Coping with Memory Effect and Serial Correlation when Estimating Reliability in a Longitudinal Framework

Autor/inn/en	Laenen, Annouschka; Alonso, Ariel; Molenberghs, Geert; Vangeneugden, Tony; Mallinckrodt, Craig H.
Titel	Coping with Memory Effect and Serial Correlation when Estimating Reliability in a Longitudinal Framework
Quelle	In: Applied Psychological Measurement, 34 (2010) 4, S.255-266 (12 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0146-6216
DOI	10.1177/0146621609349494
Schlagwörter	Psychiatry; Rating Scales; Memory; Psychometrics; Longitudinal Studies; Computation; Inferences; Reliability; Evaluation; Models; Simulation; Intervals; Case Studies; Correlation; True Scores; Error of Measurement; Equations (Mathematics) + Suchen Sie Ihr Suchwort? Psychiatrie; Rating-Skala; Gedächtnis; Psychometry; Psychometrie; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Inference; Inferenz; Reliabilität; Evaluierung; Analogiemodell; Simulation program; Simulationsprogramm; Case study; Fallstudie; Case Study; Korrelation; Messfehler; Equations; Mathematics; Gleichungslehre
Abstract	Longitudinal studies are permeating clinical trials in psychiatry. Therefore, it is of utmost importance to study the psychometric properties of rating scales, frequently used in these trials, within a longitudinal framework. However, intrasubject serial correlation and memory effects are problematic issues often encountered in longitudinal data. In the present work the authors study, via simulation, the impact of uncontrolled sources of serial correlation on newly proposed measures, designed to evaluate reliability in a longitudinal scenario. This study also addresses the relationship between serial correlation and memory effect. The simulations illustrate that ignoring serial correlation can have a severe impact on the estimates of reliability and on inferences related to it. Importantly, the authors show that the underlying modeling framework used in this new approach allows correcting for this type of correlation and avoiding bias. Moreover, it can adjust for the presence of a memory effect. Nevertheless, to achieve that, a careful model building is required. (Contains 4 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10