By Russell Waugh
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Extra resources for Applications of Rasch Measurement in Education (Education in a Competitive and Globalizing World)
J. Dorans, R. Flaugher, B. F. Green, R. J. Mislevy, L. Steinberg and D. ), Computerized Adaptive Testing: A Primer (pp. 1-22). Hillsdale, NJ: Lawrence Erlbaum Associates. Wainer, H. (1993). Some practical considerations when covering a linearly administered test to an adaptive format. Journal of Educational Measurement, 12, 15-20. , and Wright, B. D. (1980). Robust estimation of ability in the Rasch model. Psychometrika, 45, 373-391. Waugh, R. F. (2006). Rasch measurement. In N. J. ), The Encyclopedia of Measurement and Statistics.
Item 90). Item Banking and Computerized Adaptive Testing with Rasch Measurement 21 RASCH ANALYSIS FOR THE 20 EXTRA ITEMS (LINKED TO THE 78 ITEM SCALE) Further analysis with the RUMM program tested the extra 50 items (N=610) in order to create a linear scale of mathematics achievement with the 20 items that fitted the measurement model. Ten common items from the set of 78 items were included as part of the 50 items. The residuals were examined. The global item fit residuals and global student standardised fit residuals have a mean near zero and a standard deviation near one indicating a reasonable fit to the measurement model.
They found it moderately easy to find the solution to the equation M – 38 = 152 (item 197) and they found it moderately hard to find the solution to the equation 175 = E – 5 (item 117). Table 6. 95 Table 7. 25 The items relating to finding a solution to an equation involving a given condition (see Table 8) were found to be ordered from moderately hard (item 115) to very hard (item 164). For example, the students found it moderately hard to find the equation in which E has the highest value (item 115).