Crítica del análisis de la validez de constructo de la Escala de Detección de alumnos con Altas Capacidades para Padres (GRS 2)réplica a Tourón et al. (2023)

  1. José A. Martínez García 1
  1. 1 Universidad Politécnica de Cartagena
    info

    Universidad Politécnica de Cartagena

    Cartagena, España

    ROR https://ror.org/02k5kx966

Revista:
Revista de educación

ISSN: 0034-8082

Año de publicación: 2024

Número: 406

Páginas: 7-34

Tipo: Artículo

DOI: 10.4438/1988-592X-RE-2024-406-649 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista de educación

Resumen

The objective of this study is to reexamine the methods used to analyze the construct validity of the Gifted Rating Scales (GRS 2) Parent Form in Spain, originally conducted by Tourón et al. (2023). To achieve this, we build upon the proposals of these authors, offering constructive criticism of some of their procedures and suggesting alternative modeling and analysis methods. Our approach is primarily didactic, drawing extensively from the literature on structural equation modeling and psychometrics. Key elements of our critique include the distinction between reflective and formative models, the direction of causality and the underlying equations, the appropriateness of analyzing construct validity within a nomological network beyond factor analysis, and the use of chi-square testing for the structural model. Additionally, we propose various modeling options that align with current research trends in the measurement of high abilities, providing a foundation for future advancements in this discipline.

Referencias bibliográficas

  • Allen, B., & Pistone, L. F. (2023). Psychometric evaluation of a singleitem screening tool for the presence of problematic sexual behavior among preteen children. Child abuse & neglect, 143, 106327. https:// doi.org/10.1016/j.chiabu.2023.106327
  • Antonakis, J., Bendahan, S., Jacquart, P., Lalive, R. (2010). On making causal claims: A review and recommendations. Leadership Quarterly, 21, 1086-1120. https://doi.org/10.1016/j.leaqua.2010.10.010
  • Barret P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 42(5), 815-824. https://doi. org/10.1016/j.paid.2006.09.018
  • Beauducel, A., & Wittmann, W. W. (2005). Simulation Study on Fit Indexes in CFA Based on Data With Slightly Distorted Simple Structure. Structural Equation Modeling, 12(1), 41–75. https://doi. org/10.1207/s15328007sem1201_3
  • Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033- 2909.107.2.238
  • Bergkvist, L. & Rossiter, J. R. (2007). The predictive validity of multipleitem versus single-item measures of the same constructs. Journal of Marketing Research, 44 (2), 175-184. https://doi.org/10.1509/ jmkr.44.2.175
  • Bollen, K., & Lennox, R. (1991). Conventional wisdom on measurement: A structural equation perspective. Psychological Bulletin, 110(2), 305– 314. https://doi.org/10.1037/0033-2909.110.2.305
  • Bollen K. A. (2002). Latent variables in psychology and the social sciences. Annual review of psychology, 53, 605–634. https://doi. org/10.1146/annurev.psych.53.100901.135239
  • Bollen, K. A., & Diamantopoulos, A. (2017). In defense of causal-formative indicators: A minority report. Psychological Methods, 22(3), 581–596. https://doi.org/10.1037/met0000056
  • Boring, E. G. (1923). Intelligence as the test measures it. The New Republic, 35, 35-37.
  • Borsboom, D., & Cramer, A. O. (2013). Network analysis: an integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9, 91–121.
  • Borsboom, D., Mellenbergh, G. J., van Heerden, J. (2004). The concept of validity. Psychological Review, 111(4), 1061-1071.
  • Browne, M. W., MacCallum, R. C., Kim, C. T., Andersen, B. L., Glaser, R. (2002). When fit indices and residuals are incompatible. Psychological Methods, 7(4), 403–421. https://doi.org/10.1037//1082-989x.7.4.403
  • Cronbach, L. J., & Meehl, P. E. (1955). Construct validity in psychological test. Pyschological Bulletin, 52, 218-302. https://doi.org/10.1037/ h0040957
  • Durvasula, S., Sharma, S., Carter, K. (2012). Correcting the t statistic for measurement error. Marketing Letters, 23(3), 671–682. https://doi. org/10.1007/s11002-012-9170-9
  • Edwards, J. R. (2001). Multidimensional constructs in organizational behavior research: Towards an integrative and analytical framework. Organizational Research Methods, 4, 144–192.
  • Fan, X., & Sivo, S. A. (2005). Sensitivity of fit indexes to misspecified structural or measurement model components: Rationale of two-index strategy revisited. Structural Equation Modeling, 12, 334-367. https:// doi.org/10.1207/s15328007sem1203_1
  • Fonseca-Pedrero, E. (2017). Análisis de redes: ¿una nueva forma de comprender la psicopatología? Revista de Psiquiatría y Salud Mental, 10(4), 206-215.
  • Fornell, C. & Yi, Y. (1992). Assumptions of the Two-Step Approach to Latent Variable Modeling. Sociological Methods & Research, 20, 291-320.
  • Hayduk, L. A. (1996). LISREL Issues, Debates and Strategies. Baltimore, MD: Johns Hopkins University Press.
  • Hayduk, L. A. (2014a). Seeing perfectly-fitting factor models that are causally misspecified: Understanding that close-fitting models can be worse. Educational and Psychological Measurement, 74(6), 905-926. https://doi.org/10.1177/0013164414527449
  • Hayduk, L. A. (2014b). Shame for disrespecting evidence: The personal consequences of insufficient respect for structural equation model testing. BMC: Medical Research Methodology, 14, 124. https://doi. org/10.1186/1471-2288-14-124
  • Hayduk, L. A., Cummings, G. Boadu, K., Pazderka-Robinson, H., & Boulianne, S. (2007). Testing! testing! one, two, three – Testing the theory in structural equation models! Personality and Individual Differences, 42(5), 841-850.
  • Hayduk, L. A., Estabrooks, C. A., Hoben, M. (2019). Fusion Validity: Theory-Based Scale Assessment via Causal Structural Equation Modeling. Frontiers in Psychology, 10, 1139. https://doi.org/10.3389/ fpsyg.2019.01139
  • Hayduk, L. A., & Glaser, D. N. (2000a). Jiving the four-step, waltzing around factor analysis, and other serious fun. Structural Equation Modeling, 7(1), 1-35.
  • Hayduk, L. A., & Glaser, D. N. (2000b). Doing the four-step, right-2-3, wrong-2-3: A brief reply to Mulaik and Millsap; Bollen; Bentler; and Herting and Costner. Structural Equation Modeling, 7(1), 111-123.
  • Hayduk, L. A., & Littvay, L. (2012). Should researchers use single indicators, best indicators, or multiple indicators in structural equation models? BMC Medical Research Methodology, 12, 159, 1-17. https:// doi.org/10.1186/1471-2288-12-159
  • Hayduk, L.A., Ratner, P.A., Johnson, J.L., Bottorff, J.L. (1995). Attitudes, ideology and the factor model. Political Psychology, 16, 479–507
  • Hu, L. T., & Bentler, P. M. (1999). Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi. org/10.1080/10705519909540118
  • Jabůrek, M., Ťápal, A., Portešová, Š., Pfeiffer, S. I. (2020). Validity and Reliability of Gifted Rating Scales-School Form in Sample of Teachers and Parents – A Czech Contribution. Journal of Psychoeducational Assessment, 39(3), 361–371. https://doi.org/10.1177/0734282920970718
  • Jordan, P. J., & Troth, A. C. (2020). Common method bias in applied settings: The dilemma of researching in organizations. Australian Journal of Management, 45(1), 3-14. https://doi.org/10.1177/0312 896219871976
  • Jöreskog, K. G. (1978). Structural analysis of covariance and correlation matrices. Psychometrika, 43, 443-477.
  • Jung J. Y. (2022). Physical giftedness/talent: A systematic review of the literature on identification and development. Frontiers in Psychology, 13, 961624. https://doi.org/10.3389/fpsyg.2022.961624
  • Kelly, M. S. & Peverly, S. T. (1992). Identifying bright kindergartners at risk for learning difficulties: Predictive validity of a kindergarten screening tool. Journal of School Psychology, 30, 245-258.
  • Liddell, T. M. & Kruschke, J. K. (2018). Analyzing ordinal data with metric models: What could possibly go wrong? Journal of Experimental Social Psychology, 79, 328-348. https://doi.org/10.1016/j.jesp.2018.08.009
  • Lissitz, R. W. (Editor) (2009). The Concept of Validity: Revisions, New Directions, and Applications. Charlotte, NC: Information Age Publishing.
  • MacCallum, R. C., & Austin, J. T. (2000). Applications of Structural Equation Modeling in Psychological Research. Annual Review of Psychology, 51, 201-226. https://doi.org/10.1146/annurev.psych.51.1.201
  • Manzano, A. Arranz, F., Enrique B. (2008). Contexto familiar, superdotación, talento y altas capacidades». Anuario de psicología / The UB Journal of psychology, 39(3), 289-309, https://raco.cat/index. php/AnuarioPsicologia/article/view/123643
  • Marsh, H. W., Hau, K. T., Wen, Z. (2004). In search of golden rules: Comment of hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler's (1999) findings. Structural Equation Modeling, 11, 320-341. doi:10.1207/ s15328007sem11032
  • Martínez, J. A., Ko, Y. J., & Martínez, L. (2010). An application of fuzzy logic to service quality research: A case of fitness service. Journal of Sport Management, 24, 502-523. https://doi.org/10.1123/jsm. 24.5.502
  • Martínez, J. A. & Martínez, L. (2009). El análisis factorial confirmatorio y la validez de escalas en modelos causales. Anales de Psicología, 25(2), 368-374.
  • Martínez, J. A. & Martínez, L. (2010a). Re-thinking perceived service quality. An alternative to hierarchical and multidimensional models. Total Quality Management & Business Excellence, 21(1), 93-118
  • Martínez, J. A. & Martínez, L. (2010b). La medición de la satisfacción de servicios deportivos a través de la lógica borrosa. Revista de Psicología del Deporte, 19(1), 41-58.
  • Martínez, L., & Martínez, J. A. (2008). Developing a multidimensional and hierarchical service quality model for the travel agencies industry. Tourism Management, 29, 706–720.
  • Matthews, R. A., Pineault, L. & Hong, Y. H. (2022). Normalizing the use of single-item measures: Validation of the single item compendium for organizational psychology. Journal of Business and Psychology, 37, 639-673. https://doi.org/10.1007/s10869-022-09813-3
  • McIntosh, C. N. (2007). Rethinking fit assessment in structural equation modelling: A commentary and elaboration on Barrett (2007). Personality and Individual Differences, 42(5), 859–867. https://doi.org/10.1016/j. paid.2006.09.020
  • McIntosh, C. N. (2012). Improving the evaluation of model fit in confirmatory factor analysis: A commentary on Gundy, CM, Fayers, PM, Groenvold, M., Petersen, M. Aa., Scott, NW, Sprangers, MAJ, Velikov, G., Aaronson, NK (2011). Comparing higher-order models for the EORTC QLQ-C30. Quality of Life Research. Quality of Life Research, 21(9), 1619- 1621.
  • McNeish, D., & Wolf, M. G. (2023). Dynamic fit index cutoffs for confirmatory factor analysis models. Psychological Methods, 28(1), 61–88. https://doi.org/10.1037/met0000425
  • Nakano, T. D., Primi, R., Ribeiro, W. D., Almeida, L. D. (2016). Multidimensional Assessment of Giftedness: criterion Validity of Battery of Intelligence and Creativity Measures in Predicting Arts and Academic Talents. Anales De Psicologia, 32, 628-637.
  • Niemand, T. & Mai, R. (2018). Flexible Cutoff Values for Fit Indices in the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 46(6), 1148-1172.
  • Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge University Press.
  • Pfeiffer, S. I., Petscher, Y., & Kumtepe, A. (2008). The Gifted Rating Scales- School Form: A Validation Study Based on Age, Gender, and Race. Roeper review, 30(2), 140–146. https://doi.org/10.1080/02783190801955418
  • Rönkkö, M., McIntosh, C. N., Antonakis, J., Edwards, J. R. (2016). Partial least squares path modeling: Time for some serious second thoughts. Journal of Operations Management, 47–48, 9–27. https:// doi.org/10.1016/j.jom.2016.05.002
  • Ropovik, I. (2015). A cautionary note on testing latent variable models. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.01715
  • Sofologi, M., Papantoniou, G., Avgita, T., Lyraki, A., Thomaidou, C., Zaragas, H., Ntritsos, G., Varsamis, P., Staikopoulos, K., Kougioumtzis, G., Papantoniou, A., & Moraitou, D. (2022). The Gifted Rating Scales- Preschool/Kindergarten Form (GRS-P): A Preliminary Examination of Their Psychometric Properties in Two Greek Samples. Diagnostics (Basel, Switzerland), 12(11), 2809. https://doi.org/10.3390/diagno stics12112809
  • Spanos. A. (2019). Probability Theory and Statistical Inference: Empirical Modeling with Observational Data, Cambridge University Press, Cambridge
  • Spanos, A. (2021). Modeling vs. inference in frequentist statistics: ensuring the tustworthiness of empirical evidence. Descargado desde: https://errorstatistics.files.wordpress.com/2021/03/modeling-vsinference- 3-2021.pdf
  • Steiger, J. H. (2007). Understanding the limitations of global fit assessment in structural equation modelling. Personality and Individual Differences, 42(5), 893-898.
  • Tourón, M., Navarro-Asensio, E., Tourón, J. (2023). Validez de Constructo de la Escala de Detección de alumnos con Altas Capacidades para Padres, (GRS 2), en España. Revista de Educación, 1(402), 55-83. https://doi.org/10.4438/1988-592X-RE-2023-402-595
  • Wulf, J. N., Sajons, G. B., Pogrebna, G. et al. (2023). Common methodological mistakes. The Leadership Quarterly, 34(1), 101677, https://doi.org/10.1016/j.leaqua.2023.101677
Fuente de los datos: Dialnet