Statistics for Research - L27 - Analyze, Interpret, & Report Independent Samples T Test in SPSS

An independent-samples t test in SPSS was used to check whether perceptions about “vision” differ between male and female employees. The analysis treated “Vision” as the dependent variable and gender as the grouping variable (male = 1, female = 2), with a non-directional (two-sided) hypothesis because no prior expectation was set about which gender would score higher.

Before comparing group means, the procedure required testing the Levene’s test for equality of variances. The Levene’s test produced a significance value of 0.082, which is greater than 0.05, so equal variances were assumed. With that assumption in place, the results indicated a partially significant difference between male and female respondents on the vision-related perception measure. The t statistic was 1.744, with a two-sided p value reported as 0.082 (and the effect size reported as Cohen’s d ≈ 0.26). The effect size was characterized as small, meaning any gap between groups is modest even though the difference trends in the expected direction.

The group statistics showed males scoring higher than females. For males, the mean was 5.08 with a standard deviation of 1.35. For females, the mean was 4.73 with a standard deviation of 1.43. The difference in means aligns with the direction of the observed effect: male respondents reported higher levels of understanding/communication of vision than female respondents, though the statistical evidence did not reach the conventional 0.05 threshold.

The transcript also emphasized how to report these findings cleanly in a write-up. It recommended including the hypothesis statement, the test description (independent-samples t test), the group means and standard deviations, the Levene’s test decision (equal variances assumed), and the key inferential statistics (t value, degrees of freedom if needed, and the two-sided p value). It further suggested formatting the results into a compact table with only the necessary statistics, avoiding clutter such as one-sided test columns when using a two-sided hypothesis.

Finally, because effect size availability can vary across SPSS versions, the transcript noted that Cohen’s d may require an external calculator in older versions. The workflow described was to input each group’s mean and standard deviation (and the group sizes, if required by the calculator), then compute Cohen’s d for reporting alongside the t test results. Overall, the takeaway is a small, partially significant gender difference in vision-related understanding/communication, with equal variances assumed and males averaging higher scores.