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Peer reviewedJamshidian, Mortaza; Bentler, Peter M. – Journal of Educational and Behavioral Statistics, 1999
Describes the maximum likelihood (ML) estimation of mean and covariance structure models when data are missing. Describes expectation maximization (EM), generalized expectation maximization, Fletcher-Powell, and Fisher-scoring algorithms for parameter estimation and shows how software can be used to implement each algorithm. (Author/SLD)
Descriptors: Algorithms, Estimation (Mathematics), Maximum Likelihood Statistics, Scoring
Kreft, Ita G. G.; Kim, Kyung-Sung – 1990
A detailed comparison of four computer programs for analyzing hierarchical linear models is presented. The programs are: VARCL; HLM; ML2; and GENMOD. All are compiled, stand-alone, and specialized. All use maximum likelihood (ML) estimation for decomposition of the variance into different parts; and in all cases, computing the ML estimates…
Descriptors: Algorithms, Comparative Analysis, Computer Software, Computer Software Evaluation
Skaggs, Gary; Stevenson, Jose – 1986
This study assesses the accuracy of ASCAL, a microcomputer-based program for estimating item parameters for the three-parameter logistic model in item response theory. Item responses are generated from a three-parameter model, and item parameter estimates from ASCAL are compared to the generating item parameters and to estimates produced by…
Descriptors: Algorithms, Comparative Analysis, Computer Software, Estimation (Mathematics)
Muraki, Eiji – 1984
This study examines the application of the marginal maximum likelihood (MML) EM algorithm to the parameter estimation problem of the three-parameter normal ogive and logistic polychotomous item response models. A three-parameter normal ogive model, the Graded Response model, has been developed on the basis of Samejima's two-parameter graded…
Descriptors: Algorithms, Data Analysis, Estimation (Mathematics), Goodness of Fit
Download full textBergstrom, Betty A.; Gershon, Richard – 1992
The most useful method of item selection for making pass-fail decisions with a Computerized Adaptive Test (CAT) was studied. Medical technology students (n=86) took a computer adaptive test in which items were targeted to the ability of the examinee. The adaptive algorithm that selected items and estimated person measures used the Rasch model and…
Descriptors: Adaptive Testing, Algorithms, Comparative Analysis, Computer Assisted Testing
Linacre, John M. – 1990
Advantages and disadvantages of standard Rasch analysis computer programs are discussed. The unconditional maximum likelihood algorithm allows all observations to participate equally in determining the measures and calibrations to be obtained quickly from a data set. On the advantage side, standard Rasch programs can be used immediately, are…
Descriptors: Algorithms, Computer Assisted Testing, Computer Graphics, Computer Simulation
Shermis, Mark D.; And Others – 1992
The reliability of four branching algorithms commonly used in computer adaptive testing (CAT) was examined. These algorithms were: (1) maximum likelihood (MLE); (2) Bayesian; (3) modal Bayesian; and (4) crossover. Sixty-eight undergraduate college students were randomly assigned to one of the four conditions using the HyperCard-based CAT program,…
Descriptors: Adaptive Testing, Algorithms, Bayesian Statistics, Comparative Analysis
