If the experimenter cannot control an extraneous variable, then, this variable is referred to as a confounding variable. (Ref. 2) As the name implies, the presence of a confounding variable will confound the results. It may be due to the independent variable or to a confounding variable, and therefore the result will likely be inconclusive.
Examples of Independent and Dependent Variables
- Imagine if our chef used a different type of broth each time he experimented with spices—the results would be all over the place!
- For example, students who use effective coping strategies might be less stressed but also perform better academically due to their improved mental state.
- By watching how changes in one thing (like the amount of rain) affect something else (like the height of grass), you can identify the independent variable.
- Making Educated GuessesBefore they start experimenting, scientists make educated guesses called hypotheses.
Observing the effects and changes that occur helps them deduce relationships, formulate theories, and expand our understanding of the world. Every observation is a step towards solving the mysteries of nature and human behavior. Observing how the dependent variable reacts to changes helps scientists draw conclusions and make discoveries. 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.
What Is an Independent Variable? Definition and Examples
These variables are dichotomous or binary in nature, meaning they can take on only two values. Examples of binary independent variables include yes or no questions, such as whether a participant is a smoker or non-smoker. Examples of discrete independent variables include the number of siblings, the number of children in a family, and the number of pets owned. These variables are categorical or nominal in nature and represent a group or category. Examples of categorical independent variables include gender, what are interest rates and how does interest work ethnicity, marital status, and educational level. The independent and dependent variables are key to any scientific experiment, but how do you tell them apart?
Exercises for Identifying Independent Variables
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. 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. 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.
The key point here is that we have clarified what we mean by the terms as they were studied and measured in our experiment. 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, 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. If second home tax tips the patients who were taking the real drug were able to recover significantly faster than the patients taking the placebo, that means the pill was effective in treating cough.
These variables are continuous in nature and can take any value on a continuous scale. Examples of continuous independent variables include age, height, weight, temperature, and blood pressure. The independent variable is the presumed cause in an experiment or study, while the dependent variable is the presumed effect or outcome. The relationship between the independent variable and the dependent variable is often analyzed using statistical methods to determine the strength and direction of the relationship.
Making Educated GuessesBefore they start experimenting, scientists make educated guesses called hypotheses. It often includes the independent variable and the expected effect on the dependent variable, guiding researchers as they navigate through the experiment. Now that we’re acquainted with the basic concepts and have the tools to identify independent variables, let’s dive into the fascinating ocean of theories and frameworks.
A variable may be thought to alter the dependent or independent variables, but may not actually be the focus of the experiment. So that the variable will be kept constant or monitored to try to minimize its effect on the experiment. Such variables may be designated as either a “controlled variable”, “control variable”, or “fixed variable”. The levels of independent variables pertain to the different categories or groupings of that variable. For instance, in a study about social media use and the hours of sleep per night, the independent variable is social media use and the hours of sleep per night is the dependent variable.
For example, in a study examining the effect of post-secondary education on lifetime earnings, some extraneous variables might be gender, ethnicity, social class, genetics, intelligence, age, and so forth. A variable is extraneous only when it can be assumed (or shown) to influence the dependent variable. This effect is called confounding or omitted variable bias; in these situations, design changes and/or controlling for a variable statistical control is necessary.
Overview: Variables In Research
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. In some studies, researchers may want to explore how multiple factors affect the outcome, so they include more than one independent 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.
The role of a variable as independent or dependent can vary depending on the research question and study design. Yes, it is possible to have more than one independent or dependent variable in a study. An example of a dependent variable is depression symptoms, which depend on the independent variable (type of therapy). This method is used to determine the strength and direction of the relationship between two continuous variables. Correlation coefficients such as Pearson’s r or Spearman’s rho are used to quantify the strength and direction of the relationship. It allows scientists to explore relationships, unravel patterns, and unearth the secrets hidden within the fabric of our universe.
Here are the definitions of independent and dependent variables, examples of each type, and tips for telling them apart and graphing them. Let’s put on our thinking caps and try to identify the independent variables in a few scenarios. Up next, we’ll look at tons of examples to see how independent variables work their magic in different areas.
