Hemant Kher
ABSTRACT
The purpose of this didactic study is to demonstrate how latent growth models (LGM) can be utilized to measure changes in student's computer self efficacy (CSE) over time. LGM is a special application of structural equation modeling (SEM), an analytic tool that is popular among MIS researchers. LGMs have been used to study longitudinal changes in observed and/or latent variables over time in several other fields such as psychology, sociology, and management. To promote its use within MIS research, this paper provides a primer on the application of LGM using CSE data gathered from freshmen enrolled in the introduction to MIS class. We illustrate unconditional and conditional LGMs, and highlight the types of research questions such models can address. We discuss issues related to data requirements, model identification, estimation methods, sample size requirements, and model fit assessment statistics for LGMs, and conclude by providing avenues of further longitudinal research in MIS that can benefit from the use of LGMs.
- Gefen, D. 2003. Assessing Unidimensionality Through LISREL: An Explanation and Example. CAIS. 12, 23--47.Google Scholar
Cross Ref
- Joreskog. 1970. A general method for analysis of covariance structures. Biometrika. 57(2), 239--251.Google ScholarCross Ref
- Bollen, K.A., Structural Equations with Latent Variables. 1989, New York: John Wiley & Sons.Google Scholar
- Davis, F., R.P. Bagozzi, and P.R. Warshaw. 1989. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science. 35(8), 982--1003. Google ScholarDigital Library
- McKnight, H., V. Choudhury, and C. Kacmar. 2002. Developing and Validating Trust Measures for e-Commerce: An Integrative Typology. Information Systems Research. 13(3), 334--359. Google ScholarDigital Library
- Bollen, K. and P. Curran, Latent Curve Models: A Structural Equation Perspective. 2006, Hoboken, NJ: John Wiley & Sons, Inc.Google Scholar
- Compeau, D., C.A. Higgins, and S. Huff. 1999. Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly. 23(2), 145--158. Google ScholarDigital Library
- Marakas, G., M. Yi, and R.D. Johnson. 1998. The multilevel and multifaceted character of computer self-efficacy: Toward clarification of the construct and an integrative framework for research. Information Systems Research. 9(2), 126--163. Google ScholarDigital Library
- Duncan, T., S. Duncan, and L. Strycker, An Introduction to Latent Variable Growth Curve Modeling. 2006, New York: Lawrence Erlbaum Associates.Google Scholar
- Muthen, L. and B. Muthen. 2002. How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling. 9(4), 599--620.Google ScholarCross Ref
- Shah, R. and S.M. Goldstein. 2006. Use of structural equation modeling in operations management research: Looking back and forward. Journal of Operations Management. 24, 148--169.Google ScholarCross Ref
- Singer, J.D. and J.B. Willett, Applied longitudinal data analysis: Modeling change and event occurrence. 2003, New York: Oxford University Press.Google Scholar
- Eisenberger, R., S. Hutchison, and D. Sowa. 1986. Perceived Organizational Support. Journal of Applied Psychology. 71(3), 500--507.Google ScholarCross Ref
- Stoolmiller, M. 1994. Antisocial behavior, delinquent peer association and unsupervised wandering for boys: Growth and change from childhood to early adolescence. Multivariate Behavioral Research. 29, 263--288.Google ScholarCross Ref
- Chan, D. and N. Schmitt. 2000. Interindividual differences in intraindividual changes in proactivity during organizational entry: A latent growth modeling approach to understanding newcomer adaptation. Journal of Applied Psychology. 85(2), 190--210.Google ScholarCross Ref
- Venkatesh, V. and F.D. Davis. 2000. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Studies. Management Science. 46(2), 186--204. Google ScholarDigital Library
- Enders, C.K., Analyzing structural equation models with missing data, in A Second course in structural equation modeling, G.R. Hancock and R.O. Mueller, Editors. 2006, Information Age: Greenwich, CT. p. 313--344.Google Scholar
Index Terms
- Leveraging latent growth models to better understand MIS theory: a primer
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