PSYC104 Chapter Notes - Chapter 11: Factorial Experiment, Repeated Measures Design, Analysis Of Variance
18 May 2018
School
Department
Course
Professor
PSYC104 RESEARCH DESIGN READING
Chapter 11 [Complex Experimental Designs]
11.2.1 Chapter Outline
- Complex Designs
- Main Effects in Factorial Designs
- Interaction Effects in Factorial Designs
- Effect Size and Significance
- Other Variations of Factorial Designs
- Higher Order Factorial Designs
- Repeated Measures Complex Designs
- Mixed Designs
- Covariate Designs
- Multivariate Designs
11.4 The 2 x 2 Between-Subjects Factorial Design
- Combining two separate experiments into one has important benefits
o More convenient
o Has statistical advantages
- If the researcher decides to manipulate more than one independent variable in one
experiment, the researcher is employing a complex design
- Designs with more than one independent variable are titled factorial designs
11.4.1 An Example of a Factorial Design
- I the case of a cofiratory hypothesis, the researcher’s specific what they hope to fid
- In the case of an exploratory hypothesis, the researcher is not interested in confirming a
prediction, but only in investigating how two variables are related
11.4.2 Main Effects in Factorial Designs
- Main effects refer to the differences in mean scores between the two levels of each
independent variable across the values of the other independent variable
- Interaction refers to how two or more independent variables interact with one another. The
interaction implies that you cannot treat one independent variable as separate from the
other
11.4.3 Interaction Effects in Factorial Designs
- Two ways of concluding whether there is an interaction effect in a study
o Referring to an ANOVA summary table, looking for the interaction effect, and
determining its p-value
▪ If the p-value is equal of less than 0.5, there is a statistically significant
interaction present
o Plotting the interaction on a graph and analysing the visual characteristics of the
graph
find more resources at oneclass.com
find more resources at oneclass.com
Document Summary
11. 4 the 2 x 2 between-subjects factorial design. Combining two separate experiments into one has important benefits: more convenient, has statistical advantages. If the researcher decides to manipulate more than one independent variable in one experiment, the researcher is employing a complex design. Designs with more than one independent variable are titled factorial designs. I(cid:374) the case of a co(cid:374)fir(cid:373)atory hypothesis, the researcher"s specific what they hope to fi(cid:374)d. In the case of an exploratory hypothesis, the researcher is not interested in confirming a prediction, but only in investigating how two variables are related. Main effects refer to the differences in mean scores between the two levels of each independent variable across the values of the other independent variable. Interaction refers to how two or more independent variables interact with one another. The interaction implies that you cannot treat one independent variable as separate from the other.
