Please wait a minute...
   2012, Vol. 44 Issue (1) : 121-132     DOI:
|
Item Parameter Estimation for Multidimensional Measurement: Comparisons of SEM and MIRT Based Methods
LIU Hong-Yun;LUO Fang;WANG Yue;ZHANG Yu
(School of psychology, Beijing Normal University; Beijing Key Lab of Applied Experimental Psychology, Beijing 100875, China)
Download: PDF(384 KB)  
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  Traditional factor analysis models and estimation methods for continuous (i.e., interval or ratio scale) data are not appropriate for item-level data that are categorical in nature. The authors provide a brief review and synthesis of the item factor analysis estimation literature for categorical data (e.g., 0-1 type response scales) under the multidimensional response model. Popular categorical item factor analysis models and estimation methods found in the structural equation modeling and item response theory literatures are presented.
The Monte Carlo simulation studies are conducted and revealed: (1) Similar parameter estimates have been obtained of Modified weighted least squares for categorical data method (WLSMV) from the structural equation model (SEM) framework and adoptive Restricted Maximum Likelihood (MLR) and Markov chain Monte Carlo (MCMC) methods from the multidimensional item response theory (MIRT) framework. Even with a small sample and the item response theory (IRT) estimates converted to SEM parameters, the WLSMV, MLR, and MCMC results are strikingly similar. But in small sample size and long test, weighted least squares for categorical data (WLSc) did not obtain the convergence parameter estimations, although in short test, WLSc estimates have been obtained, the estimates are consistently more discrepant than those produced by the other estimation techniques. (2) The precision of the estimators enhances as the quantity of the sample increases, and the differences between WLSMV and MLR are very trivial, and the precisions of WLSMV and MLR methods are not worse than that of the MCMC method in most conditions. (3) The precision of item factor loading and of item difficulty parameter is influenced by the test length, and the precision of item discrimination and of item difficulty parameter is influenced by the number of test dimension. (4) The precision of the estimators decreases as the number of dimensions measured by the item increases, especially for item discrimination and item factor loading parameter.
Both SEM and IRT can be used for factor analysis of dichotomous item responses. In this case, the measurement models of both approaches are formally equivalent. They were refined within and across different disciplines, and make complementary contributions to central measurement problems encountered in almost all empirical social science research fields. The authors conclude with considerations for categorical item factor analysis and give some advice for applied researchers.
Keywords Multidimensional Item Response Theory      confirmatory factor analysis      parameter estimation      categorical data     
Corresponding Authors: LUO Fang   
Issue Date: 28 January 2012
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
LIU Hong-Yun
LUO Fang
WANG Yue
ZHANG Yu
Cite this article:   
LIU Hong-Yun,LUO Fang,WANG Yue, et al. Item Parameter Estimation for Multidimensional Measurement: Comparisons of SEM and MIRT Based Methods[J]. , 2012, 44(1): 121-132.
URL:  
http://journal.psych.ac.cn/xlxb/EN/     OR     http://journal.psych.ac.cn/xlxb/EN/Y2012/V44/I1/121
[1] LIU Yue, LIU Hongyun.  Reporting overall scores and domain scores of bi-factor models[J]. Acta Psychologica Sinica, 2017, 49(9): 1234-1246.
[2] WANG Wenyi;SONG Lihong;DING Shuliang. Classification accuracy and consistency indices for complex decision rules in multidimensional item response theory[J]. Acta Psychologica Sinica, 2016, 48(12): 1612-1624.
[3] ZHAN Peida; CHEN Ping; BIAN Yufang. Using confirmatory compensatory multidimensional IRT models to do cognitive diagnosis[J]. Acta Psychologica Sinica, 2016, 48(10): 1347-1356.
[4] LIU Yue;LIU Hongyun. Comparison of MIRT Linking Methods for Different Common Item Designs[J]. Acta Psychologica Sinica, 2013, 45(4): 466-480 .
[5] LIU Hong-Yun,LI Chong,ZHANG Ping-Ping,LUO Fang. Testing Measurement Equivalence of Categorical Items’ Threshold/Difficulty Parameters: A Comparison of CCFA and (M)IRT Approaches[J]. Acta Psychologica Sinica, 2012, 44(8): 1124-1136.
[6] TU Dong-Bo,CAI Yan,DAI Hai-Qi,DING Shu-Liang. Parameters Estimation of MIRT Model and Its Application in Psychological Tests[J]. , 2011, 43(11): 1329-1340.
[7] Yang Yuhao,Long Junwei. The Structure and Measurement of Enterprise Staffs’ Knowledge-Sharing Behavior in China[J]. , 2008, 40(03): 350-357.
[8] Zou Hong,Jiang Suo. Development of the Adolescent Self-disclosure with Peers Questionnaire[J]. , 2008, 40(02): 184-192.
[9] Feng-Tingyong,Su-Ti,Hu-Xingwang,Li-Hong. The Development of A Test about Learning Adjustment of Undergraduate[J]. , 2006, 38(05): 762-769.
[10] Li-Chaoping,Xiaoxuan,Shi-Kan-,Chen-Xuefeng. Psychological Empowerment: Measurement and its Effect on Employees’ Work Attitude in China[J]. , 2006, 38(01): 99-106.
[11] Ma-Chao,Ling-Wenquan,Fang-Liluo. Construct Dimension of the Enterprise Staff’s Perceptions of Organizational Politics[J]. , 2006, 38(01): 107-115.
[12] Liu Wen,Yang Lizhu. STRUCTURE OF CHILDREN’S TEMPERAMENT AGED 3 TO 9 BASED ON TEACHERS’ DESCRIPTIONS[J]. , 2005, 37(01): 67-72.
[13] Wang Huaiming, Ma Mouchao. THE FACTORS OF CELEBRITY ENDORSER’S CREDIBILITY[J]. , 2004, 36(03): 365-369.
[14] Hua-Zhang,Lijuan-Pang,Sha-Tao,Yao-Chen,Qi-Dong. THE STRUCTURE OF CHILDREN’S EARLY MATHEMATICAL ABILITY AND ITS CHARACTERISTICS[J]. , 2003, 35(06): 810-817.
[15] Luo-Zhaosheng,Qi-Shuqing,Dai-Haiqi,Ding-Shuliang. REALIZATION TO THE IRT MULTIPLE CATEGORY SCORING MODEL[J]. , 2003, 35(04): 555-558.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
Copyright © Acta Psychologica Sinica
Support by Beijing Magtech