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Pspp anova post hoc5/18/2023 The main effect lumps together men and women, which is justifiable only if these show similar effects for adtype. But as suggested by our flowchart, we'll ignore it. There is a strong main effect for adtype: F(2,32) = 11.27, p = 0.000 too. These are called simple effects as shown in our flowchart. So we should test the effects of adtype for male and female respondents separately. This plot shows that the effects for adtype are clearly different for men and women. If p < 0.05, we usually label an effect “statistically significant” so we have an interaction effect indeed as suggested by our profile plot. Output - Within-Subjects Effectsįirst, the interaction effect between gender and adtype has a p-value (“Sig.”) of 0.017. The flowchart below suggests which results to report if sphericity does (not) hold. We don't need any correction such as Greenhouse-Geisser of Huynh-Feldt. We usually state that sphericity is met if p > 0.05, so the sphericity assumption is met by our data. The p-value (denoted by “Sig.”) is 0.264. Output - Mauchly’s TestĪs we mentioned under assumptions, repeated measures ANOVA requires sphericity and Mauchly’s test evaluates if this holds. Are the differences we see large enough for concluding anything about the entire population from which our sample was drawn? The answer is a clear “yes!” as we'll see in a minute. Keep in mind, however, that this is just a sample. For women, however, there's a huge difference between ad1 and ad2. Roughly, the line is almost horizontal for men: the three ads are rated quite similarly. This suggests an interaction effect: the effect of adtype is different for men and women. ![]() Now, what's really important is that the lines are far from parallel. By default, SPSS always tests the saturated model for any factorial ANOVA. The “estimated marginal means” are equal to the observed means for the saturated model (all possible effects included). These means are nicely visualized in our profile plot. Technical note: these means may differ from DESCRIPTIVES output because the repeated measures procedure excludes all cases with one or more missing values from the entire procedure. Adtype 2 (“youngster car”) is rated worst and adtype 3 is in between. Both men and women rate adtype 1 (“family car”, as seen in the variable labels) most attractive. Output - Means Plot and DescriptivesĪt the very core of our output, we just have 6 means: 3 ads for men and women separately. Next, we'll move our profile plots up by dragging and dropping it right underneath the descriptive statistics table. Since we're not going to inspect all of our output, we'll first delete some items as shown below. GLM ad1 ad2 ad3 BY gender /WSFACTOR=adtype 3 Polynomial /MEASURE=attractiveness /METHOD=SSTYPE(3) /PLOT=PROFILE(adtype*gender) /PRINT=DESCRIPTIVE ETASQ /CRITERIA=ALPHA(.05) /WSDESIGN=adtype /DESIGN=gender. *Basic repeated measures ANOVA with within and between subjects factor. SPSS Basic Repeated Measures ANOVA Syntax Clicking Paste in the main dialog results in the syntax below. These profile plots will nicely visualize our 6 means (3 ads for 2 genders) in a multiple line chart.įor now, we'll only tick Descriptive Statistics and Estimates of Effect Size in the Options subdialog. Move gender into the between-subjects factor box. Select and move the three adratings variables in one go to the within-subjects variables box. ![]() We recommend you choose a meaningful name for it. The within-subjects factor is whatever distinguishes the three variables we'll compare. Repeated Measures may be absent from your menu if you don't have the SPSS option “Advanced statistics” installed. The initial results will then suggest how to nicely fine tune our analysis in a second run. We'll first run a very basic analysis by following the screenshots below. Last, Mauchly’s test for the sphericity assumption will be included in the output so we'll see if that holds in a minute. Regarding the normality assumption, our previous histograms showed some skewness but nothing too alarming. 1, 2, 3įirst, since each case (row of data cells) in SPSS holds a different person, the observations are probably independent. Sphericity: the variances of all difference scores among the test variables must be equal in the population.Normality: the test variables follow a multivariate normal distribution in the population.Independent and identically distributed variables (“ independent observations”).You can now verify for yourself that all distributions look plausible and there's no missing values or other issues with these variables. frequencies gender ad1 to ad3/format notable/histogram. *Quick check for abnormal distributions / outliers / missing values.
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