In such a case, one may find that gender has an influence on how much students’ scores suffer when they’re deprived of sleep. 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.
Experimental Design
The independent variable is the factor the researcher changes or controls in an experiment. The independent variable may be called the “controlled variable” because it is the one that is changed or controlled. This is different from the “control variable,” which is variable that is held constant so it won’t influence the outcome of the experiment.
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 figuring out your form w 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.
- This method is used to determine the strength and direction of the relationship between two continuous variables.
- How Independent Variables Lead the WayIn the scientific method, the independent variable is like the captain of a ship, leading everyone through unknown waters.
- The key point here is that we have clarified what we mean by the terms as they were studied and measured in our experiment.
- It’s carefully chosen and manipulated to see how it affects the dependent variable.
- It should be noted that in some experiments there are other variables present apart from the independent and the dependent variables.
- The independent variable is the factor the researcher changes or controls in an experiment.
They’re elements, characteristics, or behaviors that can shift or vary in different circumstances. Together, we’ll uncover the magic of this scientific concept and see how it continues to shape our understanding of the world around us. To preclude the “placebo” effect — wherein the patient apparently feels better after taking the placebo pill, the patients were not informed if the pill they were taking was real or the placebo.
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. For example, a scientist is testing the effect of light and dark on the behavior of moths by turning a light on and off. The independent variable is the amount of light and the moth’s reaction is the dependent variable. The story of the independent variable begins with a quest for knowledge, a journey taken by thinkers and tinkerers who wanted to explain the wonders and strangeness of the world.
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.
Types of Independent Variables and Uses
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.
Independent and Dependent Variable Examples
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.
How Independent Variables Lead the WayIn the scientific method, the independent variable is like the captain of a ship, leading everyone through unknown waters. In Different Types of ResearchThe world of research is diverse and varied, and the independent variable dons many guises! In the field of medicine, it might manifest as the dosage of a drug administered to patients.
Detecting and controlling these hidden elements helps researchers ensure the accuracy of their findings and reach true conclusions. The independent variable plays a starring role in experiments, helping us learn about everything from the smallest particles to the vastness of space. It helps researchers create vaccines, understand social behaviors, explore ecological systems, and even develop new technologies. Latent variables are unobservable factors that can influence the behaviour of individuals and explain certain outcomes within a study.
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, ethnicity, marital status, and educational level. The independent and dependent variables are key to any scientific experiment, but how do you tell them apart?
Psychology Resources
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.
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.
By changing the independent variable, scientists can see if and how it causes changes in what they are measuring or observing, helping them make connections and draw conclusions. By manipulating differential cost in accounting the independent variable and observing its effect on the dependent variable, researchers can determine whether there is a causal relationship between the two variables. This is important for understanding how different variables affect each other and for making predictions about how changes in one variable will affect other variables.
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.