Chapter 14 Repeated Measures and Two Factor Analysis of Variance PowerPoint Lecture Slides Essentials of Statistics for the Behavioral Sciences Seventh Edition by Frederick J. Gravetter and Larry B. Wallnau

Chapter 14 Learning Outcomes Explain logic of repeated-measures ANOVA study 2 Use repeated-measures ANOVA to evaluate mean differences 3 Explain logic of two-factor study and matrix of group means 4 Describe main effects and interactions from pattern of group means 5 Compute two-factor ANVOA to evaluate mean differences

Concepts to review Independent-measures analysis of variance (Chapter 13) Repeated measures designs (Chapter 11) Individual differences

14.1 Overview Analysis of Variance Complex Analysis of Variance Evaluated mean differences of two or groups Complex Analysis of Variance Samples are related not independent (Repeated measures ANOVA) More than one factor is tested (Factorial ANOVA, here Two-Factor)

14.2 Repeated Measures ANOVA Advantages of repeated measures designs Individual differences among participants do not influence outcomes Smaller number of subjects needed to test all the treatments Repeated Measures ANOVA Compares two or more treatment conditions with the same subjects tested in all conditions Studies same group of subjects at two or more different times.

Hypotheses for repeated measures ANOVA Null hypothesis: in the population, there are no mean differences among the treatment groups Alternate hypothesis states that there are mean differences among the treatment groups. H1: At least one treatment mean μ is different from another

Individual Differences in the Repeated Measures ANOVA F ratio based on variances Same structure as independent measures Variance due to individual differences is not present

Individual differences Participant characteristics that vary from one person to another. Not systematically present in any treatment group or by research design Characteristics may influence measurements on the outcome variable Eliminated from the numerator by the research design Must be removed from the denominator statistically

Logic of repeated measures ANOVA Numerator of the F ratio includes Systematic differences caused by treatments Unsystematic differences caused by random factors (reduced because same individuals in all treatments) Denominator estimates variance reasonable to expect from unsystematic factors Effect of individual differences is removed Residual (error) variance remains

Figure 14.1 Structure of the Repeated-Measures ANOVA

Stage One Repeated-Measures ANOVA equations

Two Stages of the Repeated-Measures ANOVA First stage Identical to independent samples ANOVA Compute SSTotal, SSBetween treatments and SSWithin treatments Second stage Removing the individual differences from the denominator Compute SSBetween subjects and subtract it from SSWithin treatments to find SSError

Stage Two Repeated-Measures ANOVA equations

Degrees of freedom for Repeated Measures ANOVA dftotal = N – 1 dfwithin treatments = Σdfinside each treatment dfbetween treatments = k – 1 dfbetween subjects = n – 1 dferror = dfwithin treatments – dfbetween subjects

Mean squares and F-ratio for Repeated-Measures ANOVA

Effect size for the Repeated-Measures ANOVA Percentage of variance explained by the treatment differences Partial η2 is percentage of variability that has not already been explained by other factors

Post hoc tests with Repeated Measures ANOVA Determine exactly where significant differences exist among more than two treatment means Tukey’s HSD and Scheffé can be used Substitute SSerror and dferror in the formulas

Assumptions of the Repeated Measures ANOVA The observations within each treatment condition must be independent. The population distribution within each treatment must be normal. The variances of the population distribution for each treatment should be equivalent.

Learning Check A researcher obtains an F-ratio with df = 2, 12 from an ANOVA for a repeated-measures research study. How many subjects participated in the study? A 15 B 14 C 13 D 7

Learning Check - Answer A researcher obtains an F-ratio with df = 2, 12 from an ANOVA for a repeated-measures research study. How many subjects participated in the study? A 15 B 14 C 13 D 7

Learning Check Decide if each of the following statements is True or False. T/F For the repeated-measures ANOVA, degrees of freedom for SSerror is (N–k) – (n–1). The first stage of the repeated-measures ANOVA is the same as the independent-measures ANOVA.

Answer False N is the number of scores and n is the number of participants True After the first stage, the second stage adjusts for individual differences

14.2 Two-Factor ANOVA Factorial designs Consider more than one factor Joint impact of factors is considered. Three hypotheses tested by three F-ratios Each tested with same basic F-ratio structure

Main effects Mean differences among levels of one factor Differences are tested for statistical significance Each factor is evaluated independently of the other factor(s) in the study

Interactions between factors The mean differences between individuals treatment conditions, or cells, are different from what would be predicted from the overall main effects of the factors H0: There is no interaction between Factors A and B H1: There is an interaction between Factors A and B

Interpreting Interactions Dependence of factors The effect of one factor depends on the level or value of the other Non-parallel lines (cross or converge) in a graph Indicate interaction is occurring Typically called the A x B interaction

Figure 14.2 Graph of group means with and without interaction

Structure of the Two-Factor Analysis Three distinct tests Main effect of Factor A Main effect of Factor B Interaction of A and B A separate F test is conducted for each

Two Stages of the Two-Factor Analysis of Variance First stage Identical to independent samples ANOVA Compute SSTotal, SSBetween treatments and SSWithin treatments Second stage Partition the SSBetween treatments into three separate components, differences attributable to Factor A, to Factor B, and to the AxB interaction

Figure 14.3 Structure of the Two- Factor Analysis of Variance

Stage One of the Two-Factor Analysis of Variance

Stage 2 of the Two Factor Analysis of Variance This stage determines the numerators for the three F-ratios by partitioning SSbetween treatments

Degrees of freedom for Two-Factor ANOVA dftotal = N – 1 dfwithin treatments = Σdfinside each treatment dfbetween treatments = k – 1 dfA = number of rows – 1 dfB = number of columns– 1 dferror = dfwithin treatments – dfbetween subjects

Mean squares and F-ratio for the Two-Factor ANOVA

Effect Size for Two-Factor ANOVA η2, is computed as the percentage of variability not explained by other factors.

Figure 14.4 Sample means for Example 14.3

Assumptions for the Two-Factor ANOVA The validity of the ANOVA presented in this chapter depends on three assumptions common to other hypothesis tests The observations within each sample must be independent of each other The populations from which the samples are selected must be normally distributed The populations from which the samples are selected must have equal variances (homogeneity of variance)

Learning Check If a two-factor analysis of variance produces a statistically significant interaction, then you can conclude that _____. A either the main effect for factor A or the main effect for factor B is also significant B neither the main effect for factor A nor the main effect for factor B is significant C both the man effect for factor A and the main effect for factor B are significant D the significance of the main effects is not related to the significance of the interaction

Learning Check - Answer If a two-factor analysis of variance produces a statistically significant interaction, then you can conclude that _____. A either the main effect for factor A or the main effect for factor B is also significant B neither the main effect for factor A nor the main effect for factor B is significant C both the man effect for factor A and the main effect for factor B are significant D the significance of the main effects is not related to the significance of the interaction

Learning Check Decide if each of the following statements is True or False. T/F Two separate single-factor ANOVAs provide exactly the same information that is obtained from a two-factor analysis of variance. A disadvantage of combining 2 factors in an experiment is that you cannot determine how either factor would affect the subjects' scores if it were examined in an experiment by itself.

Answer False The two-factor ANOVA allows you to determine the effect of one variable controlling for the effect of the other. Either main effect can be examined in a Oneway ANOVA, but the Two-Factor ANOVA provides more information, not less.

Any Questions? Concepts? Equations?