Discuss the differences between a between subjects factorial…

Discuss the differences between a between subjects factorial design, a within-subjects factorial design, and a mixed factorial design. Provide an example of how a factorial design could be used to answer a research question in Educational Psychology, including an identification of the type of design it would be (that is, 2×2 between subjects, 3×4 within subjects, 2×3 mixed factorial). Make sure to identify the independent and dependent variables and their scales of measurement.

Factorial designs are widely used in research, including in the field of Educational Psychology, to investigate the effects of multiple independent variables on a dependent variable. These designs allow researchers to assess the main effects of each independent variable as well as any interactions between them. Three common types of factorial designs are between subjects factorial designs, within-subjects factorial designs, and mixed factorial designs. Each design has unique characteristics, and the choice of design depends on the research question and the nature of the variables involved.

A between subjects factorial design involves manipulating two or more independent variables and assigning different groups of participants to different levels of these variables. Each group experiences only one level of each independent variable. For example, consider a study that aims to examine the effects of teaching method (traditional vs. technology-based) and class size (small vs. large) on students’ academic performance. In this scenario, participants would be randomly assigned to one of four conditions: traditional teaching method with small class size, traditional teaching method with large class size, technology-based teaching method with small class size, or technology-based teaching method with large class size. The independent variables are teaching method and class size, and the dependent variable is academic performance. The scales of measurement for both the independent variables and the dependent variable can vary depending on the specific nature of the variables, but in this example, teaching method and class size could be categorical variables (nominal or ordinal), while academic performance could be a continuous variable (interval or ratio).

On the other hand, a within-subjects factorial design involves exposing each participant to all levels of the independent variables. This design is also known as a repeated measures design. Using the previous example, instead of assigning participants to different groups, all participants would experience both teaching methods (traditional and technology-based) and both class sizes (small and large). Each participant’s academic performance would be measured under each condition. The order in which the conditions are presented can be counterbalanced to control for potential order effects. In this design, the independent variables and their levels remain the same, but the dependent variable may involve repeated measures. For instance, academic performance could be measured as a continuous variable using a standardized test score.

Lastly, a mixed factorial design combines elements of both between subjects and within-subjects designs. In this design, at least one independent variable is manipulated between subjects, while another independent variable is manipulated within subjects. Using the same example, participants could still be assigned to different groups based on class size (between subjects), but all participants within each group would experience both teaching methods (within subjects). This allows researchers to assess individual and interactive effects of the two independent variables. Both the independent variables and the dependent variable can have different scales of measurement, depending on their specific nature.

To summarize, the choice of factorial design depends on the research question and the variables involved. A between subjects factorial design assigns participants to different groups, a within-subjects factorial design exposes participants to all levels of the independent variables, and a mixed factorial design combines both between subjects and within-subjects elements. These designs provide valuable insights into the main effects and interactions of multiple independent variables on a dependent variable.