Abstract
This chapter introduces Rasch analysis techniques and lays the groundwork for conducting and understanding Rasch analysis of rating scale data. Examples are given related to the use of Rasch analysis to evaluate test data. Readers are introduced to a 13-item self-efficacy scale used frequently in examples and end-of-chapter exercises. Readers are guided through the logistics of entering raw data in spreadsheets in preparation for a Rasch analysis. Next, the chapter progresses to a discussion of Flaws in the Use of Non-Rasch Techniques to Confront Missing Data and Action and Consequence of Just Entering Data and Not Conducting a Rasch Analysis. The final section of the chapter presents an introduction to logits and the Rasch equation for dichotomous items is explained. The chapter finishes up with Keywords and Phrases, Quick Tips, Data Files, References, and Additional Readings.
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Notes
- 1.
Our purpose in using the STEBI-B is to present useful examples for colleagues in and beyond science education. The number of rating categories is not central. Our past work suggests that removal of the original middle category of Uncertain (and the addition of the Barely Agree and Barely Disagree categories) yields added measurement information (see Bradley, Cunningham, Akers, & Knutson, 2011). As a result, readers will note that our STEBI-B examples present a six-category scale.
References
Boone, W., Townsend, S., & Staver, J. (2011). Using Rasch theory to guide the practice of survey development and survey data analysis in science education and to inform science reform efforts: An exemplar utilizing STEBI self-efficacy data. Science Education, 95(2), 258–280.
Bradley, K., Cunningham, J., Akers, K., Knutson, N. (2011, April). Middle category or survey pitfall: Using Rasch modeling to illustrate the middle category measurement flaw. Paper presented at the annual meeting of the American Educational Research Association.
Enochs, L. G., & Riggs, I. M. (1990). Further development of an elementary science teaching efficacy belief instrument: A pre-service elementary scale. School Science and Mathematics, 90(8), 694–706. doi:10.1111/j.1949-8594.1990.tb12048.x.
Glass, G. V., & Stanley, J. C. (1970). Statistical methods in education and psychology. Englewood Cliffs, NJ: Prentice-Hall, Inc.
Linacre, J. M. (2012) Winsteps (Version 3.74) [Software]. Available from http://www.winsteps.com/index.html
Stevens, S. S. (1959). Chap. 2: Measurement, psychophysics and utility. In C. W. Churchman & P. Ratoosh (Eds.), Measurement: Definitions and theories. New York: Wiley.
Wright, B. D., & Masters, G. N. (1982). Rating scale analysis. Chicago: MESA Press.
Additional Readings
A classic text that specifically considers the ins and outs of Rasch analysis for rating scales.
Wright, B. D., & Masters, G. N. (1982). Rating scale analysis. Chicago: MESA Press.
In this article Ben Wright presents a summary of the work of Stevens and discusses in what manner Rasch measurement addresses the issue of “measurement”.
Wright, B. D. (1997) S. S. Stevens revisited. Rasch Measurement Transactions 11(1), 552–553. http://www.rasch.org/rmt/rmt111n.htm
A brief sample article that provides readers with an exemplar of Rasch measurement applications in the field of medicine.
Gothwal, V. K., Wright, T. A., Lamoureux, E. L., & Pesudovs, K. (2009, October). Rasch analysis of visual function and quality of life questionnaires. Optometry and Vision Science, 86(10), 1160–1168.
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Boone, W.J., Staver, J.R., Yale, M.S. (2014). Rating Scale Surveys. In: Rasch Analysis in the Human Sciences. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-6857-4_2
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DOI: https://doi.org/10.1007/978-94-007-6857-4_2
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