The Effects of Factorial Invariance and Factor Scaling on Model Fit and Parameter Estimates in the Multiple-Indicator Latent Growth Model

박송이; 장승민

doi:10.22257/kjp.2018.03.37.1.153

Korean Journal of Psychology: General

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P-ISSN1229-067X
E-ISSN2734-1127
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Vol.37 No.1

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The Effects of Factorial Invariance and Factor Scaling on Model Fit and Parameter Estimates in the Multiple-Indicator Latent Growth Model

Korean Journal of Psychology: General / Korean Journal of Psychology: General, (P)1229-067X; (E)2734-1127

2018, v.37 no.1, pp.153-183

https://doi.org/10.22257/kjp.2018.03.37.1.153

& (2018). The Effects of Factorial Invariance and Factor Scaling on Model Fit and Parameter Estimates in the Multiple-Indicator Latent Growth Model. Korean Journal of Psychology: General, 37(1), 153-183, https://doi.org/10.22257/kjp.2018.03.37.1.153

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Abstract

The multiple-indicator latent growth model (MI-LGM) is a second-order confirmatory factor model that analyzes latent trajectories of a factor measured by multiple indicators over time. Although MI-LGM can test the factorial invariance of indicators and estimate trajectories of a latent variable controlling measurement error, model fit and parameter estimates of the model may vary depending on factor scaling methods. The purpose of this study is to investigate how factor scaling methods, given a specified level of factorial invariance, change the meaning of the factor mean and thus affects the model fit and parameter estimates of MI-LGM. The authors first explored how factorial invariance and factor scaling affect the definition of factor means and the model fit in longitudinal factor analysis models. Next, they showed that constraining the sum of the indicator’ intercepts to zero creates a clear definition of the factor mean and the constraint provides consistent results and interpretation of the means of growth factors in the MI-LGM even under the weak factorial invariance. An analysis of actual panel data then illustrated such characteristics of the MI-LGM. Finally, the authors discussed the importance of factorial invariance and factor scaling in the analysis of mean and covariance structure models and that of using the strong factorial invariance when modeling the MI-LGM.

keywords: 다지표 잠재성장모형, 요인척도 설정, 요인동일성, 평균구조, 모형 적합도, multiple-indicator latent growth model, factor scaling, factorial invariance, mean structure, model fit

Reference

곽의현, 정경미, 김은성 (2016). 후기 남자 청소년의 우울․불안 발달궤적과 사회적 지지. 한국심리학회지: 임상, 35(1), 243-264.

구재선, 서은국 (2012). 행복은 4년 후 학업성취를 예측한다. 한국심리학회지: 사회 및성격, 26(2), 35-50.

권선중, 임숙희, 김영호 (2015). 청소년기 게임관련 신념과 게임 중독의 관계에 대한 재탐색 - 잠재성장 모형을 활용한 단기 종단연구. 한국심리학회지: 건강, 20(1), 267-283.

김수영, 석혜은 (2015). 잠재성장모형의 사용을위한 표본크기 결정. 한국심리학회지: 일반, 34(2), 599-617.

김수정, 곽금주 (2013). 3세부터 7세까지 어머니의 애정/온정적 양육태도의 변화가 아동의 학교적응에 주는 영향. 한국심리학회지: 발달, 26(2), 1-17.

김연수, 곽금주 (2016). 영아기 기질의 변화가 아동초기 외현화 문제행동에 미치는 영향. 한국심리학회지: 발달, 29(3), 95-111.

남순현 (2015). 청소년의 성격, 애착, 온라인 자기개방, 가족친밀감과 온라인 친구관계의 질 간의 종단적 관계. 한국심리학회지:발달, 28(4), 1-32.

박송이 (2016). 다지표 잠재성장모형에서 요인척도화와 측정동일성의 문제. 성균관대학교 석사학위청구논문.

우성범, 김성연, 임승우, 백인규, 남덕현, 양은주 (2012). 남녀청소년의 자존감, 친구애착, 비행 간의 관계에 대한 다변량 잠재성장모형 분석. 한국심리학회지: 일반, 31(4), 1093- 1113.

10.

이수진, 오경자 (2012). 이동의 기질과 부모 양육이 부주의와 과잉행동-충동성 문제의 발달경로에 미치는 영향. 한국심리학회지:임상, 31(4), 945-969.

11.

이순묵, 금은희, 이찬순 (2010). 다집단 분석의 문제: 평균구조분석에서의 측정원점 동일성 검증 필요 여부. 교육평가연구, 23(2), 391-416.

12.

이순묵, 김한조 (2011). 구조방정식 모형의 일반화 또는 집단차 연구를 위한 다집단 분석의 관행과 문제점. 사회과학, 43(1), 63-112.

13.

장혜인 (2015). 아동의 부정적 정서성과 어머니의 우울간의 종단적 상호관계: 교류모형적 접근. 한국심리학회지: 발달, 28(2), 19-39.

14.

정승아, 이수진 (2016). 학령 초기 내재화 문제행동의 발달경로에 미치는 아동 인성과 부모 양육의 효과. 한국심리학회지: 임상, 35(2), 499-518.

15.

주혜선, 이나빈, 민문경, 안현의 (2014). 대학생의 우울증상 진행경로에 미치는 정서조절곤란과 외상 기억 특성의 효과: 잠재성장모형을 통한 단기종단연구. 한국심리학회지: 상담, 26(3), 617-636.

16.

허묘연 (2000). 청소년이 지각한 부모 양육행동척도 개발 연구. 이화여자대학교 박사학위청구논문.

17.

Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation perspective. Hoboken, NJ: John Wiley & Sons.

18.

Brown, T. A. (2015). Confirmatory factor analysis for applied research (2nd ed.). New York: Guilford Publications.

19.

Chan, D. (1998). The conceptualization and analysis of change over time: An integrative approach incorporating longitudinal mean and covariance structures analysis(LMACS) and multiple indicator latent growth modeling (MLGM). Organizational Research Methods, 1(4), 421-483.

20.

Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural equation modeling, 14(3), 464-504.

21.

Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9, 233-255.

22.

Ferrer, E., Balluerka, N., & Widaman, K. F. (2008). Factorial invariance and the specification of second-order latent growth models. Methodology, 4(1), 22-36.

23.

Geiser, C., Keller, B. T., & Lockhart, G. (2013). First-versus second-order latent growth curve models: some insights from latent state-trait theory. Structural equation modeling: a multidisciplinary journal, 20(3), 479-503.

24.

Grimm, K. J., Ram, N., & Estabrook, R. (2017). Growth Modeling: Structural Equation and Multilevel Modeling Approaches. New York:Guilford Publications.

25.

Hancock, G. R., Kuo, W., & Lawrence, F. R. (2001). An illustration of second-order latent growth models. Structural Equation Modeling, 8, 470-489.

26.

Kim, E. S., & Willson, V. L. (2014). Testing Measurement Invariance Across Groups in Longitudinal Data: Multi group Second-Order Latent Growth Model. Structural Equation Modeling: A Multidisciplinary Journal, 21(4), 566-576.

27.

Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.). New York: Guilford Publications.

28.

Leite, W. L. (2007). A comparison of latent growth models for constructs measured by multiple items. Structural Equation Modeling, 14(4), 581-610.

29.

Little, T. D. (2013). Longitudinal structural equation modeling. New York: Guilford Press.

30.

Little, T. D., Slegers, D. W., & Card, N. A. (2006). A non-arbitrary method of identifying and scaling latent variables in SEM and MACS models. Structural Equation Modeling, 13(1), 59-72.

31.

MacCallum, R. C., Kim, C., Malarkey, W. B., & Kiecolt-Glaser, J. K. (1997). Studying multivariate change using multilevel models and latent curve models. Multivariate Behavioral Research, 32, 215-253.

32.

McArdle, J. J. (1988). Dynamic but structural equation modeling of repeated measures data. In J. Nesselroade & R. B. Cattel (Eds.), Handbook of multivariate experimental psychology (2nd ed., pp. 561-614). New York: Plenum.

33.

Meredith, W. (1993). Measurement invariance, factor analysis and factorial invariance. Psychometrika, 58(4), 525-543.

34.

Millsap, R. E. (2001). When trivial constraints are not trivial: The choice of uniqueness constraints in confirmatory factor analysis. Structural Equation Modeling: A Multidisciplinary Journal, 8(1), 1-17.

35.

Millsap, R. E., & Meredith, W. (2007). Factorial invariance: Historical perspectives and new problems. In R. Cudeck & R. C. MacCallum (Eds.), Factor Analysis at 100: Historical Developments and Future Directions(pp. 131-152). Mahwah, NJ: Lawrence Erlbaum.

36.

Newsom, J. T. (2015). Longitudinal Structural Equation Modeling: A Comprehensive Introduction. New York: Routledge.

37.

Preacher, K. J., Wichman, A. L., MacCallum, R. C., & Briggs, N. E. (2008). Latent Growth Curve Modeling. Oaks, CA: Sage.

38.

Sayer, A. G., & Cumsille, P. E. (2001). Second-order latent growth models. In. L. M. Collins & A. G. Sayer (Eds.), New methods for the analysis of change (pp. 179-200). Washington DC: American Psychological Association.

39.

Stoel, R. D., van den Wittenboer, G., & Hox, J. (2004). Methodological issues in the application of the latent growth curve model. In K. van Montfort, J. Oud, & A. Satorra (Eds.), Recent Developments on Structural Equation Models (pp. 241-261). Norwell, MA: Kluwer Academic Publishers.

40.

Vandenberg, R. J., & Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational research methods, 3(1), 4-70.

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Article Contents

Vol.37 No.1

The Effects of Factorial Invariance and Factor Scaling on Model Fit and Parameter Estimates in the Multiple-Indicator Latent Growth Model

Abstract

Reference

Korean Journal of Psychology: General