The Using of Latent Growth Models for Educational Researches

Year 2009, Volume: 8 Issue: 2, 534 - 555, 26.06.2009

Petek Aşkar , Halil Yurdugül

Abstract

The concept of education is explained with some concepts such as development, change, and
growth. Because of it, educational measurements used to determine learners’ achievement growth and cognitive
development, or learners’ status of achievement. Linear or nonlinear traditional statistical models used for status
of analysis. To determine the change commonly used paired t test statistics, constructed on the pre-test and posttest
experimental design, and variance of analysis based on repeated measurements. However, these methods
include some limitations and not enough to define the growth. On the other side, the latent growth modelling has
been increasingly used to analyze longitudinal data, especially for educational researches. In this article, the
latent growth modelling was dealt with for educational purposes and discussed. Also to determine the
achievement growth (cognitive development), the all of latent growth models have been applied on a simulated
data set.

Keywords

-

Örtük Büyüme Modellerinin Eğitim Araştırmalarında Kullanımı

Abstract

Eğitim açısından bir gelişim tanımini ifade eder. Bu kalmak eğitimdeki ölçmeler, içki başarı durumları belirleme. Geleneksel yöntemler yöntemler yöntemler yöntemler yöntemler yöntemler yöntemler yöntemler yöntemler yöntemler. Yeni Gelişimin Tanımlanması Yaygın bir sekilde öntest-sontest Deneysel düzeneğinde Bağımlı örneklem t testi ya da tekrarlı ölçümler düzeneğindeki varyans analizi. Gelişimin belirlenmesi bir çok olumsuzluğu da. Sondaki sıkılma zamanımaya başlanan örtük büyüme modelleri eğitim konusu da kullanılmaya başlanmıştır. Bu çalışmanın amacı örtük büyüme modellerin eğitimsel anlam kullanımını tartışmak ve eğitsel değişkenler  üzerinde kullanımını ele almaktır.

Keywords

-

References

• Anderson, J. C., & Gerbing, D. W. (1984). The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49, 155–173.
• Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423.
• Bentler, P. M. (1995). EQS structural equations program manual. Encino, CA: Multivariate Software.
• Bentler, P.M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238– 246.
• Bereiter, C. (1963). Some persisting dilemmas in the measurement of change. In C. W. Harris (Ed.), Problems in the measurement of change (3-20). Madison, WI: University of Wisconsin Press.
• Blozis, S. A., Conger, K.J., & Harring, J. R. (2007). Nonlinear latent curve models for multivariate longitudinal data, International Journal of Behavioral Development, 31(4), 340–346.
• Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation approach.Hoboken, NJ: Wiley.
• Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In Bollen, K., and Long, S. (eds.), Testing Structural Equation Models. Sage, Beverly Hills, CA, 136–162.
• Byrne, B. M., & Crombie, G. (2003). Modeling and testing change: An introduction to the latent growth curve model. Understanding Statistics, 2(3), 177-203.
• Coffman, D. L., & Millsap, R. E. (2005). Evaluating latent growth curve models using individual fit statistics. Structural Equation Modeling, 13, 1–27.
• Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological tests. Psychological Bulletin, 52, 281-302.
• Duncan, T. E., & Duncan, S. C. (2004). An Introduction to Latent Growth Curve Modeling. Behavior Therapy 35,333-363.
• Duncan, T. E., Duncan, S. C., Strycker, L. A., Li, F., & Alpert, A. (2006). An introduction to latent variable growth curve modeling: Concepts, issues, and applications. 2nd Edition. Mahwah, NJ: Erlbaum.
• Duncan, T. E., Duncan, S. C., Strycker, L. A., & Li, F. (2002). A latent variable framework for power estimation within intervention contexts. Journal of Psychopathology & Behavioral Assessment,24(1), 1-12.
• Fan, X. (2001). Parental involvement and students' academic achievement: A growth modeling analysis. The Journal of Experimental Education, 70, 27-61.
• Fan, X. (2003). Power of latent growth modeling for detecting group differences in linear growth trajectory parameters. Structural Equation Modeling, 10, 380-400.
• Fan, X., & Fan, X. (2005). Power of latent growth modeling for detecting linear growth: Number of measurements and comparison with other analytic approaches. Journal of Experimental Education, 73, 121-139.
• Grimm, K. J. (2007). Multivariate longitudinal methods for studying developmental relationships between depression and academic achievement. International Journal of Behavioral Development, 31(4), 328–339.
• Hamilton, J., Gagne, P. E., & Hancock, G. R. (2003). The effect of sample size on latent growth models. Paper presented at the annual meeting of the American Educational Research Association, Chicago.
• Hancock, G. R., Kuo, W. L., & Lawrence, F. R. (2001). An illustration of second-order latent growth models. Structural Equation Modeling, 8, 470–489.
• Hess, B. (2000). Assessing program impact using latent growth modeling: a primer for the evaluator. Evaluation and Program Planning, 23, 419-428.
• Hong, S., & Ho, H.Z. (2005). Direct and indirect longitudinal effects of parental involvement on student achievement: second order latent growth modeling across ethnic groups. Journal of Educational Psychology, 97, 32-42.
• Hu, L., Bentler, P.M., & Kano, Y. (1992). Can test statistics in covariance structure analysis be trusted? Psychological Bulletin, 112, 351-362.
• Jackson, D. L. (2003). Revisiting sample size and number of parameter estimates: Some support for the N:q hypothesis. Structural Equation Modeling, 10(1), 128-141.
• Kaplan, D. (2002). Methodological advances in the analysis of individual growth with relevance to education policy. Peabody Journal of Education, 77, 189-215.
• Kline, R. B. (1998). Principles and practice of structural equation modeling. New York: Guilford Press.
• Leite, W. L. (2007). A comparison of latent growth models for constructs measured by multiple items. Structural Equation Modeling, 14(4), 581-610.
• Lohman, D. F. (1999). Minding our p's and q's: On finding relationships between learning and intelligence. In P. L. Ackerman, P. C. Kyllonen, & R. D. Roberts (Eds.), The future of learning and individual differences: Process, traits, and content (55f72). Washington, DC: American Psychological Association.
• Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103, 391–410.
• McArdle, J. J. (1988). Dynamic but structural equation modeling of repeated measures data. In R. B. Cattell & J. Nesselroade (Eds.), Handbook of multivariate experimental psychology (2nd ed., 561-614). New York: Plenum Press.
• MacCallum, R. C., Widaman, K. F., Zhang, S. & Hong, S., (1999), Sample size in factor analysis, Psychological Methods, 4, 84-99.
• Meredith,W., & Tisak, J. (1990). Latent curve analysis. Psychometrika 55: 107–122.
• Muthén, B., & Curran, P. (1997). General growth modeling of individual differences in experimental designs: A latent variable framework for analysis and power estimation. Psychological Methods, 2, 371-402.
• Muthén, B. O., & Khoo, S. T. (1998). Longitudinal studies of achievement growth using latent variable modeling. Learning and individual differences, 10(2), 73-101.
• Newmann, F. M., Smith, B., Ainsworth, E., & Bryk, A. S. (2001). Instructional program coherence: What it is and why it should guide school improvement policy. Educational Evaluation and Policy Analysis, 23, 297–321.
• Rogosa, D. (1988). Myths about longitudinal research. In K. W. Schaie, R. T. Campbell, W. Meredith, & S. C, Rawlings (Eds.), Methodological issues in aging research, 171-209. New York: Springer.
• 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, 179–200.
• Schumacker R.E., & Lomax, R.G. (2004). A beginner’s guide to structural equation modeling, Lawrence Erlbaum, Mahwah, NJ.
• Steiger, J. H. (1990). Structural model evaluation and modification: an interval estimation approach. Multivariate Behavioral Research, 25 (2), 173-80.
• Wittmann, W. W. (1988). Multivariate reliability theory. Principles of symmetry and successful validation strategies. In J. R. Nesselroade & R.B. Cattell (Eds.), Handbook of multivariate experimental psychology (505f560). New York: Plenum Press.
• Yin, R. K., Schmidt, R. C, & Besag, F. (2006). Aggregating student achievement trends across states with different tests: Using standardized slopes as effect sizes. Peabody Journal of Education, 81(2), 47-61.
• Yurdugül, H. (2007). Çoktan seçmeli test sonuçlarFndan elde edilen farklF korelâsyon türlerinin birinci ve ikinci sFralF faktör analizlerindeki uyum indekslerine etkisi. Flkö retim Online, 6(1). 160-185.
• Ek 1: Tekde i*kenli Ko*ulsuz Örtük Büyüme Modellerinin Ketsimi