scholarly journals Entropy Fit Indices: New Fit Measures for Assessing the Structure and Dimensionality of Multiple Latent Variables.

2019 ◽  
Author(s):  
Hudson Golino ◽  
Robert Glenn Moulder ◽  
Dingjing Shi ◽  
Alexander P. Christensen ◽  
Luis E. Garrido ◽  
...  
Keyword(s):  
Information Theory ◽  
Quantum Information ◽  
Latent Variables ◽  
Categorical Variables ◽  
Graph Analysis ◽  
Fit Indices ◽  
Latent Factors ◽  
Accurate Identification ◽  
Number Of Factors ◽  
Fit Index

The accurate identification of the content and number of latent factors underlying multivariate data is an important endeavor in many areas of Psychology and related fields. Recently, a new dimensionality assessment technique based on network psychometrics was proposed (Exploratory Graph Analysis, EGA), but a measure to check the fit of the dimensionality structure to the data estimated via EGA is still lacking. Although traditional factor-analytic fit measures are widespread, recent research has identified limitations for their effectiveness in categorical variables. Here, we propose three new fit measures (termed entropy fit indices) that combines information theory, quantum information theory and structural analysis: Entropy Fit Index (EFI), EFI with Von Neumman Entropy (EFI.vn) and Total EFI.vn (TEFI.vn). The first can be estimated in complete datasets using Shannon entropy, while EFI.vn and TEFI.vn can be estimated in correlation matrices using quantum information metrics. We show, through several simulations, that TEFI.vn, EFI.vn and EFI are as accurate or more accurate than traditional fit measures when identifying the number of simulated latent factors. However, in conditions where more factors are extracted than the number of factors simulated, only TEFI.vn presents a very high accuracy. In addition, we provide an applied example that demonstrates how the new fit measures can be used with a real-world dataset, using exploratory graph analysis.

scholarly journals Using Fit Statistic Differences to Determine the Optimal Number of Factors to Retain in an Exploratory Factor Analysis

2019 ◽  
Vol 80 (2) ◽  
pp. 217-241 ◽  
Author(s):  
W. Holmes Finch
Keyword(s):  
Factor Analysis ◽  
Exploratory Factor Analysis ◽  
Optimal Number ◽  
Parallel Analysis ◽  
Fit Indices ◽  
Number Of Factors ◽  
Indicator Variables ◽  
Model Fit Indices ◽  
Fit Index ◽  
Normally Distributed

Exploratory factor analysis (EFA) is widely used by researchers in the social sciences to characterize the latent structure underlying a set of observed indicator variables. One of the primary issues that must be resolved when conducting an EFA is determination of the number of factors to retain. There exist a large number of statistical tools designed to address this question, with none being universally optimal across applications. Recently, researchers have investigated the use of model fit indices that are commonly used in the conduct of confirmatory factor analysis to determine the number of factors to retain in EFA. These results have yielded mixed results, appearing to be effective when used in conjunction with normally distributed indicators, but not being as effective for categorical indicators. The purpose of this simulation study was to compare the performance of difference values for several fit indices as a method for identifying the optimal number of factors to retain in an EFA, with parallel analysis, which is one of the most reliable such extant methods. Results of the simulation demonstrated that the use of fit index difference values outperformed parallel analysis for categorical indicators, and for normally distributed indicators when factor loadings were small. Implications of these findings are discussed.

scholarly journals Investigating the performance of Exploratory Graph Analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial

2018 ◽  
Author(s):  
Hudson Golino ◽  
Dingjing Shi ◽  
Luis E. Garrido ◽  
Alexander P. Christensen ◽  
Maria Dolores Nieto ◽  
...  
Keyword(s):  
Social Desirability ◽  
Traditional Method ◽  
Graph Analysis ◽  
Social Desirability Scale ◽  
Latent Factors ◽  
Dichotomous Data ◽  
Factor Analytic ◽  
Number Of Factors ◽  
Wide Range ◽  
A New Technique

Exploratory graph analysis (EGA) is a new technique that was recently proposed within the framework of network psychometrics to estimate the number of factors underlying multivariate data. Unlike other methods, EGA produces a visual guide––network plot––that not only indicates the number of dimensions to retain, but also which items cluster together and their level of association. Although previous studies have found EGA to be superior to traditional methods, they are limited in the conditions considered. These issues are here addressed through an extensive simulation study that incorporates a wide range of plausible structures that may be found in practice, including continuous and dichotomous data, and unidimensional and multidimensional structures. Additionally, two new EGA techniques are presented, one that extends EGA to also deal with unidimensional structures, and the other based on the triangulated maximally filtered graph approach (EGAtmfg). Both EGA techniques are compared with five widely used factor analytic techniques. Overall, EGA and EGAtmfg are found to perform as well as the most accurate traditional method, parallel analysis, and to produce the best large-sample properties of all the methods evaluated. To facilitate the use and application of EGA, we present a straightforward R tutorial on how to apply and interpret EGA, using scores from a well-known psychological instrument: the Marlowe-Crowne Social Desirability Scale.

scholarly journals Exploratory Graph Analysis for Factor Retention: Simulation Results for Continuous and Binary Data

2021 ◽  
pp. 001316442110590
Author(s):  
Tim Cosemans ◽  
Yves Rosseel ◽  
Sarah Gelper
Keyword(s):  
Sample Size ◽  
Latent Variables ◽  
Binary Data ◽  
Graph Analysis ◽  
Correlation Measure ◽  
Social Scientists ◽  
Popular Belief ◽  
Number Of Factors ◽  
Simulation Results ◽  
Major Factors

Exploratory graph analysis (EGA) is a commonly applied technique intended to help social scientists discover latent variables. Yet, the results can be influenced by the methodological decisions the researcher makes along the way. In this article, we focus on the choice regarding the number of factors to retain: We compare the performance of the recently developed EGA with various traditional factor retention criteria. We use both continuous and binary data, as evidence regarding the accuracy of such criteria in the latter case is scarce. Simulation results, based on scenarios resulting from varying sample size, communalities from major factors, interfactor correlations, skewness, and correlation measure, show that EGA outperforms the traditional factor retention criteria considered in most cases in terms of bias and accuracy. In addition, we show that factor retention decisions for binary data are preferably made using Pearson, instead of tetrachoric, correlations, which is contradictory to popular belief.

scholarly journals Calculating level-specific SEM fit indices for multilevel mediation analyses

2021 ◽  
Vol 21 (1) ◽  
pp. 195-205
Author(s):  
W. Scott Comulada
Keyword(s):  
Methadone Maintenance Treatment ◽  
Maintenance Treatment ◽  
Multilevel Models ◽  
Mean Squared Error ◽  
Methadone Maintenance ◽  
Fit Indices ◽  
Mediation Analyses ◽  
Multilevel Mediation ◽  
Squared Error ◽  
Fit Index

Stata’s gsem command provides the ability to fit multilevel structural equation models (SEM) and related multilevel models. A motivating example is provided by multilevel mediation analyses (MA) conducted on patient data from Methadone Maintenance Treatment clinics in China. Multilevel MA conducted through the gsem command examined the mediating effects of patients’ treatment progression and rapport with counselors on their treatment satisfaction. Multilevel models accounted for the clustering of patient observations within clinics. SEM fit indices, such as the comparative fit index and the root mean squared error of approximation, are commonly used in the SEM model selection process. Multilevel models present challenges in constructing fit indices because there are multiple levels of hierarchy to account for in establishing goodness of fit. Level-specific fit indices have been proposed in the literature but have not been incorporated into the gsem command. I created the gsemgof command to fill this role. Model results from the gsem command are used to calculate the level-specific comparative fit index and root mean squared error of approximation fit indices. I illustrate the gsemgof command through multilevel MA applied to two-level Methadone Maintenance Treatment data.

QALO-MOR: Improved antlion optimizer based on quantum information theory for model order reduction

2021 ◽  
pp. 1-11
Author(s):  
Rosy Pradhan ◽  
Mohammad Rafique Khan ◽  
Prabir Kumar Sethy ◽  
Santosh Kumar Majhi
Keyword(s):  
Information Theory ◽  
Quantum Information ◽  
Real World ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Quantum Information Theory ◽  
Hunting Behavior ◽  
Model Order ◽  
Ant Lion Optimization ◽  
Real World Problems

The field of optimization science is proliferating that has made complex real-world problems easy to solve. Metaheuristics based algorithms inspired by nature or physical phenomena based methods have made its way in providing near-ideal (optimal) solutions to several complex real-world problems. Ant lion Optimization (ALO) has inspired by the hunting behavior of antlions for searching for food. Even with a unique idea, it has some limitations like a slower rate of convergence and sometimes confines itself into local solutions (optima). Therefore, to enhance its performance of classical ALO, quantum information theory is hybridized with classical ALO and named as QALO or quantum theory based ALO. It can escape from the limitations of basic ALO and also produces stability between processes of explorations followed by exploitation. CEC2017 benchmark set is adopted to estimate the performance of QALO compared with state-of-the-art algorithms. Experimental and statistical results demonstrate that the proposed method is superior to the original ALO. The proposed QALO extends further to solve the model order reduction (MOR) problem. The QALO based MOR method performs preferably better than other compared techniques. The results from the simulation study illustrate that the proposed method effectively utilized for global optimization and model order reduction.

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