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Latent variable models for categorical data

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Latent variable models for categorical data. / Lancaster, Gillian; Green, Michael.
In: Statistics and Computing, Vol. 12, No. 2, 2002, p. 153-161.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Lancaster, G & Green, M 2002, 'Latent variable models for categorical data', Statistics and Computing, vol. 12, no. 2, pp. 153-161. https://doi.org/10.1023/A:1014886619553

APA

Lancaster, G., & Green, M. (2002). Latent variable models for categorical data. Statistics and Computing, 12(2), 153-161. https://doi.org/10.1023/A:1014886619553

Vancouver

Lancaster G, Green M. Latent variable models for categorical data. Statistics and Computing. 2002;12(2):153-161. doi: 10.1023/A:1014886619553

Author

Lancaster, Gillian ; Green, Michael. / Latent variable models for categorical data. In: Statistics and Computing. 2002 ; Vol. 12, No. 2. pp. 153-161.

Bibtex

@article{b3c347aa75064c568adbc51df7999033,
title = "Latent variable models for categorical data",
abstract = "Two useful statistical methods for generating a latent variable are described and extended to incorporate polytomous data and additional covariates. Item response analysis is not well-known outside its area of application, mainly because the procedures to fit the models are computer intensive and not routinely available within general statistical software packages. The linear score technique is less computer intensive, straightforward to implement and has been proposed as a good approximation to item response analysis. Both methods have been implemented in the standard statistical software package GLIM 4.0, and are compared todetermine their effectiveness.",
keywords = "latent variable, item-response analysis, linear score model , empirical Bayes estimate , linear score, log-bilinear model",
author = "Gillian Lancaster and Michael Green",
year = "2002",
doi = "10.1023/A:1014886619553",
language = "English",
volume = "12",
pages = "153--161",
journal = "Statistics and Computing",
issn = "0960-3174",
publisher = "Springer Netherlands",
number = "2",

}

RIS

TY - JOUR

T1 - Latent variable models for categorical data

AU - Lancaster, Gillian

AU - Green, Michael

PY - 2002

Y1 - 2002

N2 - Two useful statistical methods for generating a latent variable are described and extended to incorporate polytomous data and additional covariates. Item response analysis is not well-known outside its area of application, mainly because the procedures to fit the models are computer intensive and not routinely available within general statistical software packages. The linear score technique is less computer intensive, straightforward to implement and has been proposed as a good approximation to item response analysis. Both methods have been implemented in the standard statistical software package GLIM 4.0, and are compared todetermine their effectiveness.

AB - Two useful statistical methods for generating a latent variable are described and extended to incorporate polytomous data and additional covariates. Item response analysis is not well-known outside its area of application, mainly because the procedures to fit the models are computer intensive and not routinely available within general statistical software packages. The linear score technique is less computer intensive, straightforward to implement and has been proposed as a good approximation to item response analysis. Both methods have been implemented in the standard statistical software package GLIM 4.0, and are compared todetermine their effectiveness.

KW - latent variable

KW - item-response analysis

KW - linear score model

KW - empirical Bayes estimate

KW - linear score

KW - log-bilinear model

U2 - 10.1023/A:1014886619553

DO - 10.1023/A:1014886619553

M3 - Journal article

VL - 12

SP - 153

EP - 161

JO - Statistics and Computing

JF - Statistics and Computing

SN - 0960-3174

IS - 2

ER -