2012
DOI: 10.1097/psy.0b013e3182736971
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Multilevel Modeling in Psychosomatic Medicine Research

Abstract: The primary purpose of this manuscript is to provide an overview of multilevel modeling for Psychosomatic Medicine readers and contributors. The manuscript begins with a general introduction to multilevel modeling. Multilevel regression modeling at two-levels is emphasized because of its prevalence in psychosomatic medicine research. Simulated datasets based on some core ideas from the Familias Unidas effectiveness study are used to illustrate key concepts including: communication of model specification, param… Show more

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Cited by 22 publications
(11 citation statements)
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“…This allowed us to account for the nested data structure of patient ratings within physician burnout ratings and to examine variance and model fit at two levels. 23,24 Patient variables were measured at level 1, and physician variables at level 2.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…This allowed us to account for the nested data structure of patient ratings within physician burnout ratings and to examine variance and model fit at two levels. 23,24 Patient variables were measured at level 1, and physician variables at level 2.…”
Section: Discussionmentioning
confidence: 99%
“…We hypothesized that these physician characteristics were related to patient perceptions of empathy and enablement. To compare fit of successive nested models, we used absolute fit indices (Akaike information criterion; Bayesian information criterion), with lower values indicating better fit, 23,27 and chi-square deviance statistics. 28 …”
Section: Discussionmentioning
confidence: 99%
“…Data analysis was primarily conducted in multi-level models with visits at Level 1 and people at Level 2 using SAS PROC MIXED with maximum likelihood estimation (for detailed description of multilevel models, see (26)). Indices of model quality included variance estimates at Level 1 and Level 2, Aikake’s information criterion (AIC), and Bayes’s information criterion (BIC).…”
Section: Methodsmentioning
confidence: 99%
“…LMMs account for dependence between observations and allow for different numbers of participant observations. [22] A random (subject-specific) intercept was employed. Parameter estimates are reported as B (SE) or as OR (95% CI); t values with associated p values test the null hypotheses that the B value is equal to zero, or that the OR is equal to one, in the population.…”
Section: Methodsmentioning
confidence: 99%