A Mixture Rasch Model Analysis of Data from a Survey of Novice Teacher Core Competencies


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DOI:

https://doi.org/10.52380/ijcer.2023.10.1.349

Keywords:

Mixture rasch model, Validity, Survey research, Teacher effectiveness

Abstract

Although the Rasch model is used to measure latent traits like attitude or ability where there are multiple latent structures within the dataset it is best to use a technique called the Mixture Rasch Model (MRM) which is a combination of a Rasch model and a latent class analysis (LCA). This study used data from a survey for teachers, teacher candidates, and teacher education program faculty with a sample of 296 candidates, 648 graduates, and 501 program personnel. Survey items based on these competencies asked teacher candidates, graduates, and teacher education program faculty in one Western state how well the program attended prepared candidates for the teaching profession. The 40 items common to surveys of the three groups were submitted to mixture Rasch analysis to determine whether distinct patterns of item response were discernible. Analyses yielded two classes which brings the construct validity of the survey into question. Results showed that the Mixture Rasch Model is and can be useful to determine sub-groups for survey researchers. This research presents a demonstration of usefulness of the Mixture Rasch Model for the analysis of survey data.

References

Baghaei, P., & Carstensen, C. H. (2013). Fitting the mixed Rasch model to a reading comprehension test: Identifying reader types. Practical Assessment, Research & Evaluation, 18(5), 1-13. https://doi.org/10.7275/n191-pt86

Bond,T. G., & Fox, C.M. (2007). Applying the Rasch model (2nd Ed). Erlbaum Associates.

Briggs, D. C., Circi, R., Seidel, K, & Green, K. (2013). Challenges in Measuring Core Competencies in Teacher Preparation Programs. Paper presented at the annual meeting of the American Educational Research Association, San Francisco, CA, May 1, 2013

Cressie, N., & Read, T. R. C. (1984). Multinomial Goodness-Of-Fit Tests. Journal of the Royal Statistical Society: Series B (Methodological), 46(3), 440–464. https://doi.org/10.1111/j.2517-6161.1984.tb01318.x

Dallas, A. D., & Willse, J. T. (2014). Survey analysis with mixture Rasch models. Journal of Applied Measurement, 15(4), 394-404.

Darling-Hammond, L., & Bransford, J. Preparing teachers for a changing world: What teachers should learn and be able to do. Jossey-Bass.

Ferguson, R. F. (1991). Paying for public education: New evidence on how and why money matters. Harvard Journal on Legislation, 28(2), 465-498. https://heinonline.org/HOL/LandingPage?handle=hein.journals/hjl28&div=24&id=&page=

Frick, H., Strobl, C., & Zeileis, A. (2014). Rasch Mixture Models for DIF Detection. Educational and Psychological Measurement, 75(2), 208–234. https://doi.org/10.1177/0013164414536183

Frick, H., Strobl, C., & Zeileis, A. (2015). Rasch mixture models for DIF detection: A comparison of old and new score specifications. Educational and Psychological Measurement, 75(2), 208-234. https://doi.org/10.1177/0013164414536183

Gauggel, S., Böcker, M., Heinemann, A. W., Lämmler, G., Borchelt, M.,& Steinhagen- Thiessen, E. (2004). Patient–staff agreement on Barthel Index scores at admission and discharge in a sample of elderly stroke patients. Rehabilitation Psychology, 49 (1), 21–27. https://doi.org/10.1037/0090-5550.49.1.21

Goe, L. (2007). The link between teacher quality and student outcomes: A Research synthesis. National Comprehensive Center for Teacher Quality.

Hanushek, E. A., & Rivkin, S. G. (2010, May). Using Value Added Measures of Teacher Quality. Brief 9. Urban Institute, National Center for Analysis of Longitudinal Data in Education Research (CALDER).

Hartnett-Edwards, K., Seidel, K., Spurlin, M., Anderson, S., Green, K., & Briggs, D. (2013). An exploration of novice teacher core competencies: Relationship to student achievement and depth of preparation. Presented at AACTE Annual Meeting, Orlando, Florida, February 2013.

Kaiser, F.G., Keller, C. (2001). Disclosing situational constraints to ecological behavior: A confirmatory application of the mixed Rasch model. European Journal of Psychological Assessment, 17, 212-221. https://doi.org/10.1027/1015-5759.17.3.212

Linacre, J. M. (2010). A user’s guide to winsteps ministep 3.70.0: Rasch-model computer programs. Chicago: Winsteps.

Merbitz, C., Morris, J., & Grip, J. C. (1989). Ordinal scales and foundations of misinference. Archives of Physical Medicine and Rehabilitation, 70(4), 308. https://doi.org/10.5555/uri:pii:0003999389901512

Mislevy, R. J., & Verhelst, N. (1990). Modeling item responses when different subjects employ different solution strategies. Psychometrika, 55, 195–215. https://doi.org/10.1007/BF02295283

Rost, J. (1990). Rasch models in latent classes: An integration of two approaches to item analysis. Applied Psychological Measurement, 14(3), 271-282. https://doi.org/10.1177/014662169001400305

Sanders, W.L., Horn, S.P. (1998). Research Findings from the Tennessee Value-Added Assessment System (TVAAS) Database: Implications for Educational Evaluation and Research. Journal of Personnel Evaluation in Education 12, 247–256 https://doi.org/10.1023/A:1008067210518

Sanders, W.L., Rivers, J.C. (1996). Cumulative and residual effects of teachers on future student academic achievement. Research Progress Report. University of Tennessee Value-Added Research and Assessment Center.

Sanders, W.L., Wright, S.P. & Horn, S.P. (1997). Teacher and Classroom Context Effects on Student Achievement: Implications for Teacher Evaluation. Journal of Personnel Evaluation in Education 11, 57–67 https://doi.org/10.1023/A:1007999204543

Şen, S. (2016). Applying the Mixed Rasch Model to the Runco Ideational Behavior Scale. Creativity Research Journal, 28(4), 426-434. https://doi.org/10.1080/10400419.2016.1229985

Toker, T. & Green, K. (2021). A Comparison of Latent Class Analysis and the Mixture Rasch Model Using 8th Grade Mathematics Data in the Fourth International Mathematics and Science Study (TIMSS-2011) . International Journal of Assessment Tools in Education , 8 (4) , 959-974 . DOI: 10.21449/ijate.1024251

Smith, E. V., Jr. (2001). Evidence for the reliability of measures and validity of measure interpretation: A Rasch measurement perspective. Journal of Applied Measurement, 2 (3), 281–311.

von Davier, M. (2001). WINMIRA [Computer software]. Institut für die Pädagogik der Naturwissenschaften

von Davier, M. (2001b). WINMIRA user manual [Computer software manual]. Institut für die Pädagogik der Naturwissenschaften

Wenglinsky, H. (2002). The link between teacher classroom practices and student academic performance. Education Policy Analysis Archives, 10, 1-30. https://doi.org/10.14507/epaa.v10n12.2002

Wright, B. D., & Stone, M. H. (2004). Best test design. MESA Press.

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19.03.2023 — Updated on 20.03.2023

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Toker, T., & Seıdel, K. (2023). A Mixture Rasch Model Analysis of Data from a Survey of Novice Teacher Core Competencies. International Journal of Contemporary Educational Research, 10(1), 147–156. https://doi.org/10.52380/ijcer.2023.10.1.349 (Original work published March 19, 2023)

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