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Bergner, Yoav; Droschler, Stefan; Kortemeyer, Gerd; Rayyan, Saif; Seaton, Daniel; Pritchard, David E. – International Educational Data Mining Society, 2012
We apply collaborative filtering (CF) to dichotomously scored student response data (right, wrong, or no interaction), finding optimal parameters for each student and item based on cross-validated prediction accuracy. The approach is naturally suited to comparing different models, both unidimensional and multidimensional in ability, including a…
Descriptors: Factor Analysis, Prediction, Item Response Theory, Student Reaction
Thoemmes, Felix J.; West, Stephen G. – Multivariate Behavioral Research, 2011
In this article we propose several modeling choices to extend propensity score analysis to clustered data. We describe different possible model specifications for estimation of the propensity score: single-level model, fixed effects model, and two random effects models. We also consider both conditioning within clusters and conditioning across…
Descriptors: Probability, Scores, Statistical Analysis, Models
Cafri, Guy; Kromrey, Jeffrey D.; Brannick, Michael T. – Multivariate Behavioral Research, 2010
This article uses meta-analyses published in "Psychological Bulletin" from 1995 to 2005 to describe meta-analyses in psychology, including examination of statistical power, Type I errors resulting from multiple comparisons, and model choice. Retrospective power estimates indicated that univariate categorical and continuous moderators, individual…
Descriptors: Periodicals, Effect Size, Sampling, Psychology
Cragg, Kristina M. – Research in Higher Education, 2009
The purpose of this study is to identify student and institutional characteristics that influence the probability for graduation. The study delves further into the probability for graduation by examining how far the student deviates from the institutional mean with respect to academics and affordability; this concept is referred to as the "match."…
Descriptors: Graduation, Institutional Characteristics, Multivariate Analysis, Probability

Wang, Yuchung J. – Psychometrika, 1997
A k-dimensional multivariate normal distribution is made discrete by partitioning the k-dimensional Euclidean space with rectangular grids. The probability integrals over the partitioned cubes forms a k-dimensional contingency table with ordered categories. A loglinear model with main effects plus two-way interactions provides an approximation for…
Descriptors: Classification, Multivariate Analysis, Probability, Statistical Distributions
Rupp, Andre A.; Zumbo, Bruno D. – Educational and Psychological Measurement, 2006
One theoretical feature that makes item response theory (IRT) models those of choice for many psychometric data analysts is parameter invariance, the equality of item and examinee parameters from different examinee populations or measurement conditions. In this article, using the well-known fact that item and examinee parameters are identical only…
Descriptors: Psychometrics, Probability, Simulation, Item Response Theory
Hussey, Jon M.; Marshall, Jane Marie; English, Diana J.; Knight, Elizabeth Dawes; Lau, Anna S.; Dubowitz, Howard; Kotch, Jonathan B. – Child Abuse and Neglect: The International Journal, 2005
Objective: To examine whether children with substantiated maltreatment reports between 4 and 8 years of age differ from children with unsubstantiated reports on any of 10 behavioral and developmental outcomes. Method: Longitudinal data from 806 children and their adult caregivers collected in four US study sites were pooled and analyzed using…
Descriptors: Regression (Statistics), Probability, Caregivers, Social Services
Jarrell, Michele G. – 1991
A probability distribution was developed for the Andrews-Pregibon (AP) statistic. The statistic, developed by D. F. Andrews and D. Pregibon (1978), identifies multivariate outliers. It is a ratio of the determinant of the data matrix with an observation deleted to the determinant of the entire data matrix. Although the AP statistic has been used…
Descriptors: Computer Simulation, Error of Measurement, Matrices, Multivariate Analysis

Bockenholt, Ulf – Psychometrika, 1990
This paper proposes a generalization of Thurstonian probabilistic choice models for analyzing both multiple preference responses and their relationships. The approach is illustrated by modeling data from two multivariate preference experiments. Preliminary data analyses show that the extension can yield an adequate representation of multivariate…
Descriptors: Equations (Mathematics), Individual Differences, Mathematical Models, Multidimensional Scaling

De Soete, Geert; Winsberg, Suzanne – Psychometrika, 1993
A probabilistic choice model, based on L. L. Thurstone's Law of Comparative Judgment Case V, is developed for paired comparisons data about psychological stimuli. The model assumes that each stimulus is measured on a small number of physical variables. An algorithm for estimating parameters is illustrated with real data. (SLD)
Descriptors: Algorithms, Equations (Mathematics), Estimation (Mathematics), Graphs
Elliott, Ronald S.; Barcikowski, Robert S. – 1993
This Monte Carlo study examines whether, given various numbers of variables, treatments, and sample sizes, in a one-way multivariate analysis of variance, Type I error rates of the test approximations provided by the BMDP program, the Statistical Analysis System (SAS), and the Statistical Package for the Social Sciences (SPSS) for Roy's largest…
Descriptors: Analysis of Variance, Computer Simulation, Estimation (Mathematics), Monte Carlo Methods
Slade, Eric P.; Wissow, Lawrence S. – Economics of Education Review, 2007
Evidence that childhood maltreatment is associated with emotional and behavioral problems throughout childhood suggests that maltreatment could lead to impaired academic performance in middle and high school. This article explores these effects using data on siblings. An index measure of the intensity of childhood maltreatment was included as a…
Descriptors: Probability, Family Characteristics, Adolescents, Siblings

Gardner, William – Psychometrika, 1990
This paper provides a method for analyzing data consisting of event sequences and covariate observations associated with Markov chains. The objective is to use the covariate data to explain differences between individuals in the transition probability matrices characterizing their sequential data. (TJH)
Descriptors: Cognitive Development, Equations (Mathematics), Estimation (Mathematics), Individual Differences