İkinci derece örtük gelişme modelleri ve ölçme eşdeğerliği

Bireylerin davranış ya da tutumlarına ilişkin birçok araştırma problemi, zamandaki değişimin incelenmesini gerektirmektedir. Öğrenme konusunun, değişim kavramından ayrı tutulamayacağı göz önünde bulundurulduğunda, özellikle eğitim alanında bu tarz araştırmaların desenlenmesi, bu alandaki bilgilerimizin zenginleşmesine önemli katkılarda bulunacaktır. Bu çalışmada belirli bir özelliğin zaman içerisindeki doğrusal değişimi, Monte Carlo simülasyonu ile üretilmiş veri seti kullanılarak, ikinci derece örtük gelişme modelleri kapsamında ele alınmıştır. Analizlerin tümünde Mplus 5.1 programı kullanılmış; ilgili Mplus sentaksları açıklanmış ve model parametrelerinin yorumlanması üzerinde durulmuştur. Sunulan çalışmada ek olarak, örtük gelişme modellerinde ölçme eşdeğerliğinin nasıl test edileceği açıklanmıştır. Üretilen veri seti için analizler (1) temel örtük gelişme modeli, (2) örtük gelişme modelinde zayıf ölçme eşdeğerliği ve (3) örtük gelişme modelinde güçlü ölçme eşdeğerliği olmak üzere üç aşamada yapılmıştır.

Second order latend growth models and measurement equivalence

Research problems related to individuals' behaviors and attitudes requires examining inevitable changes over time. Because learning by nature implies change, analysis of longitudinal data becomes an important topic especially in the field of education. In this article, linear changes of a particular attribute over time was studied in the framework of the second order latent growth models by using data generated from Monte Carlo simulation. All analyses were performed by using Mplus 5.1 software. Related Mplus syntaxes were introduced and the interpretation of the model parameters was discussed. Additionally, it was explained how to study measurement equivalence in these models. Analyses were performed in three steps: (1) basic latent growth model, (2) latent growth model with weak measurement equivalence, and (3) strong measurement equivalence.

___

  • Bollen, K. A. ve Curran, P. J. (2006). Latent curve models: A structural equation perspective. New Jersey: A John Wiley & Sons, Inc., Publication.
  • Byrne, B. M. ve Stewart, S. M. (2006). The MACS approach to testing for multigroup invariance of a second - order structure: A walk through the process. Structural Equating Modeling, 13, 2, 287-321.
  • Cacioppo, J. T., Hughes, M. E., Waite, L. J., Hawkley, L. C., & Thisted, R. A. (2006). Loneliness as a specific risk factor for depressive symptoms: Cross sectional and longitudinal analyses. Psychology and Aging, 21,140-151.
  • Chan, D. (1998). The conceptualization and analysis of change over time: An integrative approach incorporating longitudinal means and covariance structures analysis (LMACS) and multiple indicator latent growth modeling (MLGM). Organizational Research Methods, 1, 421-483.
  • Chan, D. ve Schmitt, N. (2000). Interindividual differences inintraindividual changes inproactivity during organizational entry: A latent growth modeling approach to understanding newcomer adaptation. Journal ofApplied Psychology, 85,190-210.
  • De Frame, B., Van Damme, J., & Onghena, P. (2007). A longitudinal analysis of gender differences in academic self-concept and language achievement: A multivariate multilevel latent growth approach. Contemporary Educational Psychology, 32, 132-150.
  • Duncan, S. C. ve Duncan, T. E. (1996). A multivariate latent growth curve analysis of adolescent substance use. Structural Equation Modeling, 3, 323-347.
  • Duncan, S. C., Duncan, T. E., & Hops, H. (1996). Analysis of longitudinal data within accelerated longitudinal designs. Psychological Methods, 1, 236-248.
  • Duncan, S. C., Duncan, T. E., & Strycker, L. A. (2006). An introduction to latent variable growth curve modeling: Concepts, issues, and applications. New Jersey: Lawrence Erlbaum Associates, Publishers.
  • Farrell, A. D., Sullivan, T. N., Esposito, L. E., & Meyer, A. L. (2005). A latent growth curve analysis of the structure of aggression, drug use, and delinquent behaviors and their interrelations over time in urban and rural adolescents. Journal ofResearch on Adolescence, 15, 179-204.
  • Ferrer, E., Balluerka, N., & Widaman, K. F. (2008). Factorial invariance and the specification of the second-order latent growth models. Methodology, 4, 22-36.
  • Ingels, S. J., Dowd, K. L., Baldridge, J. D., Stipe, J. L., Bartot, V. H., & Frankel, M. R. (1994). National education longitudinal study of 1988, second follow-up: Student component data file user's manual (Report No. NCES 94-374). Washington, DC: U.S. Dept. of Education, Office of Educational Research and Improvement.
  • Jones, C. J. ve Meredith, W. (1996). Patterns of personality change across the life span. Psychology and Aging, 11, 57-65.
  • Kline, R. B. (2005). Principles and practice of structural equation modeling. New York: The Guilford Press.
  • Lance, C. E., Vandenberg, R. J., & Self, R. M. (2000). Latent growth models of individual change: The case of newcomer adjustment. Organizational Behavior and Human Decision Processes, 83, 207-140.
  • Lawrence, F. R. ve Hancock, G. R. (1998). Assessing change over time using latent growth modeling. Measurement and Evaluation in Counseling and Development, 30, 211-224.
  • Li, F. ve Acock, A. C. (1999). Latent curve analysis: A manual for research data analysis. Presented at the National Council on Family Relations, Irvine, CA.
  • McArdle, J. J. (1988). Dynamic but structural equation modeling of repeated measures data. In J. R. Nesselroade ve R. B. Cattell (Eds.), Handbook of multivariate experimental psychology (2nd ed., pp. 561-614). New York: Plenum.
  • McArdle, J. J. ve Aber, M. S. (1990). Patterns of change within latent variable structural equation models. In A. von Eye (Ed.), Statistical methods in longitudinal research (1st ed., pp.151-224). Boston: Academic Press.
  • McArdle, J. J. ve Anderson, E. (1990). Latent growth models for research on aging. In J. E. Birren ve K. W. Schaie (Eds.), Handbook of the psychology of aging (3rd ed., pp. 21-44), San Diego: Academic Press.
  • McArdle, J. J., Hamagami, F., Ellias, M. F., & Robbins, M. A. (1991). Structural modeling of mixed longitudinal and cross-sectional data. Experimental Aging Research, 17, 29-52.
  • McArdle, J. J., Prescott, C. A., Hamagami, F., & Horn, J. L. (1998). A contemporary method for developmental-genetic analyses of age changes in intellectual abilities. Developmental Neuropsychology, 14, 69-114.
  • McCoach, D. B., O'Connell, A. A., Reis, S. M., & Levitt, H. A. (2006). Growing readers: A hierarchical linear model of children's reading growth during the first 2 years of school.Journal ofEducational Psychology, 98, 14-28.
  • Mellenbergh, G. J. (1989). Item bias and item response theory. International Journal ofEducational Research, 13, 127-143.
  • Meredith, W. (1993). MI, factor analysis and factorial invariance. Psychometrika, 58, 525-543.
  • Meredith, W. ve Millsap, R. E. (1992). On the misuse of manifest variables in the detection of measurement invariance. Psychometrika, 57 (2), 289-311.
  • Millsap, R. E. (2008). Introduction to the special issue on growth models for longitudinal data in educational research. Educational Research and Evaluation, 14, 283-285.
  • Muthen, B. (1991). Multilevel factor analysis of class and student achievement components.Journal of Educational Measurement, 28, 338-354.
  • Muthen, L. K. ve Muthen, B. O. (2007). Mplus User's Guide. Fifth Edition. Los Angeles, CA: Muthen & Muthen.
  • Muthen, L. K. ve Muthen, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling: A Multidisciplinary Journal, 9, 599-620.
  • Muthen, L. K. ve Muthen, B. O. (2008). Mplus (Version 5.1) [Computer software]. Los Angeles: Muthen, ve Muthen.
  • Ployhart, R. E. ve Hakel, M. D. (1998). The substantive nature of performance variability: Predicting interindividual differences in intraindividual performance. Personnel Psychology, 51, 859901.
  • Rao, C. R. (1958). Some statistical methods for the comparison of growth curves. Biometrics, 14, 1-17.
  • Raykov, T. (1992). Structural models for studying correlates and predictors of change. Australian Journal ofPsychology, 44, 101-112.
  • Raykov, T. (1993). A structural equation model for measuring residualized change and discerning patterns of growth or decline. Applied Psychological Measurement, 17, 53-71.
  • Raykov, T. (1994). Studying correlates and predictors of longitudinal change using structural equation modeling. Applied Psychological Measurement, 18, 63-77.
  • Raykov, T. (1999). Are simple change scores obsolete? An approach to studying correlates and predictors of change. Applied Psychological Measurement, 23, 120-126.
  • Raykov, T. ve Marcoulides, G. A. (2006). A first course in structural equation modeling. New Jersey: Lawrence Erlbaum Associates.
  • Sayer, A. G. ve Cumsille, P. E. (2001). Second-order latent growth models. In L. M. Collins ve A. G. Sayer (Eds.), New methods for the analysis of change (1st ed., pp. 179-200). Washington, DC: American Psychological Association.
  • Shapka, J. D., Domene, J. F., & Keating, D. P. (2006). Trajectories of career aspirations through adolescence and young adulthood: Early math achievement as a critical filter. Educational Research & Evaluation, 12, 347-358.
  • Shevlin, M. ve Millar, R. (2006). Career education: An application of latent growth curve modeling to career information-seeking behavior of school pupils. British Journal of Educational Psychology, 76, 141-153.
  • Tucker, L. R. (1958). Determination of parameters of a functional relation by factor analysis. Psychometrika, 23,19-23.
  • Vandenberg, R. J. ve Lance, C. E. (2000). A review and synthesis of the measurement invariance literature: Suggestions, practices, and recommendations for organizational research. Organizational Research Methods, 3, 4-69.
  • Vandenberg, R. J. ve Self, R. M. (1993). Assessing newcomers' changing commitments to the organization during the first 6 months of work. Journal ofApplied Psychology, 78, 557-568.
  • Walker, A. J. Jr., Acock, A. C., Bowman, S. R., & Li, F. (1996). Amount of care given and care giving satisfaction: A latent growth curve analysis. Journal of Gerontology: Psychological Sciences, 3, 130-142.
  • Welch, G.W. (2007). Model fit and interpretation of non-linear latent growth curve models. Unpublished dissertation thesis, University of Pittsburg.
  • Willett, J. B. ve Sayer, A. G. (1994). Using covariance structure analysis to detect correlates and predictors of individual change over time. Psychological Bulletin, 116, 363-381.
  • Willett, J. B. ve Sayer, A. G. (1996). Cross-domain analyses of change over time: Combining growth modeling and covariance structure analysis. In G. A. Marcoulides ve R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 125-157). Mahwah, NJ: Erlbaum.
  • Wu, A. D., Li, Z. ve Zumbo, B. D. (2007). Decoding the meaning of factorial invariance and updating the practice of multi-group confirmatory factor analysis: A demonstration with TIMSS data. Practical Assessment, Research & Evaluation, 12,1-26.
Eğitim ve Bilim-Cover
  • ISSN: 1300-1337
  • Yayın Aralığı: Yılda 4 Sayı
  • Yayıncı: Türk Eğitim Derneği (TED) İktisadi İşletmesi