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
Evaluation Methods | 14 |
Factor Analysis | 14 |
Simulation | 6 |
Goodness of Fit | 5 |
Models | 5 |
Behavioral Science Research | 4 |
Comparative Analysis | 4 |
Correlation | 4 |
Personality Traits | 4 |
Computation | 3 |
Equations (Mathematics) | 3 |
More ▼ |
Source
Multivariate Behavioral… | 14 |
Author
Davison, Mark L. | 2 |
Kim, Se-Kang | 2 |
Zhang, Guangjian | 2 |
Amemiya, Yasuo | 1 |
Carlson, Marianne | 1 |
Chow, Sy-Miin | 1 |
Close, Catherine | 1 |
Dodou, D. | 1 |
Edwards, Michael C. | 1 |
Farley, John U. | 1 |
Frisby, Craig L. | 1 |
More ▼ |
Publication Type
Journal Articles | 13 |
Reports - Research | 6 |
Reports - Descriptive | 4 |
Reports - Evaluative | 3 |
Education Level
Higher Education | 2 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Wall, Melanie M.; Guo, Jia; Amemiya, Yasuo – Multivariate Behavioral Research, 2012
Mixture factor analysis is examined as a means of flexibly estimating nonnormally distributed continuous latent factors in the presence of both continuous and dichotomous observed variables. A simulation study compares mixture factor analysis with normal maximum likelihood (ML) latent factor modeling. Different results emerge for continuous versus…
Descriptors: Sample Size, Simulation, Form Classes (Languages), Diseases
Chow, Sy-Miin; Zu, Jiyun; Shifren, Kim; Zhang, Guangjian – Multivariate Behavioral Research, 2011
Dynamic factor analysis models with time-varying parameters offer a valuable tool for evaluating multivariate time series data with time-varying dynamics and/or measurement properties. We use the Dynamic Model of Activation proposed by Zautra and colleagues (Zautra, Potter, & Reich, 1997) as a motivating example to construct a dynamic factor…
Descriptors: Simulation, Factor Analysis, Item Response Theory, Models
Lee, Chun-Ting; Zhang, Guangjian; Edwards, Michael C. – Multivariate Behavioral Research, 2012
Exploratory factor analysis (EFA) is often conducted with ordinal data (e.g., items with 5-point responses) in the social and behavioral sciences. These ordinal variables are often treated as if they were continuous in practice. An alternative strategy is to assume that a normally distributed continuous variable underlies each ordinal variable.…
Descriptors: Personality Traits, Intervals, Monte Carlo Methods, Factor Analysis
Schweizer, Karl – Multivariate Behavioral Research, 2011
The standardization of loadings gives a metric to the corresponding latent variable and thus scales the variance of this latent variable. By assigning an appropriately estimated weight to all the loadings on the same latent variable it can be achieved that the average squared loading is 1 as the result of standardization. As a consequence, there…
Descriptors: Structural Equation Models, Short Term Memory, Evaluation Methods, Comparative Analysis
Lorenzo-Seva, Urbano; Timmerman, Marieke E.; Kiers, Henk A. L. – Multivariate Behavioral Research, 2011
A common problem in exploratory factor analysis is how many factors need to be extracted from a particular data set. We propose a new method for selecting the number of major common factors: the Hull method, which aims to find a model with an optimal balance between model fit and number of parameters. We examine the performance of the method in an…
Descriptors: Simulation, Research Methodology, Factor Analysis, Item Response Theory
Zhong, Xiaoling; Yuan, Ke-Hai – Multivariate Behavioral Research, 2011
In the structural equation modeling literature, the normal-distribution-based maximum likelihood (ML) method is most widely used, partly because the resulting estimator is claimed to be asymptotically unbiased and most efficient. However, this may not hold when data deviate from normal distribution. Outlying cases or nonnormally distributed data,…
Descriptors: Structural Equation Models, Simulation, Racial Identification, Computation
Davison, Mark L.; Kim, Se-Kang; Close, Catherine – Multivariate Behavioral Research, 2009
A profile is a vector of scores for one examinee. The mean score in the vector can be interpreted as a measure of overall profile height, the variance can be interpreted as a measure of within person variation, and the ipsatized vector of score deviations about the mean can be said to describe the pattern in the score profile. A within person…
Descriptors: Vocational Interests, Interest Inventories, Profiles, Scores
de Winter, J. C. F.; Dodou, D.; Wieringa, P. A. – Multivariate Behavioral Research, 2009
Exploratory factor analysis (EFA) is generally regarded as a technique for large sample sizes ("N"), with N = 50 as a reasonable absolute minimum. This study offers a comprehensive overview of the conditions in which EFA can yield good quality results for "N" below 50. Simulations were carried out to estimate the minimum required "N" for different…
Descriptors: Sample Size, Factor Analysis, Enrollment, Evaluation Methods
Kim, Se-Kang; Davison, Mark L.; Frisby, Craig L. – Multivariate Behavioral Research, 2007
This paper describes the Confirmatory Factor Analysis (CFA) parameterization of the Profile Analysis via Multidimensional Scaling (PAMS) model to demonstrate validation of profile pattern hypotheses derived from multidimensional scaling (MDS). Profile Analysis via Multidimensional Scaling (PAMS) is an exploratory method for identifying major…
Descriptors: Profiles, Factor Analysis, Multidimensional Scaling, Evaluation Methods
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

McDonald, Roderick P.; Mok, Magdalena M.-C. – Multivariate Behavioral Research, 1995
It is shown that goodness-of-fit criteria developed for the evaluation of multivariate structural models can be applied to assist in evaluating the dimensionality of a test consisting of binary items, and correlative methods regularly used in factor analysis can be employed to diagnose causes of misfit. (Author)
Descriptors: Correlation, Criteria, Evaluation Methods, Factor Analysis

Farley, John U.; And Others – Multivariate Behavioral Research, 1974
Evaluation of attributes of a subcompact car were combined in linear regressions predicting liking and purchase intention. Of two forms--raw scales and scales weighted by the importance attached to each attribute by each subject--unweighted evaluations proved more consistent and important predictors than those weighted by their saliency. (Author)
Descriptors: Attitudes, Decision Making, Design Preferences, Design Requirements

O'Grady, Kevin E.; Medoff, Deborah R. – Multivariate Behavioral Research, 1991
A procedure for evaluating a variety of rater reliability models is presented. A multivariate linear model is used to describe and assess a set of ratings. Parameters are represented in terms of a factor analytic model, and maximum likelihood methods test the model parameters. Illustrative examples are presented. (SLD)
Descriptors: Comparative Analysis, Correlation, Equations (Mathematics), Estimation (Mathematics)

Carlson, Marianne; Mulaik, Stanley A. – Multivariate Behavioral Research, 1993
The role that language plays in mediating the influence of verbal descriptions of persons on trait ratings of those persons was studied for 280 college students. Results suggest that the influence of verbally communicated descriptions of persons on trait ratings is mediated by the expected latent factors. (SLD)
Descriptors: Behavior Patterns, College Students, Evaluation Methods, Factor Analysis