Small Sample Solutions for Structural Equation Modeling

We propose an extension of the Wilcoxon–Mann–Whitney test to compare two groups when the outcome variable is latent. We empirically demonstrate that the test can have superior power properties relative to tests based on Structural Equation Modeling for a variety of settings. In addition, several other advantages of the Wilcoxon–Mann–Whitney test are retained such as robustness to outliers and good small sample performance. We demonstrate the proposed methodology on a case study.

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MANUFACTURING AND CONTINUOUS IMPROVEMENT AREAS USING PARTIAL LEAST SQUARE PATH MODELING WITH MULTIPLE REGRESSION COMPARISON

CBU International Conference Proceedings ◽

10.12955/cbup.v2.442 ◽

2014 ◽

Vol 2 ◽

pp. 15-26 ◽

Cited By ~ 2

Author(s):

Carlos Monge Perry ◽

Jesús Cruz Álvarez ◽

Jesús Fabián López

Keyword(s):

Structural Equation Modeling ◽

Multiple Regression ◽

Continuous Improvement ◽

Structural Equation ◽

Statistical Power ◽

Scientific Research ◽

Small Sample ◽

Partial Least Square ◽

Least Square ◽

Equation Modeling

Structural equation modeling (SEM) has traditionally been deployed in areas of marketing, consumer satisfaction and preferences, human behavior, and recently in strategic planning. These areas are considered their niches; however, there is a remarkable tendency in empirical research studies that indicate a more diversified use of the technique. This paper shows the application of structural equation modeling using partial least square (PLS-SEM), in areas of manufacturing, quality, continuous improvement, operational efficiency, and environmental responsibility in Mexico’s medium and large manufacturing plants, while using a small sample (n = 40). The results obtained from the PLS-SEM model application mentioned, are highly positive, relevant, and statistically significant. Also shown in this paper, for purposes of validity, reliability, and statistical power confirmation of PLS-SEM, is a comparative analysis against multiple regression showing very similar results to those obtained by PLS-SEM. This fact validates the use of PLS-SEM in areas of untraditional scientific research, and suggests and invites the use of the technique in diversified fields of the scientific research

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A Practical Guide to Variable Selection in Structural Equation Modeling by Using Regularized Multiple-Indicators, Multiple-Causes Models

Advances in Methods and Practices in Psychological Science ◽

10.1177/2515245919826527 ◽

2019 ◽

Vol 2 (1) ◽

pp. 55-76 ◽

Cited By ~ 9

Author(s):

Ross Jacobucci ◽

Andreas M. Brandmaier ◽

Rogier A. Kievit

Keyword(s):

Structural Equation Modeling ◽

Structural Equation ◽

Structural Equation Models ◽

Short Term Memory ◽

Small Sample Size ◽

Small Sample ◽

Model Complexity ◽

Equation Modeling ◽

Linear Regression Models ◽

Demographic Correlates

Methodological innovations have allowed researchers to consider increasingly sophisticated statistical models that are better in line with the complexities of real-world behavioral data. However, despite these powerful new analytic approaches, sample sizes may not always be sufficiently large to deal with the increase in model complexity. This difficult modeling scenario entails large models with a limited number of observations given the number of parameters. Here, we describe a particular strategy to overcome this challenge: regularization, a method of penalizing model complexity during estimation. Regularization has proven to be a viable option for estimating parameters in this small-sample, many-predictors setting, but so far it has been used mostly in linear regression models. We show how to integrate regularization within structural equation models, a popular analytic approach in psychology. We first describe the rationale behind regularization in regression contexts and how it can be extended to regularized structural equation modeling. We then evaluate our approach using a simulation study, showing that regularized structural equation modeling outperforms traditional structural equation modeling in situations with a large number of predictors and a small sample size. Next, we illustrate the power of this approach in two empirical examples: modeling the neural determinants of visual short-term memory and identifying demographic correlates of stress, anxiety, and depression.

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