ‘exposure to a 15-minute film showing scenes of physical assault’; “aggression” is operationally defined as ‘levels of electrical shocks administered to a second ‘participant’ in another room.
In another example, the hypothesis “Young participants will have significantly better memories than older participants” is not operationalized. How do we define “young,” “old,” or “memory”? “Participants aged between 16 – 30 will recall significantly more nouns from a list of twenty than participants aged between 55 – 70” is operationalized.
The key point here is that we have clarified what we mean by the terms as they were studied and measured in our experiment.
If we didn’t do this, it would be very difficult (if not impossible) to compare the findings of different studies to the same behavior.
Operationalization has the advantage of generally providing a clear and objective definition of even complex variables. It also makes it easier for other researchers to replicate a study and check for reliability .
For the following hypotheses, name the IV and the DV and operationalize both variables.
1. Women are more attracted to men without earrings than men with earrings.
I.V._____________________________________________________________
D.V. ____________________________________________________________
Operational definitions:
I.V. ____________________________________________________________
2. People learn more when they study in a quiet versus noisy place.
I.V. _________________________________________________________
D.V. ___________________________________________________________
3. People who exercise regularly sleep better at night.
Can there be more than one independent or dependent variable in a study?
Yes, it is possible to have more than one independent or dependent variable in a study.
In some studies, researchers may want to explore how multiple factors affect the outcome, so they include more than one independent variable.
Similarly, they may measure multiple things to see how they are influenced, resulting in multiple dependent variables. This allows for a more comprehensive understanding of the topic being studied.
What are some ethical considerations related to independent and dependent variables?
Ethical considerations related to independent and dependent variables involve treating participants fairly and protecting their rights.
Researchers must ensure that participants provide informed consent and that their privacy and confidentiality are respected. Additionally, it is important to avoid manipulating independent variables in ways that could cause harm or discomfort to participants.
Researchers should also consider the potential impact of their study on vulnerable populations and ensure that their methods are unbiased and free from discrimination.
Ethical guidelines help ensure that research is conducted responsibly and with respect for the well-being of the participants involved.
Can qualitative data have independent and dependent variables?
Yes, both quantitative and qualitative data can have independent and dependent variables.
In quantitative research, independent variables are usually measured numerically and manipulated to understand their impact on the dependent variable. In qualitative research, independent variables can be qualitative in nature, such as individual experiences, cultural factors, or social contexts, influencing the phenomenon of interest.
The dependent variable, in both cases, is what is being observed or studied to see how it changes in response to the independent variable.
So, regardless of the type of data, researchers analyze the relationship between independent and dependent variables to gain insights into their research questions.
Can the same variable be independent in one study and dependent in another?
Yes, the same variable can be independent in one study and dependent in another.
The classification of a variable as independent or dependent depends on how it is used within a specific study. In one study, a variable might be manipulated or controlled to see its effect on another variable, making it independent.
However, in a different study, that same variable might be the one being measured or observed to understand its relationship with another variable, making it dependent.
The role of a variable as independent or dependent can vary depending on the research question and study design.
Educational resources and simple solutions for your research journey
Independent vs Dependent Variables: Definitions & Examples
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
What is a variable?
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)
What is an independent variable?
Here is a list of the important characteristics of independent variables .( 2,3)
- An independent variable is the factor that is being manipulated in an experiment.
- In a research study, independent variables affect or influence dependent variables and cause them to change.
- Independent variables help gather evidence and draw conclusions about the research subject.
- They’re also called predictors, factors, treatment variables, explanatory variables, and input variables.
- On graphs, independent variables are usually placed on the X-axis.
- Example: In a study on the relationship between screen time and sleep problems, screen time is the independent variable because it influences sleep (the dependent variable).
- In addition, some factors like age are independent variables because other variables such as a person’s income will not change their age.
Types of independent variables
Independent variables in research are of the following two types:( 4)
Quantitative
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 :
- Differences in treatment dosages and frequencies: Useful in determining the appropriate dosage to get the desired outcome.
- Varying salinities: Useful in determining the range of salinity that organisms can tolerate.
Qualitative
Qualitative independent variables are non-numerical variables.
A few qualitative independent variables examples are listed below:
- Different strains of a species: Useful in identifying the strain of a crop that is most resistant to a specific disease.
- Varying methods of how a treatment is administered—oral or intravenous.
A quantitative variable is represented by actual amounts and a qualitative variable by categories or groups.
What is a dependent variable ?
Here are a few characteristics of dependent variables: ( 3)
- A dependent variable represents a quantity whose value depends on the independent variable and how it is changed.
- The dependent variable is influenced by the independent variable under various circumstances.
- It is also known as the response variable and outcome variable.
- On graphs, dependent variables are placed on the Y-axis.
Here are a few dependent variable examples :
- In a study on the effect of exercise on mood, the dependent variable is mood because it may change with exercise.
- In a study on the effect of pH on enzyme activity, the enzyme activity is the dependent variable because it changes with changing pH.
Types of dependent variables
Dependent variables are of two types:( 5)
Continuous dependent variables
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.
Categorical or discrete dependent variables
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.
Differences between independent and dependent variables
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 |
Independent and dependent variable examples
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 |
Independent vs dependent variables in research
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 |
How to identify independent vs dependent variables
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)
- Keep in mind that there are no specific words that will always describe dependent and independent variables.
- If you’re given a paragraph, convert that into a question and identify specific words describing cause and effect.
- The word representing the cause is the independent variable and that describing the effect is the dependent variable.
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.
Visualizing independent vs dependent variables
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.
Key takeaways
Let’s summarize the key takeaways about independent vs dependent variables from this article:
- A variable is any entity being measured in a study.
- A dependent variable is often the focus of a research study and is the response or outcome. It depends on or varies with changes in other variables.
- Independent variables cause changes in dependent variables and don’t depend on other variables.
- An independent variable can influence a dependent variable, but a dependent variable cannot influence an independent variable.
- An independent variable is the cause and dependent variable is the effect.
Frequently asked questions
- What are the different types of variables used in research?
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:
- Dependent variables are not liable to any form of bias because they cannot be manipulated by researchers or other external factors.
- Independent variables are easily obtainable and don’t require complex mathematical procedures to be observed, like dependent variables. This is because researchers can easily manipulate these variables or collect the data from respondents.
- Some independent variables are natural factors and cannot be manipulated. They are also easily obtainable because less time is required for data collection.
Disadvantages:
- Obtaining dependent variables is a very expensive and effort- and time-intensive process because these variables are obtained from longitudinal research by solving complex equations.
- Independent variables are prone to researcher and respondent bias because they can be manipulated, and this may affect the study results.
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.
- Kaliyadan F, Kulkarni V. Types of variables, descriptive statistics, and sample size. Indian Dermatol Online J. 2019 Jan-Feb; 10(1): 82–86. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6362742/
- What Is an independent variable? (with uses and examples). Indeed website. Accessed March 11, 2024. https://www.indeed.com/career-advice/career-development/what-is-independent-variable
- Independent and dependent variables: Differences & examples. Statistics by Jim website. Accessed March 10, 2024. https://statisticsbyjim.com/regression/independent-dependent-variables/
- Independent variable. Biology online website. Accessed March 9, 2024. https://www.biologyonline.com/dictionary/independent-variable#:~:text=The%20independent%20variable%20in%20research,how%20many%20or%20how%20often .
- Dependent variables: Definition and examples. Clubz Tutoring Services website. Accessed March 10, 2024. https://clubztutoring.com/ed-resources/math/dependent-variable-definitions-examples-6-7-2/
- Research topics with independent and dependent variables. Good research topics website. Accessed March 12, 2024. https://goodresearchtopics.com/research-topics-with-independent-and-dependent-variables/
- Levels of measurement and using the correct statistical test. Univariate quantitative methods. Accessed March 14, 2024. https://web.pdx.edu/~newsomj/uvclass/ho_levels.pdf
- Easiest way to identify dependent and independent variables. Afidated website. Accessed March 15, 2024. https://www.afidated.com/2014/07/how-to-identify-dependent-and.html
- Choosing data visualizations. Math for the people website. Accessed March 14, 2024. https://web.stevenson.edu/mbranson/m4tp/version1/environmental-racism-choosing-data-visualization.html
- Trivedi C. Types of variables in scientific research. Concepts Hacked website. Accessed March 15, 2024. https://conceptshacked.com/variables-in-scientific-research/
- Variables in experimental and non-experimental research. Statistics solutions website. Accessed March 14, 2024. https://www.statisticssolutions.com/variables-in-experimental-and-non-experimental-research/#:~:text=The%20independent%20variable%20would%20be,state%20instead%20of%20manipulating%20them .
- Dependent vs independent variables: 11 key differences. Formplus website. Accessed March 15, 2024. https://www.formpl.us/blog/dependent-independent-variables
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Neag School of Education
Educational Research Basics by Del Siegle
Each person/thing we collect data on is called an OBSERVATION (in our work these are usually people/subjects. Currently, the term participant rather than subject is used when describing the people from whom we collect data).
OBSERVATIONS (participants) possess a variety of CHARACTERISTICS .
If a CHARACTERISTIC of an OBSERVATION (participant) is the same for every member of the group (doesn’t vary) it is called a CONSTANT .
If a CHARACTERISTIC of an OBSERVATION (participant) differs for group members it is called a VARIABLE . In research we don’t get excited about CONSTANTS (since everyone is the same on that characteristic); we’re more interested in VARIABLES. Variables can be classified as QUANTITATIVE or QUALITATIVE (also known as CATEGORICAL).
QUANTITATIVE variables are ones that exist along a continuum that runs from low to high. Ordinal, interval, and ratio variables are quantitative. QUANTITATIVE variables are sometimes called CONTINUOUS VARIABLES because they have a variety (continuum) of characteristics. Height in inches and scores on a test would be examples of quantitative variables.
QUALITATIVE variables do not express differences in amount, only differences. They are sometimes referred to as CATEGORICAL variables because they classify by categories. Nominal variables such as gender, religion, or eye color are CATEGORICAL variables. Generally speaking, categorical variables
Categorical variables are groups…such as gender or type of degree sought. Quantitative variables are numbers that have a range…like weight in pounds or baskets made during a ball game. When we analyze data we do turn the categorical variables into numbers but only for identification purposes…e.g. 1 = male and 2 = female. Just because 2 = female does not mean that females are better than males who are only 1. With quantitative data having a higher number means you have more of something. So higher values have meaning. |
A special case of a CATEGORICAL variable is a DICHOTOMOUS VARIABLE. DICHOTOMOUS variables have only two CHARACTERISTICS (male or female). When naming QUALITATIVE variables, it is important to name the category rather than the levels (i.e., gender is the variable name, not male and female).
Variables have different purposes or roles…
Independent (Experimental, Manipulated, Treatment, Grouping) Variable- That factor which is measured, manipulated, or selected by the experimenter to determine its relationship to an observed phenomenon. “In a research study, independent variables are antecedent conditions that are presumed to affect a dependent variable. They are either manipulated by the researcher or are observed by the researcher so that their values can be related to that of the dependent variable. For example, in a research study on the relationship between mosquitoes and mosquito bites, the number of mosquitoes per acre of ground would be an independent variable” (Jaeger, 1990, p. 373)
While the independent variable is often manipulated by the researcher, it can also be a classification where subjects are assigned to groups. In a study where one variable causes the other, the independent variable is the cause. In a study where groups are being compared, the independent variable is the group classification.
Dependent (Outcome) Variable- That factor which is observed and measured to determine the effect of the independent variable, i.e., that factor that appears, disappears, or varies as the experimenter introduces, removes, or varies the independent variable. “In a research study, the independent variable defines a principal focus of research interest. It is the consequent variable that is presumably affected by one or more independent variables that are either manipulated by the researcher or observed by the researcher and regarded as antecedent conditions that determine the value of the dependent variable. For example, in a study of the relationship between mosquitoes and mosquito bites, the number of mosquito bites per hour would be the dependent variable” (Jaeger, 1990, p. 370). The dependent variable is the participant’s response.
The dependent variable is the outcome. In an experiment, it may be what was caused or what changed as a result of the study. In a comparison of groups, it is what they differ on.
Moderator Variable- That factor which is measured, manipulated, or selected by the experimenter to discover whether it modifies the relationship of the independent variable to an observed phenomenon. It is a special type of independent variable.
The independent variable’s relationship with the dependent variable may change under different conditions. That condition is the moderator variable. In a study of two methods of teaching reading, one of the methods of teaching reading may work better with boys than girls. Method of teaching reading is the independent variable and reading achievement is the dependent variable. Gender is the moderator variable because it moderates or changes the relationship between the independent variable (teaching method) and the dependent variable (reading achievement).
Suppose we do a study of reading achievement where we compare whole language with phonics, and we also include students’ social economic status (SES) as a variable. The students are randomly assigned to either whole language instruction or phonics instruction. There are students of high and low SES in each group.
Let’s assume that we found that whole language instruction worked better than phonics instruction with the high SES students, but phonics instruction worked better than whole language instruction with the low SES students. Later you will learn in statistics that this is an interaction effect. In this study, language instruction was the independent variable (with two levels: phonics and whole language). SES was the moderator variable (with two levels: high and low). Reading achievement was the dependent variable (measured on a continuous scale so there aren’t levels).
With a moderator variable, we find the type of instruction did make a difference, but it worked differently for the two groups on the moderator variable. We select this moderator variable because we think it is a variable that will moderate the effect of the independent on the dependent. We make this decision before we start the study.
If the moderator had not been in the study above, we would have said that there was no difference in reading achievement between the two types of reading instruction. This would have happened because the average of the high and low scores of each SES group within a reading instruction group would cancel each other an produce what appears to be average reading achievement in each instruction group (i.e., Phonics: Low—6 and High—2; Whole Language: Low—2 and High—6; Phonics has an average of 4 and Whole Language has an average of 4. If we just look at the averages (without regard to the moderator), it appears that the instruction types produced similar results).
Extraneous Variable- Those factors which cannot be controlled. Extraneous variables are independent variables that have not been controlled. They may or may not influence the results. One way to control an extraneous variable which might influence the results is to make it a constant (keep everyone in the study alike on that characteristic). If SES were thought to influence achievement, then restricting the study to one SES level would eliminate SES as an extraneous variable.
Here are some examples similar to your homework:
Null Hypothesis: Students who receive pizza coupons as a reward do not read more books than students who do not receive pizza coupon rewards. Independent Variable: Reward Status Dependent Variable: Number of Books Read
High achieving students do not perform better than low achieving student when writing stories regardless of whether they use paper and pencil or a word processor. Independent Variable: Instrument Used for Writing Moderator Variable: Ability Level of the Students Dependent Variable: Quality of Stories Written When we are comparing two groups, the groups are the independent variable. When we are testing whether something influences something else, the influence (cause) is the independent variable. The independent variable is also the one we manipulate. For example, consider the hypothesis “Teachers given higher pay will have more positive attitudes toward children than teachers given lower pay.” One approach is to ask ourselves “Are there two or more groups being compared?” The answer is “Yes.” “What are the groups?” Teachers who are given higher pay and teachers who are given lower pay. Therefore, the independent variable is teacher pay (it has two levels– high pay and low pay). The dependent variable (what the groups differ on) is attitude towards school.
We could also approach this another way. “Is something causing something else?” The answer is “Yes.” “What is causing what?” Teacher pay is causing attitude towards school. Therefore, teacher pay is the independent variable (cause) and attitude towards school is the dependent variable (outcome).
Research Questions and Hypotheses
The research question drives the study. It should specifically state what is being investigated. Statisticians often convert their research questions to null and alternative hypotheses. The null hypothesis states that no relationship (correlation study) or difference (experimental study) exists. Converting research questions to hypotheses is a simple task. Take the questions and make it a positive statement that says a relationship exists (correlation studies) or a difference exists (experiment study) between the groups and we have the alternative hypothesis. Write a statement that a relationship does not exist or a difference does not exist and we have the null hypothesis.
Format for sample research questions and accompanying hypotheses:
Research Question for Relationships: Is there a relationship between height and weight? Null Hypothesis: There is no relationship between height and weight. Alternative Hypothesis: There is a relationship between height and weight.
When a researcher states a nondirectional hypothesis in a study that compares the performance of two groups, she doesn’t state which group she believes will perform better. If the word “more” or “less” appears in the hypothesis, there is a good chance that we are reading a directional hypothesis. A directional hypothesis is one where the researcher states which group she believes will perform better. Most researchers use nondirectional hypotheses.
We usually write the alternative hypothesis (what we believe might happen) before we write the null hypothesis (saying it won’t happen).
Directional Research Question for Differences: Do boys like reading more than girls? Null Hypothesis: Boys do not like reading more than girls. Alternative Hypothesis: Boys do like reading more than girls.
Nondirectional Research Question for Differences: Is there a difference between boys’ and girls’ attitude towards reading? –or– Do boys’ and girls’ attitude towards reading differ? Null Hypothesis: There is no difference between boys’ and girls’ attitude towards reading. –or– Boys’ and girls’ attitude towards reading do not differ. Alternative Hypothesis: There is a difference between boys’ and girls’ attitude towards reading. –or– Boys’ and girls’ attitude towards reading differ.
Del Siegle, Ph.D. Neag School of Education – University of Connecticut [email protected] www.delsiegle.com
Types of Variables – A Comprehensive Guide
Published by Carmen Troy at August 14th, 2021 , Revised On October 26, 2023
A variable is any qualitative or quantitative characteristic that can change and have more than one value, such as age, height, weight, gender, etc.
Before conducting research, it’s essential to know what needs to be measured or analysed and choose a suitable statistical test to present your study’s findings.
In most cases, you can do it by identifying the key issues/variables related to your research’s main topic.
Example: If you want to test whether the hybridisation of plants harms the health of people. You can use the key variables like agricultural techniques, type of soil, environmental factors, types of pesticides used, the process of hybridisation, type of yield obtained after hybridisation, type of yield without hybridisation, etc.
Variables are broadly categorised into:
- Independent variables
- Dependent variable
- Control variable
Independent Vs. Dependent Vs. Control Variable
Type of variable | Definition | Example |
Independent Variable (Stimulus) | It is the variable that influences other variables. | |
Dependent variable (Response) | The dependent variable is the outcome of the influence of the independent variable. | You want to identify “How refined carbohydrates affect the health of human beings?” : refined carbohydrates : the health of human beings You can manipulate the consumption of refined carbs in your human participants and measure how those levels of consuming processed carbohydrates influence human health. |
Control Variables | Control variables are variables that are not changed and kept constant throughout the experiment. | The research includes finding ways: - To change the independent variables.
- To prevent the controlled variables from changing.
- To measure the dependent variables.
Note: The term dependent and independent is not applicable in correlational research as this is not a controlled experiment. A researcher doesn’t have control over the variables. The association and between two or more variables are measured. If one variable affects another one, then it’s called the predictor variable and outcome variable. Example: Correlation between investment (predictor variable) and profit (outcome variable) What data collection best suits your research?- Find out by hiring an expert from ResearchProspect today!
- Despite how challenging the subject may be, we are here to help you.
Types of Variables Based on the Types of DataA data is referred to as the information and statistics gathered for analysis of a research topic. Data is broadly divided into two categories, such as: Quantitative/Numerical data is associated with the aspects of measurement, quantity, and extent. Categorial data is associated with groupings. A qualitative variable consists of qualitative data, and a quantitative variable consists of a quantitative variable. Quantitative VariableThe quantitative variable is associated with measurement, quantity, and extent, like how many . It follows the statistical, mathematical, and computational techniques in numerical data such as percentages and statistics. The research is conducted on a large group of population. Example: Find out the weight of students of the fifth standard studying in government schools. The quantitative variable can be further categorised into continuous and discrete. Type of variable | Definition | Example | Continuous Variable | A continuous variable is a quantitative variable that can take a value between two specific values. | | Discrete Variable | A discrete variable is a quantitative variable whose attributes are separated from each other. | Literacy rate, gender, and nationality. Scale: Nominal and ordinal. | Categorial VariableThe categorical variable includes measurements that vary in categories such as names but not in terms of rank or degree. It means one level of a categorical variable cannot be considered better or greater than another level. Example: Gender, brands, colors, zip codes The categorical variable is further categorised into three types: Type of variable | Definition | Example | Dichotomous (Binary) Variable | This is the categorical variable with two possible results (Yes/No) | Alcoholic (Yes/No) | Nominal Variable | Nominal Variable can take the value that is not organised in terms of groups, degree, or rank. | | Ordinal Variable | Ordinal Variable can take the value that can be logically ordered or ranked. | | Note: Sometimes, an ordinal variable also acts as a quantitative variable. Ordinal data has an order, but the intervals between scale points may be uneven. Example: Numbers on a rating scale represent the reviews’ rank or range from below average to above average. However, it also represents a quantitative variable showing how many stars and how much rating is given. Not sure which statistical tests to use for your data?Let the experts at researchprospect do the daunting work for you.. Using our approach, we illustrate how to collect data, sample sizes, validity, reliability, credibility, and ethics, so you won’t have to do it all by yourself! Other Types of VariablesIt’s important to understand the difference between dependent and independent variables and know whether they are quantitative or categorical to choose the appropriate statistical test. There are many other types of variables to help you differentiate and understand them. Also, read a comprehensive guide written about inductive and deductive reasoning . Type of variable | Definition | Example | Confounding variables | The confounding variable is a hidden variable that produces an association between two unrelated variables because the hidden variable affects both of them. | There is an association between water consumption and cold drink sales. The confounding variable could be the and compels people to drink a lot of water and a cold drink to reduce heat and thirst caused due to the heat. | Latent Variable | These are the variables that cannot be observed or measured directly. | Self-confidence and motivation cannot be measured directly. Still, they can be interpreted through other variables such as habits, achievements, perception, and lifestyle. | Composite variables | A composite variable is a combination of multiple variables. It is used to measure multidimensional aspects that are difficult to observe. | - Entertainment
- Online education
- Database management, storage, and retrieval
Frequently Asked QuestionsWhat are the 10 types of variables in research. The 10 types of variables in research are: - Independent
- Confounding
- Categorical
- Extraneous.
What is an independent variable?An independent variable, often termed the predictor or explanatory variable, is the variable manipulated or categorized in an experiment to observe its effect on another variable, called the dependent variable. It’s the presumed cause in a cause-and-effect relationship, determining if changes in it produce changes in the observed outcome. What is a variable?In research, a variable is any attribute, quantity, or characteristic that can be measured or counted. It can take on various values, making it “variable.” Variables can be classified as independent (manipulated), dependent (observed outcome), or control (kept constant). They form the foundation for hypotheses, observations, and data analysis in studies. What is a dependent variable?A dependent variable is the outcome or response being studied in an experiment or investigation. It’s what researchers measure to determine the effect of changes in the independent variable. In a cause-and-effect relationship, the dependent variable is presumed to be influenced or caused by the independent variable. What is a variable in programming?In programming, a variable is a symbolic name for a storage location that holds data or values. It allows data storage and retrieval for computational operations. Variables have types, like integer or string, determining the nature of data they can hold. They’re fundamental in manipulating and processing information in software. What is a control variable?A control variable in research is a factor that’s kept constant to ensure that it doesn’t influence the outcome. By controlling these variables, researchers can isolate the effects of the independent variable on the dependent variable, ensuring that other factors don’t skew the results or introduce bias into the experiment. What is a controlled variable in science?In science, a controlled variable is a factor that remains constant throughout an experiment. It ensures that any observed changes in the dependent variable are solely due to the independent variable, not other factors. By keeping controlled variables consistent, researchers can maintain experiment validity and accurately assess cause-and-effect relationships. How many independent variables should an investigation have?Ideally, an investigation should have one independent variable to clearly establish cause-and-effect relationships. Manipulating multiple independent variables simultaneously can complicate data interpretation. However, in advanced research, experiments with multiple independent variables (factorial designs) are used, but they require careful planning to understand interactions between variables. You May Also LikeSampling methods are used to to draw valid conclusions about a large community, organization or group of people, but they are based on evidence and reasoning. Discourse analysis is an essential aspect of studying a language. It is used in various disciplines of social science and humanities such as linguistic, sociolinguistics, and psycholinguistic. Content analysis is used to identify specific words, patterns, concepts, themes, phrases, or sentences within the content in the recorded communication. USEFUL LINKS LEARNING RESOURCES COMPANY DETAILS Search form- Experiments
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Research VariablesThe research variables, of any scientific experiment or research process, are factors that can be manipulated and measured. This article is a part of the guide:- Experimental Research
- Pretest-Posttest
- Third Variable
- Research Bias
- Independent Variable
Browse Full Outline- 1 Experimental Research
- 2.1 Independent Variable
- 2.2 Dependent Variable
- 2.3 Controlled Variables
- 2.4 Third Variable
- 3.1 Control Group
- 3.2 Research Bias
- 3.3.1 Placebo Effect
- 3.3.2 Double Blind Method
- 4.1 Randomized Controlled Trials
- 4.2 Pretest-Posttest
- 4.3 Solomon Four Group
- 4.4 Between Subjects
- 4.5 Within Subject
- 4.6 Repeated Measures
- 4.7 Counterbalanced Measures
- 4.8 Matched Subjects
Any factor that can take on different values is a scientific variable and influences the outcome of experimental research . Most scientific experiments measure quantifiable factors, such as time or weight, but this is not essential for a component to be classed as a variable. As an example, most of us have filled in surveys where a researcher asks questions and asks you to rate answers. These responses generally have a numerical range, from ‘1 - Strongly Agree’ through to ‘5 - Strongly Disagree’. This type of measurement allows opinions to be statistically analyzed and evaluated. Dependent and Independent VariablesThe key to designing any experiment is to look at what research variables could affect the outcome. There are many types of variable but the most important, for the vast majority of research methods, are the independent and dependent variables. The independent variable is the core of the experiment and is isolated and manipulated by the researcher. The dependent variable is the measurable outcome of this manipulation, the results of the experimental design . For many physical experiments , isolating the independent variable and measuring the dependent is generally easy. If you designed an experiment to determine how quickly a cup of coffee cools, the manipulated independent variable is time and the dependent measured variable is temperature. In other fields of science, the variables are often more difficult to determine and an experiment needs a robust design. Operationalization is a useful tool to measure fuzzy concepts which do not have one obvious variable. The Difficulty of Isolating VariablesIn biology , social science and geography, for example, isolating a single independent variable is more difficult and any experimental design must consider this. For example, in a social research setting, you might wish to compare the effect of different foods upon hyperactivity in children. The initial research and inductive reasoning leads you to postulate that certain foods and additives are a contributor to increased hyperactivity. You decide to create a hypothesis and design an experiment , to establish if there is solid evidence behind the claim. The type of food is an independent variable, as is the amount eaten, the period of time and the gender and age of the child. All of these factors must be accounted for during the experimental design stage. Randomization and controls are generally used to ensure that only one independent variable is manipulated. To eradicate some of these research variables and isolate the process, it is essential to use various scientific measurements to nullify or negate them. For example, if you wanted to isolate the different types of food as the manipulated variable, you should use children of the same age and gender. The test groups should eat the same amount of the food at the same times and the children should be randomly assigned to groups. This will minimize the physiological differences between children. A control group , acting as a buffer against unknown research variables, might involve some children eating a food type with no known links to hyperactivity. In this experiment, the dependent variable is the level of hyperactivity, with the resulting statistical tests easily highlighting any correlation . Depending upon the results , you could try to measure a different variable, such as gender, in a follow up experiment. Converting Research Variables Into ConstantsEnsuring that certain research variables are controlled increases the reliability and validity of the experiment, by ensuring that other causal effects are eliminated. This safeguard makes it easier for other researchers to repeat the experiment and comprehensively test the results. What you are trying to do, in your scientific design, is to change most of the variables into constants, isolating the independent variable. Any scientific research does contain an element of compromise and inbuilt error , but eliminating other variables will ensure that the results are robust and valid . - Psychology 101
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Martyn Shuttleworth (Aug 9, 2008). Research Variables. Retrieved Aug 18, 2024 from Explorable.com: https://explorable.com/research-variables You Are Allowed To Copy The TextThe text in this article is licensed under the Creative Commons-License Attribution 4.0 International (CC BY 4.0) . This means you're free to copy, share and adapt any parts (or all) of the text in the article, as long as you give appropriate credit and provide a link/reference to this page. That is it. You don't need our permission to copy the article; just include a link/reference back to this page. You can use it freely (with some kind of link), and we're also okay with people reprinting in publications like books, blogs, newsletters, course-material, papers, wikipedia and presentations (with clear attribution). Want to stay up to date? Follow us!Get all these articles in 1 guide. Want the full version to study at home, take to school or just scribble on? Whether you are an academic novice, or you simply want to brush up your skills, this book will take your academic writing skills to the next level. Download electronic versions: - Epub for mobiles and tablets - For Kindle here - For iBooks here - PDF version here Save this course for laterDon't have time for it all now? No problem, save it as a course and come back to it later. Footer bottom- Subscribe to our RSS Feed
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Elements of Research | | The purpose of all research is to describe and explain in the world. Variance is simply the difference; that is, variation that occurs naturally in the world or change that we create as a result of a manipulation. Variables are names that are given to the variance we wish to explain. A variable is either a result of some force or is itself the force that causes a change in another variable. In experiments, these are called and variables respectively. When a researcher gives an active drug to one group of people and a placebo , or inactive drug, to another group of people, the independent variable is the drug treatment. Each person's response to the active drug or is called the dependent variable. This could be many things depending upon what the drug is for, such as high blood pressure or muscle pain. Therefore in experiments, a researcher manipulates an independent variable to determine if it causes a change in the dependent variable. As we learned earlier in a descriptive study, variables are not manipulated. They are observed as they naturally occur and then associations between variables are studied. In a way, all the variables in descriptive studies are dependent variables because they are studied in relation to all the other variables that exist in the setting where the research is taking place. However, in descriptive studies, variables are not discussed using the terms "independent" or "dependent." Instead, the names of the variables are used when discussing the study. For example, there is more diabetes in people of Native American heritage than people who come from Eastern Europe. In a descriptive study, the researcher would examine how diabetes (a variable) is related to a person's genetic heritage (another variable). Variables are important to understand because they are the basic units of the information studied and interpreted in research studies. Researchers carefully analyze and interpret the value(s) of each variable to make sense of how things relate to each other in a descriptive study or what has happened in an experiment. | Dependent vs. Independent Variables in ResearchIntroductionIndependent and dependent variables in research, can qualitative data have independent and dependent variables. Experiments rely on capturing the relationship between independent and dependent variables to understand causal patterns. Researchers can observe what happens when they change a condition in their experiment or if there is any effect at all. It's important to understand the difference between the independent variable and dependent variable. We'll look at the notion of independent and dependent variables in this article. If you are conducting experimental research, defining the variables in your study is essential for realizing rigorous research . In experimental research, a variable refers to the phenomenon, person, or thing that is being measured and observed by the researcher. A researcher conducts a study to see how one variable affects another and make assertions about the relationship between different variables. A typical research question in an experimental study addresses a hypothesized relationship between the independent variable manipulated by the researcher and the dependent variable that is the outcome of interest presumably influenced by the researcher's manipulation. Take a simple experiment on plants as an example. Suppose you have a control group of plants on one side of a garden and an experimental group of plants on the other side. All things such as sunlight, water, and fertilizer being equal, both plants should be expected to grow at the same rate. Now imagine that the plants in the experimental group are given a new plant fertilizer under the assumption that they will grow faster. Then you will need to measure the difference in growth between the two groups in your study. In this case, the independent variable is the type of fertilizer used on your plants while the dependent variable is the rate of growth among your plants. If there is a significant difference in growth between the two groups, then your study provides support to suggest that the fertilizer causes higher rates of plant growth. What is the key difference between independent and dependent variables?The independent variable is the element in your study that you intentionally change, which is why it can also be referred to as the manipulated variable. You manipulate this variable to see how it might affect the other variables you observe, all other factors being equal. This means that you can observe the cause and effect relationships between one independent variable and one or multiple dependent variables. Independent variables are directly manipulated by the researcher, while dependent variables are not. They are "dependent" because they are affected by the independent variable in the experiment. Researchers can thus study how manipulating the independent variable leads to changes in the main outcome of interest being measured as the dependent variable. Note that while you can have multiple dependent variables, it is challenging to establish research rigor for multiple independent variables. If you are making so many changes in an experiment, how do you know which change is responsible for the outcome produced by the study? Studying more than one independent variable would require running an experiment for each independent variable to isolate its effects on the dependent variable. This being said, it is certainly possible to employ a study design that involves multiple independent and dependent variables, as is the case with what is called a factorial experiment. For example, a psychological study examining the effects of sleep and stress levels on work productivity and social interaction would have two independent variables and two dependent variables, respectively. Such a study would be complex and require careful planning to establish the necessary research rigor , however. If possible, consider narrowing your research to the examination of one independent variable to make it more manageable and easier to understand. Independent variable examplesLet's consider an experiment in the social studies. Suppose you want to determine the effectiveness of a new textbook compared to current textbooks in a particular school. The new textbook is supposed to be better, but how can you prove it? Besides all the selling points that the textbook publisher makes, how do you know if the new textbook is any good? A rigorous study examining the effects of the textbook on classroom outcomes is in order. The textbook given to students makes up the independent variable in your experimental study. The shift from the existing textbooks to the new one represents the manipulation of the independent variable in this study. Dependent variable examplesIn any experiment, the dependent variable is observed to measure how it is affected by changes to the independent variable. Outcomes such as test scores and other performance metrics can make up the data for the dependent variable. Now that we are changing the textbook in the experiment above, we should examine if there are any effects. To do this, we will need two classrooms of students. As best as possible, the two sets of students should be of similar proficiency (or at least of similar backgrounds) and placed within similar conditions for teaching and learning (e.g., physical space, lesson planning). The control group in our study will be one set of students using the existing textbook. By examining their performance, we can establish a baseline. The performance of the experimental group, which is the set of students using the new textbook, can then be compared with the baseline performance. As a result, the change in the test scores make up the data for our dependent variable. We cannot directly affect how well students perform on the test, but we can conclude from our experiment whether the use of the new textbook might impact students' performance. Turn data into valuable insights with ATLAS.tiRely on our powerful data analysis interface for your research, starting with a free trial. How do you know if a variable is independent or dependent?We can typically think of an independent variable as something a researcher can directly change. In the above example, we can change the textbook used by the teacher in class. If we're talking about plants, we can change the fertilizer. Conversely, the dependent variable is something that we do not directly influence or manipulate. Strictly speaking, we cannot directly manipulate a student's performance on a test or the rate of growth of a plant, not without other factors such as new teaching methods or new fertilizer, respectively. Understanding the distinction between a dependent variable and an independent variable is key to experimental research. Ultimately, the distinction can be reduced to which element in a study has been directly influenced by the researcher. Other variablesGiven the potential complexities encountered in research, there is essential terminology for other variables in any experimental study. You might employ this terminology or encounter them while reading other research. A control variable is any factor that the researcher tries to keep constant as the independent variable changes. In the plant experiment described earlier in this article, the sunlight and water are each a controlled variable while the type of fertilizer used is the manipulated variable across control and experimental groups. To ensure research rigor, the researcher needs to keep these control variables constant to dispel any concerns that differences in growth rate were being driven by sunlight or water, as opposed to the fertilizer being used. Extraneous variables refer to any unwanted influence on the dependent variable that may confound the analysis of the study. For example, if bugs or animals ate the plants in your fertilizer study, this was greatly impact the rates of plant growth. This is why it would be important to control the environment and protect it from such threats. Finally, independent variables can go by different names such as subject variables or predictor variables. Dependent variables can also be referred to as the responding variable or outcome variable. Whatever the language, they all serve the same role of influencing the dependent variable in an experiment. The use of the word " variables " is typically associated with quantitative and confirmatory research. Naturalistic qualitative research typically does not employ experimental designs or establish causality. Qualitative research often draws on observations , interviews , focus groups , and other forms of data collection that are allow researchers to study the naturally occurring "messiness" of the social world, rather than controlling all variables to isolate a cause-and-effect relationship. In limited circumstances, the idea of experimental variables can apply to participant observations in ethnography , where the researcher should be mindful of their influence on the environment they are observing. However, the experimental paradigm is best left to quantitative studies and confirmatory research questions. Qualitative researchers in the social sciences are oftentimes more interested in observing and describing socially-constructed phenomena rather than testing hypotheses . Nonetheless, the notion of independent and dependent variables does hold important lessons for qualitative researchers. Even if they don't employ variables in their study design, qualitative researchers often observe how one thing affects another. A theoretical or conceptual framework can then suggest potential cause-and-effect relationships in their study. With ATLAS.ti, insightful data analysis is at your fingertipsDownload a free trial of ATLAS.ti to see how you can make the most of your data. An official website of the United States government The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site. The site is secure. 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- Indian Dermatol Online J
- v.10(1); Jan-Feb 2019
Types of Variables, Descriptive Statistics, and Sample SizeFeroze kaliyadan. Department of Dermatology, King Faisal University, Al Hofuf, Saudi Arabia Vinay Kulkarni1 Department of Dermatology, Prayas Amrita Clinic, Pune, Maharashtra, India This short “snippet” covers three important aspects related to statistics – the concept of variables , the importance, and practical aspects related to descriptive statistics and issues related to sampling – types of sampling and sample size estimation. What is a variable?[ 1 , 2 ] To put it in very simple terms, a variable is an entity whose value varies. A variable is an essential component of any statistical data. It is a feature of a member of a given sample or population, which is unique, and can differ in quantity or quantity from another member of the same sample or population. Variables either are the primary quantities of interest or act as practical substitutes for the same. The importance of variables is that they help in operationalization of concepts for data collection. For example, if you want to do an experiment based on the severity of urticaria, one option would be to measure the severity using a scale to grade severity of itching. This becomes an operational variable. For a variable to be “good,” it needs to have some properties such as good reliability and validity, low bias, feasibility/practicality, low cost, objectivity, clarity, and acceptance. Variables can be classified into various ways as discussed below. Quantitative vs qualitativeA variable can collect either qualitative or quantitative data. A variable differing in quantity is called a quantitative variable (e.g., weight of a group of patients), whereas a variable differing in quality is called a qualitative variable (e.g., the Fitzpatrick skin type) A simple test which can be used to differentiate between qualitative and quantitative variables is the subtraction test. If you can subtract the value of one variable from the other to get a meaningful result, then you are dealing with a quantitative variable (this of course will not apply to rating scales/ranks). Quantitative variables can be either discrete or continuousDiscrete variables are variables in which no values may be assumed between the two given values (e.g., number of lesions in each patient in a sample of patients with urticaria). Continuous variables, on the other hand, can take any value in between the two given values (e.g., duration for which the weals last in the same sample of patients with urticaria). One way of differentiating between continuous and discrete variables is to use the “mid-way” test. If, for every pair of values of a variable, a value exactly mid-way between them is meaningful, the variable is continuous. For example, two values for the time taken for a weal to subside can be 10 and 13 min. The mid-way value would be 11.5 min which makes sense. However, for a number of weals, suppose you have a pair of values – 5 and 8 – the midway value would be 6.5 weals, which does not make sense. Under the umbrella of qualitative variables, you can have nominal/categorical variables and ordinal variablesNominal/categorical variables are, as the name suggests, variables which can be slotted into different categories (e.g., gender or type of psoriasis). Ordinal variables or ranked variables are similar to categorical, but can be put into an order (e.g., a scale for severity of itching). Dependent and independent variablesIn the context of an experimental study, the dependent variable (also called outcome variable) is directly linked to the primary outcome of the study. For example, in a clinical trial on psoriasis, the PASI (psoriasis area severity index) would possibly be one dependent variable. The independent variable (sometime also called explanatory variable) is something which is not affected by the experiment itself but which can be manipulated to affect the dependent variable. Other terms sometimes used synonymously include blocking variable, covariate, or predictor variable. Confounding variables are extra variables, which can have an effect on the experiment. They are linked with dependent and independent variables and can cause spurious association. For example, in a clinical trial for a topical treatment in psoriasis, the concomitant use of moisturizers might be a confounding variable. A control variable is a variable that must be kept constant during the course of an experiment. Descriptive StatisticsStatistics can be broadly divided into descriptive statistics and inferential statistics.[ 3 , 4 ] Descriptive statistics give a summary about the sample being studied without drawing any inferences based on probability theory. Even if the primary aim of a study involves inferential statistics, descriptive statistics are still used to give a general summary. When we describe the population using tools such as frequency distribution tables, percentages, and other measures of central tendency like the mean, for example, we are talking about descriptive statistics. When we use a specific statistical test (e.g., Mann–Whitney U-test) to compare the mean scores and express it in terms of statistical significance, we are talking about inferential statistics. Descriptive statistics can help in summarizing data in the form of simple quantitative measures such as percentages or means or in the form of visual summaries such as histograms and box plots. Descriptive statistics can be used to describe a single variable (univariate analysis) or more than one variable (bivariate/multivariate analysis). In the case of more than one variable, descriptive statistics can help summarize relationships between variables using tools such as scatter plots. Descriptive statistics can be broadly put under two categories: - Sorting/grouping and illustration/visual displays
- Summary statistics.
Sorting and groupingSorting and grouping is most commonly done using frequency distribution tables. For continuous variables, it is generally better to use groups in the frequency table. Ideally, group sizes should be equal (except in extreme ends where open groups are used; e.g., age “greater than” or “less than”). Another form of presenting frequency distributions is the “stem and leaf” diagram, which is considered to be a more accurate form of description. Suppose the weight in kilograms of a group of 10 patients is as follows: 56, 34, 48, 43, 87, 78, 54, 62, 61, 59 The “stem” records the value of the “ten's” place (or higher) and the “leaf” records the value in the “one's” place [ Table 1 ]. Stem and leaf plot | |
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0 | - | 1 | - | 2 | - | 3 | 4 | 4 | 3 8 | 5 | 4 6 9 | 6 | 1 2 | 7 | 8 | 8 | 7 | 9 | - |
Illustration/visual display of dataThe most common tools used for visual display include frequency diagrams, bar charts (for noncontinuous variables) and histograms (for continuous variables). Composite bar charts can be used to compare variables. For example, the frequency distribution in a sample population of males and females can be illustrated as given in Figure 1 . Composite bar chart A pie chart helps show how a total quantity is divided among its constituent variables. Scatter diagrams can be used to illustrate the relationship between two variables. For example, global scores given for improvement in a condition like acne by the patient and the doctor [ Figure 2 ]. Scatter diagram Summary statisticsThe main tools used for summary statistics are broadly grouped into measures of central tendency (such as mean, median, and mode) and measures of dispersion or variation (such as range, standard deviation, and variance). Imagine that the data below represent the weights of a sample of 15 pediatric patients arranged in ascending order: 30, 35, 37, 38, 38, 38, 42, 42, 44, 46, 47, 48, 51, 53, 86 Just having the raw data does not mean much to us, so we try to express it in terms of some values, which give a summary of the data. The mean is basically the sum of all the values divided by the total number. In this case, we get a value of 45. The problem is that some extreme values (outliers), like “'86,” in this case can skew the value of the mean. In this case, we consider other values like the median, which is the point that divides the distribution into two equal halves. It is also referred to as the 50 th percentile (50% of the values are above it and 50% are below it). In our previous example, since we have already arranged the values in ascending order we find that the point which divides it into two equal halves is the 8 th value – 42. In case of a total number of values being even, we choose the two middle points and take an average to reach the median. The mode is the most common data point. In our example, this would be 38. The mode as in our case may not necessarily be in the center of the distribution. The median is the best measure of central tendency from among the mean, median, and mode. In a “symmetric” distribution, all three are the same, whereas in skewed data the median and mean are not the same; lie more toward the skew, with the mean lying further to the skew compared with the median. For example, in Figure 3 , a right skewed distribution is seen (direction of skew is based on the tail); data values' distribution is longer on the right-hand (positive) side than on the left-hand side. The mean is typically greater than the median in such cases. Location of mode, median, and mean Measures of dispersionThe range gives the spread between the lowest and highest values. In our previous example, this will be 86-30 = 56. A more valuable measure is the interquartile range. A quartile is one of the values which break the distribution into four equal parts. The 25 th percentile is the data point which divides the group between the first one-fourth and the last three-fourth of the data. The first one-fourth will form the first quartile. The 75 th percentile is the data point which divides the distribution into a first three-fourth and last one-fourth (the last one-fourth being the fourth quartile). The range between the 25 th percentile and 75 th percentile is called the interquartile range. Variance is also a measure of dispersion. The larger the variance, the further the individual units are from the mean. Let us consider the same example we used for calculating the mean. The mean was 45. For the first value (30), the deviation from the mean will be 15; for the last value (86), the deviation will be 41. Similarly we can calculate the deviations for all values in a sample. Adding these deviations and averaging will give a clue to the total dispersion, but the problem is that since the deviations are a mix of negative and positive values, the final total becomes zero. To calculate the variance, this problem is overcome by adding squares of the deviations. So variance would be the sum of squares of the variation divided by the total number in the population (for a sample we use “n − 1”). To get a more realistic value of the average dispersion, we take the square root of the variance, which is called the “standard deviation.” The box plotThe box plot is a composite representation that portrays the mean, median, range, and the outliers [ Figure 4 ]. The concept of skewness and kurtosisSkewness is a measure of the symmetry of distribution. Basically if the distribution curve is symmetric, it looks the same on either side of the central point. When this is not the case, it is said to be skewed. Kurtosis is a representation of outliers. Distributions with high kurtosis tend to have “heavy tails” indicating a larger number of outliers, whereas distributions with low kurtosis have light tails, indicating lesser outliers. There are formulas to calculate both skewness and kurtosis [Figures [Figures5 5 – 8 ]. Positive skew High kurtosis (positive kurtosis – also called leptokurtic) Negative skew Low kurtosis (negative kurtosis – also called “Platykurtic”) Sample SizeIn an ideal study, we should be able to include all units of a particular population under study, something that is referred to as a census.[ 5 , 6 ] This would remove the chances of sampling error (difference between the outcome characteristics in a random sample when compared with the true population values – something that is virtually unavoidable when you take a random sample). However, it is obvious that this would not be feasible in most situations. Hence, we have to study a subset of the population to reach to our conclusions. This representative subset is a sample and we need to have sufficient numbers in this sample to make meaningful and accurate conclusions and reduce the effect of sampling error. We also need to know that broadly sampling can be divided into two types – probability sampling and nonprobability sampling. Examples of probability sampling include methods such as simple random sampling (each member in a population has an equal chance of being selected), stratified random sampling (in nonhomogeneous populations, the population is divided into subgroups – followed be random sampling in each subgroup), systematic (sampling is based on a systematic technique – e.g., every third person is selected for a survey), and cluster sampling (similar to stratified sampling except that the clusters here are preexisting clusters unlike stratified sampling where the researcher decides on the stratification criteria), whereas nonprobability sampling, where every unit in the population does not have an equal chance of inclusion into the sample, includes methods such as convenience sampling (e.g., sample selected based on ease of access) and purposive sampling (where only people who meet specific criteria are included in the sample). An accurate calculation of sample size is an essential aspect of good study design. It is important to calculate the sample size much in advance, rather than have to go for post hoc analysis. A sample size that is too less may make the study underpowered, whereas a sample size which is more than necessary might lead to a wastage of resources. We will first go through the sample size calculation for a hypothesis-based design (like a randomized control trial). The important factors to consider for sample size calculation include study design, type of statistical test, level of significance, power and effect size, variance (standard deviation for quantitative data), and expected proportions in the case of qualitative data. This is based on previous data, either based on previous studies or based on the clinicians' experience. In case the study is something being conducted for the first time, a pilot study might be conducted which helps generate these data for further studies based on a larger sample size). It is also important to know whether the data follow a normal distribution or not. Two essential aspects we must understand are the concept of Type I and Type II errors. In a study that compares two groups, a null hypothesis assumes that there is no significant difference between the two groups, and any observed difference being due to sampling or experimental error. When we reject a null hypothesis, when it is true, we label it as a Type I error (also denoted as “alpha,” correlating with significance levels). In a Type II error (also denoted as “beta”), we fail to reject a null hypothesis, when the alternate hypothesis is actually true. Type II errors are usually expressed as “1- β,” correlating with the power of the test. While there are no absolute rules, the minimal levels accepted are 0.05 for α (corresponding to a significance level of 5%) and 0.20 for β (corresponding to a minimum recommended power of “1 − 0.20,” or 80%). Effect size and minimal clinically relevant differenceFor a clinical trial, the investigator will have to decide in advance what clinically detectable change is significant (for numerical data, this is could be the anticipated outcome means in the two groups, whereas for categorical data, it could correlate with the proportions of successful outcomes in two groups.). While we will not go into details of the formula for sample size calculation, some important points are as follows: In the context where effect size is involved, the sample size is inversely proportional to the square of the effect size. What this means in effect is that reducing the effect size will lead to an increase in the required sample size. Reducing the level of significance (alpha) or increasing power (1-β) will lead to an increase in the calculated sample size. An increase in variance of the outcome leads to an increase in the calculated sample size. A note is that for estimation type of studies/surveys, sample size calculation needs to consider some other factors too. This includes an idea about total population size (this generally does not make a major difference when population size is above 20,000, so in situations where population size is not known we can assume a population of 20,000 or more). The other factor is the “margin of error” – the amount of deviation which the investigators find acceptable in terms of percentages. Regarding confidence levels, ideally, a 95% confidence level is the minimum recommended for surveys too. Finally, we need an idea of the expected/crude prevalence – either based on previous studies or based on estimates. Sample size calculation also needs to add corrections for patient drop-outs/lost-to-follow-up patients and missing records. An important point is that in some studies dealing with rare diseases, it may be difficult to achieve desired sample size. In these cases, the investigators might have to rework outcomes or maybe pool data from multiple centers. Although post hoc power can be analyzed, a better approach suggested is to calculate 95% confidence intervals for the outcome and interpret the study results based on this. Financial support and sponsorshipConflicts of interest. There are no conflicts of interest. Title Page SetupA title page is required for all APA Style papers. There are both student and professional versions of the title page. Students should use the student version of the title page unless their instructor or institution has requested they use the professional version. APA provides a student title page guide (PDF, 199KB) to assist students in creating their title pages. Student title pageThe student title page includes the paper title, author names (the byline), author affiliation, course number and name for which the paper is being submitted, instructor name, assignment due date, and page number, as shown in this example. Title page setup is covered in the seventh edition APA Style manuals in the Publication Manual Section 2.3 and the Concise Guide Section 1.6 Related handouts- Student Title Page Guide (PDF, 263KB)
- Student Paper Setup Guide (PDF, 3MB)
Student papers do not include a running head unless requested by the instructor or institution. Follow the guidelines described next to format each element of the student title page. | | | Paper title | Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms. | | Author names | Place one double-spaced blank line between the paper title and the author names. Center author names on their own line. If there are two authors, use the word “and” between authors; if there are three or more authors, place a comma between author names and use the word “and” before the final author name. | Cecily J. Sinclair and Adam Gonzaga | Author affiliation | For a student paper, the affiliation is the institution where the student attends school. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author name(s). | Department of Psychology, University of Georgia | Course number and name | Provide the course number as shown on instructional materials, followed by a colon and the course name. Center the course number and name on the next double-spaced line after the author affiliation. | PSY 201: Introduction to Psychology | Instructor name | Provide the name of the instructor for the course using the format shown on instructional materials. Center the instructor name on the next double-spaced line after the course number and name. | Dr. Rowan J. Estes | Assignment due date | Provide the due date for the assignment. Center the due date on the next double-spaced line after the instructor name. Use the date format commonly used in your country. | October 18, 2020 18 October 2020 | | Use the page number 1 on the title page. Use the automatic page-numbering function of your word processing program to insert page numbers in the top right corner of the page header. | 1 | Professional title pageThe professional title page includes the paper title, author names (the byline), author affiliation(s), author note, running head, and page number, as shown in the following example. Follow the guidelines described next to format each element of the professional title page. | | | Paper title | Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms. | | Author names | Place one double-spaced blank line between the paper title and the author names. Center author names on their own line. If there are two authors, use the word “and” between authors; if there are three or more authors, place a comma between author names and use the word “and” before the final author name. | Francesca Humboldt | When different authors have different affiliations, use superscript numerals after author names to connect the names to the appropriate affiliation(s). If all authors have the same affiliation, superscript numerals are not used (see Section 2.3 of the for more on how to set up bylines and affiliations). | Tracy Reuter , Arielle Borovsky , and Casey Lew-Williams | Author affiliation | For a professional paper, the affiliation is the institution at which the research was conducted. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author names; when there are multiple affiliations, center each affiliation on its own line. | Department of Nursing, Morrigan University | When different authors have different affiliations, use superscript numerals before affiliations to connect the affiliations to the appropriate author(s). Do not use superscript numerals if all authors share the same affiliations (see Section 2.3 of the for more). | Department of Psychology, Princeton University Department of Speech, Language, and Hearing Sciences, Purdue University | Author note | Place the author note in the bottom half of the title page. Center and bold the label “Author Note.” Align the paragraphs of the author note to the left. For further information on the contents of the author note, see Section 2.7 of the . | n/a | | The running head appears in all-capital letters in the page header of all pages, including the title page. Align the running head to the left margin. Do not use the label “Running head:” before the running head. | Prediction errors support children’s word learning | | Use the page number 1 on the title page. Use the automatic page-numbering function of your word processing program to insert page numbers in the top right corner of the page header. | 1 | - Small Business
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There is a critical need to generate age– and sex-specific survival curves to characterize chronological aging consistently across nonhuman primates (NHP) used in biomedical research. Accurate measures of chronological aging are essential for inferences into genetic, demographic, and physiological variables driving differences in NHP lifespan within and between species. Understanding NHP lifespans is relevant to public health because unraveling the demographic, molecular, and clinical bases of health across the life course in translationally relevant NHP species is fundamentally important to the study of human aging. Data from more than 110,000 captive individual NHP were contributed by 15 major research institutions to generate sex-specific Kaplan-Meier survival curves using uniform methods in 12 translational aging models: Callithrix jacchus (common marmoset), Chlorocebus aethiops sabaeus (vervet/African green), Macaca fascicularis (cynomolgus macaque), M. fuscata (Japanese macaque), M. mulatta (rhesus macaque), M. nemestrina (pigtail macaque), M. radiata (bonnet macaque), Pan troglodytes spp. (chimpanzee), Papio hamadryas spp. (baboon), Plecturocebus cupreus (coppery titi monkey), Saguinus oedipus (cotton-top tamarin), and Saimiri spp. (squirrel monkey). After employing strict inclusion criteria, primary analysis results are based on 12,269 NHP that survived to adulthood and died of natural/health-related causes. A secondary analysis was completed for 32,616 NHP that died of any cause. For the primary analyses, we report ages of 25 th , 50 th , 75 th , and 85 th percentiles of survival, maximum observed ages, rates of survivorship, and sex-based differences captured by quantile regression models and Kolmogorov-Smirnov tests. Our findings show a pattern of reduced male survival among catarrhines (African and Asian primates), especially macaques, but not platyrrhines (Central and South American primates). For many species, median lifespans were lower than previously reported. An important consideration is that these analyses may offer a better reflection of healthspan than lifespan. Captive NHP used in research are typically euthanized for humane welfare reasons before their natural end of life, often after diagnosis of their first major disease requiring long-term treatment with reduced quality of life (e.g., endometriosis, cancer, osteoarthritis). Supporting the idea that these data are capturing healthspan, for several species typical age at onset of chronic disease is similar to the median lifespan estimates. This data resource represents the most comprehensive characterization of sex-specific lifespan and age-at-death distributions for 12 biomedically relevant species, to date. The results clarify the relationships among NHP ages and will provide a valuable resource for the aging research community, improving human-NHP age equivalencies, informing investigators of the expected survival rates of NHP assigned to studies, providing a metric for comparisons in future studies, and contributing to our understanding of the factors that drive lifespan differences within and among species. Competing Interest StatementThe authors have declared no competing interest. Code availability statement added to main file; custom code added to Supplementary document. View the discussion thread. Supplementary Material Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Citation Manager Formats- EndNote (tagged)
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Free Webinar Replay – Why Sex Matters from Research to Patient CareCiti program replay: why sex matters from research to patient care, wednesday, july 17, 2024 at 10:00 a.m. et. Join us for a replay of the CITI Program’s original webinar “Why Sex Matters from Research to Patient Care.” Note: This webinar is a recording of the original broadcast, and will not be moderated. More information about this webinar: Sex and gender are variables that have been found to impact all health conditions. Unfortunately, clinical research has not consistently assessed and reported results based on sex, impacting patient care now and in the future. Sex-specific data is needed to provide optimal, personalized care; without it, it is impossible to know if a given intervention is effective and safe for everyone. Data disaggregated and reported by sex is also needed to inform the education of current healthcare professionals across the learning spectrum and serve as the basis of future research. In this webinar, we differentiate the concepts of sex and gender and consider the impact of these variables on research outcomes and patient care. The presenters review the National Institutes of Health (NIH) policy on sex as a biological variable (SABV) and how it relates to research. They also discuss the importance of including of sex and gender in research proposals. Register for additional free live webinars and available webinar replays. This Webinar Has ClosedClick below to browse our other upcoming webinars. - Free Live Webinar – Studying Up, Studying Down, and Studying Sideways: Ethical Considerations in Research
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Types of Variables in Research. Types of Variables in Research are as follows: Independent Variable. This is the variable that is manipulated by the researcher. It is also known as the predictor variable, as it is used to predict changes in the dependent variable. Examples of independent variables include age, gender, dosage, and treatment type ...
Types of Variables in Research & Statistics | Examples. Published on September 19, 2022 by Rebecca Bevans. Revised on June 21, 2023. In statistical research, a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good ...
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. The independent variable is the cause.
The Role of Variables in Research. In scientific research, variables serve several key functions: Define Relationships: Variables allow researchers to investigate the relationships between different factors and characteristics, providing insights into the underlying mechanisms that drive phenomena and outcomes. Establish Comparisons: By manipulating and comparing variables, scientists can ...
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 ...
Types of Variables in Research | Definitions & Examples. Published on 19 September 2022 by Rebecca Bevans. Revised on 28 November 2022. In statistical research, a variable is defined as an attribute of an object of study. Choosing which variables to measure is central to good experimental design.
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 ...
Examples of categorical variables include gender (male, female, other), type of vehicle (car, truck, motorcycle), or marital status (single, married, divorced). These categories help researchers organize data into groups for comparison and analysis. Categorical variables can be further classified into two subtypes: nominal and ordinal.
The definition of a variable changes depending on the context. Typically, a letter represents them, and it stands in for a numerical value. In algebra, a variable represents an unknown value that you need to find. For mathematical functions and equations, you input their values to calculate the output. In an equation, a coefficient is a fixed ...
In research, the independent variable is manipulated to observe its effect, while the dependent variable is the measured outcome. Essentially, the independent variable is the presumed cause, and the dependent variable is the observed effect. Variables provide the foundation for examining relationships, drawing conclusions, and making ...
Variables in Research. The definition of a variable in the context of a research study is some feature with the potential to change, typically one that may influence or reflect a relationship or ...
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 ...
Categorical variables are groups…such as gender or type of degree sought. Quantitative variables are numbers that have a range…like weight in pounds or baskets made during a ball game. When we analyze data we do turn the categorical variables into numbers but only for identification purposes…e.g. 1 = male and 2 = female.
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.
Quantitative Variables. Quantitative variables, also called numeric variables, are those variables that are measured in terms of numbers. A simple example of a quantitative variable is a person's age. Age can take on different values because a person can be 20 years old, 35 years old, and so on.
A control variable in research is a factor that's kept constant to ensure that it doesn't influence the outcome. By controlling these variables, researchers can isolate the effects of the independent variable on the dependent variable, ensuring that other factors don't skew the results or introduce bias into the experiment. ...
The independent variable is the core of the experiment and is isolated and manipulated by the researcher. The dependent variable is the measurable outcome of this manipulation, the results of the experimental design.For many physical experiments, isolating the independent variable and measuring the dependent is generally easy.. If you designed an experiment to determine how quickly a cup of ...
The purpose of research is to describe and explain variance in the world, that is, variance that. occurs naturally in the world or chang e that we create due to manipulation. Variables are ...
Variables are important to understand because they are the basic units of the information studied and interpreted in research studies. Researchers carefully analyze and interpret the value (s) of each variable to make sense of how things relate to each other in a descriptive study or what has happened in an experiment. previous next.
Variables are an important concept in experimental and hypothesis-testing research, so understanding independent/dependent variables is key to understanding research design. In this article, we will talk about what separates a dependent variable from an independent variable and how the concept applies to research.
Variables. What is a variable?[1,2] To put it in very simple terms, a variable is an entity whose value varies.A variable is an essential component of any statistical data. It is a feature of a member of a given sample or population, which is unique, and can differ in quantity or quantity from another member of the same sample or population.
A variable that changes the relationship between dependent and independent variables by strengthening or weakening the intervening variable's effect Example Access to health care: If wealth is the independent variable, and a long life span is a dependent variable, a researcher might hypothesize that access to quality health care is the ...
For examining relationships between variables, use correlation or regression analysis. For categorical data comparisons, use the Chi-Square test. If data does not meet normality assumptions ...
For a professional paper, the affiliation is the institution at which the research was conducted. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author names; when there are multiple affiliations, center ...
Important information. NerdWallet UK website is a free service with no charge to the user. Find out more details about how our site works.. Registered Office: Floor 3 Haldin House, Old Bank of ...
There is a critical need to generate age- and sex-specific survival curves to characterize chronological aging consistently across nonhuman primates (NHP) used in biomedical research. Accurate measures of chronological aging are essential for inferences into genetic, demographic, and physiological variables driving differences in NHP lifespan within and between species. Understanding NHP ...
This data set contains Tealbook/Greenbook projections for many of the variables also forecast in the Federal Reserve Bank of Philadelphia's Survey of Professional Forecasters (SPF). Updated: 04 Mar '24
In this webinar, we differentiate the concepts of sex and gender and consider the impact of these variables on research outcomes and patient care. The presenters review the National Institutes of Health (NIH) policy on sex as a biological variable (SABV) and how it relates to research. They also discuss the importance of including of sex and ...