One-WAY ANOVA

One-way ANOVA is the go-to method when researchers need to test whether the mean of a continuous outcome differs across three or more independent groups defined by a single categorical factor. Instead of comparing means pairwise (as with independent-samples t tests), one-way ANOVA evaluates whether the variability in scores between groups is large enough to conclude that at least one group mean differs. That matters because it controls the overall error rate when multiple groups are involved and provides a single, interpretable “overall” test before drilling into which specific groups differ.

The transcript lays out a practical setup: there is one dependent variable measured on a continuous scale and one independent grouping variable with multiple levels. Examples include checking whether lecturer income differs across London, Paris, and Islamabad; whether optimism scores vary across age groups; or whether staff satisfaction differs among permanent, part-time, and casual employees. In each case, the grouping variable defines categories (cities, age groups, employment types), while the dependent variable is the score being compared (income, optimism, satisfaction).

Running the analysis in SPSS starts with selecting Analyze → Compare Means → One-Way ANOVA. The dependent variable entered here is “organizational commitment,” while the factor is “rank,” with three levels: junior, middle, and senior. Options are set to request descriptives and homogeneity of variance. The output then produces multiple tables.

The descriptives table summarizes sample sizes and group means (including standard deviation, standard error, and 95% confidence intervals). The key decision point comes from the ANOVA significance value: it is reported as less than 0.05 (and even less than 0.01), indicating statistically significant differences in organizational commitment across the three rank groups overall. However, that overall result does not identify which specific pairs of groups differ.

To determine pairwise differences, the transcript emphasizes the need for post hoc testing, guided by the homogeneity of variances test (often Levene’s test). In this case, the homogeneity test is significant, meaning equal variances across groups are not assumed. That drives the choice of post hoc procedure: Dunnett’s T3 is selected rather than an equal-variance option. The multiple comparisons table then identifies where differences occur. The results described indicate significant differences between junior and senior, and between middle and senior, while junior and middle show no significant difference.

Finally, the transcript provides a reporting template: state the hypothesis (that organizational commitment differs across management levels), report the overall ANOVA statistics (including corrected degrees of freedom and the p-value), then report the post hoc findings that specify which group means differ. It also notes a common write-up issue—avoiding redundant rows in the post hoc table when only significant comparisons are relevant—especially as the number of groups grows and tables become crowded.