## Monday, February 19, 2018

### Repeated Measures Analysis of Variance (SPSS)

Previously, in a prior article, we discussed the concept of repeated measures analysis of variance (repeated measures ANOVA). In this article, we will be utilizing the same example data set, however, instead of creating the model within the R platform, we will demonstrate the creation of the model through the utilization of SPSS.

Repeated Measures Analysis of Variance (Repeated Measures ANOVA) Example:

Repeated-Measures ANOVA

A repeated measures ANOVA is similar to a paired t-test in that it samples from the same set more than once. This model contains one factor with at least two levels, and the levels are dependent.

Stated Problem:

Researchers want to test the impact of reading existential philosophy on a group of 8 individuals. They measure the happiness of the participants three times, once prior to reading, once after reading the materials for one week, and once after reading the materials for two weeks. We will assume an alpha of .05.

To create the model, first choose “Analyze” from the topmost menu. After this selection has been made, choose “General Linear Model”, and then select “Repeated Measures”.

This series of selections should cause the following interface to appear:

“Within-Subject Factor Name:” should be pre-populated with “factor1”. You can leave this entry as is. The “Number of Levels” entry must be modified to the level which is necessary for inclusion within the model. In the case of our example, we will set this value to “3”. I have created the graphic below which illustrates the levels:

Once the required data has been entered into the appropriate fields, we can continue with the model creation by clicking “Define”.

Doing such, will present the following menu:

“Within-Subject Variables” will initially be listed as ” _?_(1)" , " _?_(2)", and "_?_(3)". Clicking the topmost arrow pointing right will allow us to modify this list so that instead of the default place holder values, our model is prepared to populate with the necessary level variables. Once the variables have been listed in order, select “OK”.

This populates numerous tables within the output section of SPSS. However, only the table below is worth inspecting.

Since (.377 > .05) we will not reject the null hypothesis.

With this information, we can conclude that the three conditions did not significantly differ pertaining to level of happiness.