Confirmatory factor analysis for applied research, 2nd ed.

By T. Brown. 2015

2,264 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/c8710f4a250e079b67325c0a5f45347f29b8f444

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Citations in the corpus

The Development and Validation of the Game User Experience Satisfaction Scale (GUESS) (2016, background): “Third, standard errors of parameter estimates would be underestimated (Benson & Fleishman, 1994; Brown, 2014; Finney & DiStefano, 2013; Russell, 2002).”
Your Coefficient Alpha Is Probably Wrong, but Which Coefficient Omega Is Right? A Tutorial on Using R to Obtain Better Reliability Estimates (2020, methodology): “The lavaan package is loaded as follows: > library(lavaan) In this example, I fitted the one-factor model depicted in Figure 1 to the data for the five openness items using direct ML estimation to incorporate cases with incomplete data (see Brown, 2015).”
Exploratory Factor Analysis: A Guide to Best Practice (2018, methodology): “Conceptually, simple structure is an attempt to find a solution where each factor is loaded by several salient variables and each variable has a salient loading on one factor and trivial loadings on the remaining factors (Brown, 2015).”
Evidence Regarding the Internal Structure: Confirmatory Factor Analysis (2017, methodology): “In the complex process of scale development, confirmatory factor analysis (CFA) is an important tool to assess the internal, or latent, structure of an instrument or questionnaire (Brown, 2015).”
Exploratory bifactor analysis of the Wechsler Intelligence Scale for Children—Fifth Edition with the 16 primary and secondary subtests (2015, background): “Gorsuch (1983) and others (e.g. Brown, 2015; Carroll, 1993; Reise, 2012) indicated that EFA and CFA are complementary, and test users can have greater confidence in an instrument's structure when both procedures are in agreement, particularly when an instrument has been revised and reformulated.”
The Hierarchical Taxonomy of Psychopathology (HiTOP): A Dimensional Alternative to Traditional Nosologies (2017, unknown): “This family of techniques includes exploratory factor analysis, which searches for the optimal organization of variables, and confirmatory factor analysis, which tests the fit of hypothesized structures to data (Brown, 2015; Fabrigar et al., 1999).”
Defining and measuring youth digital citizenship (2016, methodology): “…in defining digital citizenship as a two-factor combination of online respect and online civic engagement, CFA with structural equation modeling was used to test the hypothesized structure of the digital citizenship scale (Brown, 2015; Jackson et al., 2009; Schreiber et al., 2006; SPSS, 2012).”
Academic self-efficacy, growth mindsets, and university students' integration in academic and social support networks (2018, methodology): “To account for missing values, MPlus provides maximum likelihood estimation (Brown, 2015; Little & Rubin, 2002).”
A review of the methodological misconceptions and guidelines related to the application of structural equation modeling: A Malaysian scenario (2017, methodology): “We would recommend some good articles on the subject matter, and they are authored by Hair et al. (2017a), Fan et al. (2016), Green et al. (2016), Brown (2015), Fabrigar and Wegener (2011) and Winter, Dodou, and Wieringa (2009).”
Corporate social responsibility and employee performance: Mediation role of job satisfaction and affective commitment (2019, background): “However, several authors pointed that in CFA and structural equation modeling parameter estimates, chi‐square tests and SMRM are sensitive to sample size (Brown, 2015; Byrne, 2012; Hu & Bentler, 1999).”
Testing Measurement Invariance for a Second-Order Factor. A Cross-National Test of the Alienation Scale (2018, methodology): “, the most constrained, with a highest possible level of invariance) which is still well-fitting (Brown, 2015).”
Message fatigue: Conceptual definition, operationalization, and correlates (2017, methodology): “Specifically, in order to examine the feasibility of the scale’s unidimensionality, a higher-order CFA was pursued (see Brown, 2015).”
Measuring consumers attitudes towards health and taste and their association with food-related life-styles and preferences (2019, methodology): “CFAs were performed by arbitrarily setting one indicator to unity, for each different latent construct, to define the scale of the factor (Kline, 2011).”
Lack of Theory Building and Testing Impedes Progress in The Factor and Network Literature (2020, background): “(Vaidyanathan et al., 2015) Factor models require little introduction, and represent a sophisticated statistical framework (Brown, 2015; Hoyle, 2012; Kline, 2015).”