Univariate and Bivariate Loglinear Models for Discrete Test Score Distributions

2000 ◽  
Vol 25 (2) ◽  
pp. 133 ◽  
Author(s):  
Paul W. Holland ◽  
Dorothy T. Thayer
Keyword(s):  
Test Score ◽  
Loglinear Models ◽  
Score Distributions

Testing for Differences in Test Score Distributions Using Loglinear Models

1996 ◽  
Vol 9 (4) ◽  
pp. 305-321 ◽  
Author(s):  
Bradley A. Hanson
Keyword(s):  
Test Score ◽  
Loglinear Models ◽  
Score Distributions

scholarly journals UNIVARIATE AND BIVARIATE LOGLINEAR MODELS FOR DISCRETE TEST SCORE DISTRIBUTIONS

1998 ◽  
Vol 1998 (2) ◽  
pp. i-56 ◽  
Author(s):  
Paul W. Holland ◽  
Dorothy T. Thayer
Keyword(s):  
Test Score ◽  
Loglinear Models ◽  
Score Distributions

Commentary on “Validation Methods for Aggregate-Level Test Scale Linking: A Case Study Mapping School District Test Score Distributions to a Common Scale”

2020 ◽  
pp. 107699862095666
Author(s):  
Alina A. von Davier
Keyword(s):  
School District ◽  
Test Score ◽  
Applied Research ◽  
Aggregate Level ◽  
Validation Methods ◽  
Common Scale ◽  
Scale Linking ◽  
Score Distributions

In this commentary, I share my perspective on the goals of assessments in general, on linking assessments that were developed according to different specifications and for different purposes, and I propose several considerations for the authors and the readers. This brief commentary is structured around three perspectives (1) the context of this research, (2) the methodology proposed here, and (3) the consequences for applied research.

Loglinear Models for Observed-Score Distributions

2017 ◽  
pp. 71-86
Author(s):  
Tim Moses
Keyword(s):  
Loglinear Models ◽  
Observed Score ◽  
Score Distributions

scholarly journals PSEUDO BAYES ESTIMATES FOR TEST SCORE DISTRIBUTIONS AND CHAINED EQUIPERCENTILE EQUATING

2009 ◽  
Vol 2009 (2) ◽  
pp. i-37 ◽  
Author(s):  
Tim Moses ◽  
Hyeonjoo J. Oh
Keyword(s):  
Test Score ◽  
Bayes Estimates ◽  
Equipercentile Equating ◽  
Score Distributions

Smoothing Methods for Estimating Test Score Distributions

1991 ◽  
Vol 28 (3) ◽  
pp. 257-282 ◽  
Author(s):  
Michael J. Kolen
Keyword(s):  
Test Score ◽  
Smoothing Methods ◽  
Score Distributions

A Survey of Observed Test-Score Distributions With Respect to Skewness and Kurtosis1

1955 ◽  
Vol 15 (4) ◽  
pp. 383-389 ◽  
Author(s):  
Frederic M. Lord
Keyword(s):  
Test Score ◽  
Score Distributions

Using Heteroskedastic Ordered Probit Models to Recover Moments of Continuous Test Score Distributions From Coarsened Data

2016 ◽  
Vol 42 (1) ◽  
pp. 3-45 ◽  
Author(s):  
Sean F. Reardon ◽  
Benjamin R. Shear ◽  
Katherine E. Castellano ◽  
Andrew D. Ho
Keyword(s):  
Test Score ◽  
Mean Squared Error ◽  
Population Distribution ◽  
Ordered Probit ◽  
Parameter Estimates ◽  
Demographic Groups ◽  
Continuous Test ◽  
Standard Deviations ◽  
Ordered Probit Models ◽  
Score Distributions

Test score distributions of schools or demographic groups are often summarized by frequencies of students scoring in a small number of ordered proficiency categories. We show that heteroskedastic ordered probit (HETOP) models can be used to estimate means and standard deviations of multiple groups’ test score distributions from such data. Because the scale of HETOP estimates is indeterminate up to a linear transformation, we develop formulas for converting the HETOP parameter estimates and their standard errors to a scale in which the population distribution of scores is standardized. We demonstrate and evaluate this novel application of the HETOP model with a simulation study and using real test score data from two sources. We find that the HETOP model produces unbiased estimates of group means and standard deviations, except when group sample sizes are small. In such cases, we demonstrate that a “partially heteroskedastic” ordered probit (PHOP) model can produce estimates with a smaller root mean squared error than the fully heteroskedastic model.

An IBM 1401 Program for Computing Test Score Distributions

1967 ◽  
Vol 27 (1) ◽  
pp. 175-176 ◽  
Author(s):  
Leroy A. Olson ◽  
Robert L. Royce
Keyword(s):  
Test Score ◽  
Score Distributions
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