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Sterba, Sonya K.; MacCallum, Robert C. – Multivariate Behavioral Research, 2010
Different random or purposive allocations of items to parcels within a single sample are thought not to alter structural parameter estimates as long as items are unidimensional and congeneric. If, additionally, numbers of items per parcel and parcels per factor are held fixed across allocations, different allocations of items to parcels within a…
Descriptors: Sampling, Computation, Statistical Analysis, Computer Software
Sterba, Sonya K. – Multivariate Behavioral Research, 2009
A model-based framework, due originally to R. A. Fisher, and a design-based framework, due originally to J. Neyman, offer alternative mechanisms for inference from samples to populations. We show how these frameworks can utilize different types of samples (nonrandom or random vs. only random) and allow different kinds of inference (descriptive vs.…
Descriptors: Statistical Inference, Models, Sampling, Psychology
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
Marsh, Herbert W.; Ludtke, Oliver; Robitzsch, Alexander; Trautwein, Ulrich; Asparouhov, Tihomir; Muthen, Bengt; Nagengast, Benjamin – Multivariate Behavioral Research, 2009
This article is a methodological-substantive synergy. Methodologically, we demonstrate latent-variable contextual models that integrate structural equation models (with multiple indicators) and multilevel models. These models simultaneously control for and unconfound measurement error due to sampling of items at the individual (L1) and group (L2)…
Descriptors: Educational Environment, Context Effect, Models, Structural Equation Models

Hakstian, A. Ralph; Barchard, Kimberly A. – Multivariate Behavioral Research, 2000
Developed a sample-based nonanalytical degrees-of-freedom correction factor for situations sampling both subjects and conditions with measurement data departing from essentially parallel form. Assessed the application of this correction factor through a simulation study involving data sets with a range of design characteristics and manifesting…
Descriptors: Robustness (Statistics), Sampling, Simulation, Statistical Inference

Raykov, Tenko – Multivariate Behavioral Research, 1997
The population discrepancy between Cronbach's Coefficient Alpha (L. Cronbach, 1951) and scale reliability with fixed congeneric measure, uncorrelated errors, and sampling of subjects was studied. The difference is expressed in terms of the individual component violations of the assumption of equal tau-equivalence that is necessary and sufficient…
Descriptors: Error of Measurement, Reliability, Sampling, Scaling

Lambert, Zarrel V.; And Others – Multivariate Behavioral Research, 1989
Bootstrap methodology is presented that yields approximations of the sampling variation of redundancy estimates while assuming little a priori knowledge about the distributions of these statistics. Results of numerical demonstrations suggest that bootstrap confidence intervals may offer substantial assistance in interpreting the results of…
Descriptors: Estimation (Mathematics), Predictor Variables, Sampling, Statistical Analysis
Ruscio, John; Ruscio, Ayelet Meron; Meron, Mati – Multivariate Behavioral Research, 2007
Meehl's taxometric method was developed to distinguish categorical and continuous constructs. However, taxometric output can be difficult to interpret because expected results for realistic data conditions and differing procedural implementations have not been derived analytically or studied through rigorous simulations. By applying bootstrap…
Descriptors: Sampling, Equated Scores, Data Interpretation, Inferences

Barchard, Kimberly A.; Hakstian, A. Ralph – Multivariate Behavioral Research, 1997
Two studies, both using Type 12 sampling, are presented in which the effects of violating the assumption of essential parallelism in setting confidence intervals are studied. Results indicate that as long as data manifest properties of essential parallelism, the two methods studied maintain precise Type I error control. (SLD)
Descriptors: Error of Measurement, Robustness (Statistics), Sampling, Statistical Analysis

Lanning, Kevin – Multivariate Behavioral Research, 1996
Effects of sample size and composition are systematically examined on the replicability of principal components, using observer ratings of personality from the California Adult Q-Set for 192 series of principal components analyses. Results indicate that dimensionality cannot be inferred from component robustness; they are empirically and logically…
Descriptors: Factor Analysis, Personality Measures, Robustness (Statistics), Sample Size

Duncan, Terry E.; Duncan, Susan C.; Alpert, Anthony; Hops, Hyman; Stoolmiller, Mike; Muthen, Bengt – Multivariate Behavioral Research, 1997
Demonstrates the use of a general model for latent variable growth analysis that takes into account cluster sampling. Multilevel Latent Growth Modeling was used to analyze longitudinal and multilevel data for adolescent and parent substance use measured at four annual time points for 435 families. (SLD)
Descriptors: Adolescents, Cluster Analysis, Longitudinal Studies, Mathematical Models

La Du, Terence J.; Tanaka, J. S. – Multivariate Behavioral Research, 1995
After reviewing the multiple fit indices in structural equation models, evidence on their behavior is presented through simulation studies in which sample size, estimation method, and model misspecification varied. Two sampling studies, with and without known populations, are presented, and implications for the use of fit indices are discussed.…
Descriptors: Estimation (Mathematics), Goodness of Fit, Sample Size, Sampling

Kaplan, David – Multivariate Behavioral Research, 1989
The sampling variability and zeta-values of parameter estimates for misspecified structural equation models were examined. A Monte Carlo study was used. Results are discussed in terms of asymptotic theory and the implications for the practice of structural equation models. (SLD)
Descriptors: Error of Measurement, Estimation (Mathematics), Mathematical Models, Monte Carlo Methods

Bentler, 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

Lambert, Zarrel V.; And Others – Multivariate Behavioral Research, 1991
A method is presented that eliminates some interpretational limitations arising from assumptions implicit in the use of arbitrary rules of thumb to interpret exploratory factor analytic results. The bootstrap method is presented as a way of approximating sampling distributions of estimated factor loadings. Simulated datasets illustrate the…
Descriptors: Behavioral Science Research, Computer Simulation, Estimation (Mathematics), Factor Structure
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