The Impact of Inappropriate Modeling of Cross-Classified Data Structures
| Autor: | Jason L. Meyers, S. Natasha Beretvas |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Statistics and Probability
Structure (mathematical logic) Psychometrics Computer science Experimental and Cognitive Psychology General Medicine Data structure Random effects model Data set Standard error Arts and Humanities (miscellaneous) Statistics Econometrics Analysis of variance Social science research |
| Zdroj: | Multivariate Behavioral Research. 41:473-497 |
| ISSN: | 1532-7906 0027-3171 |
| DOI: | 10.1207/s15327906mbr4104_3 |
| Popis: | Cross-classified random effects modeling (CCREM) is used to model multilevel data from nonhierarchical contexts. These models are widely discussed but infrequently used in social science research. Because little research exists assessing when it is necessary to use CCREM, 2 studies were conducted. A real data set with a cross-classified structure was analyzed by comparing parameter estimates when ignoring versus modeling the cross-classified data structure. A follow-up simulation study investigated potential factors affecting the need to use CCREM. Results indicated that when the structure is ignored, fixed-effect estimates were unaffected, but standard error estimates associated with the variables modeled incorrectly were biased. Estimates of the variance components also displayed bias, which was related to several study factors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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