Psychometric properties of the reading comprehension test ECOMPLEC.Sec

  1. Ricardo Olmos Albacete 1
  2. José Antonio León Cascón 1
  3. Lorena Alicia Martín Arnal 1
  4. José David Moreno Pérez 1
  5. Inmaculada Escudero Domínguez 2
  6. Fernando Sánchez Sánchez 3
  1. 1 Universidad Autónoma de Madrid
    info

    Universidad Autónoma de Madrid

    Madrid, España

    ROR https://ror.org/01cby8j38

  2. 2 Universidad Nacional de Educación a Distancia
    info

    Universidad Nacional de Educación a Distancia

    Madrid, España

    ROR https://ror.org/02msb5n36

  3. 3 TEA ediciones
Mostrar afiliaciones +
Revista:
Psicothema

ISSN: 0214-9915

Año de publicación: 2016

Volumen: 28

Número: 1

Páginas: 89-95

Tipo: Artículo

DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Psicothema

Resumen

Background: ECOMPLEC.Sec is a reading comprehension test for secondary students, conceived from a multidimensional perspective in line with large-scale educational surveys such as PISA or PIRLS. The objective of this study was to validate the theoretical model of ECOMPLEC.Sec. A bifactor model that postulates the existence of a general reading comprehension factor and three specific factors provided a good fit to the data. Method: 1,912 adolescents (13-18 years) participated in this study. Data analysis included construct validity via confirmatory factor analysis, and factors were regressed onto observed covariates for the interpretation of the constructs. Reliability was calculated from a non-linear SEM in order to justify the interpretability of the observed scale and subscale scores. Results: the bifactor model exhibited a significantly better fit to the data than the second-order model. Furthermore, construct validity analysis suggests the existence of specific reading comprehension factors. Finally, the reliability study also supports the idea of using a total score to obtain a measure of reading comprehension. Conclusions: ECOMPLEC.Sec displays a valid parsimonious factor structure, as well as metric properties that make it a suitable tool to assess reading comprehension.

Referencias bibliográficas

  • Abad, F., Olea, J., Ponsoda, V., & García, C. (2011). Medición en Ciencias Sociales y de la Salud [Measurement in social and health sciences]. Madrid: Editorial Síntesis.
  • Anmarkrud, O., & Braten, I. (2009). Motivation for reading comprehension. Learning and Individual Differences, 19, 252-256.
  • Bentler, P. M. (1990). Comparative fit indices in structural models. Psychological Bulletin, 107, 238-246.
  • Bentler, P. M. (2009). Alpha, dimension-free, and model-based internal consistency reliability. Psychometrika, 74(1), 137-143.
  • Brown, T.A. (2006). Confirmatory factor analysis for applied research. New York: Guilford Press.
  • Chen, F. F., Hayes, A., Carver, C. S., Laureceau, J-P., & Zhang, Z (2012). Modeling general and specific variance in multifaceted constructs: A comparison of the bifactor model to other approaches. Journal of Personality, 80(1), 219-251.
  • Cromley, J. G., Snyder-Hogan, L. E., & Luciw-Dubas, U. A. (2010). Reading comprehension of scientific text: A domain-specific test of the direct and inferential mediation model of reading comprehension. Journal of Educational Psychology, 102, 687-700.
  • Graesser, A. C. (2007). An introduction to strategic Reading comprehension. In D. S. McNamara (Ed.), Reading comprehension strategies (pp. 3-26). Mahwah, NJ: Lawrence Erlbaum Associates.
  • Graesser, A. C., Singer, M., & Trabasso, T. (1994). Constructing inferences during narrative text comprehension. Psychological Review, 101, 371395.
  • Gignac, G. E. (2007). Multi-facto modelling in individual differences research: Some suggestions and recommendations. Personality and Individual Differences, 42, 37-48.
  • Green, B. A. (1995). Comprehension of expository text from an unfamiliar domain: Effects of instruction that provides either domain-specific or strategy knowledge. Contemporary Educational Psychology, 2, 313-319.
  • Green, S. B., & Yang, Y. (2009). Reliability of summed items scores using structural equation modeling: An alternative to coefficient alpha. Psychometrika, 74(1), 155-167.
  • Hidi, S., & Harackiewicz, J. M. (2000). Motivating the academically unmotivated: A critical issue for the 21st century. Review of Educational Research, 70, 151-179.
  • Hyde, J. (1981). How large are cognitive gender differences? A meta-analysis. Psychological Bulletin, 104, 53-69.
  • Kintsch, W. (1988). The use of knowledge in discourse processing: A construction-integration model. Psychological Review, 95, 163-182.
  • León, J. A. (2004). Un nuevo enfoque de la competencia lectora basado en diferentes tipos de comprensión [A new approach to reading skills based on different kinds of comprehension]. Seminario de primavera 2004. Fundación Santillana, 39-50.
  • León, J.A., & Escudero, I. (2015). Understanding causality in Science discourse for Middle and High School Students. Summary task as a Strategy for Improving Comprehension. In K.L. Santi and D. Reed (Eds.), Improving Comprehension for Middle and High School Students (pp. 75-98). Springer International Publishing Switzerland.
  • León, J. A., Escudero, I., & Olmos, R. (2012). Ecomplec: una propuesta de evaluación de la comprensión lectora en Primaria y Secundaria [A proposal for the assessment of reading comprehension in primary and secondary education]. Madrid: TEA Ediciones (ISBN 978-84-1526245-9).
  • León, J. A., Escudero, I., Olmos, R., Sanz, M. M., Dávalos, T., & García, T. (2009). ECOMPLEC: un modelo de evaluación de la comprensión lectora en diversos tramos de la Educación Secundaria. Psicología Educativa, 15(2), 123-142.
  • McVey, J., & Kane, M. J. (2012). Why does working memory capacity predict variation in reading comprehension? On the influence of mind wandering and executive attention. Journal of Experimental Psychology, 141(2), 302-320.
  • Murray, A. L., & Johnson, W. (2013). The limitations of model fit in comparing the bi-factor versus higher-order models of human cognitive ability structure. Intelligence, 41, 407-422.
  • Muthén, L. K., & Muthén, B. O. (1998-2010). Mplus User’s Guide. Sixth Edition. Los Angeles, CA: Muthén & Muthén.
  • OECD (2013). PISA 2012 Assessment and Analytical Framework: Mathematics, Reading, Science, Problem Solving and Financial Literacy. PISA, OECD Publishing, Paris.
  • Ozuru, Y., Dempsey, K., & McNamara, D. S. (2009). Prior knowledge, reading skill, and text cohesion in the comprehension of science texts. Learning and Instruction, 19(3), 228-242.
  • Raykov, T. (2001). Bias of coefficient afor fixed congeneric measures with correlated errors. Applied Psychological Measuremalet, 25(1), 69-76.
  • Reise, S. P., Moore, T. M., & Haviland, M. G. (2012). Bifactor models and rotations: Exploring the extent to which multidimensional data Yield univocal Scale Scores. Journal of Personality Assessment, 92, 6, 544559.
  • Rijmen, F. (2011). Hierarchical factor item response theory models for PIRLS: Capturing clustering effects at multiple levels. Research Report. ETS, Princeton, New Jersey.
  • Sáenz, L. M., & Fuchs, L. S. (2002). Examining the reading difficulty of secondary students with learning disabilities. Expository versus narrative text. Remedial and Special Education, 23(1), 31-41.
  • Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1-10.
  • Unsworth, N., & McMillan, B. D. (2013). Mind wandering and reading comprehension: Examining the roles of wording memory capacity, interest, motivation, and topic experience. Journal of Experimental Psychology: Learning, Memory and Cognition, 39(3), 832-842.
  • Van den Broek, P., &, Espin, C. A. (2012). Connecting cognitive theory and assessment: Measuring individual differences in reading comprehension. School Psychology Review, 41(3), 315-325.
  • Vizcarro, C., & León, J. A. (Coords.) (1998). Nuevas tecnologías para el aprendizaje [New technologies for learning]. Madrid: Pirámide.
  • Zwaan, R., & Singer, M. (2003). Text comprehension. In A. C. Graesser, M. A. Gernsbacher & S. R. Goldman (Eds.), Handbook of discourse processes (pp. 83-121). Mahwah, NJ: Lawrence Erlbaum Associates.
Fuente de los datos: Dialnet