NDLI: Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches

Please wait, while we are loading the content...

Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches

Content Provider	Scilit
Author	Lubke, Gitta Tueller, Stephen
Copyright Year	2010
Description	Taxometric procedures such as MAXEIG and factor mixture modeling (FMM) are used in latent class clustering, but they have very different sets of strengths and weaknesses. Taxometric procedures, popular in psychiatric and psychopathology applications, do not rely on distributional assumptions. Their sole purpose is to detect the presence of latent classes. The procedures capitalize on the assumption that, due to mean differences between two classes, item covariances within class are smaller than item covariances between the classes. FMM goes beyond class detection and permits the specification of hypothesis-based within-class covariance structures ranging from local independence to multidimensional within-class factor models. In principle, FMM permits the comparison of alternative models using likelihood-based indexes. These advantages come at the price of distributional assumptions. In addition, models are often highly parameterized and susceptible to misspecifications of the within-class covariance structure.
Related Links	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3955757/pdf
Ending Page	628
Page Count	24
Starting Page	605
ISSN	10705511
e-ISSN	15328007
DOI	10.1080/10705511.2010.510050
Journal	Structural Equation Modeling: A Multidisciplinary Journal
Issue Number	4
Volume Number	17
Language	English
Publisher	Informa UK Limited
Publisher Date	2010-10-06
Access Restriction	Open
Subject Keyword	Journal: Structural Equation Modeling: A Multidisciplinary Journal Mathematical Social Sciences Models Comparison Structures Latent Class Permits Item Maxeig Taxometric Procedures
Content Type	Text
Resource Type	Article
Subject	Sociology and Political Science Economics, Econometrics and Finance Decision Sciences Modeling and Simulation

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches

Evaluation of Structural Equation Mixture Models: Parameter Estimates and Correct Class Assignment

PROC LCA: A SAS Procedure for Latent Class Analysis

Eliminating Bias in Classify-Analyze Approaches for Latent Class Analysis

Models and Strategies for Factor Mixture Analysis: An Example Concerning the Structure Underlying Psychological Disorders

The Detection and Modeling of Direct Effects in Latent Class Analysis

Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach

Evaluation of structural equation mixture models Parameter estimates and correct class assignment

Mostly Harmless Direct Effects: A Comparison of Different Latent Markov Modeling Approaches

Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches

Similar Documents

Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches

Evaluation of Structural Equation Mixture Models: Parameter Estimates and Correct Class Assignment

PROC LCA: A SAS Procedure for Latent Class Analysis

Eliminating Bias in Classify-Analyze Approaches for Latent Class Analysis

Models and Strategies for Factor Mixture Analysis: An Example Concerning the Structure Underlying Psychological Disorders

The Detection and Modeling of Direct Effects in Latent Class Analysis

Latent Class Analysis With Distal Outcomes: A Flexible Model-Based Approach

Evaluation of structural equation mixture models Parameter estimates and correct class assignment

Mostly Harmless Direct Effects: A Comparison of Different Latent Markov Modeling Approaches

Latent Class Detection and Class Assignment: A Comparison of the MAXEIG Taxometric Procedure and Factor Mixture Modeling Approaches