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| 3 Sep | 24 Sep
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Oct| 21 Oct | 22 Oct | 29 Oct |
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3 Sep (Tue) |
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Research
Challenges and Opportunities in Test Publishing Dr Interested in learning what it is like to conduct
research in business and industry? Visiting Scholar Rich Patz,
on sabbatical from his role as director of research for CTB/McGraw-Hill, will
discuss the business of educational testing and assessment, interesting and
active areas of research in the current assessment climate, and career paths
in research organizations. Rich will discuss some of the research he will be
engaged in during the tenure of his |
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24 Sep (Tue) |
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The Hierarchical IRT
Model for Criterion-referenced Measurement & the Problem of Standard
Setting In the hierarchical IRT
model for criterion-referenced measurement (Janssen, Tuerlinckx,
Meulders, & De Boeck,
2000) items are grouped under criteria (e.g., "standards") and for
each criterion a set of hyperparameters is
introduced. This allows one to estimate a success probability on a criterion
itself, rather than on individual items, as is standard in IRT. It will be
argued that this model can be relevant for the problem of standard setting in
two interrelated ways. First, the success probability on a criterion can be
interpreted as a domain score. Bock (1997) has already discussed the use of
domain scores for standard setting. Second, the hierarchical IRT model can be
supplemented with item judgments according to the method of Jaeger (1978).
This would lead to a weighted success probability on the criterion, with each
item weight expressing the importance of the item according to the judges.
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1 Oct (Tue) |
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Multilevel item-response and structural equation
modeling Dr Sophia Rabe-Hesketh,
In item-response
models, ability is typically treated as a latent (unobserved) variable,
assumed to be independently normally distributed with constant mean and
variance. A useful extension of this model is to allow mean ability to depend
on observed covariates. In multilevel or
hierarchical settings such as schools, where students are nested in classes
and classes in schools, important predictors of students' abilities are
likely to include class and school level covariates such as teacher to
student ratio. However, in practice not all these higher level covariates
will be observed, leading to unexplained differences in mean abilities
between schools and between classes within schools, even after adjusting for
the observed covariates. As a result, the abilities of students in the same
class or school can no longer be assumed to be independent (conditional on
the covariates). We can model this dependence by including random effects of
class and school in the model for ability. The model can be further extended
by including latent covariates, for instance teacher's attitude, leading to
multilevel structural equation models. These kinds of models
can all be formulated as special cases of a general model framework, GLLAMM
(Generalized Linear Latent And Mixed Models), and estimated using the Stata program, gllamm. |
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21 Oct (Mon) |
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Workshop: Bayesian IRT Modeling with WinBUGS Dr Werner Wothke,
CTB/McGraw-Hill WinBUGS is a software
which aims to make practical MCMC methods available to applied statisticians
and quantitative researchers.Ý The
workshop will cover setting up a 1PL IRT model, diagnostic with MCMC
simulation and other IRT models (2PL, 3PL). |
ÝÝÝÝÝÝ |
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22 Oct (Tue) |
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Paradigms, Models, and
Methods for Statistical Inference in Educational Measurement Dr The
application of Bayesian statistical methodology in physical and social
sciences has seen a dramatic increase in recent years.Ý Powered on the one hand by an attractive
formal framework for inference and on the other by increasingly powerful set
of statistical computing tools, Bayesian inference is affecting educational
research in significant and sometimes controversial ways.Ý ÝÝÝÝÝÝÝÝ In this talk we will first examine
several interesting issues in the foundations of statistical inference that
have implications for the way we interpret information collected in
educational measurement and assessment contexts.Ý Second, we consider student proficiency,
and the data and models used in its assessment.Ý We explore the suitability of Bayesian
analysis and the applicability of Bayesian statistical computing methods,
including Markov chain |
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29 Oct (Tue) |
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SAT Coaching, Bias and Causal Inference Dr This presentation considers the extent to which
unbiased causal inferences can be drawn about the effect of coaching on SAT
performance.Ý Following a review of the
literature, I present the linear regression model and the Heckman Model as
two statistical approaches that might be used control for bias in an
estimated coaching effect.Ý The
assumptions necessary before an estimated effect can be given a causal
interpretation are described in some detail.Ý
I estimate coaching effects for both sections of the SAT using data
from the National Education Longitudinal Study of 1988 (NELS).Ý There is some indication that the linear
regression model successfully reduces bias due to omitted variables.Ý It appears that commercial coaching
programs have an effect of about 3 to 20 points on the verbal section of the
SAT, and an effect of about 10 to 28 points on the math section of the
SAT.Ý These effects may be somewhat
bigger or smaller if coaching is defined more broadly.Ý There is some evidence that coaching is
more effective for certain types of students.Ý
I demonstrate the sensitivity of the Heckman Model to the choice of
variables included in the selection function.Ý
Small changes in the selection function are shown to have a big impact
on estimated coaching effects. |
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19 Nov (Tue) |
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Dr Jason Ravitz, Buck
Institute for Education Title to be announced. |
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26 Nov Ý(Tue) |
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Title
to be Announced |
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