4275 Words Jan 30th, 2012 18 Pages
Help Sheet for Reading SPSS Printouts
Heather Walen-Frederick, Ph.D.

This document will give you annotated SPSS output for the following statistics:

1. Correlation 2. Regression 3. Paired Samples t-test 4. Independent Samples t-test 5. ANOVA 6. Chi Square

Note that the version of SPSS used for this handout was 13.0 (Basic). Therefore, if you have advanced add-ons, or a more recent version there may be some slight differences, but the bulk should be the same. One possible difference would be for later versions or advanced packages to give the option of things like effect size, etc. In addition, the data used for these printouts were based on data available in the text: Statistics for the
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When conducting a correlation, be sure to look at your data, including scatterplots, to make sure assumptions are met (for correlation, outliers can be a concern).

This example is from Chapter 14 (problem #49). The question was whether there was a relationship between the amount of time it took to complete a test and the test score. Do people who take longer get better scores, maybe due to re-checking questions and taking their time (positive correlation), or do people who finish sooner do better possibly because they are more prepared (negative correlation)?

This is the default output.

This box gives the results.

You see the correlation in the last column, first row: .083. Clearly, this is a small correlation (remember they range from 0-1 and this is almost 0). The p-value (in the row below this) is .745. This is consistent with the correlation. This is nowhere near either alpha (.05 or .01); in other words, because the p-value EXCEEDS alpha, it is not statistically significant. Thus we fail to reject the null, and conclude that the time someone takes to complete a test is not related to the score they will receive.

The write up would look like this: r(18) = .08, p > .05 (if you were using an alpha of .01, it would read: r(18) = .08, p > .01. Alternatively, you could write: r(18) = .08, p = .75 (the difference is that in the last example, you are reporting the actual

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