Do individuals or groups have preferences, or does their performance change over time?
Repeated Measures ANOVA compares averages within the same group (within subjects effects) across multiple conditions or time points.
In our own B2B Buying Experience research, we asked buyers to tell us how helpful they thought various roles on sales teams were. Thus, the same group of buyers gave scores across multiple conditions (multiple sales team roles). Here, Repeated Measures ANOVA was used to test whether participants’ ratings of various roles were reliably different.
A p value of < .001 told us that there is at least one statistically reliable difference in how useful buyers rate the various sales roles. Just as in standard ANOVA, a post-hoc analysis is used to compare each of the seller roles and determine between which pairs lie a true difference.
Our post-hoc analysis showed that BDR/SDRs are rated less helpful than all other sales roles. Sales engineers are more helpful than Account Executives (AEs), and Senior Leadership is also more helpful than AEs.
The example just explained is a test of within subjects differences – how buyers’ opinions differ regarding the helpfulness of various roles on sales teams.
Repeated Measures ANOVA also allows us to test for between subjects effects, or within the context of our example, if there are differences in helpfulness ratings across buyers of different groups. For instance, if we wanted to test whether a buyer’s organizational level influences their perception of seller personnel, we could introduce the respondent’s organizational level as a Between-Subjects factor. This allows us to compare perceptions across various levels of organizational hierarchy, effectively separating buyers opinions on seller personal based on their level.
Both the within-subjects and between subjects tests were statistically significant, indicating that people vary in how they perceive the different seller roles (within subjects effect) and their perception of the different roles varies according to their Level in the organization (between subject effect).
The eta-squared value is larger for the Between Subjects factor, which tells us that a person’s Level is a bigger influencer on perception of the Helpfulness of seller personnel than is the actual role of the individuals.
As in many statistical analyses, the numbers can be hard to parse, but a chart clears things up. Below is a graph depicting the differences described above. The chart clearly shows how a person’s level impacts perception of helpfulness. The highest rungs on the corporate hierarchy rate seller personnel higher than do the lowest. That is especially true of how they rate the helpfulness of BDRs and Senior Leadership.
