This page shows you how to run the common tests in SPSS, which number in the output actually matters, and how to write the result in APA. When the deadline is tight or the data will not behave, a statistician can run and interpret it on your own file.
SPSS is less about clicking buttons than knowing which buttons to click and what the output means. Pick the wrong test for the design, skip the assumption checks, or read the wrong row of a table, and the analysis is wrong no matter how tidy it looks. This guide is part of our statistics homework help. It gives the exact menu path for each common test, points at the one value in the output that decides the result, and shows how to report it. Where a test can fail, it names the check and the fallback test to use instead.
Two version notes that trip people up. In SPSS version 29 and later the Compare Means menu is named Compare Means and Proportions. In version 27 and later the old Sig. (2-tailed) column is split into One-Sided p and Two-Sided p; for an ordinary non-directional hypothesis you read Two-Sided p.
Match your research question and variable types to the right test, then jump to its guide.
| Your question | Your data | Test |
|---|---|---|
| Do two unrelated groups differ on average? | Continuous outcome, two independent groups | Independent-samples t-test |
| Did the same people change between two points? | Continuous outcome, two paired measures | Paired-samples t-test |
| Do three or more unrelated groups differ? | Continuous outcome, three or more groups | One-way ANOVA |
| Did the same people change across three or more points? | Continuous outcome, repeated measures | Repeated-measures ANOVA |
| Are two categories associated? | Two categorical variables | Chi-square test of independence |
| Do two continuous variables move together? | Two continuous variables | Pearson correlation |
Not sure your data meets the requirements? Every guide below lists the assumptions and the test to switch to when one fails.
Every SPSS dataset has two tabs at the bottom left of the Data Editor. Data View is the data itself, with columns as variables and rows as cases. Variable View describes the variables, with one row per variable and columns for its Name, Label, Values and Measure.
The setting that quietly breaks analyses is Measure. SPSS tags every numeric variable as Scale by default. If you code a category as a number, for example one for male and two for female, SPSS still treats it as Scale until you change it. Set it to Nominal for unordered categories, Ordinal for ordered categories such as a Likert scale, and leave it as Scale for genuine continuous measures. Kent State University notes this setting affects everything from the charts SPSS offers to the tests it will run, so fix it before you analyze anything.
To attach value labels, click the Values cell in Variable View, then the three-dot button, type the value and its label, click Add, and repeat. The labels then show in your output in place of the raw codes. The common tests live under one menu: t-tests and one-way ANOVA under Analyze > Compare Means and Proportions, factorial and repeated-measures ANOVA under Analyze > General Linear Model, correlation under Analyze > Correlate, and chi-square under Analyze > Descriptive Statistics > Crosstabs.
Menu path, the assumption checks, the exact number to read, and how to report it. Example values follow standard teaching datasets.
Tests whether two categorical variables are associated, for example gender and a preferred learning format.
Assumptions. Both variables categorical, independent observations, and expected counts large enough: no more than 20 percent of cells may have an expected count below five. SPSS states the exact percentage in the footnote under the Chi-Square Tests table. If that rule fails in a two-by-two table, read the Fisher's Exact Test row, which SPSS prints automatically.
What to read. The Chi-Square Tests table, the Pearson Chi-Square row. Ignore the Likelihood Ratio and Linear-by-Linear rows.
| Value | df | Asymptotic Sig. (2-sided) | |
|---|---|---|---|
| Pearson Chi-Square | 0.487 | 1 | .485 |
| Likelihood Ratio | 0.488 | 1 | .485 |
| N of Valid Cases | 50 |
Common mistakes
Compares the means of one continuous outcome across two unrelated groups.
Assumptions. Continuous outcome, two independent groups, no serious outliers, approximate normality within each group (Shapiro-Wilk in Analyze > Descriptive Statistics > Explore), and equal variances, which SPSS checks with Levene's Test inside the results table. If normality is badly broken, switch to the Mann-Whitney U test.
What to read, and the Levene's rule. In the Independent Samples Test table, let Levene's decide the row. If Levene's Sig. is above .05, read the top row, Equal variances assumed. If Levene's Sig. is .05 or below, read the bottom row, Equal variances not assumed. Then read t, df, and Two-Sided p on that row.
| Levene F | Levene Sig. | t | df | Two-Sided p | |
|---|---|---|---|---|---|
| Equal variances assumed | 1.57 | .217 | 2.428 | 38 | .020 |
| Equal variances not assumed | 2.428 | 34.9 | .021 |
Common mistakes
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Compares two measurements taken on the same people, for example a score before and after training.
Assumptions. A continuous outcome measured twice on the same cases, no serious outliers, and normality of the difference scores, not the raw variables. Compute the difference with Transform > Compute Variable, then check it with a boxplot and Shapiro-Wilk. If the differences are badly non-normal, use the Wilcoxon signed-rank test.
What to read. The Paired Samples Test table, reading across to t, df, which equals the number of pairs minus one, and Two-Sided p. Do not read the Sig. in the Paired Samples Correlations table above it.
Common mistakes
Compares the means of one continuous outcome across three or more unrelated groups.
Assumptions. Continuous outcome, three or more independent groups, no serious outliers, normality within groups (Shapiro-Wilk), and equal variances (Levene's, from the Options checkbox). If Levene's Sig. is below .05, use Welch's ANOVA with the Games-Howell post-hoc instead of Tukey.
What to read. The ANOVA table, the Between Groups row, for F and Sig. Then the Multiple Comparisons table for the Tukey pairwise results, where an asterisk on the mean difference flags a significant pair.
| Sum of Squares | df | Mean Square | F | Sig. | |
|---|---|---|---|---|---|
| Between Groups | 85.5 | 2 | 42.7 | 4.467 | .021 |
| Within Groups | 258.3 | 27 | 9.6 |
Common mistakes
Compares three or more measurements on the same people, for example scores at three time points.
Assumptions. A continuous outcome measured three or more times, no serious outliers, normality at each level, and sphericity, meaning the variances of the differences between all level pairs are roughly equal. SPSS tests sphericity automatically with Mauchly's Test. Sphericity only applies with three or more levels; with two levels it is met by definition.
The decision rule. Read Mauchly's Test of Sphericity first. If its Sig. is above .05, sphericity holds, and in the Tests of Within-Subjects Effects table you read the Sphericity Assumed row. If Mauchly's Sig. is .05 or below, sphericity is violated, and you read the Greenhouse-Geisser row instead. The F ratio is the same on both rows; only the degrees of freedom change, becoming fractional, which is why the corrected result has decimal df.
Common mistakes
Repeated-measures designs are where most SPSS marks are lost. Send your file and we will run it, apply the right correction, and write the result. Get a quote →
Measures the strength and direction of the linear relationship between two continuous variables.
Assumptions. Two continuous variables, a linear relationship and no serious outliers, both checked on a scatterplot before you trust the coefficient, and approximate normality. Pearson only captures linear association, so a strong curved relationship can still return a small r. For ordinal data or a monotonic but non-linear relationship, use Spearman instead.
What to read. The Correlations table is a matrix with ones on the diagonal. Read an off-diagonal cell: Pearson Correlation is r, from minus one to plus one, Sig. (2-tailed) is p, and N is the number of complete pairs.
Common mistakes
Most SPSS marks are lost not on the software but on ignoring a broken assumption. Check the assumption, and when it fails, switch to the matched test rather than reporting an invalid result.
| Assumption | How SPSS checks it | If it fails |
|---|---|---|
| Normality | Shapiro-Wilk and Q-Q plots in Explore | Use the non-parametric equivalent (Mann-Whitney, Wilcoxon, Kruskal-Wallis) |
| Equal variances | Levene's Test | Read the "equal variances not assumed" row, or use Welch and Games-Howell |
| Sphericity | Mauchly's Test | Read the Greenhouse-Geisser row |
| Expected cell counts | Footnote under Chi-Square Tests | Use Fisher's Exact Test |
| Linearity | Scatterplot | Use Spearman's correlation |
Procedures on this page were checked against the IBM SPSS Statistics documentation and the UCLA statistical computing SPSS guides.
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It is the p-value for a non-directional test. If it is below your alpha level, usually 0.05, the result is statistically significant. In SPSS version 27 and later this column is labelled Two-Sided p. It is not the test statistic; the statistic is the t, F, r or Chi-Square value in the same table.
Use an independent-samples t-test for two unrelated groups, a paired-samples t-test for two measurements on the same people, and a one-sample t-test to compare one group mean against a known value. For three or more groups use ANOVA.
SPSS rounds very small p-values to .000, but a probability is never exactly zero. Report it as p < .001.
Large samples make the parametric tests fairly tolerant of non-normality, but you should still inspect histograms, Q-Q plots and the Shapiro-Wilk test, and always check for outliers, which a large sample does not fix.
Levene's Test checks equality of variances. If its Sig. is above 0.05, read the top row, Equal variances assumed. If its Sig. is 0.05 or below, read the bottom row, Equal variances not assumed.
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