Latent Class Analysis of Incomplete Data via an Entropy-Based Criterion.
Latent class analysis is used to group categorical data into classes via a probability model. Model selection criteria then judge how well the model fits the data. When addressing incomplete data, the current methodology restricts the imputation to a single, pre-specified number of classes. We seek to develop an entropy-based model selection criterion that does not restrict the imputation to one number of clusters. Simulations show the new criterion performing well against the current standards of AIC and BIC, while a family studies application demonstrates how the criterion provides more detailed and useful results than AIC and BIC.
School of Business, State University of New York at New Paltz, New Paltz, NY, USA.
Chantal
C
Larose
Department of Statistics, University of Connecticut, Storrs, CT, USA.
Ofer
O
Harel
University of Bristol, Senate House, Tyndall Avenue, Bristol BS8 1TH, UK.
Katarzyna
K
Kordas
Department of Statistics, University of Connecticut, Storrs, CT, USA.
Dipak K
DK
Dey
entropy; latent class analysis; missing data; model selection; multiple imputation
Neurotoxicology. 2011 Dec;32(6):693-701
21925208
Am Fam Physician. 2010 Jun 15;81(12):1462-71
20540485
Stat Med. 2007 Jul 20;26(16):3057-77
17256804
Arch Environ Occup Health. 2014;69(1):46-54
23930796
Psychol Methods. 2001 Dec;6(4):330-51
11778676
Stat Methods Med Res. 1999 Mar;8(1):3-15
10347857
Addict Behav. 2008 Jan;33(1):206-10
17728069
Biom J. 2013 Jul;55(4):541-53
23712802
AIDS Behav. 2012 Aug;16(6):1382-93
22223301
Health Serv Outcomes Res Methodol. 2012 Jun;12(2-3):219-233
22879799
Psychol Methods. 2002 Jun;7(2):147-77
12090408