M. İ. SOYSAL, M. KEKEÇOĞLU, E.K. GÜRCAN, G. G. ŞİMŞEK

Türkiye bal arıları populasyonu morfometrik karekterleri örnekleminde (Apis mellifera L.) iki seviyeli faktör analizi

Klasik factor analizi örneklemenin bağımsız gözlemlerden oluştuğunu varsayar, oysa örnek verileri genellikle hiyerrarşik yapıdadır. Bu yapı içerisinde bal arıları farklı bölgelerde ve farklı lokasyonlarda koloniler halinde yaşarlar. Bu nedenle gözlemler arasında bağımsızlığın düşünülmesi gerçekçi değildir. Çünkü örneklenen birimler aynı bakım koşullarını ve aynı çevresel etkileri paylaşmazlar. Bu tür hiyerarşik yapıdaki verilerin analizi için çok seviyeli faktör analiz modeli önerilebilir. Bu araştırmada modeli bal arılarının morfometrik ölçüm sonuçlarına uygulayarak çok seviyeli faktör analizini basamak basamak anlatan bir model sunulmuştur. Beklenildiği gibi kanat, bacak, ve damar karakterlerinden oluşan üç factor yapısı oluşmuştur. Sonuçlar, koloniler arası ve koloni içi morfometrik faktör yapısının beklenen ile uyumlu olduğunu göstermektedir.

Two-Level factor analysis of morphometric characters of honeybees population sampled (Apis mellifera L.) in Turkey

Classical factor analysis assumes that sampling is independent observations. In morphometric researches, however, the data belonged to honeybee usually have hierarchical structure in which individuals are grouped within colonies within different localities and regions. The assumption of independence among observations is not realistic, because sampling units not share common environment, experiences and interactions. Multilevel factor analysis model is an appropriate methodological tool which has been proposed as an extension to confirmatory factor analysis models for analyzing data with hierarchical structure. In this study, we provide a didactic step-by-step guide to exploratory multilevel factor analysis of morphometric characters of honeybees. The results illustrated that the within and between level factor structure of morphometric characters conformed to expectation which is factor solution with three factors of wing, leg and vacular.

PDF

___

Amssalu, B., A. Nuru, S.E. Rudolph and H.R. Hepburn, 2004. Multivariate morphometric analysis of honeybees (Apis mellifera) in the Ethiopian region. Apidologie, 35: 71-81.
Andere, C., C. Garcia, C. Marinelli, R. Cepeda, E.M. Rodriguez and A. Palacio, 2008. Morphometric variables of honeybees Apis mellifera used in ecotypes characterization in Argentina. Ecological Modelling. (In Press).
Bentler, P.M. and E.J.C. Wu, 2002. EQS 6 for Windows user’s guide, Multivariate Software, Encino.
Cheung, M.W.L. and K.Au, 2005. Applications of multilevel structural equation modeling to cross-cultural research. Structural Equation Modeling. 12 (4): 598-619.
Bliese, P.D. and P.J. Hanges, 2004. Being both too liberal and too conservative: The perils of treating grouped data as though it is independent, Organizational Research Methods. 7, 400-417.
Dansereau, F., J.A. Alutto and F.J. Yammarino, 1984. Theory testing in organizational behavior: The varient approach, Prentice-Hall, Englewood Cliffs, NJ.
Dyer, G.N., P.J. Hanges and R.J. Hall, 2005. Applying multilevel confirmatory factor analysis techniques to the study of leadership. The leadership quarterly. 16: 149-167.
Heck, R.H.. 1999. Multilevel modeling with SEM, in Thomas S. L. and Heck R. H. (Ed.), Introduction to Multilevel Modeling Techniques. Lawrence Erlbaum Associates, Mahwah NJ, pp: 89-127.
Hofmann, D A., 1997. An overview of the logic and rationale of hierarchical linear models. Journal of Management. 23: 723-744.
Hox, J., 1993. Factor analysis of multilevel data: Gauging the Muthén model, in Oud J.H.L. and van Blokland-Vogelesang R.A.W. (Ed.), Advances in longitudinal and multivariate analysis in the behavioral sciences. ITS, Nijmegen, The Netherlands, pp:141-156.
Hox, J., 2002. Multilevel Analysis: Techniques and applications, Lawrence Erlbaum Associates, Mahwah, NJ. Hox, J.J. and C.J.M Maas, 2001. The accuracy of multilevel structural equation modeling with pseudobalanced groups and small samples, Structural Equation Modeling. 8: 157-174.
James, L.R., R.G. Demaree and G. Wolf,1984. Estimating within-group interrater reliability with and without response bias. Journal of Applied Psychology 69: 85–98.
James, L.R., R.G.Demaree and G.Wolf, 1993. Rwg: An assessment of within-group interrater agreement. Journal of Applied Psychology 78: 306–309.
Kandemir, I., M. Kence, A. Kence, 2000. Genetic and morphometric variation in honeybee (Apis mellifera L.) populations of Turkey. Apidologie 31: 343-356.
Kandemir, I., M. Kence and A. Kence, 2005. Morphometric and Electrophoretic Variation in Different Honeybee (Apis mellifera L.) Populations, Turk J Vet Anim Sci. 29: 885-890.
Kekeçoğlu M., 2007. Türkiye Balarılarının mtDNA ve Bazı Morfolojik Özellikleri bakımından Karşılaştırılmasına Yönelik Bir Araştırma. (Doktora Tezi). NKÜ Fen Bilimleri Enstitüsü, Tekirdağ, Türkiye.
Kenny, D.A. and C.M. Judd, 1986. Consequences of violating the independence assumption in analysis of variance. Psychological Bulletin 99: 422-431.
Klein, K.J. and S.W.J. Kowzlowski, 2000. From micro to meso: Critical steps in conceptualizing and conducting multilevel research. Organizational Research Methods 3: 211–236.
Lee, S.Y., 1990. Multilevel Analysis of Structural Equation Models, Biometrika. 77(4), 763-772.
Lee, S.Y. and W.Y.Poon, 1995. Estimation of factor scores in a two-level confirmatory factor analysis model, Computational Statistics & Data Analysis. 20: 275-284.
Long, J.S., 1983. Confirmatory factor analysis: A preface to LISREL, Sage Publications, California. Longford, N.T. and B.O. Muthén, 1992. Factor analysis for clustered observations. Psychometrika 57: 581-597.
Mok, M., 1995. Sample size requirements for 2-level designs in educational research. Macquarie University, Sydney, Australia. Muthén, B.O., 1989. Latent variable modeling in heterogeneous populations. Psychometrika 54: 557-585.
Muthén, B.O., 1990. Mean and covariance structure analysis of hierarchical data. UCLA Statistics Series, 62.
Muthén, B.O., 1991. Multilevel factor analysis of class and student achievement components. Journal of Educational Measurement 28(4): 338-354.
Muthén, B.O., 1994. Multilevel covariance structure analysis. Sociological Methods & Research 22(3): 376-398. Muthén, B.O. and A. Sattora, 1989. Multilevel aspects of varying parameters in structural models, in Bock, R.D. (Ed.), Multilevel analysis of educational data. Academic, San Diego, CA, pp: 87-99.
Muthén, L.K. and B. O. Muthén, 2004. Mplus user’s guide. Muthén & Muthén, Los Angeles.
Raudenbush, S.W. and A. S. Bryk, 2002. Hierarchical linear models: Applications and data analysis methods. Sage Publications, Newbury Park, CA.
Reise, S.P, J.Ventura, K.H. Nuechterlein and K.H. Kim, 2005. An illustration of multilevel factor analysis. Journal of Personality Assessment. 84(2): 126-136.
Stapleton, L. M. 2006. Using multilevel structural equation modeling techniques with complex sample data, in Hancock G. R. and Mueller R. O. (Ed.), Structural equation modeling: A second course. Information Age Publishing, Greenwood, CT, 345-383.