A hierarchical bayesian model with correlated residuals for investigating stability and change in intensive longitudinal data settings.

Autor/inn/en	Gasimova, Fidan; Robitzsch, Alexander; Wilhelm, Oliver; Hülür, Gizem
Titel	A hierarchical bayesian model with correlated residuals for investigating stability and change in intensive longitudinal data settings.
Quelle	In: Methodology, 10 (2014) 4, S. 126-137Infoseite zur Zeitschrift PDF als Volltext Link als defekt meldenVerfügbarkeit
Beigaben	Literaturangaben
Zusatzinformation	Forschungsdaten, Studiendetails und Erhebungsinstrumente
Sprache	englisch
Dokumenttyp	online; Zeitschriftenaufsatz
ISSN	1614-2241
DOI	10.1027/1614-2241/a000083
Schlagwörter	Korrelation; Stochastik; Stochastische Methode; Bezugsgruppe; Verhältnis; Stichprobe; Schuljahr 09; Bayes-Formel; Messdaten; Stochastisches Modell; Verhältnisrechnung; Wahrscheinlichkeitsverteilung; Bayes-Statistik; Statistik; Messergebnis; Plausibilität; Verteilung
Abstract	The present paper's focus is the modeling of interindividual and intraindividual variability in longitudinal data. We propose a hierarchical Bayesian model with correlated residuals, employing an autoregressive parameter AR(1) for focusing on intraindividual variability. The hierarchical model possesses four individual random effects: intercept, slope, variability, and autocorrelation. The performance of the proposed Bayesian estimation is investigated in simulated longitudinal data with three different sample sizes (N=100, 200, 500) and three different numbers of measurement points (T=10, 20, 40). The initial simulation values are selected according to the results of the first 20 measurement occasions from a longitudinal study on working memory capacity in 9th graders. Within this simulation study, we investigate the root mean square error (RMSE), bias, relative percentage bias, and the 90% coverage probability of parameter estimates. Results indicate that more accurate estimates are associated with a larger sample size. One exception to this tendency is the autocorrelation parameter, which shows more sensitivity to an increasing number of time points. (Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2022/1