Chat with us, powered by LiveChat You mention interactions as an advantage in factorial design. What is a possible interaction that might happen with your research example? Week6Questions.docx - Writingforyou

You mention interactions as an advantage in factorial design. What is a possible interaction that might happen with your research example?  Week6Questions.docx

 Nice start. You mention interactions as an advantage in factorial design. What is a possible interaction that might happen with your research example?  


Week 6 Questions

Student’s Name

Institutional Affiliation


Instructor’s Name

Date Due

Week 6 Questions

A. Discuss the problem of compounding Type I error and explain how the ANOVA addresses this problem.

When several statistical tests are carried out inside a single investigation, there is a more prominent chance of creating a false rejection of the null hypothesis, known as compounding Type I error. The probability that chance increments with the number of tests performed will obtain at least one essential result. In numerous comparisons, this intensification of Type I error could cause stress (Rogers & Revesz, 2019). This issue is solved by ANOVA (Analysis of Variance), which enables analysts to test the null hypothesis concurrently over several groups. An ANOVA is utilized to decide whether there are any noteworthy contrasts between the groups instead of doing person t-tests for each group (Gravetter et al., 2021). When handling different comparisons, ANOVA may be a more solid approach since it considers all groups together, which helps control the overall Type I error rate.

B. Factor is another word for independent variable or manipulation. A factorial design then has multiple factors or manipulations. Provide an example of a factorial design

Consider a study in which the impacts of two free variables—coffee admissions (low vs. high) and noise level (calm vs. noisy)—on cognitive work are being inspected within the setting of mental research. The two factors in this case are noise level and coffee admissions. Each member would be given one of four conditions—low caffeine/quiet, high caffeine/quiet, low caffeine/noisy, or high caffeine/noisy— (Gravetter et al., 2021). Analysts can examine the essential impacts of noise level and caffeine on cognitive work, as well as any conceivable intuition between the two factors, due to the factorial plan.

C. Describe the following figure (i.e., what at the IVs, what is the DV, describe the results)? Please note that the Y axis represents the Mean PERFORMANCE score.

The graph shows the association between mean performance scores under low and high humidity conditions and temperature (in degrees Fahrenheit). Three temperature levels—70°F, 80°F, and 90°F—are displayed on the x-axis, and the mean performance score is represented on the y-axis. A factorial design using temperature as one independent variable (IV) and humidity as another is suggested by the graph, which has two lines: one for low humidity and another for high humidity circumstances. Analyzing the graph shows how the mean performance ratings vary depending on the humidity and temperature (Miller et al., 2020). The primary impacts of humidity are probably represented by the low and high humidity lines, illustrating how mean performance scores fluctuate with temperature under each humidity level.

D. What are some advantages of a factorial design?

Factorial designs in experimental research give various benefits, as can be seen from the graph. First, they make it conceivable to explore several factors and their intuition simultaneously, driving a more exhaustive comprehension of the impacts on the subordinate variable (Gravetter et al., 2021). Second, factorial designs progress outside legitimacy by precisely representing the complexity of real-world circumstances in which various factors might influence a subject's behavior. Thirdly, they make it conceivable to recognize interactions when one component's degree impacts another's effect (Rogers & Revesz, 2019). This advanced information gives a more careful examination of how several members may interact to influence the conclusion variable, going beyond essential impacts.


Gravetter, F. J., Wallnau, L. B., Forzano, L. A. B., & Witnauer, J. E. (2021).  Essentials of statistics for the behavioral sciences. Cengage Learning.

Miller, C. J., Smith, S. N., & Pugatch, M. (2020). Experimental and quasi-experimental designs in implementation research.  Psychiatry Research283, 112452.

Rogers, J., & Revesz, A. (2019). Experimental and quasi-experimental designs. In  The Routledge Handbook of Research Methods in applied linguistics (pp. 133-143). Routledge.