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Journal ArticleDOI

Current Methodological Considerations in Exploratory and Confirmatory Factor Analysis

TLDR
The present article provides a current overview of these areas in an effort to provide researchers with up-to-date methods and considerations in both exploratory and confirmatory factor analysis.
Abstract
Researchers must make numerous choices when conducting factor analyses, each of which can have significant ramifications on the model results. They must decide on an appropriate sample size to achieve accurate parameter estimates and adequate power, a factor model and estimation method, a method for determining the number of factors and evaluating model fit, and a rotation criterion. Unfortunately, researchers continue to use outdated methods in each of these areas. The present article provides a current overview of these areas in an effort to provide researchers with up-to-date methods and considerations in both exploratory and confirmatory factor analysis. A demonstration was provided to illustrate current approaches. Choosing between confirmatory and exploratory methods is also discussed, as researchers often make incorrect assumptions about the application of each.

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Journal ArticleDOI

Testing Measurement Invariance and Comparing Latent Factor Means Within a Confirmatory Factor Analysis Framework

TL;DR: In this article, the authors emphasize the importance of testing for measurement invariance (MI) and provide guidance when conducting these tests and discuss potential causes of non-invariant items, the difference between measurement bias and invariance, remedies for non-informal measures, and considerations associated with model estimation.
Journal ArticleDOI

On exploratory factor analysis: A review of recent evidence, an assessment of current practice, and recommendations for future use

TL;DR: Five major decisions made in conducting factor analysis are focused on, including establishing how large the sample needs to be, choosing between factor analysis and principal components analysis, determining the number of factors to retain, selecting a method of data extraction, and deciding upon the methods of factor rotation.
Journal ArticleDOI

Variable-Centered, Person-Centered, and Person-Specific Approaches: Where Theory Meets the Method

TL;DR: This article detail the purpose of each approach, describe how to determine when each approach is most appropriate, and delineate when the approaches diverge to give differing results, suggesting that no single approach is the “best.”
Journal ArticleDOI

Data-driven subtypes of major depressive disorder: a systematic review

TL;DR: The studies performed to date do not provide conclusive evidence for the existence of depressive symptom dimensions or symptomatic subtypes of depression, and the wide diversity of identified factors and classes might result from the absence of patterns to be found.
Journal ArticleDOI

Translation and validation of body image instruments: Challenges, good practice guidelines, and reporting recommendations for test adaptation.

TL;DR: An operational framework for conducting effective test adaptation of existing measurement tools is offered and good-practice guidelines for instrument translation and effective strategies for achieving semantic equivalence of translated instruments are suggested.
References
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Book

Statistical Power Analysis for the Behavioral Sciences

TL;DR: The concepts of power analysis are discussed in this paper, where Chi-square Tests for Goodness of Fit and Contingency Tables, t-Test for Means, and Sign Test are used.
Journal ArticleDOI

Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives

TL;DR: In this article, the adequacy of the conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice were examined, and the results suggest that, for the ML method, a cutoff value close to.95 for TLI, BL89, CFI, RNI, and G...
Journal ArticleDOI

Alternative Ways of Assessing Model Fit

TL;DR: In this paper, two types of error involved in fitting a model are considered, error of approximation and error of fit, where the first involves the fit of the model, and the second involves the model's shape.
Book

Structural Equations with Latent Variables

TL;DR: The General Model, Part I: Latent Variable and Measurement Models Combined, Part II: Extensions, Part III: Extensions and Part IV: Confirmatory Factor Analysis as discussed by the authors.
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