Discussion Of Confounding Variables And Internal Validity In

Discussion Confounding Variablesinternal Validity In A Research Study

Discussion: Confounding Variables Internal validity in a research study is the extent to which changes in the dependent variable can confidently be attributed to the influence of the independent variable rather than to possible confounding variables. In other words, was it really the independent variable that had an effect on the dependent variable or did something else cause the effect? For example, presume that an instructor wants to try a new reading efficiency strategy to improve reading comprehension of college students in an online course. The instructor divides the class into two equal groups. One group (the experimental group) reads a passage using the new reading strategy taught by the instructor.

The other group (the control group) reads the same passage but is not exposed to the new strategy. Next, both groups of students complete a reading comprehension test. The assumption is that if the group exposed to the new reading strategy scored higher than the other group, the new reading strategy is effective. What else could explain the difference in scores? Is it possible that the students enrolled in the group that learned the new strategy already had high reading comprehension skills?

What if this group already knew more about the information in the reading passage? Could the instructor have inadvertently biased the study because he taught the new reading strategy, and he selected the reading passages that both groups read? Possible situations, or reasons, that could interfere with obtaining accurate results are called confounds, and could be a threat to the internal validity of a study. It is important to keep in mind though, that the presence of a possible confounding variable in a study does not necessarily mean it is responsible for obtained results. Rather, the independent variable (e.g., the reading intervention) may have actually had an effect on the dependent variable (the test results).

In this Discussion, you will examine possible confounding variable(s) in research studies from your course textbook and apply methodology for addressing and/or eliminating the possible confound(s). To prepare: Read Chapter 12 in your course text. Read the “Thinking Critically About Research” scenarios (a—i) in Chapter 12, pages 256–258. Choose the scenario that most interests you. Note: Before selecting a scenario, view the Discussion 4 Forum to see if any colleagues have already posted.

If so, select a letter that has not yet been chosen. All nine (a–i) letters should be addressed before a student repeats a letter. For your chosen scenario, determine the possible confounding variable(s) (there may be more than one), and consider how they might be eliminated using research designs presented in the readings (e.g., 2x2 factorial design). Note: You can assume that random assignment took care of any potential differences in the groups; therefore, group differences are not a potential confound. With these thoughts in mind: By Day 3 Indicate the letter of the scenario you selected in the “Subject” field of your post.

You should be addressing a scenario different from those posted, unless your colleagues have already addressed all nine scenarios. Identify and explain the possible confounding variable(s) (e.g., demand characteristics, placebo effect) in your chosen scenario. Drawing from the Learning Resources this week, explain a specific research design (e.g., 2x2 factorial design, repeated measures design) the researcher(s) could use to control for confounding variables. Note: Be sure to support the responses within your Discussion post, and in your colleague reply, with evidence from the assigned Learning Resources.

Paper For Above instruction

Internal validity is a fundamental concept in research methodology, denoting the degree to which a study can establish a causal relationship between variables without interference from extraneous factors. When considering internal validity, a primary concern is the presence of confounding variables—variables other than the independent variable that could influence the dependent variable and thus threaten the integrity of the findings. Recognizing and controlling for confounds is vital to ensure that results accurately reflect the effect of the independent variable (Cozby & Bates, 2012).

From the scenarios outlined in Chapter 12 of Cozby and Bates (2012), suppose we select scenario (b), which investigates the impact of a new cognitive-behavioral therapy (CBT) program on reducing anxiety levels among college students. In this scenario, the possible confounding variables could include participants' baseline anxiety levels, motivation to participate, or even external stressors unrelated to the therapy. If students with higher initial motivation are more likely to adhere to the program and also experience greater reductions in anxiety, motivation becomes a confounding variable influencing the results.

Furthermore, external factors such as concurrent medication use or life events could also confound the relationship by independently affecting anxiety levels. To mitigate these confounds, researchers could implement a randomized controlled trial (RCT) with multiple control groups. Random assignment minimizes systematic differences among participants, effectively controlling for unmeasured variables (Shadish, Cook, & Campbell, 2002). Additionally, employing a 2x2 factorial design allows researchers to examine the interaction effects of therapy type and other variables such as medication status or session frequency, thereby isolating the specific effects of the CBT intervention (Cook & Campbell, 1979).

Another robust method is the repeated measures design, where the same participants are assessed before and after the intervention. This within-subjects approach controls for individual differences in baseline anxiety, as each participant serves as their own control. Using statistical controls such as covariance analysis (ANCOVA) further helps adjust for any remaining confounding factors, ensuring that the observed changes are more likely attributable to the intervention itself (Tabachnick & Fidell, 2013).

In achieving high internal validity, methodological rigor is essential. Carefully selecting participants through randomization, utilizing appropriate experimental designs such as factorial or repeated measures, and applying statistical controls are all techniques that help eliminate or reduce confounding variables. By doing so, researchers can produce more reliable and valid conclusions about causal relationships in their studies (Shadish, Cook, & Campbell, 2002).

References

• Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues for field settings. Houghton Mifflin.
• Cozby, P. C., & Bates, R. (2012). Methods in behavioral research (11th ed.). McGraw-Hill.
• Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-experimental designs for generalized causal inference. Houghton Mifflin.
• Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
• Salkind, N. J. (2010). Statistics for people who (think they) know statistics. Sage Publications.
• McLeod, S. (2019). Internal validity in research studies. Simply Psychology. https://www.simplypsychology.org/internal-validity.html
• Morling, B. (2017). Research methods in psychology: Evaluating a world of information (3rd ed.). W. W. Norton & Company.
• Greenland, S., & Robins, J. M. (1988). Conceptual problems in the analysis of cohort studies. Annual Review of Public Health, 9(1), 173–196.
• Rosenbaum, P. R. (2002). Observational Studies. Springer.
• Becker, H. S. (1970). Social Problems. The Student-Teacher Relationship in Research. Harper & Row.