Açıklayıcı Grafik Analizi: EGAnet R paketiyle Bir Uygulama

Öz

Amaç: Boyut sayısının belirlenmesinde her tekniğin kendine göre sınırlılıkları mevcuttur. Bu durum doğru tahminler sağlayabilecek yeni faktör belirleme yöntemlerine ihtiyaç duyulmasına neden olmuştur. Bu araştırmanın amacı faktör belirleme yöntemlerine alternatif bir yaklaşım olan açıklayıcı grafik analiz yöntemi ve bu yöntemin analizleri için kullanılan R programlama dilindeki EGAnet paketi tanıtmaktır. Yöntem: Makale, pakette yer alan ölçek geliştirme çalışmalarında kullanılan fonksiyonların göstermesi amaçlamıştır. Bu amaçla, verinin analiz için hazırlanması, geleneksel ve bostraping açıklayıcı grafik analiz ile boyut belirleme, madde ve boyutlara ilişkin istatistiklerin elde edilmesi, yapısal tutarlılık, doğrulayıcı faktör analizi ve ölçme değişmezliği gibi uygulamaların nasıl kullanılabileceği gösterilmiştir. EGAnet paketinin işlevselliğini göstermek için gerçek veri seti üzerinden analizler yapılmıştır. Bunun için Çevrimiçi Oyun Oynama Motivasyon Ölçeği’nden elde edilen veriler ile açıklamalı R kodları gösterilmiştir. Sonuç: Bu araştırma sonucunda, geleneksel ve bostraping açıklayıcı grafik analiz sonuçlarıyla doğrulayıcı faktör analizi sonuçlarının aynı olduğu elde edilmiştir. Ayrıca cinsiyete göre yapılan ölçme değişmezliği sonucunda kısmi metrik değişmezlik sağlanmıştır. Özgünlük: Bu çalışmanın ölçek geliştirme sürecinde ölçeğin bütünsel olarak incelenmesi ve boyut belirleme konularında araştırmacılara yol göstereceği düşünülmektedir.

Anahtar Kelimeler

• Boyut belirleme
• Keşifsel grafik analizi
• EGAnet
• ağ analizi.

Exploratory Graphic Analysis: An Application with the EGAnet R Package

Öz

Purpose: Each technique has its own limitations in determining the number of dimensions. This situation has led to the need for new factor determination methods that can provide accurate predictions. The aim of this research is to introduce the explanatory graphical analysis method, which is an alternative approach to factor determination methods, and the EGAnet package in the R programming language used for the analysis of this method. Method: The article aims to show the functions used in the scale development studies in the package. For this purpose, it has been shown how applications such as preparation of data for analysis, dimension determination with traditional and bostraping explanatory graphic analysis, obtaining statistics regarding items and dimensions, structural consistency, confirmatory factor analysis and measurement invariance can be used. To demonstrate the functionality of the EGAnet package, analyzes are performed on the real data set. For this purpose, R codes are shown using data obtained from the Online Game Playing Motivation Scale. Conclusion: As a result of this research, it is found that the results of traditional and bostraping explanatory graphic analysis and confirmatory factor analysis are same. In addition, partial metric invariance is achieved as a result of measurement invariance based on gender. Originality: It is thought that this study will guide researchers in holistic examination of the scale and dimension determination during the scale development process.

Anahtar Kelimeler

• Exploratory graph analysis
• EGAnet
• network analysis
• factor numbers.

Kaynakça

1. Avcu, A. (2021). Investigating the performance of exploratory graph analysis when the data are unidimensional and polytomous. Journal of Measurement and Evaluation in Education and Psychology, 12(1), 1-14. doi: 10.21031/epod.784128
2. Bandalos, D. L., ve Boehm-Kaufman, M. R. (2009). Four common misconceptions in exploratory factor analysis. In C. E. Lance & R. J. Vandenberg (Eds.), Statistical and methodological myths and urban legends: Doctrine, verity and fable in the organizational and social sciences (ss. 61–87). Routledge/Taylor & Francis Group.
3. Bell, V. ve O’Driscoll, C. (2018). The network structure of paranoia in the general population. Soc Psychiatry Psychiatr Epidemiol, 53, 737–744. https://doi.org/10.1007/s00127-018-1487-0.
4. Borsboom, D., ve Cramer, A. O. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91–121. doi: 10.1146/annurev-clinpsy-050212-185608.
5. Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245–276. https://doi.org/10.1207/s15327906mbr0102_10.
6. Chen, J. ve Chen, Z. (2008) Extended Bayesian information criteria for model selection with large model spaces. Biometrika. 95(3), 759–71. https://www.jstor.org/stable/20441500
7. Christensen, A. P. ve Golino, H. (2021). On the equivalency of factor and network loadings. Behavior research methods, 53, 1563–1580. https://orcid.org/10.3758/s134 28-020-01500-6
8. Christensen, A.P., Gross, G.M., Golino, H.F., Silvia, P.J. ve Kwapil, T.R. (2019). Exploratory graph analysis of the multidimensional schizotypy scale. Schizophr. Res. 206, 43–51. doi: 10.1016/j.schres.2018.12.018

9. Christensen, A. P., Golino, H. F., ve Silvia, P. (2019). A psychometric network perspective on the validity and validation of personality trait questionnaires. PsyArXiv. https://doi.or g/10.1002/per.2265.
10. Christensen, A. P. ve Golino, H. (2021b). Estimating the stability of psychological dimensions via bootstrap exploratory graph analysis: A monte carlo simulation and tutorial. Psych, 3(3), 479–500. https://doi.org/10.3390/psych3030032
11. Cohen, L., Manion, L., & Morrison, K. (2005). Research methods in education. (5th Ed.). London: Routledge Falmer.
12. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37-46. https://doi.org/10.3390/psych3030032
13. Crawford, A. V., Green, S. B., Levy, R., Lo, W. J., Scott, L., Svetina, D., ve Thompson, M. S. (2010). Evaluation of parallel analysis methods for determining the number of factors. Educational and Psychological Measurement, 70, 885–901. https://doi.org/10.1177/00131644103793
14. Epskamp, S., Maris, G., Waldorp, L. J., ve Borsboom, D. (2017). Network Psychometrics. P. Irwing, D. Hughes, ve T. Booth (Ed.), Handbook of psychometrics. New York: Wiley.
15. Epskamp, S. ve Fried, E. (2018). A tutorial on regularized partial correlation networks. Psychological Methods, 23(4), 617–634. https://doi.org/10.1037/met0000167
16. Epskamp, S. ve Fried, E. I. (2016). A primer on estimating regularized psychological networks. arXiv:Applications. Available at: http://arxiv.org/abs/1607. 013677.
17. Evren, C., Evren, B., Dalbudak, E., Topçu, M. ve Kutlu, N. (2020). Psychometric validation of the Turkish motives for Online Gaming Questionnaire (MOGQ) across university students and video game players. Addicta: The Turkish Journal on Addictions, 7(2), 81-89. doi: 10.5152/ADDICTA.2020.19093.
18. Foygel, R. ve Drton, M. (2010). Extended bayesian information criteria for gaussian graphical models. In Proceedings of the 23rd international conference on neural information processing systems - volume 1 (Vol. 1, ss. 604–612). Vancouver, Canada.
19. Garcia-Garzon, E., Abad, F. J. ve Garrido, L. E. (2019a). Improving bi-factor exploratory modelling: Empirical target rotation based on loading differences. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 15(2), 45–55. https://doi.org/10.1027/1614-2241/a000163
20. Garcia-Garzon, E., Abad, F. J. ve Garrido, L. E. (2019b). Searching for G: A new evaluation of SPM-LS dimensionality. Journal of Intelligence, 7(3), 14. https://doi.org/10.3390/jintelligence7030014.
21. Garrido, L. E., Abad, F. J. ve Ponsoda, V. (2011). Performance of Velicer's minimum average partial factor retention method with categorical variables. Educational and Psychological Measurement, 71, 551–570. https://doi.org/10.1177/00131644103894
22. Golino, H. ve Christensen, A. P. (2020). EGAnet: Exploratory Graph Analysis - A framework for estimating the number of dimensions in multivariate data using network psychometrics. R package version 0.9.4. https://cran.r-project.org/web/packages/EGAnet/index.html.
23. Golino, H. F. ve Epskamp, S. (2016). Exploratory graph analysis: a new approach for estimating the number of dimensions in psychological research. arXiv preprint. Stat-Ap/ 1605.02231. Available at: http://arxiv.org/abs/1605.02231
24. Golino, H. F. ve Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PloS One, 12(6), e0174035. https://doi.org/10.1371/journal.pone.0174035.
25. Golino, H.F. ve Demetriou, A. (2017). Estimating the dimensionality of intelligence like data using Exploratory Graph Analysis. Intelligence. 62, 54–70. https://doi.org/10.1016/j. intell.2017.02.007.
26. Golino, H., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Sadana, R., Thiyagarajan, J. A. ve Martinez-Molina, A. (2020). Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. Psychological Methods, 25(3), 292-320. https://doi.org/10.1037/met0000255
27. Gorsuch, R. L. (1983). Factor Analysis. Philadelphia: Saunders.
28. Guttman L. (1954). Some necessary conditions for common-factor analysis. Psychometrika. 19(2), 149–61. https://doi.org/10.1007/BF02289162
29. Horn, J. L.(1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 30(2), 179–85. https://doi.org/10.1007/BF02289447
30. Hu, L. T. ve Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
31. Isvoranu, A.M., Borsboom, D., van Os, J., ve Guloksuz, S. (2016). A network approach to environmental impact in psychotic disorder: Brief theoretical framework. Schizophr Bull, 42, 870–873. https://doi.org/10.1093/schbul/sbw049.
32. Jamison, L., Christensen, A. P., ve Golino, H. (2021). Optimizing Walktrap’s Community Detection in Networks Using the Total Entropy Fit Index. https://doi.org/10.31234/osf.io/9pj2m
33. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational And Psychological Measurement. 20, 141–151. https://doi.org/10.1007/BF02289447
34. Keith, T.Z., Caemmerer, J.M. ve Reynolds, M.R. (2016). Comparison of methods for factor extraction for cognitive test-like data: Which overfactor, which underfactor? Intelligence, 54, 37–54. https://doi.org/10.1016/j.intell.2015.11.003
35. Kline, P. (1994). An Easy Guide To Factor Analysis. New York: Routledge.
36. Kossakowski, J. J., Epskamp, S., Kieffer, J. M., van Borkulo, C. D., Rhemtulla, M., ve Borsboom, D. (2015). The application of a network approach to Health-Related Quality of Life (HRQoL): Introducing a new method for assessing HRQoL in healthy adults and cancer patients. Qual Life Res, 25, 781–792. https://doi.org/10.1007/s11136-015-1127-z
37. Koyuncu, M. ve Kılıç, A.(2021). Classification of Scale Items with Exploratory Graph Analysis and Machine Learning Methods. International Journal of Assessment Tools in Education, 8(4), 928–947 https://doi.org/10.21449/ijate.880914.
38. Lubbe, D. (2019). Parallel analysis with categorical variables: Impact of category probability proportions on dimensionality assessment accuracy. Psychological Methods, 24(3), 339–351. https://doi.org/http://dx.doi.org/10.1037/met0000171.
39. Massara, G. P., Di Matteo, T., ve Aste, T. (2016). Network filtering for big data: Triangulated maximally filtered graph. Journal of Complex Networks, 5(2), 161–178. https://doi.org/10.48550/arXiv.1505.02445
40. Pons, P. ve Latapy, M. (2006). Computing communities in large networks using random walks. J. Graph Algorithms and Applications, 10, 191–218. https://doi.org/10.7155/jgaa.00189.
41. Preacher, K. J., Zhang, G., Kim, C. ve Mels, G. (2013). Choosing the optimal number of factors in exploratory factor analysis: A model selection perspective. Multivariate Behavioral Research, 48(1), 28-56. https://doi.org/10.1080/00273171.2012.710386.
42. R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
43. Raiche, G. (2010). nFactors: An R package for parallel analysis and non graphical solutions to the Cattell's scree test. R package version 2.3.3. https://cran.r-project.org/web/packages/nFactors/index.html.
44. Raiche, G., Riopel. M. ve Blais, J.G. (2006). Non graphical solutions for the Cattell’s scree test. Paper presented at the International Annual Meeting of the Psychometric Society, Montreal.
45. Revelle, W. ve Rocklin, T. (1979). Very simple structure: An alternative procedure for estimating the optimal number of interpretable factors. Multivariate Behavioral Research, 14(4), 403-414. doi: 10.1207/s15327906mbr1404_2.
46. Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1-36. https://doi.org/10.18637/jss.v048.i02
47. Royce, J.R. (1963). Factors as theoretical constructs. D.N. Jackson ve S. Messick (Ed.), Problems in Human Assessment. New York: McGrawHilI.
48. Schwarz, G. (1978). Estimating the dimension of a model. The annals of statistics, 6(2), 461–4. https://www.jstor.org/stable/2958889
49. Smith Bassett, D. S. ve Bullmore, E. (2006). Small-world brain networks. Neuroscientist, 12(6), 512–523. doi: 10.1177/1073858406293182.
50. Spearman, C. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15, 201–292. https://doi.org/10.2307/1412107
51. Tabachnick, B. G. ve Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston: Allyn and Bacon.
52. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 58(1), 267–288. https://www.jstor.org/stable/2346178
53. Timmerman, M. E. ve Lorenzo-Seva, U. (2011). Dimensionality assessment of ordered polytomous items with parallel analysis. Psychological Methods, 16(2), 209-220. https:/ /doi.org/10.1037/a0023353.
54. van der Maas, H. L., Dolan, C. V., Grasman, R. P., Wicherts, J. M., Huizenga, H. M., ve Raijmakers, M. E. (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychological review, 113(4), 842-861. doi: 10.1037/0033-295X.113.4.842
55. Velicer, W.F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3), 321–7. https://doi.org/10.1007/BF02293557
56. Velicer, W. F., Eaton, C. A. ve Fava, J. L. (2000). Construct explication through factor or component analysis: A reviewand evaluation ofalternative procedures for determining the number of factors or components. Richard D. Goffin, ve Edward Helmes (Ed.), Problems and solutions in human assessment (s. 41–71). New York, NY: Springer.
57. Velicer, W.F. ve Jackson, D. N. (1990). Component analysis versus common factor analysis: Some issues in selecting an appropriate procedure. Multivariate Behavioral Research, 25(1), 1–28. https://doi.org/ 10.1207/s15327906mbr2501_1 PMID: 26741964.
58. Wickham, H., Miller, E. ve Smith, D. (2023). haven: Import and Export 'SPSS', 'Stata' and 'SAS' Files. R package version 2.5.4, https://github.com/WizardMac/ReadStat, https://haven.tidyverse.org.
59. Zwick, W. R. ve Velicer, W. F. (1986). Comparison of five rules for determining the number of components to retain. Psychological Bulletin, 99(3), 432-442. http://dx.doi.org/10.1037/0033-2909.99.3.432.