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Wang, Shudong; Jiao, Hong; Jin, Ying; Thum, Yeow Meng – Online Submission, 2010
The vertical scales of large-scale achievement tests created by using item response theory (IRT) models are mostly based on cluster (or correlated) educational data in which students usually are clustered in certain groups or settings (classrooms or schools). While such application directly violated assumption of independent sample of person in…
Descriptors: Scaling, Achievement Tests, Data Analysis, Item Response Theory
Raiche, Gilles; Blais, Jean-Guy – Applied Psychological Measurement, 2006
Monte Carlo methodologies are frequently applied to study the sampling distribution of the estimated proficiency level in adaptive testing. These methods eliminate real situational constraints. However, these Monte Carlo methodologies are not currently supported by the available software programs, and when these programs are available, their…
Descriptors: Computer Assisted Instruction, Computer Software, Sampling, Adaptive Testing
Kim, Jee-Seon; Bolt, Daniel M. – Educational Measurement: Issues and Practice, 2007
The purpose of this ITEMS module is to provide an introduction to Markov chain Monte Carlo (MCMC) estimation for item response models. A brief description of Bayesian inference is followed by an overview of the various facets of MCMC algorithms, including discussion of prior specification, sampling procedures, and methods for evaluating chain…
Descriptors: Placement, Monte Carlo Methods, Markov Processes, Measurement
De Corte, Wilfried – Educational and Psychological Measurement, 2004
The article describes a Windows program to estimate the expected value and sampling distribution function of the adverse impact ratio for general multistage selections. The results of the program can also be used to predict the risk that a future selection decision will result in an outcome that reflects the presence of adverse impact. The method…
Descriptors: Sampling, Measurement Techniques, Evaluation Methods, Computer Software
Nevitt, Johnathan; Hancock, Gregory R. – 1998
Though common structural equation modeling (SEM) methods are predicated upon the assumption of multivariate normality, applied researchers often find themselves with data clearly violating this assumption and without sufficient sample size to use distribution-free estimation methods. Fortunately, promising alternatives are being integrated into…
Descriptors: Chi Square, Computer Software, Error of Measurement, Estimation (Mathematics)
Correcting for Systematic Bias in Sample Estimates of Population Variances: Why Do We Divide by n-1?

Mittag, Kathleen Cage – 1992
An important topic presented in introductory statistics courses is the estimation of population parameters using samples. Students learn that when estimating population variances using sample data, we always get an underestimate of the population variance if we divide by n rather than n-1. One implication of this correction is that the degree of…
Descriptors: College Mathematics, Computer Software, Equations (Mathematics), Estimation (Mathematics)