Identifying an appropriate measurement modeling approach for the Mini-Mental State Examination.
The Mini-Mental State Examination (MMSE) is a 30-item, dichotomously scored test of general cognition. A number of benefits could be gained by modeling the MMSE in an item response theory (IRT) framework, as opposed to the currently used classical additive approach. However, the test, which is built...
| Publicado en: | Psychological Assessment Vol. 28; no. 2; pp. 125 - 134 |
|---|---|
| Autores principales: | , , |
| Formato: | journal article |
| Publicado: |
American Psychological Association
Feb2016
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=112713069&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 112713069 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10403590 POL jtl: Psychological Assessment issn: 10403590 maglogo: N pubinfo: dt: Feb2016 vid: 28 iid: 2 pid: 34 pub: American Psychological Association artinfo: ui: 112713069 10.1037/pas0000146 ppf: 125 ppct: 9 formats: tig: atl: Identifying an appropriate measurement modeling approach for the Mini-Mental State Examination. aug: au: Rubright, Jonathan D. Nandakumar, Ratna Karlawish, Jason affil: University of Delaware University of Pennsylvania su: Cognition Item response theory Psychological tests Psychometrics Mini-Mental State Examination Measurement Hypothesis Mathematical models Cognition disorders diagnosis Factor analysis Neuropsychological tests Theory Statistical models sug: subj: Cognition Item response theory Psychological tests Psychometrics Mini-Mental State Examination Measurement Hypothesis Mathematical models Cognition disorders diagnosis Factor analysis Neuropsychological tests Theory Statistical models keyword: dimensionality item response theory dimensionality item response theory ab: The Mini-Mental State Examination (MMSE) is a 30-item, dichotomously scored test of general cognition. A number of benefits could be gained by modeling the MMSE in an item response theory (IRT) framework, as opposed to the currently used classical additive approach. However, the test, which is built from groups of items related to separate cognitive subdomains, may violate a key assumption of IRT: local item independence. This study aimed to identify the most appropriate measurement model for the MMSE: a unidimensional IRT model, a testlet response theory model, or a bifactor model. Local dependence analysis using nationally representative data showed a meaningful violation of the local item independence assumption, indicating multidimensionality. In addition, the testlet and bifactor models displayed superior fit indices over a unidimensional IRT model. Statistical comparisons showed that the bifactor model fit MMSE respondent data significantly better than the other models considered. These results suggest that application of a traditional unidimensional IRT model is inappropriate in this context. Instead, a bifactor model is suggested for future modeling of MMSE data as it more accurately represents the multidimensional nature of the scale. (PsycINFO Database Record pubtype: Academic Journal doctype: journal article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|