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, an employee survey is reported in terms of its N’s, means, distribution of responses, and benchmarking scores, but it is analyzed with a Root Cause Analysis, including random forest using managed machine learning and 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, again require random forest with managed machine learning, but also 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. NBRI provides the analyses and the training to respond to the results. This expertise is a critical component of all scientific, psychological research, and the means by which NBRI provides clients with a clear path to dramatic organizational improvement.