Stat Methods Ch.10

factorial analysis of design

ANOVA for a factorial research design

factorial research designs

way of organizing a study in which the effects of two or more variables are studied at once by making grouping of every combination of the variables

interaction effect

situation in the factorial ANOVA in which a combination of variables has an effect that could not be predicted rom the effects of the two variables

• main point of the research

two-way factorial research design

factorial research design in an ANOVA with two variables that each divide the groups

two-way ANOVA

ANOVA for a two-way factorial research design

grouping variable

a variable that separates groups in an ANOVA

one-way ANOVA

ANOVA in which there is only one grouping variable

main effect

difference between groups on one grouping variable in a factorial design in ANOVA; result for a grouping variable, averaging across the levels of the other grouping variable(s)

cell

in a factorial design, particular combination of levels of the variables that divide the group

cell mean

mean of a particular combination of levels of the variables that divide the groups in a factorial design in an ANOVA

marginal means

in a factorial design in ANOVA, mean score for all the participants at a particular level of one of the grouping variables

repeated measures analysis of variance

ANOVA for a repeated measures design in which each person is tested more than once so that the levels of the grouping variable(s) are different times or types of testing for the same persons

dichotomizing

dividing the scores for a variable into two groups. AKA median split.

what are the advantages and disadvantages to dichotomizing?

• advantage: you can do a factorial ANOVA, with all of its advantages of efficiency and testing interaction effects

• disadvantage: you lose information when you reduce a whole range of scores to just two, high and low; it’s just less accurate

what are the three F-ratios for in a two-way ANOVA?

• one for the grouping variables spread across the columns (column main effect)

• one for the grouping variables spread across the rows (the row main effect)

• one for interaction effect