Literature DB >> 16675147

Item response mixture modeling: application to tobacco dependence criteria.

Bengt Muthen1, Tihomir Asparouhov.   

Abstract

This paper illustrates new hybrid latent variable models that are promising for phenotypical analyses. The hybrid models combine features of dimensional and categorical analyses seen in the conventional techniques of factor analysis and latent class analysis. The paper focuses on the analysis of categorical items, which presents especially challenging analyses with hybrid models and has recently been made practical in the Mplus program. The hybrid models are typically seen to fit data better than conventional models of factor analysis (IRT) and latent class analysis. An illustration is given in the form of analysis of tobacco dependence in a general population survey.

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Year:  2006        PMID: 16675147     DOI: 10.1016/j.addbeh.2006.03.026

Source DB:  PubMed          Journal:  Addict Behav        ISSN: 0306-4603            Impact factor:   3.913


  58 in total

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7.  A data mining and item response mixture modeling method to retrospectively measure Diagnostic and Statistical Manual of Mental Disorders-5 attention deficit hyperactivity disorder in the 1970 British Cohort Study.

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Review 8.  Seeking verisimilitude in a class: a systematic review of evidence that the criterial clinical symptoms of schizophrenia are taxonic.

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Journal:  Schizophr Bull       Date:  2009-01-27       Impact factor: 9.306

9.  Modeling the Structure of Acute Sport-Related Concussion Symptoms: A Bifactor Approach.

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Journal:  J Int Neuropsychol Soc       Date:  2018-08-06       Impact factor: 2.892

10.  A new approach to measuring absolute pitch on a psychometric theory of isolated pitch perception: Is it disentangling specific groups or capturing a continuous ability?

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Journal:  PLoS One       Date:  2021-02-22       Impact factor: 3.240

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