How to Run One Way ANOVA in SPSS: Concept, Interpretation, and Reporting One Way ANOVA

One-way ANOVA in SPSS is the go-to method for testing whether a continuous outcome differs across three or more independent groups—provided the group variances meet the test’s assumptions. The core workflow is: run the ANOVA, check homogeneity of variance (Levene’s test), then choose the correct post-hoc multiple-comparisons procedure based on whether variances are equal or not. That sequence matters because it determines which pairwise comparisons are statistically valid when group spread differs.

The transcript frames one-way ANOVA as an extension of earlier mean-comparison tests: instead of comparing one sample to a standard (one-sample t test) or two independent groups (independent-samples t test), one-way ANOVA compares means across three or more groups using one independent grouping variable with multiple levels and one dependent continuous variable. Common examples include testing whether income differs across cities, whether optimism scores differ across age categories, or whether staff satisfaction varies by employment type (permanent, part-time, casual). In each case, the dependent variable is continuous (e.g., income, optimism, satisfaction), and the independent variable is categorical with at least three levels (e.g., city, age group, staff type).

A worked SPSS example targets organizational commitment. Employees are grouped by job rank—junior, middle, and senior—and organizational commitment score is the dependent variable (higher score means higher commitment). The hypothesis is that organizational commitment differs across management levels: if job rank changes, commitment changes too. In SPSS, the analysis is set up under Analyze → Compare Means → One-Way ANOVA, with organizational commitment as the dependent variable and job rank as the factor. The process also includes selecting Options → Homogeneity of variance to run Levene’s test.

Levene’s test is reported as significant (based on the transcript’s “significant differences in the homogeneity of variance” wording), meaning the equal-variance assumption fails. The ANOVA table still shows a statistically significant overall effect (p < .01, with an F value reported as 9.248 and corresponding degrees of freedom). With the overall ANOVA significant, the next step is post-hoc testing to identify which specific group pairs differ.

Because variances are not homogeneous, the transcript instructs choosing a post-hoc test that does not assume equal variances—specifically Dunnett’s T3. Pairwise results indicate no significant difference between junior and middle employees, but significant differences emerge between junior and senior, and between middle and senior. Descriptives are used to interpret direction: junior has the lowest mean commitment, middle is higher, and senior has the highest mean commitment.

Finally, the transcript provides guidance for reporting results in a Word document: state the hypothesis and group structure, report the ANOVA statistics (F, degrees of freedom, p value), report Levene’s test outcome and the post-hoc method used (Dunnett’s T3), then report group means and standard deviations plus the key pairwise comparisons with mean differences and 95% confidence intervals (not crossing zero for significant differences). The takeaway is that correct assumption checking and matching the post-hoc test to Levene’s result are essential for credible one-way ANOVA reporting.