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Volumn 69, Issue 6, 2009, Pages 929-947

The effect of auxiliary variables and multiple imputation on parameter estimation in confirmatory factor analysis

Author keywords

Auxiliary variables; Confirmatory factor analysis; Missing data; Multiple imputation; Test validity

Indexed keywords


EID: 70449834993     PISSN: 00131644     EISSN: 15523888     Source Type: Journal    
DOI: 10.1177/0013164409332225     Document Type: Article
Times cited : (12)

References (31)
  • 1
  • 2
    • 0345475379 scopus 로고    scopus 로고
    • Missing data techniques for structural equation modeling
    • Allison, P.D. (2003). Missing data techniques for structural equation modeling. Journal of Abnormal Psychology, 112, 545-557.
    • (2003) Journal of Abnormal Psychology , vol.112 , pp. 545-557
    • Allison, P.D.1
  • 3
    • 0002914202 scopus 로고    scopus 로고
    • Full information estimation in the presence of incomplete data
    • In G. A. Marcoulides & R. E. Schumacker (Eds.), Mahwah, NJ: Lawrence Erlbaum
    • Arbuckle, J.L. (1996). Full information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling (pp. 243-277). Mahwah, NJ: Lawrence Erlbaum.
    • (1996) Advanced Structural Equation Modeling , pp. 243-277
    • Arbuckle, J.L.1
  • 4
    • 0032954507 scopus 로고    scopus 로고
    • Applications of multiple imputation in medical studies: From AIDS to NHANES
    • Barnard, J., & Meng, X. (1999). Applications of multiple imputation in medical studies: From AIDS to NHANES. Statistical Methods in Medical Research, 8, 17-36.
    • (1999) Statistical Methods in Medical Research , vol.8 , pp. 17-36
    • Barnard, J.1    Meng, X.2
  • 5
    • 0000783293 scopus 로고
    • Efficacy of the indirect approach for estimating structural equation models with missing data: A comparison of five methods
    • Brown, R.L. (1994). Efficacy of the indirect approach for estimating structural equation models with missing data: A comparison of five methods. Structural Equation Modeling, 1, 287-316.
    • (1994) Structural Equation Modeling , vol.1 , pp. 287-316
    • Brown, R.L.1
  • 6
    • 0035755636 scopus 로고    scopus 로고
    • A comparison of inclusive and restrictive strategies in modern missing data procedures
    • Collins, L.M., Schafer, J.L., & Kam, C. (2001). A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychological Methods, 6, 330-351.
    • (2001) Psychological Methods , vol.6 , pp. 330-351
    • Collins, L.M.1    Schafer, J.L.2    Kam, C.3
  • 7
    • 0035756118 scopus 로고    scopus 로고
    • The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data
    • Enders, C.K. (2001). The impact of nonnormality on full information maximum-likelihood estimation for structural equation models with missing data. Psychological Methods, 6, 352-370.
    • (2001) Psychological Methods , vol.6 , pp. 352-370
    • Enders, C.K.1
  • 8
    • 33645894571 scopus 로고    scopus 로고
    • Analyzing structural equation models with missing data
    • In G. Hancock & R. Mueller (Eds.), Greenwich, CT: Information Age
    • Enders, C.K. (2006). Analyzing structural equation models with missing data. In G. Hancock & R. Mueller (Eds.), Structural equation modeling: A second course (pp. 313-342). Greenwich, CT: Information Age.
    • (2006) Structural Equation Modeling: A Second Course , pp. 313-342
    • Enders, C.K.1
  • 9
    • 0000885702 scopus 로고    scopus 로고
    • The relative performance of full information maximum likelihood estimation for missing data in structural equation models
    • Enders, C.K., & Bandalos, D.L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling, 8, 430-457.
    • (2001) Structural Equation Modeling , vol.8 , pp. 430-457
    • Enders, C.K.1    Bandalos, D.L.2
  • 10
    • 2642541763 scopus 로고    scopus 로고
    • Using an EM covariance matrix to estimate structural equation models with missing data: Choosing an adjusted sample size to improve the accuracy of inferences
    • Enders, C.K., & Peugh, J.L. (2004). Using an EM covariance matrix to estimate structural equation models with missing data: Choosing an adjusted sample size to improve the accuracy of inferences. Structural Equation Modeling, 11, 1-19.
    • (2004) Structural Equation Modeling , vol.11 , pp. 1-19
    • Enders, C.K.1    Peugh, J.L.2
  • 12
    • 0000497010 scopus 로고    scopus 로고
    • Treatments of missing data: A Monte Carlo comparison of RBHDI, iterative stochastic regression imputation, and expectation-maximization
    • Gold, M.S., & Bentler, P.M. (2000). Treatments of missing data: A Monte Carlo comparison of RBHDI, iterative stochastic regression imputation, and expectation-maximization. Structural Equation Modeling, 7, 319-355.
    • (2000) Structural Equation Modeling , vol.7 , pp. 319-355
    • Gold, M.S.1    Bentler, P.M.2
  • 13
    • 3042829565 scopus 로고    scopus 로고
    • A comparison of maximum-likelihood and asymptotically distribution-free methods of treating incomplete nonnormal data
    • Gold, M.S., Bentler, P.M., & Kim, K.H. (2003). A comparison of maximum-likelihood and asymptotically distribution-free methods of treating incomplete nonnormal data. Structural Equation Modeling, 10, 47-79.
    • (2003) Structural Equation Modeling , vol.10 , pp. 47-79
    • Gold, M.S.1    Bentler, P.M.2    Kim, K.H.3
  • 14
    • 0347249765 scopus 로고    scopus 로고
    • Adding missing-data-relevant variables to FIML-based structural equation models
    • Grahan, J.W. (2003). Adding missing-data-relevant variables to FIML-based structural equation models. Structural Equation Modeling, 10, 80-100.
    • (2003) Structural Equation Modeling , vol.10 , pp. 80-100
    • Grahan, J.W.1
  • 15
    • 33846873244 scopus 로고    scopus 로고
    • Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models
    • Horton, N.J., & Kleinman, K.P. (2007). Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models. The American Statistician, 61, 79-90.
    • (2007) The American Statistician , vol.61 , pp. 79-90
    • Horton, N.J.1    Kleinman, K.P.2
  • 18
    • 0040731105 scopus 로고    scopus 로고
    • Pairwise deletion for missing data in structural equation models: Nonpositive definite matrices, parameter estimates, goodness of fit, and adjusted sample sizes
    • Marsh, H.W. (1998). Pairwise deletion for missing data in structural equation models: Nonpositive definite matrices, parameter estimates, goodness of fit, and adjusted sample sizes. Structural Equation Modeling, 5, 22-36.
    • (1998) Structural Equation Modeling , vol.5 , pp. 22-36
    • Marsh, H.W.1
  • 19
    • 15244345570 scopus 로고    scopus 로고
    • A short version of the Self Description Questionnaire II: Operationalizing criteria for short-form evaluation with new applications of confirmatory factor analyses
    • Marsh, H.W., Ellis, L., Parada, L., Richards, G., & Heubeck, B.G. (2005). A short version of the Self Description Questionnaire II: Operationalizing criteria for short-form evaluation with new applications of confirmatory factor analyses. Psychological Assessment, 17, 81-102.
    • (2005) Psychological Assessment , vol.17 , pp. 81-102
    • Marsh, H.W.1    Ellis, L.2    Parada, L.3    Richards, G.4    Heubeck, B.G.5
  • 20
    • 0001010853 scopus 로고
    • On structural equation modeling with data that are not missing completely at random
    • Muthen, B., Kaplan, D., & Hollis, M. (1987). On structural equation modeling with data that are not missing completely at random. Psychometrika, 52, 431-462.
    • (1987) Psychometrika , vol.52 , pp. 431-462
    • Muthen, B.1    Kaplan, D.2    Hollis, M.3
  • 21
    • 0038009574 scopus 로고    scopus 로고
    • Longitudinal modeling with randomly and systematically missing data: A simulation of ad hoc, maximum likelihood, and multiple imputation techniques
    • Newman, D.A. (2003). Longitudinal modeling with randomly and systematically missing data: A simulation of ad hoc, maximum likelihood, and multiple imputation techniques. Organizational Research Methods, 6, 328-362.
    • (2003) Organizational Research Methods , vol.6 , pp. 328-362
    • Newman, D.A.1
  • 22
    • 0041589601 scopus 로고    scopus 로고
    • The comparative efficacy of imputation methods for missing data in structural equation modeling
    • Olinsky, A., Chen, S., & Harlow, L. (2003). The comparative efficacy of imputation methods for missing data in structural equation modeling. European Journal of Operational Research, 15, 53-79.
    • (2003) European Journal of Operational Research , vol.15 , pp. 53-79
    • Olinsky, A.1    Chen, S.2    Harlow, L.3
  • 24
    • 0003526297 scopus 로고    scopus 로고
    • SAS Institute., Cary, NC: SAS Institute
    • SAS Institute. (2001). The SAS system for windows. Cary, NC: SAS Institute.
    • (2001) The SAS System for Windows
  • 25
    • 18444385925 scopus 로고    scopus 로고
    • A statistically justified pairwise ML method for incomplete nonnormal data: A comparison with direct ML and pairwise ADF
    • Savalei, V., & Bentler, P.M. (2005). A statistically justified pairwise ML method for incomplete nonnormal data: A comparison with direct ML and pairwise ADF. Structural Equation Modeling, 12, 183-214.
    • (2005) Structural Equation Modeling , vol.12 , pp. 183-214
    • Savalei, V.1    Bentler, P.M.2
  • 28
    • 85047673373 scopus 로고    scopus 로고
    • Missing data: Our view of the state of the art
    • Schafer, J.L., & Graham, J.W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177.
    • (2002) Psychological Methods , vol.7 , pp. 147-177
    • Schafer, J.L.1    Graham, J.W.2
  • 29
    • 0032219074 scopus 로고    scopus 로고
    • Multiple imputation for multivariate missing data problems: A data analyst's perspective
    • Schafer, J.L., & Olsen, M.K. (1998). Multiple imputation for multivariate missing data problems: A data analyst's perspective. Multivariate Behavioral Research, 33, 545-571.
    • (1998) Multivariate Behavioral Research , vol.33 , pp. 545-571
    • Schafer, J.L.1    Olsen, M.K.2
  • 30
    • 0033616909 scopus 로고    scopus 로고
    • Multiple imputation of missing blood pressure covariates in survival analysis
    • Van Buuren, S., Boshuizen, H.C., & Knook, D.L. (1999). Multiple imputation of missing blood pressure covariates in survival analysis. Statistics in Medicine, 18, 681-694.
    • (1999) Statistics in Medicine , vol.18 , pp. 681-694
    • van Buuren, S.1    Boshuizen, H.C.2    Knook, D.L.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.