Different types of data require different types of statistical analyses. This is especially true of psychological research, including employee surveys, customer surveys, and market research.
For instance, a stand-alone employee survey is reported in terms of its N’s, means, distribution of responses, and benchmarking scores, but it may be analyzed with a Root Cause Analysis, including inter-correlations among the predictors for identifying multi-collinearity in the regression. The output of the Root Cause Analysis provides Management with those 3 to 5 perceptions in employee thinking that are driving behavior. When these are addressed, employee productivity, engagement, and loyalty are improved in the most expedient manner possible.
Longitudinal studies, such as those of customers or guests with frequent data collections, require generalized estimating equations to fit a repeated measures logistic regression. Generalized linear mixed models may also be used by extending the linear model so that the target is linearly related to the factors and covariates via a specified link function.
With this type of analysis, you are able to ‘cut to the chase.’ There is no time or money wasted trying to decide what to work on or what actions should be taken. We recommend that those without doctoral level educations in statistics do not attempt to analyze the data. This expertise is a critical component of all scientific, psychological research, and the means by which you may find a clear path to dramatic organizational improvement.