Current Methodological Considerations in Exploratory and Confirmatory Factor Analysis

By Thomas A. Schmitt. 2011

972 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/58d92d220381a733b92c35ae737d1d2aa3601eef

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Citations in the corpus

Measuring Tourists’ Emotional Solidarity with One Another—A Modification of the Emotional Solidarity Scale (2020, methodology): “When interpreting the results, promax rotation, which allows factors to correlate (Schmitt 2011; Tabachnick and Fidell 2013), was used.”
Improving Your Exploratory Factor Analysis for Ordinal Data: A Demonstration Using FACTOR. (2014, methodology): “The outcomes obtained by the EFA using the recommended procedures through FACTOR are compared to the default techniques currently available in SPSS. Exploratory factor analysis (EFA) is a cluster of common methods used to explore the underlying pattern of relationships among multiple observed…”
Can You Really Measure That? Combining Critical Race Theory and Quantitative Methods (2018, background): “EFA is highly discretionary, with extrastatistical decisions being made by the researcher (Henson & Roberts, 2006; Schmitt, 2011; Worthington & Whittaker, 2006).”
Self-determination and STEM education: Effects of autonomy, motivation, and self-regulated learning on high school math achievement (2015, methodology): “Because the observed variables or the scale items were ordered categorically, we decided to use weighted least square mean and variance adjusted (WLSMV) as the estimation method which is more accurate than Maximun Likelihood (Schmitt, 2011).”
Comparing Exploratory Structural Equation Modeling and Existing Approaches for Multiple Regression with Latent Variables (2018, methodology): “…loadings, exploratory structural equation modeling (ESEM) was proposed as an alternative approach for latent variables analysis, which evaluates the measurement model of latent variables using exploratory factor analysis (EFA) instead of CFA (Marsh et al., 2009; Sass, 2011; Schmitt, 2011).”
An Easy Approach to Exploratory Factor Analysis: Marketing Perspective (2016, methodology): “As anticipated, these three factors emerge when factor analyzed using oblique rotation principal as shown in Table 2.3 for details see (Kim and Mueller, 1978; Schmitt, 2011).”
Investigating the performance of Exploratory Graph analysis and traditional techniques to identify the number of latent factors: a simulation and tutorial (2018, methodology): “Parallel analysis has been one of the most studied and accurate dimensionality assessment methods for continuous and categorical variables to date (Crawford et al., 2010; Garrido et al., 2013, 2016; Ruscio & Roche, 2012; Schmitt, 2011; Timmerman & Lorenzo-Seva, 2011).”
Testing Measurement Invariance and Comparing Latent Factor Means Within a Confirmatory Factor Analysis Framework (2011, methodology): “When conducting measurement equivalence analyses, researchers are also faced with selecting the most approximate statistical model (e.g., MCFA, ESEM, IRT), estimation method (e.g., MLE, MLR, WLSMV), correlation or covariance matrix, among other modeling decisions (Schmitt, 2011).”
Variable-Centered, Person-Centered, and Person-Specific Approaches (2018, methodology): “Once the data were appropriately averaged, an exploratory factor analysis (EFA), which is the traditional method to identify the dimensionality of underlying constructs across a sample, was performed (Conway & Huffcutt, 2003; Costello & Osborne, 2005; Fabrigar, Wegener, MacCallum, & Strahan, 1999; S”