Repeated Measures ANOVA An ANOVA with repeated measures is used to compare three or more group means where the participants are the same in each group; it is also referred to as within-subjects ANOVA or ANOVA for correlated samples (Frankfort-Nachmias, & Nachmias, 2008). We can analysis data using repeated measures ANOVA for two types of study design. Studies that investigate either: (1) changes in mean scores over three or more time points that is, when participants are measured multiple times to see changes to an intervention, or (2) differences in mean scores under three or more different conditions that is, when participants are subjected to more than one condition/trial and the response to each of these conditions wants to be compared (Field, 2013). The purpose of this paper is to conduct statistical analysis on data in TutorMarks.sav data set from the Field text and report in APA format. Section 1 Assumptions for Repeated Measures ANOVA (RMA) (1) Dependent variables and independent level of measurement The first assumption of repeated measures ANOVA (RMA) requires that the dependent variables should be measured at the continuous level of measurement or group that is they are ratio or interval variables (Field, 2013). Independent variables should consist of at least two categorical, related groups or matched pairs (Frankfort-Nachmias, & Nachmias, 2008). (2) No Outlier There should be no significant outliers in the related groups. Outliers are simply single data points within the data that do not follow the usual pattern. The problem with outliers is that they can have a negative effect on the repeated measures ANOVA, distorting the differences between the related groups (whether increasing or decreasing the scores on the dependent variable), and can reduce the accuracy of your results (field, 2013). (3) Normality (Normal distribution) The fourth assumption is that the distribution of the dependent variable in the two or more related groups should be approximately normally distributed. Repeated measures ANOVA only requiring approximately normal data because it is quite “robust” to violations of normality, meaning that the assumption can be a little violated and still provide valid results. The normality test can be done using the Shapiro-Wilk test of normality, which is easily tested for using SPSS Statistics (Field, 2013).
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