Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 0 |
Since 2016 (last 10 years) | 0 |
Since 2006 (last 20 years) | 10 |
Descriptor
Monte Carlo Methods | 19 |
Statistical Analysis | 19 |
Comparative Analysis | 6 |
Computation | 5 |
Correlation | 4 |
Data Analysis | 4 |
Multivariate Analysis | 4 |
Sample Size | 4 |
Hypothesis Testing | 3 |
Models | 3 |
Scores | 3 |
More ▼ |
Source
Multivariate Behavioral… | 19 |
Author
Mendoza, Jorge L. | 2 |
West, Stephen G. | 2 |
Aiken, Leona S. | 1 |
Barcikowski, Robert S. | 1 |
Biesanz, Jeremy C. | 1 |
Cham, Heining | 1 |
Chun, So Yeon | 1 |
Enders, Craig K. | 1 |
Estabrook, Ryne | 1 |
Falk, Carl F. | 1 |
Fan, Xitao | 1 |
More ▼ |
Publication Type
Journal Articles | 14 |
Reports - Evaluative | 8 |
Reports - Research | 6 |
Education Level
Elementary Education | 1 |
Grade 5 | 1 |
Grade 7 | 1 |
Higher Education | 1 |
Intermediate Grades | 1 |
Audience
Location
Virginia | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Estabrook, Ryne; Neale, Michael – Multivariate Behavioral Research, 2013
Factor score estimation is a controversial topic in psychometrics, and the estimation of factor scores from exploratory factor models has historically received a great deal of attention. However, both confirmatory factor models and the existence of missing data have generally been ignored in this debate. This article presents a simulation study…
Descriptors: Factor Analysis, Scores, Computation, Regression (Statistics)
Gottschall, Amanda C.; West, Stephen G.; Enders, Craig K. – Multivariate Behavioral Research, 2012
Behavioral science researchers routinely use scale scores that sum or average a set of questionnaire items to address their substantive questions. A researcher applying multiple imputation to incomplete questionnaire data can either impute the incomplete items prior to computing scale scores or impute the scale scores directly from other scale…
Descriptors: Questionnaires, Data Analysis, Computation, Monte Carlo Methods
Biesanz, Jeremy C.; Falk, Carl F.; Savalei, Victoria – Multivariate Behavioral Research, 2010
Theoretical models specifying indirect or mediated effects are common in the social sciences. An indirect effect exists when an independent variable's influence on the dependent variable is mediated through an intervening variable. Classic approaches to assessing such mediational hypotheses (Baron & Kenny, 1986; Sobel, 1982) have in recent years…
Descriptors: Computation, Intervals, Models, Monte Carlo Methods
Chun, So Yeon; Shapiro, Alexander – Multivariate Behavioral Research, 2009
The noncentral chi-square approximation of the distribution of the likelihood ratio (LR) test statistic is a critical part of the methodology in structural equation modeling. Recently, it was argued by some authors that in certain situations normal distributions may give a better approximation of the distribution of the LR test statistic. The main…
Descriptors: Statistical Analysis, Structural Equation Models, Validity, Monte Carlo Methods
Cham, Heining; West, Stephen G.; Ma, Yue; Aiken, Leona S. – Multivariate Behavioral Research, 2012
A Monte Carlo simulation was conducted to investigate the robustness of 4 latent variable interaction modeling approaches (Constrained Product Indicator [CPI], Generalized Appended Product Indicator [GAPI], Unconstrained Product Indicator [UPI], and Latent Moderated Structural Equations [LMS]) under high degrees of nonnormality of the observed…
Descriptors: Monte Carlo Methods, Computation, Robustness (Statistics), Structural Equation Models
Pituch, Keenan A.; Stapleton, Laura M. – Multivariate Behavioral Research, 2008
A Monte Carlo study compared the statistical performance of standard and robust multilevel mediation analysis methods to test indirect effects for a cluster randomized experimental design under various departures from normality. The performance of these methods was examined for an upper-level mediation process, where the indirect effect is a fixed…
Descriptors: Research Design, Monte Carlo Methods, Statistical Analysis, Error Patterns
Wanstrom, Linda – Multivariate Behavioral Research, 2009
Second-order latent growth curve models (S. C. Duncan & Duncan, 1996; McArdle, 1988) can be used to study group differences in change in latent constructs. We give exact formulas for the covariance matrix of the parameter estimates and an algebraic expression for the estimation of slope differences. Formulas for calculations of the required sample…
Descriptors: Sample Size, Effect Size, Mathematical Formulas, Computation
Yuan, Ke-Hai – Multivariate Behavioral Research, 2008
In the literature of mean and covariance structure analysis, noncentral chi-square distribution is commonly used to describe the behavior of the likelihood ratio (LR) statistic under alternative hypothesis. Due to the inaccessibility of the rather technical literature for the distribution of the LR statistic, it is widely believed that the…
Descriptors: Monte Carlo Methods, Graduate Students, Social Sciences, Data Analysis
Wang, Lijuan; Zhang, Zhiyong; McArdle, John J.; Salthouse, Timothy A. – Multivariate Behavioral Research, 2008
Score limitation at the top of a scale is commonly termed "ceiling effect." Ceiling effects can lead to serious artifactual parameter estimates in most data analysis. This study examines the consequences of ceiling effects in longitudinal data analysis and investigates several methods of dealing with ceiling effects through Monte Carlo simulations…
Descriptors: Longitudinal Studies, Data Analysis, Evaluation Methods, Monte Carlo Methods
Fan, Xitao; Sivo, Stephen A. – Multivariate Behavioral Research, 2007
The search for cut-off criteria of fit indices for model fit evaluation (e.g., Hu & Bentler, 1999) assumes that these fit indices are sensitive to model misspecification, but not to different types of models. If fit indices were sensitive to different types of models that are misspecified to the same degree, it would be very difficult to establish…
Descriptors: Structural Equation Models, Criteria, Monte Carlo Methods, Factor Analysis

Mendoza, Jorge L.; And Others – Multivariate Behavioral Research, 1978
Four testing procedures for establishing the number of non-zero population roots in canonical analysis are investigated. Results of a Monte Carlo study indicate that three well-established procedures were effective, and a new procedure designed to correct a supposed flaw in the other procedures was ineffective. (JKS)
Descriptors: Correlation, Hypothesis Testing, Monte Carlo Methods, Multivariate Analysis

Thorndike, Robert M. – Multivariate Behavioral Research, 1976
In their Monte Carlo study of canonical analysis, Barcikowski and Stevens evaluated the relative stability of canonical weights and loadings. This paper identifies some weaknesses in their study, suggests directions for future research in this area, and discusses interpretation of canonical analysis both in development and in cross-validation. For…
Descriptors: Correlation, Measurement Techniques, Monte Carlo Methods, Multivariate Analysis

Barcikowski, Robert S.; Stevens, James P. – Multivariate Behavioral Research, 1976
This article is a rejoinder to TM 502 249. Each of Thorndike's comments are examined. A possible solution to the large number of subjects necessary for stable weights and variate-variable correlations using ridge regression procedures is suggested. (RC)
Descriptors: Correlation, Measurement Techniques, Monte Carlo Methods, Multivariate Analysis

Lance, Charles E.; And Others – Multivariate Behavioral Research, 1988
Supporting the use of separate analyses of measurement and structural portions of latent or mixed manifest and latent variable models, limited information (single equation) procedures are presented for estimating structural parameters. These procedures are recommended for testing specific causal hypotheses and locating specific structural model…
Descriptors: Equations (Mathematics), Estimation (Mathematics), Mathematical Models, Monte Carlo Methods

Hummel, Thomas J.; Feltovich, Paul J. – Multivariate Behavioral Research, 1975
Monte Carlo methods were used to investigate the robustness of techniques used in judging the magnitude of a sample correlation coefficient when observations are correlated. Empirical distributions of r, t, and Fisher's z were generated. A technique for controlling error rates in certain situations is suggested. (Author/BJG)
Descriptors: Computer Science, Correlation, Error Patterns, Monte Carlo Methods
Previous Page | Next Page ยป
Pages: 1 | 2