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Factor structure and a multiple indicators multiple cause model of internet addiction test: the effect of socio-demographic and internet use variables

Purpose: This study aimed to identify factor structure of a bilingual version (English and Malay) of Internet Addiction Test, a measure of severity on pathological Internet use (PIU); examine the measurement invariant by adding the socio-demographic and Internet use variables as covariates. Method:...

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Main Authors: Lu, X., Yeo, K. J., Guo, F., Zhao, Z.
Format: Article
Published: Springer 2020
Subjects:
L Education (General)
Online Access:http://eprints.utm.my/id/eprint/87455/
http://www.dx.doi.org/10.1007/s12144-019-00234-9
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spelling my.utm.874552020-11-08T04:00:02Z http://eprints.utm.my/id/eprint/87455/ Factor structure and a multiple indicators multiple cause model of internet addiction test: the effect of socio-demographic and internet use variables Lu, X. Yeo, K. J. Guo, F. Zhao, Z. L Education (General) Purpose: This study aimed to identify factor structure of a bilingual version (English and Malay) of Internet Addiction Test, a measure of severity on pathological Internet use (PIU); examine the measurement invariant by adding the socio-demographic and Internet use variables as covariates. Method: Pencil-paper questionnaire was distributed to undergraduates in Univerisiti Teknologi Malaysia (UTM). A total of 1120 valid responses were collected. EFA and CFA were first applied to identify the factor structure of IAT. Multiple indicator multiple cause (MIMIC) model was used to examine the differential item functioning (DIF) and the effect of covariates on the latent factors. Result: A four-factor model demonstrated moderate fit to the data for 17-item of IAT with significant high factor loading. Comparison of MIMIC model with and without DIF indicated that the most meaningful and significant DIF effect were on three items showing direct effect from Chinese to IAT18, IAT19 and IAT20. By controlling the DIF effect, some covariates still present the significant impact on latent factors of IAT, which were female, time spent online, effect on study, years of Internet use experience, general users, other users and Chinese. Conclusions: the risk factors of PIU were male, time spent online, years of Internet use experiences, game users, SNS users, perceived less effect on study. More research is encouraged to examine the racial group difference, including the risk of PIU and item response bias. Springer 2020-06 Article PeerReviewed Lu, X. and Yeo, K. J. and Guo, F. and Zhao, Z. (2020) Factor structure and a multiple indicators multiple cause model of internet addiction test: the effect of socio-demographic and internet use variables. Current Psychology, 39 (3). pp. 769-781. ISSN 1046-1310 http://www.dx.doi.org/10.1007/s12144-019-00234-9 DOI: 10.1007/s12144-019-00234-9
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic L Education (General)
spellingShingle L Education (General)
Lu, X.
Yeo, K. J.
Guo, F.
Zhao, Z.
Factor structure and a multiple indicators multiple cause model of internet addiction test: the effect of socio-demographic and internet use variables
description Purpose: This study aimed to identify factor structure of a bilingual version (English and Malay) of Internet Addiction Test, a measure of severity on pathological Internet use (PIU); examine the measurement invariant by adding the socio-demographic and Internet use variables as covariates. Method: Pencil-paper questionnaire was distributed to undergraduates in Univerisiti Teknologi Malaysia (UTM). A total of 1120 valid responses were collected. EFA and CFA were first applied to identify the factor structure of IAT. Multiple indicator multiple cause (MIMIC) model was used to examine the differential item functioning (DIF) and the effect of covariates on the latent factors. Result: A four-factor model demonstrated moderate fit to the data for 17-item of IAT with significant high factor loading. Comparison of MIMIC model with and without DIF indicated that the most meaningful and significant DIF effect were on three items showing direct effect from Chinese to IAT18, IAT19 and IAT20. By controlling the DIF effect, some covariates still present the significant impact on latent factors of IAT, which were female, time spent online, effect on study, years of Internet use experience, general users, other users and Chinese. Conclusions: the risk factors of PIU were male, time spent online, years of Internet use experiences, game users, SNS users, perceived less effect on study. More research is encouraged to examine the racial group difference, including the risk of PIU and item response bias.
format Article
author Lu, X.
Yeo, K. J.
Guo, F.
Zhao, Z.
author_facet Lu, X.
Yeo, K. J.
Guo, F.
Zhao, Z.
author_sort Lu, X.
title Factor structure and a multiple indicators multiple cause model of internet addiction test: the effect of socio-demographic and internet use variables
title_short Factor structure and a multiple indicators multiple cause model of internet addiction test: the effect of socio-demographic and internet use variables
title_full Factor structure and a multiple indicators multiple cause model of internet addiction test: the effect of socio-demographic and internet use variables
title_fullStr Factor structure and a multiple indicators multiple cause model of internet addiction test: the effect of socio-demographic and internet use variables
title_full_unstemmed Factor structure and a multiple indicators multiple cause model of internet addiction test: the effect of socio-demographic and internet use variables
title_sort factor structure and a multiple indicators multiple cause model of internet addiction test: the effect of socio-demographic and internet use variables
publisher Springer
publishDate 2020
url http://eprints.utm.my/id/eprint/87455/
http://www.dx.doi.org/10.1007/s12144-019-00234-9
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score 13.211869

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