Dimensionality assessment of ordered polytomous items with parallel analysis.

By Marieke E. Timmerman, Urbano Lorenzo-Seva. Psychological methods, 2011

1,424 citations recorded by the source corpus.

Subfields: Food Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3c0a7160dd918afd6cff3ead8ba6f1a483e170f8

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Citations in the corpus

Development of the Educational and Career Interest Scale in Science, Technology, and Mathematics for High School Students (2013, result): “In terms of parallel analysis, a factor was retained when its eigenvalue obtained from the real data was equal or larger than the one from the simulation data (Timmerman and Lorenzo-Seva 2011).”
Inclusion: Conceptualization and measurement (2014, methodology): “Therefore, a PA allows for a more statistically informed decision of the number of factors to be extracted in an EFA than the more commonly used “eigenvalue greater than one” criterion (Kaiser criterion; Timmerman & Lorenzo-Seva, 2011).”
Multiple Imputation of missing values in exploratory factor analysis of multidimensional scales : estimating latent trait scores (2016, methodology): “This approach was adopted in earlier research on the common factor model (see for example, Timmerman & Lorenzo-Seva, 2011).”
Understanding charging behaviour of electric vehicle users (2013, methodology): “Parallel analysis (Timmerman & Lorenzo-Seva, 2011), however, indicated that two factors best accounted for the data when n = 75.”
Robust Promin: A method for diagonally weighted factor rotation (2019, positive): “e inter-item correlation matrix had good sample adequacy (KMO = .930; 95% conﬁdence interval = .928 and .935), and the Optimal Implementation of Parallel Analysis (Timmerman & Lorenzo-Seva, 2011) suggested that three factors should be extracted.”
Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research (2016, background): “Why does psychology need a new way to estimate the number of dimensions? The answer lies in the several studies published about the performance of the parallel analysis [18, 21, 22, 23, 24, 25], the MAP [18, 24, 26], the BIC [27, 28, 29, 30] and the Kaiser-Guttman eigenvalue rule [2, 18, 24, 25, 31]”
Improving Your Exploratory Factor Analysis for Ordinal Data: A Demonstration Using FACTOR. (2014, methodology): “Therefore, it’s not surprising that researchers recommend the use of polychoric correlations for EFA performed on data from ordinal variables (Garrido et al., 2013; Holgado–Tello et al., 2008; Timmerman & Lorenzo-Seva, 2011).”
Investigating the performance of Exploratory Graph analysis and traditional techniques to identify the number of latent factors: a simulation and tutorial (2018, methodology): “…number of factors, dimensionality, exploratory factor analysis, parallel analysis Investigating the number of latent factors or dimensions that underlie multivariate data is an important aspect in the construction and validation of instruments in psychology (Timmerman & Lorenzo-Seva, 2011).”
Estimating the dimensionality of intelligence like data using Exploratory Graph Analysis (2017, neutral): “…size is equal to or N500 and when the factor loadings are frommoderate to high (Buja & Eyuboglu, 1992; Crawford et al., 2010; Garrido, Abad & Ponsoda, 2011; Green, Redell, Thompson & Levy, 2016; Timmerman & Lorenzo-Seva, 2011; Velicer, Eaton, & Fava, 2000, Velicer, 1976; Zwick & Velicer, 1986).”