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National Centre for Vocational Education Research (NCVER), 2016
This work asks one simple question: "how reliable is the method used by the National Centre for Vocational Education Research (NCVER) to estimate projected rates of VET program completion?" In other words, how well do early projections align with actual completion rates some years later? Completion rates are simple to calculate with a…
Descriptors: Vocational Education, Graduation Rate, Predictive Measurement, Predictive Validity
Guarino, Cassandra M.; Reckase, Mark D.; Stacy, Brian W.; Wooldridge, Jeffrey M. – Education Policy Center at Michigan State University, 2014
We study the properties of two specification tests that have been applied to a variety of estimators in the context of value-added measures (VAMs) of teacher and school quality: the Hausman test for choosing between random and fixed effects and a test for feedback (sometimes called a "falsification test"). We discuss theoretical…
Descriptors: Achievement Gains, Evaluation Methods, Teacher Effectiveness, Educational Quality
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Lecoutre, Bruno; Lecoutre, Marie-Paule; Poitevineau, Jacques – Psychological Methods, 2010
P. R. Killeen's (2005a) probability of replication ("p[subscript rep]") of an experimental result is the fiducial Bayesian predictive probability of finding a same-sign effect in a replication of an experiment. "p[subscript rep]" is now routinely reported in "Psychological Science" and has also begun to appear in…
Descriptors: Research Methodology, Guidelines, Probability, Computation
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Roberts, James S.; Fang, Haw-ren – Applied Psychological Measurement, 2006
The GGUM2004 computer program estimates parameters for a family of unidimensional unfolding item response theory (IRT) models. These unfolding IRT models predict higher item scores to the extent that a respondent is located close to an item on an underlying latent continuum. This prediction is often consistent with responses to traditional…
Descriptors: Computer Software, Item Response Theory, Scores, Computation
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Curran, Patrick J.; Bauer, Daniel J.; Willoughby, Michael T. – Psychological Methods, 2004
A key strength of latent curve analysis (LCA) is the ability to model individual variability in rates of change as a function of 1 or more explanatory variables. The measurement of time plays a critical role because the explanatory variables multiplicatively interact with time in the prediction of the repeated measures. However, this interaction…
Descriptors: Multiple Regression Analysis, Predictive Measurement, Models, Item Response Theory