Validation of the Spanish adaptation of the School Atitude Assessment Survey-Revised using multidimensional Rasch analysis

  1. Alejandro Veas
  2. Juan-Luis Castejón
  3. Raquel Gilar
  4. Pablo Miñano
Journal:
Anales de psicología

ISSN: 0212-9728,  1695-2294

Year of publication: 2017

Volume: 33

Issue: 1

Pages: 74-81

Type: Article

DIALNET GOOGLE SCHOLAR

More publications in: Anales de psicología

Abstract

The School Attitude Assessment Survey-Revised (SAAS-R) was developed by McCoach and Siegle (2003b) and validated in Spain by Mi- ñano, Castejón, and Gilar (2014) using Classical Test Theory. The objective of the current research is to validate SAAS-R using multidimensional Rasch analysis. Data were collected from 1398 students attending different high schools. Principal Component Analysis supported the multidimensional SAAS-R. The item difficulty and person ability were calibrated along the same latent trait scale. 10 items were removed from the scale due to misfit with the Rasch model. Differential Item Functioning revealed no significant differences across gender for the remaining 25 items. The 7- category rating scale structure did not function well, and the subscale goal valuation obtained low reliability values. The multidimensional Rasch model supported 25 item-scale SAAS-R measures from five latent factors. Therefore, the advantages of multidimensional Rasch analysis are demonstrated in this study

€ View funding

Funding information

The present work was supported by the Vice Chancellor for Research of the University of Alicante [GRE11-15] and the Spanish Ministry of Economy and Competitiveness [EDU2012-32156]. The corresponding author is funded by the Ministry of Economy and Competitiveness (Reference of the grant: BES-2013064331).

Funders

Bibliographic References

  • Adams, R. J., Wilson, M., & Wang, W. C. (1997). The multidimensional random coefficient multinomial logit model. Applied Psychological Measurement, 21(1), 1-23.
  • Andrich, D. (1978). A rating formulation for ordered response catego-ries. Psychometrika, 43, 561-573.
  • Bond, T. G., & Fox, C. M. (2007). Applying the Rasch model: Fundamental measurement in the human sciences (2nd ed.). Mahwah, N. J.: Erlbaum.
  • Cadime, I., Ribeiro, I., Viana, F. L., Santos, S., & Prieto, G. (2014). Cali-bration of a reading comprehension test for Portuguese students. Anales de psicología, 30(3), 1025-1034.
  • Elliot, A. J. (2005). A conceptual history of the achievement goal con-struct. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 52-72). New York, USA: The Guilford Press.
  • Green, J., Liem, G.D., Martin, A.J., Colmar, S., Marsh. H.W., & McIner-ney, D. (2012). Academic motivation, self-concept, engagement, and performance in high school : Key processes from a longitu-dinal perspective. Journal of Adolescence, 35(5), 1111-1122.
  • Inglés, C. J., Martínez-Monteagudo, M. C., García-Fernández, J. M., Va-lle, A., & Castejón, J. L. (2014). Goal orientation profiles and self-concept of secondary school students. Revista de Psicodidáctica, 20(1), 99-116.
  • Kitsantas, A., & Zimmerman, B. J. (2009). College students´ homework and academic achievement: The mediating role of self-regulatory beliefs. Metacognition and Learning, 4(2), 97-110.
  • Lee, M., Zhu, W., Ackley-Holbrook, E., Brower, D. G., & McMurray, B. (2014). Calibration and validation of the Psysical Activity Barrier Scale for persons who are blind or visually impaired. Disability and Health Journal, 7, 309-317.
  • Linacre, J. M. (1998). Structure in Rasch residuals: Why principal com-ponent analysis? Rasch Measurement Transsactions, 12, 636.
  • Linacre, J. M. (2002). Optimizing rating scale category effectiveness. Journal of Applied Measurement, 3, 85-106.
  • Linacre, J. M. (2011). Winsteps (version 3.81) [computer software]. Chi-cago: MESA.
  • Linacre, J. M., & Wright, B. D. (1998). A user´s guide to Bigsteps/Winsteps. Chicago, IL:Winsteps.com.
  • Linacre, J. (2012). A user’s guide to Winsteps & Ministeps Rasch-Model Com-puter Programs. Program Manual 3.74.0. 2012. Access in October 2014, from http://www.winsteps/com/winman
  • Matthews, J.S., Pointz, C.C., & Morrison, F.J. (2009). Early gender dif-ferences in self-regulation and academic achievement. Journal of Educational Psychology, 101(3), 689-704.
  • McCoach, D. B. (2002). A validation study of the School Attitude As-sessment Survey. Measurement and Evaluation in Counseling and Devel-opment, 35, 66-77.
  • McCoach, D. B., & Siegle, D. (2001). A comparison of high achievers’ and low achievers’ attitudes, perceptions, and motivations. Aca-demic Exchange Quarterly, 5, 71-76.
  • McCoach, D. B., & Siegle, D. (2003a). Factors that differentiate under-achieving gifted students from high-achieving gifted students. Gifted Child Quarterly, 47, 144-154.
  • McCoach, D. B., & Siegle, D. (2003b). The School Attitude Assessment Survey-Revised: A new instrument to identify academically able students who underachieve. Educational and Psychological Measurement, 63, 414-429.
  • Meece, J. L., Anderman, E. M., & Anderman, L. H. (2006). Classroom goal structure, student motivation, and academic achievement. Annual Review Psychology, 57, 487-503.
  • Meece, J. L., Bowwer, B., & Burg, S. (2006). Gender and motivation. Journal of School Psychology, 44(5), 351-373.
  • Miñano, P., & Castejón, J. L. (2011). Variables cognitivas y motivacio-nales en el rendimiento académico en Lengua y Matemáticas: un modelo estructural. Revista de Psicodidáctica, 16(2), 203-230.
  • Miñano, P., Castejón, J. L., & Gilar, R. (2014). Psychometric properties of the Spanish Adaptation of the School Attitude Assessment Sur-vey-Revised. Psicothema, 26(3), 423-430.
  • Muñiz, J. (1996). Psicometría. Madrid: Universitas S.A.
  • Prieto, G., & Delgado, A. R. (2003). Análisis de un test mediante el modelo de Rasch. Psicothema, 15(1), 94-100.
  • Rasch, G. (1960). Probabilistic models for some intelligence and achievement test. Copenhagan: Danish Institute for Educational Research.
  • Rasch, G. (1980). Probabilistic models for some intelligence and achievement test (Expanded ed.). Chicago: University of Chicago Press.
  • Reckase, M. M. (1997). The past and the future of multidimensional item response theory. Applied Psychological Measurement, 21(1), 25-36.
  • Smith, L., Sinclair, K. E., & Chapman, E. S. (2002). Student´s goals, self-efficacy, self-handicapping and negative affective responses: An Australian senior school student study. Contemporary Educational Psychology, 27, 471-485.
  • Vecchione, M., Alessandri, G., & Marsicano, G. (2014). Academic mo-tivation predicts educational attainment: Does gender make a dif-ference? Learning and Individual Differences, 32, 124-131.
  • Wang, W. C., Cheng, Y. Y., & Wilson, M. (2005). Local item depend-ence for items across tests connected by common stimuli. Educa-tional and Psychological Measurement, 65(1), 5-27.
  • Wang, W. C., Yao, G., Tsai, Y. J., Wang, J. D., & Hsieh, C. L. (2006). Val-idating, improving reliability, and estimating correlation of the four subscales in the WHOQOL-BREF using multidimensional item response models. Psychological Methods, 9(1), 116-136.
  • Wright, B. D. (1996). Local dependency, correlations and principal components. Rasch Measurement Transactions, 10(3), 509-511.
  • Wright, B. D. (1997). A history of social science measurement. Educa-tional Measurement: Issues and Practice, 16(4), 33-45.
  • Wright, B. D., & Linacre, J. M. (1989). Observations are always ordinal; measurements, however, must be interval. Archives of Physical Medi-cine and rehabilitation, 70(12), 857-860.
  • Wright, B. D., & Masters, G. N. (1982). Rating scale analysis. Chicago: MESA Press.
  • Wu, M. L., Adams, R. J., Wilson, M. R., & Haldane, S. A. (2007). ACER ConQuest, version 2.0: Generalized item response modelling software. Cam-berwell, Victoria: Australian Council for Educational Research.
  • Yen, W. M. (1984). Effect of local item dependence on the fit and equating performance of the three parameter logistic model. Ap-plied Psychological Measurement, 8, 125-145.
  • Yen, W. M. (1993). Scaling performance assessments: strategies for managing local item dependence. Journal of Educational Measurement, 30, 187-213.
  • Zeegers, P. (2004). Student learning in higher education: a path analy-sis of academic achievement in science. Higher Education Research and Development, 23, 35-56.
Data source: Dialnet