The Multitrait-Multimethod Matrix at 50!

Eid M, Nussbeck FW. The Multitrait-Multimethod Matrix at 50!. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences. 2009;5(3):71-71

Multitrait-multimethod analysis in psychotherapy research: New methodological approaches

Multimethod measurement plays a crucial role in psychology to analyze the convergent and discriminant validity, estimate the degree of method specificity, and scrutinize the generalizability of results of empirical studies and assessment procedures across methods. The implications of multimethod approaches for test validation, multimethod measurement, indication, and evaluation in psychotherapy research are discussed. Moreover, an overview of modern methodological approaches of multitrait-multimethod analysis for cross-sectional and longitudinal data is given. In particular, basic principles of confirmatory factor analysis models for interchangeable and structurally different methods are explained

A CTC(M-1) Model for Different Types of Raters

Many psychologists collect multitrait-multimethod (MTMM) data to assess the convergent and discriminant validity of psychological measures. In order to choose the most appropriate model, the types of methods applied have to be considered. It is shown how the combination of interchangeable and structurally different raters can be analyzed with an extension of the correlated trait-correlated method minus one [CTC(M-1)] model. This extension allows for disentangling individual rater biases (unique method effects) from shared rater biases (common method effects). The basic ideas of this model are presented and illustrated by an empirical example

On the meaning of the latent variables in the CT-C(M-1) model: A comment on Maydeu-Olivares and Coffman (2006)

In a recent article, A. Maydeu-Olivares and D. L. Coffman (2006) presented a random intercept factor approach for modeling idiosyncratic response styles in questionnaire data and compared this approach with competing confirmatory factor analysis models. Among the competing models was the CT-C(M-1) model (M. Eid, 2000). In an application to the Life Orientation Test (M. F. Scheier & C. S. Carver, 1985), Maydeu-Olivares and Coffman found that results obtained from the CT-C(M-1) model were difficult to interpret. In particular, Maydeu-Olivares and Coffman challenged the asymmetry of the CT-C(M-1) model. In the present article, the authors show that the difficulties faced by Maydeu-Olivares and Coffman rest upon an improper interpretation of the meaning of the latent factors. The authors' aim is to clarify the meaning of the latent variables in the CT-C(M-1) model. The authors explain how to properly interpret the results from this model and introduce an alternative restricted model that is conceptually similar to the CT-C(M-1) model and nested within it. The fit of this model is invariant across different reference methods. Finally, the authors provide guidelines as to which model should be used in which research context

On the meaning of the latent variables in the CT-C(M-1) model: A comment on Maydeu-Olivares and Coffman (2006)

In a recent article, A. Maydeu-Olivares and D. L. Coffman (2006) presented a random intercept factor approach for modeling idiosyncratic response styles in questionnaire data and compared this approach with competing confirmatory factor analysis models. Among the competing models was the CT-C(M-1) model (M. Eid, 2000). In an application to the Life Orientation Test (M. F. Scheier & C. S. Carver, 1985), Maydeu-Olivares and Coffman found that results obtained from the CT-C(M-1) model were difficult to interpret. In particular, Maydeu-Olivares and Coffman challenged the asymmetry of the CT-C(M-1) model. In the present article, the authors show that the difficulties faced by Maydeu-Olivares and Coffman rest upon an improper interpretation of the meaning of the latent factors. The authors' aim is to clarify the meaning of the latent variables in the CT-C(M-1) model. The authors explain how to properly interpret the results from this model and introduce an alternative restricted model that is conceptually similar to the CT-C(M-1) model and nested within it. The fit of this model is invariant across different reference methods. Finally, the authors provide guidelines as to which model should be used in which research context

Neuere psychometrische Ansätze der Veränderungsmessung

Die Arbeit gibt einen Überblick über neuere psychometrische Ansätze der Veränderungsmessung, die für die Klinische Psychologie von Bedeutung sind. Gegliedert nach Modellen der Erfassung (1) der situationsbedingten Variabilität des Verhaltens und Erlebens, (2) entwicklungsbedingter Veränderung und (3) interventionsbedingter Veränderung, werden Modelle für kontinuierliche und kategoriale Variablen dargestellt. Insbesondere werden neuere Entwicklungen im Bereich der Mischverteilungsanalyse (z. B. Mischverteilungs-State-Trait-Modelle, Mischverteilungs-Wachstumskurvenmodelle) behandelt, die eine Klassifikation von Personen in Bezug auf ihr Veränderungsmuster erlauben. Abschließend wird aufgezeigt, wie Mischverteilungsmodelle herangezogen werden können, um analysieren zu können, ob sich (a priori unbekannte) Subpopulationen in Treatmentwirkungen unterscheiden

Trajectories of posttraumatic stress symptoms in significant others of patients with severe traumatic brain injury

Pielmaier L, Milek A, Nussbeck FW, Walder B, Maercker A. Trajectories of Posttraumatic Stress Symptoms in Significant Others of Patients With Severe Traumatic Brain Injury. Journal of Loss and Trauma. 2013;18(6):521-538