Parameter constraints of the logit form of the reduced RUM

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Reduced Reparameterized Unified Model (Reduced RUM) has received considerable attention among educational researchers. Markov chain Monte Carlo (MCMC) or Expectation Maximization (EM) is typically used for estimating the Reduced RUM. Implementations of the EM algorithm are available in the latent class analysis (LCA) routines of commercial software packages (e.g., Latent GOLD, Mplus). Using a commercial LCA routine as a vehicle for fitting the Reduced RUM with the EM algorithm requires that it be reparameterized as a logit model, with complex constraints imposed on the parameters. This article summarizes the general parameterization of the Reduced RUM as a logit model and the associated parameter constraints.

Original languageEnglish (US)
Title of host publicationQuantitative Psychology - 81st Annual Meeting of the Psychometric Society, 2016
EditorsWen-Chung Wang, Marie Wiberg, Steven A. Culpepper, Jeffrey A. Douglas, L. Andries van der Ark
PublisherSpringer New York LLC
Pages207-213
Number of pages7
ISBN (Print)9783319562933
DOIs
StatePublished - Jan 1 2017
Event81st annual meeting of the Psychometric Society, 2016 - Asheville, United States
Duration: Jul 11 2016Jul 15 2016

Publication series

NameSpringer Proceedings in Mathematics and Statistics
Volume196
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017

Other

Other81st annual meeting of the Psychometric Society, 2016
CountryUnited States
CityAsheville
Period7/11/167/15/16

Fingerprint

Logit
Latent Class Analysis
Logit Model
Expectation-maximization Algorithm
Expectation Maximization
Markov Chain Monte Carlo
Software Package
Model
Parameterization
Form

Keywords

  • Cognitive diagnosis
  • EM algorithm
  • Reduced RUM

ASJC Scopus subject areas

  • Mathematics(all)

Cite this

Koehn, H. F. (2017). Parameter constraints of the logit form of the reduced RUM. In W-C. Wang, M. Wiberg, S. A. Culpepper, J. A. Douglas, & L. A. van der Ark (Eds.), Quantitative Psychology - 81st Annual Meeting of the Psychometric Society, 2016 (pp. 207-213). (Springer Proceedings in Mathematics and Statistics; Vol. 196). Springer New York LLC. https://doi.org/10.1007/978-3-319-56294-0_19

Parameter constraints of the logit form of the reduced RUM. / Koehn, Hans Friedrich.

Quantitative Psychology - 81st Annual Meeting of the Psychometric Society, 2016. ed. / Wen-Chung Wang; Marie Wiberg; Steven A. Culpepper; Jeffrey A. Douglas; L. Andries van der Ark. Springer New York LLC, 2017. p. 207-213 (Springer Proceedings in Mathematics and Statistics; Vol. 196).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Koehn, HF 2017, Parameter constraints of the logit form of the reduced RUM. in W-C Wang, M Wiberg, SA Culpepper, JA Douglas & LA van der Ark (eds), Quantitative Psychology - 81st Annual Meeting of the Psychometric Society, 2016. Springer Proceedings in Mathematics and Statistics, vol. 196, Springer New York LLC, pp. 207-213, 81st annual meeting of the Psychometric Society, 2016, Asheville, United States, 7/11/16. https://doi.org/10.1007/978-3-319-56294-0_19
Koehn HF. Parameter constraints of the logit form of the reduced RUM. In Wang W-C, Wiberg M, Culpepper SA, Douglas JA, van der Ark LA, editors, Quantitative Psychology - 81st Annual Meeting of the Psychometric Society, 2016. Springer New York LLC. 2017. p. 207-213. (Springer Proceedings in Mathematics and Statistics). https://doi.org/10.1007/978-3-319-56294-0_19
Koehn, Hans Friedrich. / Parameter constraints of the logit form of the reduced RUM. Quantitative Psychology - 81st Annual Meeting of the Psychometric Society, 2016. editor / Wen-Chung Wang ; Marie Wiberg ; Steven A. Culpepper ; Jeffrey A. Douglas ; L. Andries van der Ark. Springer New York LLC, 2017. pp. 207-213 (Springer Proceedings in Mathematics and Statistics).
@inproceedings{f2befec9f948452489042437e3c2598e,
title = "Parameter constraints of the logit form of the reduced RUM",
abstract = "The Reduced Reparameterized Unified Model (Reduced RUM) has received considerable attention among educational researchers. Markov chain Monte Carlo (MCMC) or Expectation Maximization (EM) is typically used for estimating the Reduced RUM. Implementations of the EM algorithm are available in the latent class analysis (LCA) routines of commercial software packages (e.g., Latent GOLD, Mplus). Using a commercial LCA routine as a vehicle for fitting the Reduced RUM with the EM algorithm requires that it be reparameterized as a logit model, with complex constraints imposed on the parameters. This article summarizes the general parameterization of the Reduced RUM as a logit model and the associated parameter constraints.",
keywords = "Cognitive diagnosis, EM algorithm, Reduced RUM",
author = "Koehn, {Hans Friedrich}",
year = "2017",
month = "1",
day = "1",
doi = "10.1007/978-3-319-56294-0_19",
language = "English (US)",
isbn = "9783319562933",
series = "Springer Proceedings in Mathematics and Statistics",
publisher = "Springer New York LLC",
pages = "207--213",
editor = "Wen-Chung Wang and Marie Wiberg and Culpepper, {Steven A.} and Douglas, {Jeffrey A.} and {van der Ark}, {L. Andries}",
booktitle = "Quantitative Psychology - 81st Annual Meeting of the Psychometric Society, 2016",

}

TY - GEN

T1 - Parameter constraints of the logit form of the reduced RUM

AU - Koehn, Hans Friedrich

PY - 2017/1/1

Y1 - 2017/1/1

N2 - The Reduced Reparameterized Unified Model (Reduced RUM) has received considerable attention among educational researchers. Markov chain Monte Carlo (MCMC) or Expectation Maximization (EM) is typically used for estimating the Reduced RUM. Implementations of the EM algorithm are available in the latent class analysis (LCA) routines of commercial software packages (e.g., Latent GOLD, Mplus). Using a commercial LCA routine as a vehicle for fitting the Reduced RUM with the EM algorithm requires that it be reparameterized as a logit model, with complex constraints imposed on the parameters. This article summarizes the general parameterization of the Reduced RUM as a logit model and the associated parameter constraints.

AB - The Reduced Reparameterized Unified Model (Reduced RUM) has received considerable attention among educational researchers. Markov chain Monte Carlo (MCMC) or Expectation Maximization (EM) is typically used for estimating the Reduced RUM. Implementations of the EM algorithm are available in the latent class analysis (LCA) routines of commercial software packages (e.g., Latent GOLD, Mplus). Using a commercial LCA routine as a vehicle for fitting the Reduced RUM with the EM algorithm requires that it be reparameterized as a logit model, with complex constraints imposed on the parameters. This article summarizes the general parameterization of the Reduced RUM as a logit model and the associated parameter constraints.

KW - Cognitive diagnosis

KW - EM algorithm

KW - Reduced RUM

UR - http://www.scopus.com/inward/record.url?scp=85020872175&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85020872175&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-56294-0_19

DO - 10.1007/978-3-319-56294-0_19

M3 - Conference contribution

AN - SCOPUS:85020872175

SN - 9783319562933

T3 - Springer Proceedings in Mathematics and Statistics

SP - 207

EP - 213

BT - Quantitative Psychology - 81st Annual Meeting of the Psychometric Society, 2016

A2 - Wang, Wen-Chung

A2 - Wiberg, Marie

A2 - Culpepper, Steven A.

A2 - Douglas, Jeffrey A.

A2 - van der Ark, L. Andries

PB - Springer New York LLC

ER -