[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-592811-105":59,"doc-detail-592811-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","investigating-measurement-invariance-under-different-missing-value-reduction-methods-article","Investigating Measurement Invariance under Different Missing Value Reduction Methods - article","","This study comparatively examines measurement invariance using structural equation modeling when missing data are handled through expectation-maximization (EM), regression imputation, and mean substitution. Analyses are conducted on the complete data matrix and on a randomly generated 5% missing data matrix drawn from the same source. Data come from 2822 students, with gender split reported as 1338 females and 1434 males. Results show that imputation methods alter invariance decisions across factors, with consistent outcomes only in one sub-dimension, supporting further work on missing data structures and proportions.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/investigating-measurement-invariance-under-different-missing-value-reduction-methods-article/592811/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/investigating-measurement-invariance-under-different-missing-value-reduction-methods-article/592811.png","ImageObject",300,407,{"name":92,"@type":93},"Marglet","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-10","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What missing data handling methods are compared in the study?","Question",{"text":112,"@type":113},"The study compares expectation-maximization (EM), regression imputation, and mean substitution.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is measurement invariance tested in this research?",{"text":117,"@type":113},"Measurement invariance is tested with structural equation modeling using both the complete data matrix and a 5% missing data matrix.",{"name":119,"@type":110,"acceptedAnswer":120},"Do different imputation methods lead to different measurement invariance decisions?",{"text":121,"@type":113},"Yes. The study concludes that different data imputation methods change measurement invariance decisions when considering all factors.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},592811,1792959208,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},2336477982702,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Asian Journal of Education and Training  \nVol. 6, No. 2, 237-245, 2020 ISSN(E) 2519-5387  \nDOI: 10.20448/journal.522.2020.62.237.245  \n© 2020 by the authors; licensee Asian Online Journal Publishing Group  \nInvestigating Measurement Invariance under Different Missing Value Reduction Methods  \nHüseyin Selvi 1 􀀣  \nDevrim Alıcı2  \nNezaket Bilge Uzun3  \n1Medical Education Department, Mersin University, Mersin, Turkey.  \n2,3Measurement and Evaluation Department, Mersin University, Mersin, Turkey.   \n(􀀣 Corresponding Author)  \nAbstract  \nThis study aims to comparatively examine the resultant findings by testing the measurement  \ninvariance with structural equation modeling in cases where the missing data is handled using the  \nexpectation-maximization (EM), regression imputation, and mean substitution methods in the  \ncomplete data matrix and the 5% missing data matrix that is randomly obtained from the same  \nmatrix. The data were collected from 2822 students. Of these students, who participated in the  \nstudy, 1338 (49.2%) were females while 1434 (50.8%) were males. The data were collected using  \nthe “School Attitude Scale” developed by Alıcı (2013) . In this study, the measurement invariance  \nwas tested with structural equation modeling in the complete data matrix and in cases of handling  \nthe missing data it was tested using EM, Regression-Based Imputation, and Mean Substitution  \nmethods. In the present study the measurement invariance decisions taken for the complete data  \nmatrix coincide with the mean substitution method in all sub-factors, with regression imputation  \nfrom the other suggested methods in two sub-factors, and with expectation-maximization in one  \nsub-factor. It was concluded that different data imputation methods change measurement  \ninvariance decisions, considering all the factors. The three techniques provided consistent results  \nonly in one sub-dimension. The ways of dealing with the missing data change the results; thereby,  \nincreasing fundamental studies in this regard is necessary. With reference to these results, it  \nwould be worthwhile to study different missing data structures and different proportions of  \nmissing data in terms of invariance decisions.  \nKeywords: Missing values, Missing data, Measurement invariance, Expectation maximization, Regression imputation, Mean substitution, Missing data handling methods.  \nAcknowledgement: All authors contributed to the conception and design of the study.  \nFunding: This study received no specific financial support.  \nCompeting Interests: The authors declare that they have no conflict of interests.  \nTransparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study was reported; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained.  \nEthical: This study follows all ethical practices during writing.  \nContents  \n1. Introduction .................................................................................................................................................................................... 238  \n2. Method ............................................................................................................................................................................................. 239  \n3. Results .............................................................................................................................................................................................. 240  \n4. Discussion........................................................................................................................................................................................ 243  \nReferences ............................................................................................................................................................................................ 243  \nContribu","cbCaiaCVfKn4cyzz","https://ap.wps.com/l/cbCaiaCVfKn4cyzz","pdf",506294,"English","# Abstract\n# Introduction\n## Background and problem statement\n## Missing data and measurement invariance","[{\"question\":\"What missing data handling methods are compared in the study?\",\"answer\":\"The study compares expectation-maximization (EM), regression imputation, and mean substitution.\"},{\"question\":\"How is measurement invariance tested in this research?\",\"answer\":\"Measurement invariance is tested with structural equation modeling using both the complete data matrix and a 5% missing data matrix.\"},{\"question\":\"Do different imputation methods lead to different measurement invariance decisions?\",\"answer\":\"Yes. The study concludes that different data imputation methods change measurement invariance decisions when considering all factors.\"}]","Investigating Measurement Invariance under Different Missing Value Reduction Methods - article | PDF",1792075713,23]