CUTLO= cut off responses with low expectations = 0, no
Use this if guessing or response sets are evident. CUTLO= cuts off the bottom right-hand corner of the Scalogram in Table 22.
Eliminates (cuts off) observations where examinee measure is CUTLO= logits or more (user-rescaled by USCALE=) lower than item measure, so that the examinee has a low probability of success. The elimination of off-target responses takes place after PROX has converged. After elimination, PROX is restarted, followed by JMLE estimation and point-measure and fit calculation using only the reduced set of responses. This may mean that the original score-based ordering is changed.
For further discussion of tailoring the data to remove guessing situations, see "Using a Theorem by Andersen and the Dichotomous Rasch Model to Assess the Presence of Random Guessing in Multiple Choice Items." David Andrich, Ida Marais, and Stephen Humphry, Journal of Educational and Behavioral Statistics, October, 2011.
CUTLO= is equivalent to Waller's procedure in Waller, M.I. (1976) "Estimating Parameters in the Rasch Model: Removing the Effects of Random Guessing", Report No. ETS-RB-76-0, Educational Testing Service, Princeton, N.J. http://files.eric.ed.gov/fulltext/ED120261.pdf
The CUTLO= value to USCALE=. So, if USCALE=10, then CUTLO=-1 means "omit responses where the person ability is 1 user-scaled unit or more lower than the item difficulty."
Usually with CUTLO= and CUTHI=, misfitting items aren't deleted - but miskeys etc. must be corrected first. Setting CUTLO= and CUTHI= is a compromise between fit and missing data. If you loose too much data, then increase the values. If there is still considerable misfit or skewing of equating, then decrease the values.
Here are the usual effects of CUTLO= and CUTHI=
1. Fit to the Rasch model improves.
2. The count of observations for each person and item decreases.
3. The variance in the data explained by the measures decreases.
CUTLO= is equivalent to the procedure outlined in Bruce Choppin. (1983). A two-parameter latent trait model. (CSE Report No. 197). Los Angeles, CA: University of. California, Center for the Study of Evaluation, and the procedure in David Andrich, Ida Marais, and Stephen Humphry (2012) Using a Theorem by Andersen and the Dichotomous Rasch Model to Assess the Presence of Random Guessing in Multiple Choice Items Journal of Educational and Behavioral Statistics, 37, 417-442.
Polytomous items: CUTLO= and CUTHI= trim the data relative to the item difficulty, so they tend to remove data in high and low categories. You can adjust the item difficulty relative to the response structure using SAFILE=.
Example 1: Disregard responses where examinees are faced with too great a challenge, and so might guess wildly, i.e., where examinee measure is 2 or more logits lower than item measure:
CUTLO= -2 ; 12% success
This is equivalent to a "Optimum Appropriateness Measurement" (OAM) model in which it is assumed that persons might guess on all the items, so all responses in guessing situations are eliminated.
GUTTMAN SCALOGRAM OF RESPONSES:
| 12 22 1 3231311 1322112 2 113322
147 +0001111110011010100111111000001000 154
130 +1100101111001110110101000000010100 135
93 +011110010100111010000001101000010 098
129 +111110111011011000000000000001010 134
134 +000110101110010001110010001000010 139
133 +100100111110000000101010011000000 138
137 +100000011110100101101000010000000 143
141 +110100010011100001100000000001010 147
138 +10100101011101000000000010010 144
113 +1011001000101111000000000000010 118
144 +10000010100010001000010001 151
114 +1000010010000100 119
| 12 22 1 3231311 1322112 2 113322
Example 2: Richard Gershon applied this technique in Guessing and Measurement with CUTLO=-1 ; 27% success
Example 3: We have some misbehaving children in our sample, but don't want their behavior to distort our final report.
An effective approach is in two stages:
Stage 1. calibrate the items using the good responses
Stage 2. anchor the items and measure the students using all the responses.
In Stage 1, we trim the test. We want to remove the responses by children that are so off-target that successes are probably due to chance or other off-dimensional behavior. These responses will contain most of the misfit. For this we analyze the data using
CUTLO= -2 (choose a suitable value by experimenting)
CUTLO= -1.39 ; 20% success
CUTLO= -1.10 ; 25% success
write an item file from this analysis:
In Stage 2. Anchor all the items at their good calibrations:
Include all the responses (omit CUTLO=)
We can now report all the children without obvious child mis-behavior distorting the item measures.
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|Rasch Measurement Transactions (free, online)||Rasch Measurement research papers (free, online)||Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch||Applying the Rasch Model 3rd. Ed., Bond & Fox||Best Test Design, Wright & Stone|
|Rating Scale Analysis, Wright & Masters||Introduction to Rasch Measurement, E. Smith & R. Smith||Introduction to Many-Facet Rasch Measurement, Thomas Eckes||Invariant Measurement with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments, George Engelhard, Jr. & Stefanie Wind||Statistical Analyses for Language Testers, Rita Green|
|Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar||Journal of Applied Measurement||Rasch models for measurement, David Andrich||Constructing Measures, Mark Wilson||Rasch Analysis in the Human Sciences, Boone, Stave, Yale|
|in Spanish:||Análisis de Rasch para todos, Agustín Tristán||Mediciones, Posicionamientos y Diagnósticos Competitivos, Juan Ramón Oreja Rodríguez|
|Winsteps Tutorials||Facets Tutorials||Rasch Discussion Groups|
|Coming Rasch-related Events|
|Jan. 5 - Feb. 2, 2018, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com|
|Jan. 10-16, 2018, Wed.-Tues.||In-person workshop: Advanced Course in Rasch Measurement Theory and the application of RUMM2030, Perth, Australia (D. Andrich), Announcement|
|Jan. 17-19, 2018, Wed.-Fri.||Rasch Conference: Seventh International Conference on Probabilistic Models for Measurement, Matilda Bay Club, Perth, Australia, Website|
|Jan. 22-24, 2018, Mon-Wed.||In-person workshop: Rasch Measurement for Everybody en español (A. Tristan, Winsteps), San Luis Potosi, Mexico. www.ieia.com.mx|
|April 10-12, 2018, Tues.-Thurs.||Rasch Conference: IOMW, New York, NY, www.iomw.org|
|April 13-17, 2018, Fri.-Tues.||AERA, New York, NY, www.aera.net|
|May 22 - 24, 2018, Tues.-Thur.||EALTA 2018 pre-conference workshop (Introduction to Rasch measurement using WINSTEPS and FACETS, Thomas Eckes & Frank Weiss-Motz), https://ealta2018.testdaf.de|
|May 25 - June 22, 2018, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com|
|June 27 - 29, 2018, Wed.-Fri.||Measurement at the Crossroads: History, philosophy and sociology of measurement, Paris, France., https://measurement2018.sciencesconf.org|
|June 29 - July 27, 2018, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Further Topics (E. Smith, Winsteps), www.statistics.com|
|July 25 - July 27, 2018, Wed.-Fri.||Pacific-Rim Objective Measurement Symposium (PROMS), (Preconference workshops July 23-24, 2018) Fudan University, Shanghai, China "Applying Rasch Measurement in Language Assessment and across the Human Sciences" www.promsociety.org|
|Aug. 10 - Sept. 7, 2018, Fri.-Fri.||On-line workshop: Many-Facet Rasch Measurement (E. Smith, Facets), www.statistics.com|
|Sept. 3 - 6, 2018, Mon.-Thurs.||IMEKO World Congress, Belfast, Northern Ireland www.imeko2018.org|
|Oct. 12 - Nov. 9, 2018, Fri.-Fri.||On-line workshop: Practical Rasch Measurement - Core Topics (E. Smith, Winsteps), www.statistics.com|
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