BibTeX
@article{IJASEIT14910,
author = {Samah Zakaria and Mai Sherif Hafez and Ahmed M. Gad},
title = {A Latent Class Model for Multivariate Binary Data Subject to Missingness},
journal = {International Journal on Advanced Science, Engineering and Information Technology},
volume = {11},
number = {5},
year = {2021},
pages = {1832--1840},
keywords = {binary variables; latent class model; item non-response; non-random missingness; response propensity.},
abstract = {When researchers are interested in measuring social phenomena that cannot be measured using a single variable, the appropriate statistical tool to be used is a latent variable model. A number of manifest variables is used to define the latent phenomenon. The manifest variables may be incomplete due to different forms of non-response that may or may not be random. In such cases, especially when the missingness is nonignorable, it is inevitable to include a missingness mechanism in the model to obtain valid estimates for parameters. In social surveys, categorical items can be considered the most common type of variable. We thus propose a latent class model where two categorical latent variables are defined; one represents the latent phenomenon of interest, and another represents a respondent’s propensity to respond to survey items. All manifest items are considered to be categorical. The proposed model incorporates a missingness mechanism that accounts for forms of missingness that may not be random by allowing the latent response propensity class to depend on the latent phenomenon under consideration, given a set of covariates. The Expectation-Maximization (EM) algorithm is used for estimating the proposed model. The proposed model is used to analyze data from 2014 Egyptian Demographic and Health Survey (EDHS14). Missing data is artificially created in order to study results under the three types of missingness: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).},
issn = {2088-5334},
publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14910},
doi = {10.18517/ijaseit.11.5.14910}
}
EndNote
%A Zakaria, Samah
%A Hafez, Mai Sherif
%A Gad, Ahmed M.
%D 2021
%T A Latent Class Model for Multivariate Binary Data Subject to Missingness
%B 2021
%9 binary variables; latent class model; item non-response; non-random missingness; response propensity.
%! A Latent Class Model for Multivariate Binary Data Subject to Missingness
%K binary variables; latent class model; item non-response; non-random missingness; response propensity.
%X When researchers are interested in measuring social phenomena that cannot be measured using a single variable, the appropriate statistical tool to be used is a latent variable model. A number of manifest variables is used to define the latent phenomenon. The manifest variables may be incomplete due to different forms of non-response that may or may not be random. In such cases, especially when the missingness is nonignorable, it is inevitable to include a missingness mechanism in the model to obtain valid estimates for parameters. In social surveys, categorical items can be considered the most common type of variable. We thus propose a latent class model where two categorical latent variables are defined; one represents the latent phenomenon of interest, and another represents a respondent’s propensity to respond to survey items. All manifest items are considered to be categorical. The proposed model incorporates a missingness mechanism that accounts for forms of missingness that may not be random by allowing the latent response propensity class to depend on the latent phenomenon under consideration, given a set of covariates. The Expectation-Maximization (EM) algorithm is used for estimating the proposed model. The proposed model is used to analyze data from 2014 Egyptian Demographic and Health Survey (EDHS14). Missing data is artificially created in order to study results under the three types of missingness: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14910
%R doi:10.18517/ijaseit.11.5.14910
%J International Journal on Advanced Science, Engineering and Information Technology
%V 11
%N 5
%@ 2088-5334
IEEE
Samah Zakaria,Mai Sherif Hafez and Ahmed M. Gad,"A Latent Class Model for Multivariate Binary Data Subject to Missingness," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 5, pp. 1832-1840, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.5.14910.
RefMan/ProCite (RIS)
TY - JOUR
AU - Zakaria, Samah
AU - Hafez, Mai Sherif
AU - Gad, Ahmed M.
PY - 2021
TI - A Latent Class Model for Multivariate Binary Data Subject to Missingness
JF - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 5
Y2 - 2021
SP - 1832
EP - 1840
SN - 2088-5334
PB - INSIGHT - Indonesian Society for Knowledge and Human Development
KW - binary variables; latent class model; item non-response; non-random missingness; response propensity.
N2 - When researchers are interested in measuring social phenomena that cannot be measured using a single variable, the appropriate statistical tool to be used is a latent variable model. A number of manifest variables is used to define the latent phenomenon. The manifest variables may be incomplete due to different forms of non-response that may or may not be random. In such cases, especially when the missingness is nonignorable, it is inevitable to include a missingness mechanism in the model to obtain valid estimates for parameters. In social surveys, categorical items can be considered the most common type of variable. We thus propose a latent class model where two categorical latent variables are defined; one represents the latent phenomenon of interest, and another represents a respondent’s propensity to respond to survey items. All manifest items are considered to be categorical. The proposed model incorporates a missingness mechanism that accounts for forms of missingness that may not be random by allowing the latent response propensity class to depend on the latent phenomenon under consideration, given a set of covariates. The Expectation-Maximization (EM) algorithm is used for estimating the proposed model. The proposed model is used to analyze data from 2014 Egyptian Demographic and Health Survey (EDHS14). Missing data is artificially created in order to study results under the three types of missingness: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).
UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14910
DO - 10.18517/ijaseit.11.5.14910
RefWorks
RT Journal Article
ID 14910
A1 Zakaria, Samah
A1 Hafez, Mai Sherif
A1 Gad, Ahmed M.
T1 A Latent Class Model for Multivariate Binary Data Subject to Missingness
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 5
YR 2021
SP 1832
OP 1840
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 binary variables; latent class model; item non-response; non-random missingness; response propensity.
AB When researchers are interested in measuring social phenomena that cannot be measured using a single variable, the appropriate statistical tool to be used is a latent variable model. A number of manifest variables is used to define the latent phenomenon. The manifest variables may be incomplete due to different forms of non-response that may or may not be random. In such cases, especially when the missingness is nonignorable, it is inevitable to include a missingness mechanism in the model to obtain valid estimates for parameters. In social surveys, categorical items can be considered the most common type of variable. We thus propose a latent class model where two categorical latent variables are defined; one represents the latent phenomenon of interest, and another represents a respondent’s propensity to respond to survey items. All manifest items are considered to be categorical. The proposed model incorporates a missingness mechanism that accounts for forms of missingness that may not be random by allowing the latent response propensity class to depend on the latent phenomenon under consideration, given a set of covariates. The Expectation-Maximization (EM) algorithm is used for estimating the proposed model. The proposed model is used to analyze data from 2014 Egyptian Demographic and Health Survey (EDHS14). Missing data is artificially created in order to study results under the three types of missingness: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14910
DO - 10.18517/ijaseit.11.5.14910
