Literaturnachweis - Detailanzeige
Autor/inn/en | Gasimova, Fidan; Robitzsch, Alexander; Wilhelm, Oliver; Hülür, Gizem |
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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 |
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 |