Should We Classify, and How Do We Know? Classification Accuracy in Latent Variable Mixture Models

2011 ◽  
Author(s):  
Stephen L. Tueller
Keyword(s):  
Mixture Models ◽  
Classification Accuracy ◽  
Latent Variable ◽  
Latent Variable Mixture Models

scholarly journals Are Negative Symptoms Dimensional or Categorical? Detection and Validation of Deficit Schizophrenia With Taxometric and Latent Variable Mixture Models

2014 ◽  
Vol 41 (4) ◽  
pp. 879-891 ◽  
Author(s):  
Anthony O. Ahmed ◽  
Gregory P. Strauss ◽  
Robert W. Buchanan ◽  
Brian Kirkpatrick ◽  
William T. Carpenter
Keyword(s):  
Mixture Models ◽  
Latent Variable ◽  
Negative Symptoms ◽  
Deficit Schizophrenia ◽  
Latent Variable Mixture Models

scholarly journals The use of latent variable mixture models to identify invariant items in test construction

2017 ◽  
Vol 27 (7) ◽  
pp. 1745-1755 ◽  
Author(s):  
Richard Sawatzky ◽  
Lara B. Russell ◽  
Tolulope T. Sajobi ◽  
Lisa M. Lix ◽  
Jacek Kopec ◽  
...  
Keyword(s):  
Mixture Models ◽  
Latent Variable ◽  
Test Construction ◽  
Latent Variable Mixture Models

Latent variable mixture models: a promising approach for the validation of patient reported outcomes

2011 ◽  
Vol 21 (4) ◽  
pp. 637-650 ◽  
Author(s):  
Richard Sawatzky ◽  
Pamela A. Ratner ◽  
Jacek A. Kopec ◽  
Bruno D. Zumbo
Keyword(s):  
Mixture Models ◽  
Latent Variable ◽  
Patient Reported Outcomes ◽  
Patient Reported ◽  
Latent Variable Mixture Models

scholarly journals Fitting latent variable mixture models

2017 ◽  
Vol 98 ◽  
pp. 91-102 ◽  
Author(s):  
Gitta H. Lubke ◽  
Justin Luningham
Keyword(s):  
Mixture Models ◽  
Latent Variable ◽  
Latent Variable Mixture Models

Latent variable mixture modeling in psychiatric research – a review and application

2015 ◽  
Vol 46 (3) ◽  
pp. 457-467 ◽  
Author(s):  
J. Miettunen ◽  
T. Nordström ◽  
M. Kaakinen ◽  
A. O. Ahmed
Keyword(s):  
Latent Class Analysis ◽  
Mixture Models ◽  
Latent Variable ◽  
Latent Class ◽  
Mixture Modeling ◽  
Population Heterogeneity ◽  
Psychiatric Research ◽  
Class Analysis ◽  
Psychological Phenomena ◽  
Latent Variable Mixture Models

Latent variable mixture modeling represents a flexible approach to investigating population heterogeneity by sorting cases into latent but non-arbitrary subgroups that are more homogeneous. The purpose of this selective review is to provide a non-technical introduction to mixture modeling in a cross-sectional context. Latent class analysis is used to classify individuals into homogeneous subgroups (latent classes). Factor mixture modeling represents a newer approach that represents a fusion of latent class analysis and factor analysis. Factor mixture models are adaptable to representing categorical and dimensional states of affairs. This article provides an overview of latent variable mixture models and illustrates the application of these methods by applying them to the study of the latent structure of psychotic experiences. The flexibility of latent variable mixture models makes them adaptable to the study of heterogeneity in complex psychiatric and psychological phenomena. They also allow researchers to address research questions that directly compare the viability of dimensional, categorical and hybrid conceptions of constructs.

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