Unit 5 Discussion 1

.docx

School

Capella University *

*We aren’t endorsed by this school

Course

8658

Subject

Mathematics

Date

Jan 9, 2024

Type

docx

Pages

Uploaded by ProfessorThunderKoala14

From research, there are three types of factorial design: (1) between subjects; (2) within subjects; and mixed factorial design. While having distinct characteristics, the main difference between them resides in the testing of the independent variables on the subjects. Therefore, the between-subjects factorial design is a factorial design that tests only one condition for each subject (or group of subjects). In this optic, in between-subjects factorial design, a "condition" refers to the combination of the factors, otherwise the independent variable. In this factorial design, all the independent variables are manipulated within subjects. As a matter of fact, a 2x2 factorial design has a total of four combinations/conditions. A subject needs only to be tested in one of these conditions/combinations (Cheng, 2016). On the other hand, in the within-subjects factorial design, all subjects in the study are required to be tested under all possible conditions in the experiment. Likewise, all factors/independent variables are required to be manipulated for all subjects. For the within- subjects type, the set-up can be a 3x4 factorial design which means that there are 3 levels for the first factor/independent variable and 4 levels for the second factor/independent variable. For example, if I had a 3 x 4 factorial design, I could infer that I had 2 factors and that one factor had 3 levels while the other had 4. Everything being equal, a mixed factorial design divides the factors into two groups. These two groups as within-subjects and between-subjects. For the mixed factorial design, the set-up can be 2x3. This means that there are two levels for the first factor/independent variable and 3 levels for the other factor/independent variable. For example, if one of the independent variables had a third level, then it would be a 3 × 2 factorial design, and there would be six distinct conditions (Brown et al., 1999). Reference Brown, H. D., Kosslyn, S. M., Delamater, B., Fama, A., & Barsky, A. J. (1999). Perceptual and memory biases for health-related information in hypochondriacal individuals. Journal of Psychosomatic Research, 47 , 67–78 Cheng, C. S. (2016). Theory of Factorial Design . Chapman and Hall/CRC.

Hi Autumn, I read with interest your post and your effort to understand and explore the nuances of the three levels of factorial design in applying three elements of senses that were affected by the COVID-19 virus in the body. As you mentioned, the between-subjects factorial design requires a 2x2 setup wherein there are two levels for each of the independent variables or factors. For the within-subjects type, when the set-up is a 3x4 factorial design which means that there are 3 levels for the first factor/independent variable and 4 levels for the second factor/independent variable. For the mixed factorial design, the setup is 2x3. This means that there are two levels for the first factor/independent variable and 3 levels for the other factor/independent variable. A factorial design has all levels of every factor combined with every level of every other factor (IVs). A factorial design allows the investigation of the separate main effects and interactions of two or more independent variables. In a design with two (or more) independent variables; the main effect of a variable is the overall effect of that variable after collapsing across all other levels of all other factors (e.g., the effect of caffeine averaged across all levels of alcohol gives you the main effect of caffeine).

Your preview ends here

Eager to read complete document? Join bartleby learn and gain access to the full version

Access to all documents
Unlimited textbook solutions
24/7 expert homework help