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Lingbo Tong; Wen Qu; Zhiyong Zhang – Grantee Submission, 2025
Factor analysis is widely utilized to identify latent factors underlying the observed variables. This paper presents a comprehensive comparative study of two widely used methods for determining the optimal number of factors in factor analysis, the K1 rule, and parallel analysis, along with a more recently developed method, the bass-ackward method.…
Descriptors: Factor Analysis, Monte Carlo Methods, Statistical Analysis, Sample Size
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Levy, Roy; Xia, Yan; Green, Samuel B. – Educational and Psychological Measurement, 2021
A number of psychometricians have suggested that parallel analysis (PA) tends to yield more accurate results in determining the number of factors in comparison with other statistical methods. Nevertheless, all too often PA can suggest an incorrect number of factors, particularly in statistically unfavorable conditions (e.g., small sample sizes and…
Descriptors: Bayesian Statistics, Statistical Analysis, Factor Structure, Probability
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Gonzalez, Oscar; MacKinnon, David P. – Educational and Psychological Measurement, 2018
Statistical mediation analysis allows researchers to identify the most important mediating constructs in the causal process studied. Identifying specific mediators is especially relevant when the hypothesized mediating construct consists of multiple related facets. The general definition of the construct and its facets might relate differently to…
Descriptors: Statistical Analysis, Monte Carlo Methods, Measurement, Models
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Xu, Yuning; Green, Samuel B. – AERA Online Paper Repository, 2017
Investigation of measurement invariance (MI) commonly assumes correct specification of dimensionality across multiple groups. Although research shows that violation of the dimensionality assumption can cause bias in model parameter estimation for single-group analyses, little research on this issue has been conducted for multiple-group analyses.…
Descriptors: Statistical Analysis, Factor Structure, Measurement, Goodness of Fit
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Törmänen, Juha; Hämäläinen, Raimo P.; Saarinen, Esa – Learning Organization, 2016
Purpose: Systems intelligence (SI) (Saarinen and Hämäläinen, 2004) is a construct defined as a person's ability to act intelligently within complex systems involving interaction and feedback. SI relates to our ability to act in systems and reason about systems to adaptively carry out productive actions within and with respect to systems such as…
Descriptors: Emotional Intelligence, Factor Analysis, Questionnaires, Sample Size
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Murphy, Daniel L.; Beretvas, S. Natasha; Pituch, Keenan A. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
This simulation study examined the performance of the curve-of-factors model (COFM) when autocorrelation and growth processes were present in the first-level factor structure. In addition to the standard curve-of factors growth model, 2 new models were examined: one COFM that included a first-order autoregressive autocorrelation parameter, and a…
Descriptors: Sample Size, Simulation, Factor Structure, Statistical Analysis
Doolen, Jessica – ProQuest LLC, 2012
High fidelity simulation has become a widespread and costly learning strategy in nursing education because it can fill the gap left by a shortage of clinical sites. In addition, high fidelity simulation is an active learning strategy that is thought to increase higher order thinking such as clinical reasoning and judgment skills in nursing…
Descriptors: Simulation, Nursing Education, Simulated Environment, Psychometrics
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Meade, Adam W.; Bauer, Daniel J. – Structural Equation Modeling: A Multidisciplinary Journal, 2007
This study investigates the effects of sample size, factor overdetermination, and communality on the precision of factor loading estimates and the power of the likelihood ratio test of factorial invariance in multigroup confirmatory factor analysis. Although sample sizes are typically thought to be the primary determinant of precision and power,…
Descriptors: Sample Size, Factor Structure, Factor Analysis, Statistical Analysis
Chau, Hung; Hocevar, Dennis – 1995
This study addressed which, if any, contemporary fit indices are least susceptible to the bias associated with confirmatory factor analysis (CFA) involving a large number of measured variables. Data were obtained from student responses from 1980 to 1990 on the Student Evaluations of Educational Quality (SEEQ) instrument of H. Marsh (1987). Factor…
Descriptors: Chi Square, College Students, Factor Structure, Goodness of Fit