Graphs menu

Winsteps produces bit-mapped images, using the Graphs menu. Winsteps produces character-based graphs in Table 21

 

 

Initially, select which type of curves you want to see. When the graphs display, you can select the other options. You can look at the others later without going back to this menu. Graphs are plotted relative to the central difficulty of each item or response structure. Model-based curves (such as probability and information functions) are the same for all items which share the same model definition in ISGROUPS=. Empirical curves differ across items.

 

Category Probability Curves

model-based probability of observing each category of the response structure at each point on the latent variable (relative to the item difficulty)

Empirical Category Curves

data-based relative frequencies of categories in each interval along the latent variable

Expected Score ICC

model-based Item Characteristic Curve (or Item Response Function IRF) for the item or response structure. This is controlled BYITEM= or the last two entries in this menu.

Empirical ICC

data-based empirical curve.

Empirical randomness

observed randomness (mean-square fit) in each interval on the variable with logarithmic scaling. The model expectation is 1.0

Cumulative Probabilities

model-based sum of category probabilities. The category median boundaries are the points at which the probability is .5. Click on a line to obtain the category accumulation.

Item Information Function

model-based Fisher statistical information for the item. This is also the model variance of the responses, see RSA p. 100.

Category Information

model-based item information partitioned according to the probability of observing the category. Click on a line to obtain the category number.

Conditional Probability Curves

model-based relationship between probabilities of adjacent categories. These follow dichotomous logistic ogives. Click on a line to obtain the category pairing

Test Characteristic Curve

model-based test score-to-measure characteristic curve.

Test Information Function

model-based test information function, the sum of the item information functions.

Test randomness

the observed randomness (mean-square fit) in each interval on the variable with logarithmic scaling. The model expectation is 1.0

Multiple Item ICCs

displays several model and empirical ICCs simultaneously.

Display by item

shows these curves for individual items, also controlled by BYITEM=. Model-based output is the same for all items with the same ISGROUPS= designation.

Display by scale group

for each ISGROUPS= code, a set of curves is shown. An example item number is also shown - all other items in the grouping are included in the one set of grouping plots. Also controlled by BYITEM=.

Non-Uniform DIF ICCs

the empirical item characteristic curve for each DIF= person-classification-group.

Person-item Histogram

the distributions of the person abilities and item difficulties as vertical histograms.


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Rasch Publications
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 Winsteps & Facets Events
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
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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