The importance of statistical modelling in clinical research

Abstract

SummaryBackgroundVarious studies have shown that caregiving relatives of schizophrenic patients are at risk of suffering from depression. These studies differ with respect to the applied statistical methods, which could influence the findings. Therefore, the present study analyzes to which extent different methods may cause differing results.MethodsThe present study contrasts by means of one data set the results of three different modelling approaches, Rasch Modelling (RM), Structural Equation Modelling (SEM), and Linear Regression Modelling (LRM).ResultsThe results of the three models varied considerably, reflecting the different assumptions of the respective models.ConclusionsLatent trait models (i. e., RM and SEM) generally provide more convincing results by correcting for measurement error and the RM specifically proves superior for it treats ordered categorical data most adequately.

Assessing the dimensionality of the CES-D using multi-dimensional multi-level Rasch models

“…The often applied CFA approach allows already for a multidimensional analysis (and the results of these studies support indeed a multi-dimensional structure of the CES-D), however, the CFA model has been originally developed for interval scaled data, assuming linear relationships and a multivariate normal distribution. Although extensions covering ordered categorical data and non-normality exist, the IRT family of models is specifically designed for (ordered) categorical data as we obtain from questionnaires like the CES-D. Amongst others, the IRT approach allows for a detailed analysis of items and item categories, specifically taking into account the categorical response format (for a direct comparison of the various approaches see [ 73 ]). To the authors’ knowledge, the CES-D has so far not been analysed with a multidimensional IRT model.…”