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Psychometric property and measurement invariance of internet addiction test: the effect of socio-demographic and internet use variables

BACKGROUND: According to the validation literature on items of Young’s Internet Addiction Test (IAT), this study rephrased disputable items to improve the psychometric properties of this Chinese version of IAT and identify the presence of differential item function (DIF) among demographic and Intern...

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Autores principales: Lu, Xi, Yeo, Kee Jiar, Guo, Fang, Zhao, Zhenqing, Wu, Ou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9375945/
https://www.ncbi.nlm.nih.gov/pubmed/35964103
http://dx.doi.org/10.1186/s12889-022-13915-1
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author Lu, Xi
Yeo, Kee Jiar
Guo, Fang
Zhao, Zhenqing
Wu, Ou
author_facet Lu, Xi
Yeo, Kee Jiar
Guo, Fang
Zhao, Zhenqing
Wu, Ou
author_sort Lu, Xi
collection PubMed
description BACKGROUND: According to the validation literature on items of Young’s Internet Addiction Test (IAT), this study rephrased disputable items to improve the psychometric properties of this Chinese version of IAT and identify the presence of differential item function (DIF) among demographic and Internet use factors; detect the effect of demographic and Internet use factors on IAT after adjusting for DIF. METHODS: An online questionnaire was distributed to college students in Zhe Jiang province in two stage. The 1st phase study collected 384 valid responses to examine the quality of IAT items by using Rasch Model analysis and exploring factor analysis (EFA). The online questionnaire was modified according to the 1st phase study and distributed online for the 2nd phase study which collected a total of 1131 valid responses. The 2nd phase study applied confirmatory factor analysis (CFA) and a multiple indicator multiple causes (MIMIC) model to verify the construct of IAT, potential effect of covariates on IAT latent factors, as well as the effect of differential item functioning (DIF). RESULTS: Rasch model analysis in the 1st phase study indicated a 5-point rating scale was performed better, no sever misfit was found on item. The overall property of Chinese version IAT with the 5-point scale was good to excellent person and item separation (2.66 and 6.86). A three-factor model was identified by EFA. In the 2nd phase study, IAT 13 were detected with DIF for gender in MIMIC model. After correcting DIF effect, the significant demographic and Internet use factors on IAT were time spent online per day, year 3, year 2, general users. CONCLUSION: Item improvement was efficient that the problematic items found in literature was performed good in this study. The overall psychometric property of this Chinese version IAT was good with limited DIF effect in one item. Item improvement on IAT13 was encouraged in the future study to avoid gender bias and benefit for epidemiology on PIU.
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spelling pubmed-93759452022-08-15 Psychometric property and measurement invariance of internet addiction test: the effect of socio-demographic and internet use variables Lu, Xi Yeo, Kee Jiar Guo, Fang Zhao, Zhenqing Wu, Ou BMC Public Health Research BACKGROUND: According to the validation literature on items of Young’s Internet Addiction Test (IAT), this study rephrased disputable items to improve the psychometric properties of this Chinese version of IAT and identify the presence of differential item function (DIF) among demographic and Internet use factors; detect the effect of demographic and Internet use factors on IAT after adjusting for DIF. METHODS: An online questionnaire was distributed to college students in Zhe Jiang province in two stage. The 1st phase study collected 384 valid responses to examine the quality of IAT items by using Rasch Model analysis and exploring factor analysis (EFA). The online questionnaire was modified according to the 1st phase study and distributed online for the 2nd phase study which collected a total of 1131 valid responses. The 2nd phase study applied confirmatory factor analysis (CFA) and a multiple indicator multiple causes (MIMIC) model to verify the construct of IAT, potential effect of covariates on IAT latent factors, as well as the effect of differential item functioning (DIF). RESULTS: Rasch model analysis in the 1st phase study indicated a 5-point rating scale was performed better, no sever misfit was found on item. The overall property of Chinese version IAT with the 5-point scale was good to excellent person and item separation (2.66 and 6.86). A three-factor model was identified by EFA. In the 2nd phase study, IAT 13 were detected with DIF for gender in MIMIC model. After correcting DIF effect, the significant demographic and Internet use factors on IAT were time spent online per day, year 3, year 2, general users. CONCLUSION: Item improvement was efficient that the problematic items found in literature was performed good in this study. The overall psychometric property of this Chinese version IAT was good with limited DIF effect in one item. Item improvement on IAT13 was encouraged in the future study to avoid gender bias and benefit for epidemiology on PIU. BioMed Central 2022-08-13 /pmc/articles/PMC9375945/ /pubmed/35964103 http://dx.doi.org/10.1186/s12889-022-13915-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Lu, Xi
Yeo, Kee Jiar
Guo, Fang
Zhao, Zhenqing
Wu, Ou
Psychometric property and measurement invariance of internet addiction test: the effect of socio-demographic and internet use variables
title Psychometric property and measurement invariance of internet addiction test: the effect of socio-demographic and internet use variables
title_full Psychometric property and measurement invariance of internet addiction test: the effect of socio-demographic and internet use variables
title_fullStr Psychometric property and measurement invariance of internet addiction test: the effect of socio-demographic and internet use variables
title_full_unstemmed Psychometric property and measurement invariance of internet addiction test: the effect of socio-demographic and internet use variables
title_short Psychometric property and measurement invariance of internet addiction test: the effect of socio-demographic and internet use variables
title_sort psychometric property and measurement invariance of internet addiction test: the effect of socio-demographic and internet use variables
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9375945/
https://www.ncbi.nlm.nih.gov/pubmed/35964103
http://dx.doi.org/10.1186/s12889-022-13915-1
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