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Peer reviewedRosenfeld, Barry; Turkheimer, Eric – Multivariate Behavioral Research, 1995
Analysis of the pairwise preferences of pairs of gambling stimuli elicited from each of 40 chronic schizophrenic inpatients, 32 schizophrenic outpatients, and 33 siblings or parents of these patients indicates that choice behavior was usually determined by the expected value of the stimuli, with no significant differences among groups. (SLD)
Descriptors: Decision Making, Maximum Likelihood Statistics, Multidimensional Scaling, Parents
Peer reviewedFava, Joseph L.; Velicer, Wayne F. – Multivariate Behavioral Research, 1992
Effects of overextracting factors and components within and between maximum likelihood factor analysis and principal components analysis were examined through computer simulation of a range of factor and component patterns. Results demonstrate similarity of component and factor scores during overextraction. Overall, results indicate that…
Descriptors: Computer Simulation, Correlation, Factor Analysis, Mathematical Models
Peer reviewedEveritt, B. S. – Multivariate Behavioral Research, 1981
Results show that the proposed sampling distribution of the test appears to be appropriate only for sample sizes above 50, and for data where the sample size is 10 times the number of variables. For such cases the power of the test is found to be fairly low. (Author/RL)
Descriptors: Mathematical Formulas, Maximum Likelihood Statistics, Monte Carlo Methods, Multivariate Analysis
Peer reviewedGraham, John W.; And Others – Multivariate Behavioral Research, 1996
The utility of the three-form design coupled with maximum likelihood methods for estimation of missing values was evaluated. Simulation studies demonstrate that maximum likelihood estimation and multiple imputation methods produce the most efficient and least biased estimates of variances and covariances for normally distributed and slightly…
Descriptors: Data Collection, Estimation (Mathematics), Maximum Likelihood Statistics, Research Design
A Cautionary Note on Using G[squared](dif) to Assess Relative Model Fit in Categorical Data Analysis
Maydeu-Olivares, Albert; Cai, Li – Multivariate Behavioral Research, 2006
The likelihood ratio test statistic G[squared](dif) is widely used for comparing the fit of nested models in categorical data analysis. In large samples, this statistic is distributed as a chi-square with degrees of freedom equal to the difference in degrees of freedom between the tested models, but only if the least restrictive model is correctly…
Descriptors: Goodness of Fit, Data Analysis, Simulation, Item Response Theory
Peer reviewedWoodbury, Max A.; Manton, Kenneth G. – Multivariate Behavioral Research, 1991
An empirical Bayes-maximum likelihood estimation procedure is presented for the application of fuzzy partition models in describing high dimensional discrete response data. The model describes individuals in terms of partial membership in multiple latent categories that represent bounded discrete spaces. (SLD)
Descriptors: Bayesian Statistics, Equations (Mathematics), Estimation (Mathematics), Mathematical Models
Peer reviewedBentler, Peter M.; Yuan, Ke-Hai – Multivariate Behavioral Research, 1999
Studied the small sample behavior of several test statistics based on the maximum-likelihood estimator but designed to perform better with nonnormal data. Monte Carlo results indicate the satisfactory performance of the "F" statistic recently proposed by K. Yuan and P. Bentler (1997). (SLD)
Descriptors: Estimation (Mathematics), Maximum Likelihood Statistics, Monte Carlo Methods, Sample Size
Lubke, Gitta; Neale, Michael C. – Multivariate Behavioral Research, 2006
Latent variable models exist with continuous, categorical, or both types of latent variables. The role of latent variables is to account for systematic patterns in the observed responses. This article has two goals: (a) to establish whether, based on observed responses, it can be decided that an underlying latent variable is continuous or…
Descriptors: Sample Size, Maximum Likelihood Statistics, Models, Responses
Peer reviewedChan, Wai; Bentler, Peter M. – Multivariate Behavioral Research, 1996
A method is proposed for partially analyzing additive ipsative data (PAID). Transforming the PAID according to a developed equation preserves the density of the transformed data, and maximum likelihood estimation can be carried out as usual. Simulation results show that the original structural parameters can be accurately estimated from PAID. (SLD)
Descriptors: Equations (Mathematics), Estimation (Mathematics), Goodness of Fit, Matrices
Peer reviewedBriggs, Nancy E.; MacCallum, Robert C. – Multivariate Behavioral Research, 2003
Examined the relative performance of two commonly used methods of parameter estimation in factor analysis, maximum likelihood (ML) and ordinary least squares (OLS) through simulation. In situations with a moderate amount of error, ML often failed to recover the weak factor while OLS succeeded. Also presented an example using empirical data. (SLD)
Descriptors: Error of Measurement, Estimation (Mathematics), Factor Analysis, Factor Structure
Peer reviewedBacon, Donald R. – Multivariate Behavioral Research, 1995
A maximum likelihood approach to correlational outlier identification is introduced and compared to the Mahalanobis D squared and Comrey D statistics through Monte Carlo simulation. Identification performance depends on the nature of correlational outliers and the measure used, but the maximum likelihood approach is the most robust performance…
Descriptors: Comparative Analysis, Computer Simulation, Correlation, Estimation (Mathematics)
Peer reviewedOlsson, Ulf Henning; Troye, Sigurd Villads; Howell, Roy D. – Multivariate Behavioral Research, 1999
Used simulation to compare the ability of maximum likelihood (ML) and generalized least-squares (GLS) estimation to provide theoretic fit in models that are parsimonious representations of a true model. The better empirical fit obtained for GLS, compared with ML, was obtained at the cost of lower theoretic fit. (Author/SLD)
Descriptors: Estimation (Mathematics), Goodness of Fit, Least Squares Statistics, Maximum Likelihood Statistics
Peer reviewedVelicer, Wayne F.; And Others – Multivariate Behavioral Research, 1982
Factor analysis, image analysis, and principal component analysis are compared with respect to the factor patterns they would produce under various conditions. The general conclusion that is reached is that the three methods produce results that are equivalent. (Author/JKS)
Descriptors: Comparative Analysis, Data Analysis, Factor Analysis, Goodness of Fit
Peer reviewedBrown, R. L. – Multivariate Behavioral Research, 1990
A Monte Carlo study was conducted to assess the robustness of the limited information two-stage least squares (2SLS) estimation procedure on a confirmatory factor analysis model with nonnormal distributions. Full information maximum likelihood methods were used for comparison. One hundred model replications were used to generate data. (TJH)
Descriptors: Comparative Analysis, Estimation (Mathematics), Factor Analysis, Least Squares Statistics
Peer reviewedMolenaar, Peter C. M.; Nesselroade, John R. – Multivariate Behavioral Research, 1998
Pseudo-Maximum Likelihood (p-ML) and Asymptotically Distribution Free (ADF) estimation methods for estimating dynamic factor model parameters within a covariance structure framework were compared through a Monte Carlo simulation. Both methods appear to give consistent model parameter estimates, but only ADF gives standard errors and chi-square…
Descriptors: Chi Square, Comparative Analysis, Error of Measurement, Estimation (Mathematics)

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