Modeling and Multivariate Methods - SAS

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534 Scoring Tests Using Item Response Theory Chapter 22 Technical Details Technical Details Note that P(θ) does not necessarily represent the probability of a positive response from a particular individual. It is certainly feasible that an examinee might definitely select an incorrect answer, or that an examinee may know an answer for sure, based on the prior experiences and knowledge of the examinee, apart from the trait level. It is more correct to think of P(θ) as the probability of response for a set of individuals with ability level θ. Said another way, if a large group of individuals with equal trait levels answered the item, P(θ) predicts the proportion that would answer the item correctly. This implies that IRT models are item-invariant; theoretically, they would have the same parameters regardless of the group tested. An assumption of these IRT models is that the underlying trait is unidimensional. That is to say, there is a single underlying trait that the questions measure which can be theoretically measured on a continuum. This continuum is the horizontal axis in the plots of the curves. If there are several traits being measured, each of which have complex interactions with each other, then these unidimensional models are not appropriate.
Chapter 23 Plotting Surfaces Using the Surface Plot Platform The Surface Plot platform functions both as a separate platform and as an option in model fitting platforms. Up to four dependent surfaces can be displayed in the same plot. The dependent variables section, below the plot, has four rows that correspond to the four surfaces. Depending on what you choose to view (sheets, points, isosurfaces, or density grids) and whether you supply a formula variable, different options appear in the dependent variables section. Surface Plot is built using the 3-D scene commands from JMP’s scripting language. Complete documentation of the OpenGL-style scene commands is found in the JMP Scripting Guide. In this platform, you can • use the mouse to drag the surface to a new position • Right-click on the surface to change the background color or show the virtual ArcBall (which helps position the surface) • enable hardware acceleration, which may increase performance if it is supported on your system • drag lights to different positions, assign them colors, and turn them on and off. Figure 23.1 Example of a Surface Plot
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Version 10 Modeling and Multivariat
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INDIRECT OR CONSEQUENTIAL DAMAGES O
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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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14 Response Frequencies . . . . . .
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16 19 Analyzing Principal Component
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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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30 Learn about JMP Chapter 1 Additi
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Contents Launch the Fit Model Platf
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34 Introduction to the Fit Model Pl
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Contents Example Using Standard Lea
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56 Fitting Standard Least Squares M
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100 Fitting Standard Least Squares
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Contents Overview of Stepwise Regre
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136 Fitting Stepwise Regression Mod
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Contents Example of a Multiple Resp
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158 Fitting Multiple Response Model
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Contents Overview of Generalized Li
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180 Fitting Generalized Linear Mode
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Contents Introduction to Logistic M
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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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Contents Introduction to Partitioni
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318 Recursively Partitioning Data C
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Contents Launch the Platform . . .
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354 Performing Time Series Analysis
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Contents The Categorical Platform .
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386 Performing Categorical Response
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Contents Introduction to Choice Mod
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404 Performing Choice Modeling Chap
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Contents Launch the Multivariate Pl
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444 Correlations and Multivariate T
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Contents Introduction to Clustering
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462 Clustering Data Chapter 18 The
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464 Clustering Data Chapter 18 Hier
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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
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Contents Principal Components. . .
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630 Comparing Model Performance Cha
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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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644 References Hooper, J. H. and Am
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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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