DISCOVERING STATISTICS USING SPSS Chapter 15: Mixed design ANOVA Labcoat Leni’s Real Research The objection of desire Problem Bernard, P., et al. (2012). Psychological Science, 23(5), 469–471. There is a concern that images that portray women as sexually desirable objectify them. This idea was tested in an inventive study by Philippe Bernard (Bernard, Gervais, Allen, Campomizzi, & Klein, 2012). People find it harder to recognize upside-down (inverted) pictures than ones the right way up. This ‘inversion effect’ occurs for pictures of humans, but not for pictures of objects. Bernard et al. used this effect to test whether sexualized pictures of women are processed as objects. They presented people with pictures of sexualized (i.e., not wearing many clothes) males and females. Half of these pictures were inverted (Inverted_Women and Inverted_Men) and the remainder were upright (Upright_Women and Upright_Men). They noted the Gender of the participant. After each trial participants were shown two pictures and asked to identify the one they had just seen. The outcome was the proportion of correctly identified pictures. An inversion effect is demonstrated by higher recognition scores for upright pictures than inverted ones. If sexualized females are processed as objects you would expect an inversion effect for the male pictures but not the female ones. The data are in Bernard et al (2012).sav. Conduct a three-way mixed ANOVA to see whether picture gender (male or female) and picture orientation (upright or inverted) interact. Include participant gender as the between-group factor. Follow up the analysis with t-tests looking at (1) the inversion effect for male pictures, (2) the inversion effect for female pictures, (3) the gender effect for upright pictures, and (4) the gender effect for inverted pictures. Solution To run the ANOVA select the repeated-measures ANOVA dialog box ( .). We have two repeated-measures variables: whether the target picture was of a male or female (let’s call this TargetGender) and whether the target picture was upright or inverted (let’s call this variable TargetLocation). The resulting ANOVA will be a 2 (TargetGender: male or female) × 2 (TargetLocation: upright or inverted) × 2 (Gender: male or female) three-way mixed ANOVA with repeated measures on the first two variables. First, we must define our two repeated-measures variables (Figure 1). PROFESSOR ANDY P FIELD 1 DISCOVERING STATISTICS USING SPSS Figure 1 Next, we need to define these variables by specifying the columns in the data editor that relate to the different combinations of the gender and orientation of the picture (Figure 2). Figure 2 You could also ask for an interaction graph for the three-way interaction (Figure 3). PROFESSOR ANDY P FIELD 2 DISCOVERING STATISTICS USING SPSS Figure 3 You can set other options as in the book chapter. Because both of our repeated-measures variables have only two levels, we do not need to worry about sphericity. As you can see in Output 1, SPSS still produces the sphericity table; however, in the column labelled Sig there is simply a full stop to indicate that we do not need to worry about the assumption of sphericity. Output 1 PROFESSOR ANDY P FIELD 3 DISCOVERING STATISTICS USING SPSS Output 2 Output 2 is the table of the overall descriptive statistics; these will be useful for interpreting the direction of the results in the main ANOVA table. We can also use these values when we report the results. Figure 4 is the plot for the two-way interaction between target gender and target location for female participants. Looking at the graph, we can see that when the target was of a female (i.e., when Target Gender = 1) female participants correctly recognized a similar number of inverted (blue line) and upright (green line) targets, indicating that there was no inversion effect for female pictures. We can tell this because the dots are very close together. However, when the target was of a male (Target Gender = 2), the female participants’ recognition of inverted male targets was very poor compared with their recognition of upright male targets (the dots are very far apart), indicating that the inversion effect was present for pictures of males. Figure 5 is the plot for the two-way interaction between target gender and target location for male participants. Looking at the graph, we can see that there appears to be a similar pattern of results as for the female participants: when the target was of a female (i.e., when Target Gender = 1) male participants correctly recognized a fairly similar number of inverted (blue line) and upright (green line) targets, indicating no inversion effect for the female target pictures. We can tell this because the dots are reasonably together. However, when the target was of a male (Target Gender = 2), the male participants’ recognition of inverted male targets was very poor compared with their recognition of upright male targets (the dots are very far apart), indicating the presence of the inversion effect for male target pictures. The fact that the pattern of results were very similar for male and female participants suggests that there may not be a significant three-way interaction between target gender, target location and participant gender. PROFESSOR ANDY P FIELD 4 DISCOVERING STATISTICS USING SPSS Figure 4 Figure 5 Output 3 PROFESSOR ANDY P FIELD 5 DISCOVERING STATISTICS USING SPSS Output 4 Output 4 shows the summary table of the repeated-measures effects in the ANOVA with corrected F-values. As with factorial repeated-measures ANOVA, the output is split into sections for each of the effects in the model and their associated error terms. The interactions between our between-groups variable of gender and the repeated-measures effects are included in this table also. We could report these effects as follows:  There was a significant interaction between target gender and target location, F(1, 75) = 15.07, p < .001, η2 = .167, indicating that if we ignore whether the participant was male or female, the relationship between recognition of upright and inverted targets was different for pictures depicting men and women. The two-way interaction between target location and participant gender was not significant, F(1, 75) = .96, p = .331, η2 = .013, indicating that if we ignore whether the target depicted a picture of a man or a woman, male and female participants did not significantly differ in their recognition of inverted and upright targets. There was also no significant three-way PROFESSOR ANDY P FIELD 6 DISCOVERING STATISTICS USING SPSS interaction between target gender, target location and participant gender, F(1, 75) = .02, p = .904, η2 = .000, indicating that the relationship between target location (whether the target picture was upright or inverted) and target gender (whether the target was of a male or female) was not significantly different in male and female participants. The next part of the question asks us to follow up the analysis with t-tests looking at inversion and gender effects. To do this, we need to conduct four paired-samples t-tests (See Chapter 9). Once you have the Paired-Samples T Test dialog box open, you can transfer pairs of varialbles from the left-hand side to the box labelled Paired Variables. The first pair I am going to compare is Upright Female vs. Inverted Female, to look at the inversion effect for female pictures. The next pair will be Upright Male vs. Inverted Male, and this comparison will investigate the inversion effect for male pictures. To look at the gender effect for upright pictures we need to compare Upright Female vs. Upright Male. Finally, to look at the gender effect for inverted pictures we need to compare the variables Inverted Female and Inverted Male. Your complated dialog box should look like Figure 6. Figure 6 PROFESSOR ANDY P FIELD 7 DISCOVERING STATISTICS USING SPSS Output 5 Output 6 Output 7 Output 7 shows the results of the paired samples t-tests. The results show that people recognized upright males (M = 0.85, SD = 0.17) significantly better than inverted males (M = 0.73, SD = 0.17), t(77) = 6.29, p < .001, but this pattern did not emerge for females, t(77) = 1.38, p = .171. Additionally, participants recognized inverted females (M = 0.83, SD = 0.16) significantly better than inverted males (M = 0.73, SD = 0.17), t(77) = 5.42, p < .001. This effect was not found for upright males and females, t(77) = 0.54, p = .59. Note: the sign of the t- statistic will depend on which way round you entered the variables in the Paired-Samples T Test dialog box. Consistent with the authors’ hypothesis, the results showed that the inversion effect emerged only when participants saw sexualized males. This suggests that, at a basic cognitive PROFESSOR ANDY P FIELD 8 DISCOVERING STATISTICS USING SPSS level, sexualized men were perceived as persons, whereas sexualized women were perceived as objects. Keep the faith(ful)? Problem Schützwohl, A. (2008). Personality and Individual Differences, 44, 633–644. People can be jealous. People can be especially jealous when they think that their partner is being unfaithful. An evolutionary view of jealousy suggests that men and women have evolved distinctive types of jealousy. Specifically, a woman’s sexual infidelity deprives her mate of a reproductive opportunity and could burden him with years investing in a child that is not his. Conversely, a man’s sexual infidelity does not burden his mate with unrelated children, but may divert his resources from his mate’s progeny. This diversion of resources is signalled by emotional attachment to another female. Consequently, men’s jealousy mechanism should have evolved to prevent a mate’s sexual infidelity, whereas in women it has evolved to prevent emotional infidelity. Achim Schützwohl reasoned that if this is the case, women should be on the look-out for emotional infidelity, whereas men should be watching out for sexual infidelity. He put this hypothesis to the test in a unique study in which men and women saw sentences presented on a computer screen (Schützwohl, 2008). At each trial, participants saw a target sentence that was emotionally neutral (e.g., ‘The gas station is at the other side of the street’). However, before each of these targets, a distractor sentence was presented that could also be affectively neutral, or could indicate sexual infidelity (e.g., ‘Your partner suddenly has difficulty becoming sexually aroused when he and you want to have sex’) or emotional infidelity (e.g., ‘Your partner doesn’t say “I love you” to you anymore’). The idea was that if these distractor sentences grabbed a person’s attention then (1) they would remember them, and (2) they would not remember the target sentence that came afterwards (because their attentional resources were focused on the distractor). These effects should show up only in people currently in a relationship. The outcome was the number of sentences that a participant could remember (out of 6), and the predictors were whether the person had a partner or not (Relationship), whether the trial used a neutral distractor, an emotional infidelity distractor or a sexual infidelity distractor, and whether the sentence was a distractor or the target following a distractor. Schützwohl analysed men and women’s data seperately. The predictions are that women should remember more emotional infidelity sentences (distractors) but fewer of the targets that followed those sentences (target). For men, the same effect should be found but for sexual infidelity sentences. The data from this study are in PROFESSOR ANDY P FIELD 9 DISCOVERING STATISTICS USING SPSS the file Schützwohl(2008).sav. Labcoat Leni wants you to carry out two three-way mixed ANOVAs (one for men and the other for women) to test these hypotheses. Solution We want to run these analyses on men and women separately; therefore, we could (to be efficient) split the file by the variable Gender (see Chapter 5), as shown in Figure 7. Figure 7 To run the ANOVA, select the repeated-measures ANOVA dialog box ( .). We have two repeated-measures variables: whether the sentence was a distractor or a target (let’s call this Sentence_Type) and whether the distractor used on a trial was neutral, indicated sexual infidelity or emotional infidelity (let’s call this variable Distracter_Type). The resulting ANOVA will be a 2 (relationship: with partner or not) × 2 (sentence type: distractor or target) × 3 (distractor type: neutral, emotional infidelity or sexual infidelity) three-way mixed ANOVA with repeated measures on the last two variables. First, we must define our two repeated-measures variables (Figure 8). PROFESSOR ANDY P FIELD 10