Modeling and Multivariate Methods - SAS

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532 Scoring Tests Using Item Response Theory Chapter 22 Item Analysis Output Figure 22.11 Dual Plot Questions are plotted to the left of the vertical dotted line, examinees on the right. In addition, a histogram of ability levels is appended to the right side of the plot. This example shows a wide range of abilities. Q10 is rated as difficult, with an examinee needing to be around half a standard deviation above the mean in order to have a 50% chance of correctly answering the question. Other questions are distributed at lower ability levels, with Q11 and Q4 appearing as easier. There are some questions that are off the displayed scale (Q7 and Q14). The estimated parameter estimates appear below the Dual Plot, as shown in Figure 22.12.
Chapter 22 Scoring Tests Using Item Response Theory 533 Platform Commands Figure 22.12 Parameter Estimates Item identifies the test item. Difficulty is the b parameter from the model. A histogram of the difficulty parameters is shown beside the difficulty estimates. Discrimination is the a parameter from the model, shown only for 2PL and 3PL models. A histogram of the discrimination parameters is shown beside the discrimination estimates. Threshold is the c parameter from the model, shown only for 3PL models. Platform Commands The following three commands are available from the drop-down menu on the title bar of the report. Number of Plots Across brings up a dialog to specify how many plots should be grouped together on a single line. Initially, plots are stacked one-across. Figure 22.8 on page 530 shows four plots across. Save Ability Formula creates a new column in the data table containing a formula for calculating ability levels. Since the ability levels are stored as a formula, you can add rows to the data table and have them scored using the stored ability estimates. In addition, you can run several models and store several estimates of ability in the same data table. The ability is computed using the IRT Ability function. The function has the following form IRT Ability (Q1, Q2,...,Qn, [a1, a2,..., an, b1, b2,..., bn, c1, c2, ..., cn]); where Q1, Q2,...,Qn are columns from the data table containing items, a1, a2,..., an are the corresponding discrimination parameters, b1, b2,..., bn are the corresponding difficulty parameters for the items, and c1, c2, ..., cn are the corresponding threshold parameters. Note that the parameters are entered as a matrix, surrounded by square brackets. Script contains options that are available to all platforms. See Using JMP.
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Version 10 Modeling and Multivariat
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6 Emphasis Options for Standard Lea
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8 Models with Crossed, Interaction,
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10 8 Analyzing Screening Designs Us
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12 Examining the Residuals . . . .
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18 Interpreting the Profiles . . .
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20 Discriminant Analysis . . . . .
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Contents Book Conventions. . . . .
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24 Learn about JMP Chapter 1 Book C
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26 Learn about JMP Chapter 1 Book C
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28 Learn about JMP Chapter 1 Additi
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Contents Example Using Standard Lea
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158 Fitting Multiple Response Model
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180 Fitting Generalized Linear Mode
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200 Performing Logistic Regression
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Contents Overview of the Screening
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232 Analyzing Screening Designs Cha
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Contents Introduction to the Nonlin
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244 Performing Nonlinear Regression
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Contents Overview of Neural Network
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280 Creating Neural Networks Chapte
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Contents Launching the Platform. .
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296 Modeling Relationships With Gau
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Contents Overview of the Loglinear
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306 Fitting Dispersion Effects with
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318 Recursively Partitioning Data C
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354 Performing Time Series Analysis
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386 Performing Categorical Response
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Contents Introduction to Choice Mod
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Contents Launch the Multivariate Pl
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462 Clustering Data Chapter 18 The
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470 Clustering Data Chapter 18 K-Me
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472 Clustering Data Chapter 18 K-Me
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474 Clustering Data Chapter 18 Norm
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476 Clustering Data Chapter 18 Norm
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478 Clustering Data Chapter 18 Self
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480 Clustering Data Chapter 18 Self
Page 482 and 483: Contents Principal Components. . .
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640 References Becker, R.A. and Cle
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642 References Draper, N. and Smith
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646 References Matsumoto, M. and Ni
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648 References Rawlings, J.O. (1988
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650 References Umetrics (1995), Mul
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Contents The Response Models . . .
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654 Statistical Details Appendix A
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690 Index Birth Death Subset.jmp 46
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692 Index Ellipses Transparency 451
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694 Index test for curvature 46 Kru
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696 Index Customizing 267 Nonlinear
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698 Index reliability analysis 453-
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700 Index Stable 363 Stable Inverti
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702 Index
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Modeling and Multivariate Methods - SAS

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