Independent Versus Dependent Variables And Research Question

Independent Versus Dependent Variables And Research Questionsas A Scie

Independent versus Dependent Variables and Research Questions As a scientist and a budding new researcher, you must determine what specific measures or variables you want to use for your research. For example, if you want to measure happiness, and you want to consider the different factors that might impact happiness, such as age, income, and marital status. In this example, you have different measures that you will take, or what researchers commonly refer to as variables . Broadly defined, variables are measurements that change over time (Gravetter & Foranzo, 2018). When examining these different variables or measures, it is important to differentiate that there are separate categories or types of variables that researchers typically use.

In most cases, these variables are designated as either independent or dependent (Gravetter & Foranzo, 2018). Independent variables are variables that the researcher intentionally changes or manipulates and controls, and they occur first in time during a research study, which is the principle of temporal contiguity . On the other hand, dependent variables are the measures used to assess the impact of this intentional manipulation or change. They occur second in time and can be viewed as ‘dependent’ on the changes in the independent variable. Explore the following information to learn more: Launch in a separate window For example, a true independent variable would be a study where the researcher intentionally randomly assigns participants to consume different samples of flavors of ice cream.

The researcher then measures participants’ self-reported likelihood of eating it again in the future using a Likert-type rating scale of 1 (not at all likely) to 10 (extremely likely). In this case, the different flavors of ice cream would be considered the true independent variable, or the variable that the researcher has total control over and is intentionally changing or manipulating in some way by randomly assigning subjects to consume these different flavors (Gravetter & Foranzo, 2018). The dependent variable in this example would be the subjects’ self-reported ratings of the likelihood of eating that particular flavor of ice cream again in the future. Figure 1 People May only Like Certain Flavors of Ice Cream Now, put on your thinking cap, and start to think like a scientist.

See if you can think of a few more examples of research studies that you would like to conduct, with clearly defined independent and dependent variables. Write these variables down, and then talk it over with your professor to determine if you are on the right track in terms of your ability to differentiate a true independent from a dependent variable. Just like most things in life, the more you practice at something, the better you will get – so give it a try! Here is another example for you to consider. If you want to study happiness and try to figure out how income influences individuals’ self-reported level of happiness (Santos, 2018), then, in this case, you can identify which variable is the independent variable and which one is the dependent variable.

To help you figure this out, remember that an independent variable comes first in time. This type of variable or measure is often used to predict something else in the future. Did you get it right? In this case, income level is the independent variable that is being used to predict happiness. On the other hand, the dependent variable would be the variable that you are trying to predict in some way, and it occurs after the independent variable on the timeline. In this example, it would be the outcome measure of self-reported happiness. Another question to consider here: Is the variable of income considered a true independent variable or a quasi-independent one, and if so, why? A quasi-independent variable is a variable that the researcher is unable to randomly assign participants to. Take a moment to reflect on this, and then consider your answer. Drum roll, please … If you said a ‘quasi’ independent variable, then you are correct!

Gold star for you! Remember, a quasi-experimental variable is a variable that the researcher does not have complete control over and cannot randomly assign subjects. Given that income level is not something that the researcher would have direct control over, this would be considered a quasi -independent variable, or a variable that is almost, but not quite, a true independent variable (Gravetter & Foranzo, 2018). Now that you are clearer on the differences between an independent and a dependent variable and what a quasi- versus, a true independent variable looks like, it is important for you to also think about how you will define each of the variables you have identified for your hypothetical study, and to be certain that you are able to randomly assign subjects to the different levels of the independent variable (a true independent variable).

For example, you might measure whether or not someone is smiling as a measure of happiness, or you might use a self-reported measurement where an individual rates on a scale of 1-10 how happy they are at that particular moment in time. You might also want to use more sophisticated measures such as the Satisfaction with Life Scale (SWLS) or the PANAS (Positive and Negative Affect Scale) (Santos, 2018). (For more about how to find happiness and well-being, check out the Happiness lab to determine if you can find more direction here for fulfillment in your own life). Believe it or not, the difference in the type of measure you select can influence the results and the conclusions too! You want your measures to be both reliable – or consistent time and time again, and valid – or accurate in measuring what you intend to.

Explore the concepts of reliability and validity below: Launch in a separate window Something to consider as you continue with your studies – particularly when you are trying to synthesize studies and compare results: Take time to write down some independent and dependent variables or measures that you might want to explore for your research topic of interest. Developing Research Questions Now that you have identified independent and dependent variables of interest, you are ready to begin developing a research question. Generally, a research question is a broad statement of what you want to inquire about and why it is important to do so. As you will learn this week, there are characteristics of ‘good’ research questions you should know when creating a research question.

These five characteristics of a good question are as follows: a. the question clearly defines the variables of interest; b. the question is falsifiable or testable; c. the question is worthwhile or important to study; d. the question clearly states the type of relationship that is expected; and e. the question can be answered in one study (APA, 2020). Figure 2 Characteristics of Good Research Questions After a concise research question has been identified, with specific independent and dependent variables indicated, you will then want to include a null and an alternative hypothesis. The alternative hypothesis is what you expect to occur. It is like an answer that you are providing ahead of time to your research question.

For example, you might hypothesize that the more time you spend lifting weights (independent variable), the better your physical fitness level will be as measured by your muscle mass (dependent variable). This is the alternative hypothesis or the effect you expect to observe. On the other hand, the null hypothesis states that there is no effect or impact of the independent variable (amount of time lifting weights) on the dependent variable (your muscle mass). This week, you will be asked to select a true independent variable (not a quasi-independent variable) and a dependent variable of interest, develop a research question, and provide a corresponding null and an alternative hypothesis based on what you expect to find.

Pull up your sleeves, and get ready to dig in and get to work. You have a busy week ahead! References Gravetter, F. J., & Forzano, L. B. (2018). Research methods for the behavioral sciences (6th ed.). Cengage Learning. American Psychological Association. (2020). PsycLearn research methods [Computer software]. Author. Santos, L. (2018). The Happiness Lab. Pushkin Industries. Weekly Resources and Assignments Review the resources from the Course Resources link, located in the top navigation bar, to prepare for this week’s assignments. The resources may include textbook reading assignments, journal articles, websites, links to tools or software, videos, handouts, rubrics, etc.