2010
DOI: 10.1016/j.jsp.2009.09.002
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A practical guide to multilevel modeling

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Cited by 1,083 publications
(839 citation statements)
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References 17 publications
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“…In the first analysis, the intra-class correlation (ICC) of country was examined. The ICC was .06, indicating the proportion of total variance that occurred systematically between countries was trivial and multilevel analysis with country as Level-2 units was not necessary (Lee, 2000;Peugh, 2010). Second, the standard errors of the regression models conducted with and without multilevel analyses were compared.…”
Section: Data Analysesmentioning
confidence: 99%
“…In the first analysis, the intra-class correlation (ICC) of country was examined. The ICC was .06, indicating the proportion of total variance that occurred systematically between countries was trivial and multilevel analysis with country as Level-2 units was not necessary (Lee, 2000;Peugh, 2010). Second, the standard errors of the regression models conducted with and without multilevel analyses were compared.…”
Section: Data Analysesmentioning
confidence: 99%
“…Furthermore, the development of trait and state anxiety was plotted over the course of chemotherapy treatment. Repeated measurements of anxiety and symptom severity at different time points resulted in a nested data set, calling for an analysis approach that accounts for this higher level clustering of individuals' ratings by time to avoid type I errors and biased parameter estimations (Peugh, 2010;Shek and Ma, 2011). Therefore, longitudinal mixed model analyses were used to answer our three research questions as stated above.…”
Section: Analysesmentioning
confidence: 99%
“…The best-known and most frequently used method for parameter estimation is the maximum likelihood function. Furthermore, the likelihood function can be used for the calculation of the deviance, which is used for comparison of nested models (Peugh, 2010). In addition, nested or non-nested models can be compared using the Akaike information criterion ( AIC ) and the Bayesian information criterion ( BIC ) (Kwok et al, 2008).…”
Section: Methodsmentioning
confidence: 99%
“…There are no straightforward effect sizes in MLA, but generally accepted indices like the coefficient of determination Pseudo R 2 can be computed (Raudenbush & Bryk, 2002). Pseudo R 2 gives the variance explanation by adding one or more predictors compared to a more restrictive model (Peugh, 2010). …”
Section: Methodsmentioning
confidence: 99%
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