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The importance of statistical modelling in clinical research

Comparing multidimensional Rasch-, structural equation and linear regression models for analyzing the depression of relatives of psychiatric patients

Zeitschrift:: neuropsychiatrie > Ausgabe 2/2016

Autoren:: Ao. Univ.-Prof. Mag. Dr. Rainer W. Alexandrowicz, Rebecca Jahn, Fabian Friedrich, Anne Unger

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Summary

Background

Various 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.

Methods

The 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).

Results

The results of the three models varied considerably, reflecting the different assumptions of the respective models.

Conclusions

Latent 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.

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Titel: The importance of statistical modelling in clinical research
Comparing multidimensional Rasch-, structural equation and linear regression models for analyzing the depression of relatives of psychiatric patients
Autoren:: Ao. Univ.-Prof. Mag. Dr. Rainer W. Alexandrowicz
Rebecca Jahn
Fabian Friedrich
Anne Unger
Publikationsdatum: 01.06.2016
DOI: https://doi.org/10.1007/s40211-016-0180-3
Verlag: Springer Vienna
Zeitschrift: neuropsychiatrie Psychiatrie, Psychotherapie, Public Mental Health und Sozialpsychiatrie
Ausgabe 2/2016
Print ISSN: 0948-6259
Elektronische ISSN: 2194-1327

Springer Medizin Österreich

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Background

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Results

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