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SPSS for Beginners

This guide provides a basic introduction to SPSS. It is recommended that have an in-depth understanding of the various statistical methods to use for your research study.

Non-Parametric Inferential Statistics

The Chi-Square Test is used to test whether two categorical (nominal) variables are associated with each other. This test assumes that the observations are independent, and that the expected frequencies for each category should be at least 1 (NOTE: no more than 20% of the categories should have expected frequencies less than 5). 

Note that this is a non-parametric test. There is no parametric version of a Chi-Square Test of Independence.

HOW TO RUN A CHI-SQUARE FOR CROSSTABS

  1. Click on Analyze. Select Descriptive Statistics. Select Crosstabs. 
  2. Place one or more variables in “Row(s)” and one or more variables in “Column(s)”.
    • NOTE: SPSS will generate a crosstab table for each row / column combination, as only one row and one column is used per crosstab.
  3. To run a Chi-Square test, click Statistics and check the “Chi-square” box. If you want a measure of effect size, also check the “Phi and Cramer’s V” box. Click Continue to save your choices. 
  4. Click Cells to ensure the “Observed” box is checked. Optionally, you can request the expected number of cases here if you check the “Expected” box. Click Continue to save your choices.
  5. Click OK to run the test (results will appear in the output window).

The Spearman’s rank-order correlation is used to determine the strength and direction of a relationship of the rankings of two variables. The variables can be ordinal or continuous. This test does not assume the variables are normally distributed. However, the relationship between the ranked values should be monotonic (i.e., an increasing OR decreasing relationship; not increasing AND decreasing).

Note that this is a non-parametric test; you could / should use a Spearman’s rank-order correlation if the normality assumption has been violated for your Pearson correlation (i.e., the parametric equivalent). You can also use this test if you wish to conduct a correlation on ordinal data (note: Pearson’s would not be appropriate here).

HOW TO RUN A SPEARMAN CORRELATION

  1. Click on Analyze. Select Correlate. Select Bivariate.
  2. Place two or more variables in the “Variables” box.
  3. In the Correlation Coefficients section, ensure “Spearman” is checked.
  4. Click OK to run the test (results will appear in the output window).

The Wilcoxon signed-rank test is used to determine whether the median of a single continuous variable differs from a specified constant (similar to a one-sample t-test) AND / OR whether the median of two continuous variables from the same group of participants differ (similar to a paired-samples t-test). Both versions of this test do not assume that the data are normally distributed.

Note that this is a non-parametric test; you could / should use the Wilcoxon signed-rank test if the normality assumption has been violated for your one-sample t-test or a paired-samples t-test (i.e., the parametric equivalents). 

HOW TO RUN A WILCOXON SIGNED-RANK TEST (ONE-SAMPLE T-TEST VERSION)

  1. Click on Analyze. Select Nonparametric Tests. Select One Sample. 
  2. In the “Objective” tab on the dialogue box, click the “Customize analysis” circle.

  1. In the “Fields” tab on the dialogue box, select the single column of data you wish to use for the Wilcoxon one-sample test and move it to the “Test Fields” box.

  1. In the “Settings” tab on the dialogue box, click the “Customize tests” circle, then check the “Compare median to hypothesized (Wilcoxon signed-rank test)” box. Input the constant you wish to use in the “Hypothesized median” input box.

  1. Click Run to run the test (results will appear in the output window).

The Mann-Whitney U test is used to determine whether two groups’ medians on the same continuous variable differ (similar to an independent samples t-test). This test does not assume that the data are normally distributed, but is does assume that the distributions are the same shape.

Note that this is a non-parametric test; you could / should use the Mann-Whitney U test if the normality assumption has been violated for your independent samples t-test (i.e., the parametric equivalent).

HOW TO RUN A MANN-WHITNEY U TEST

  1. Click on Analyze. Select Nonparametric Tests. Select Independent Samples.
  2. In the “Objective” tab on the dialogue box, click the “Customize analysis” circle.

  1. In the “Fields” tab on the dialogue box, select the single column of continuous data you wish to use for the Mann-Whitney independent samples test and move it to the “Test Fields” box. Select the single categorical grouping variable (which must be only two groups) you wish to use and move it to the “Groups” box.

  1. In the “Settings” tab on the dialogue box, click the “Customize tests” circle, then in the “Compare Distributions across Groups” section, check the “Mann-Whitney U (2 samples)” box.

  1. Click Run to run the test (results will appear in the output window).

The Kruskal-Wallis H test is used to determine whether three or more groups’ medians on the same continuous variable differ (similar to a one-way ANOVA, with independent groups). This test does not assume that the data are normally distributed, but it does assume the distributions are the same shape.

Note that this is a non-parametric test; you could / should use the Kruskal-Wallis H test if the normality assumption has been violated for your one-way ANOVA with independent groups (i.e., the parametric equivalent).

HOW TO RUN A KRUSKAL-WALLIS H TEST

How to run a Kruskal-Wallis H test

  1. Click on Analyze. Select Nonparametric Tests. Select Independent Samples.
  2. In the “Objective” tab on the dialogue box, click the “Customize analysis” circle.

  1. In the “Fields” tab on the dialogue box, select the single column of continuous data you wish to use for the Kruskal-Wallis H test and move it to the “Test Fields” box. Select the single categorical grouping variable (which must be three or more groups) you wish to use and move it to the “Groups” box.

  1. In the “Settings” tab on the dialogue box, click the “Customize tests” circle, then in the “Compare Distributions across Groups” section, check the “Kruskal-Wallis 1-way ANOVA (k samples)” box. Indicate whether you would like either “all pairwise” or “stepwise step-down” for your multiple comparisons.

  1. Click Run to run the test (results will appear in the output window).

The Friedman test is used to determine whether one groups’ ranking on three or more continuous or ordinal variables differ (similar to a repeated measures one-way ANOVA). This test does not assume that the data are normally distributed, but it does assume the distributions are the same shape.

Note that this is a non-parametric test; you could / should use the Friedman test if the normality assumption has been violated for your repeated measures one-way ANOVA (i.e., the parametric equivalent).

HOW TO RUN A FRIEDMAN TEST

  1. Click on Analyze. Select Nonparametric Tests. Select Related Samples.
  2. In the “Objective” tab on the dialogue box, click the “Customize analysis” circle.

  1. In the “Fields” tab on the dialogue box, select the three (or more) columns of continuous or ordinal data you would like to use for the Friedman’s test and move them to the “Test Fields” box.

  1. In the “Settings” tab on the dialogue box, click the “Customize tests” circle, then in the “Compare Distributions” section, check the “Friedman’s 2-way ANOVA by ranks (k samples)” box. Indicate whether you would like “none”, “all pairwise”, or “stepwise step-down” for your multiple comparisons.

  1. Click Run to run the test (results will appear in the output window).
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