Repeated Measures ANOVA in SPSS George please follow the instructions listed and use the datasets you already have from previous assignments. Also please use the template attached to complete the assignment, Analysis of Variance with Repeated Measures Because you are measuring the same subjects over time, your data are dependent and therefore you need a statistical test that will take this dependency into consideration. You could run multiple paired sample t tests but that would not only be inefficient, it would introduce the possibility of a type I error. The repeated measures ANOVA allows you to compare three or more means on dependent data without using multiple t tests.

Repeated Measures ANOVA in SPSS

George please follow the instructions listed and use the datasets you already have from previous assignments. Also please use the template attached to complete the assignment,
Analysis of Variance with Repeated Measures
Because you are measuring the same subjects over time, your data are dependent and therefore you need a statistical test that will take this dependency into consideration. You could run multiple paired sample t tests but that would not only be inefficient, it would introduce the possibility of a type I error. The repeated measures ANOVA allows you to compare three or more means on dependent data without using multiple t tests.
Repeated Measures ANOVA in SPSS
To prepare

• Review the datasets provided.
• Construct a research question based on one of those datasets.
• Pay attention to the assumptions of this test, and ask, âDoes it make sense to interpret the mean of this dependent variable?â

Use SPSS to answer the research question you constructed. Then, compose a 1- to 2-paragraph analysis in APA format.
Here is an example:
RQ: What is the difference in the scoring by different lecturers on the same papers?
Null Hypothesis (H0): There are no statistically significant differences in the percentage marks given by the four different lecturers on the same papers.
Alternative Hypothesis (HA): There are statistically significant differences in the percentage marks given by the four different lecturers on the same papers.
Results
The grading of four different lecturers on the same essays were analyzed to determine if there were âharderâ and âeasierâ graders. There are some differences in the mean scores given by the different professors (list means of each instructor here).
The results of Mauchlyâs test was statistically significant (p=.043) so the assumption of sphericity was violated. Due to this, the Greenhouse-Geisser test was used in order to determine if the scores given by the four different professors on the same essays were statistically significant (see Table 1).
INSERT TABLE 1 (Test of Within-Subjects Effect table)
The results of this indicated that the differences were not statistically significant (p=.063). Therefore the null hypothesis is retained.
Be sure to include your data output from SPSS at the end of the paper

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