Analysis of Covariance (ANCOVA) in SPSS | Concept, Analysis, Interpreting and Reporting ANCOVA

ANCOVA in SPSS is used to test whether a categorical factor changes a dependent outcome while statistically removing the influence of a related continuous variable (the covariate). In the example, job rank is assessed for its effect on organizational commitment, but job tenure is treated as a covariate because tenure could also shape commitment. The key payoff is that ANCOVA can prevent misleading conclusions that happen when confounding variables are ignored.

The workflow starts by setting the variables correctly: organizational commitment must be measured on an interval/ratio scale, job rank is the categorical independent variable, and job tenure is the scale covariate. Before running ANCOVA, several assumptions are checked. First, the dependent variable and covariate should not be highly correlated; the tutorial uses a correlation screen in SPSS and reports a correlation below 0.8 (about 0.8 threshold), indicating the covariate is not excessively tied to commitment in a way that would undermine the model. Second, commitment is checked for approximate normality using descriptive statistics with skewness and kurtosis, where values fall within acceptable ranges (skewness and kurtosis both under about 1). Third, homogeneity of variance across groups is evaluated using Levene’s test, with the result exceeding 0.05, supporting the assumption of similar variances.

After assumptions are satisfied, the analysis begins with a plain comparison of means (ANOVA-style summary). The group means suggest junior-level employees show higher commitment than other ranks, with standard deviations broadly similar across groups. Still, tenure could be driving part of that pattern. When ANCOVA is run in SPSS via General Linear Model → Univariate, job rank is entered as the fixed factor and job tenure as the covariate. The model options include descriptive statistics, homogeneity tests, and effect size estimation (partial eta squared).

The results show two major findings. Levene’s test supports equal error variances (insignificant), so the homogeneity assumption is not violated. More importantly, both the covariate (tenure) and the factor (job rank) have significant effects on commitment when tenure is controlled. The ANOVA table reports an F statistic (F≈4.81 with p≈0.03) for job rank, indicating significant differences in adjusted commitment across ranks. Effect size is small: partial eta squared is about 0.117, meaning roughly 11% of the variance in commitment is explained by job rank after accounting for tenure.

A central lesson comes from comparing models with and without the covariate. Without controlling for tenure, job rank appears not to be significant; once tenure is included, job rank becomes significant. That shift is attributed to removing confounding influence from tenure, which reduces unexplained variance (reported as dropping from about 79.75 to about 44.7). The tutorial then interprets estimated marginal means as adjusted means: junior employees remain higher, while other categories decline after tenure is statistically removed. Pairwise comparisons identify where differences lie—junior differs significantly from senior and from executive, while other contrasts are not significant.

Reporting guidance follows: present the overall ANCOVA result (F, p), then specify which group comparisons are significant, and include partial eta squared using effect-size guidelines. The takeaway is that ANCOVA provides stricter experimental control by accounting for confounding variables, yielding a clearer and more accurate estimate of how job rank relates to organizational commitment.