"How Big Is Big Enough?": Sample Size and Goodness of Fit in Structural Equation Models with Latent Variables

1987 ◽  
Vol 58 (1) ◽  
pp. 134 ◽  
Author(s):  
J. S. Tanaka
Keyword(s):  
Sample Size ◽  
Latent Variables ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Structural Equation Models

scholarly journals Power analysis for parameter estimation in structural equation modeling: A discussion and tutorial

2020 ◽  
Author(s):  
Yilin Andre Wang ◽  
Mijke Rhemtulla
Keyword(s):  
Structural Equation Modeling ◽  
Sample Size ◽  
Latent Variables ◽  
Power Analysis ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Regression Coefficient ◽  
Equation Modeling ◽  
Shiny App ◽  
Target Effect

Despite the widespread and rising popularity of structural equation modeling (SEM) in psychology, there is still much confusion surrounding how to choose an appropriate sample size for SEM. Currently available guidance primarily consists of sample size rules of thumb that are not backed up by research, and power analyses for detecting model misfit. Missing from most current practices is power analysis to detect a target effect (e.g., a regression coefficient between latent variables). In this paper we (a) distinguish power to detect model misspecification from power to detect a target effect, (b) report the results of a simulation study on power to detect a target regression coefficient in a 3-predictor latent regression model, and (c) introduce a Shiny app, pwrSEM, for user-friendly power analysis for detecting target effects in structural equation models.

scholarly journals Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics

1981 ◽  
Vol 18 (3) ◽  
pp. 382-388 ◽  
Author(s):  
Claes Fornell ◽  
David F. Larcker
Keyword(s):  
Statistical Analysis ◽  
Measurement Error ◽  
Structural Equation ◽  
Goodness Of Fit ◽  
Structural Equation Models ◽  
Structural Equations ◽  
Testing System

Several issues relating to goodness of fit in structural equations are examined. The convergence and differentiation criteria, as applied by Bagozzi, are shown not to stand up under mathematical or statistical analysis. The authors argue that the choice of interpretative statistic must be based on the research objective. They demonstrate that when this is done the Fornell-Larcker testing system is internally consistent and that it conforms to the rules of correspondence for relating data to abstract variables.

Clarifying the Factor Structure of the Self-Compassion Scale

2021 ◽  
Author(s):  
Noelle J. Strickland ◽  
Raquel Nogueira-Arjona ◽  
Sean Mackinnon ◽  
Christine Wekerle ◽  
Sherry H. Stewart
Keyword(s):  
Factor Structure ◽  
Lower Order ◽  
Latent Variables ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Well Being ◽  
The Self ◽  
Self Compassion ◽  
The Hierarchical Structure ◽  
Compassion Scale

Abstract. Self-compassion is associated with greater well-being and lower psychopathology. There are mixed findings regarding the factor structure and scoring of the Self-Compassion Scale (SCS). Using confirmatory factor analysis, we tested and conducted nested comparisons of six previously posited factor structures of the SCS. Participants were N = 1,158 Canadian undergraduates (72.8% women, 26.6% men, 0.6% non-binary; Mage = 19.0 years, SD = 2.3). Results best supported a two-factor hierarchical model with six lower-order factors. A general self-compassion factor was not supported at the higher- or lower-order levels; thus, a single total score is not recommended. Given the hierarchical structure, researchers are encouraged to use structural equation models of the SCS with two latent variables: self-caring and self-coldness. A strength of this study is the large sample, while the undergraduate sample may limit generalizability.

scholarly journals Aportaciones de la teoría de la autodeterminación a la predicción de la satisfacción escolar en estudiantes universitarios

2019 ◽  
Vol 37 (2) ◽  
pp. 471-485 ◽  
Author(s):  
José-Manuel Tomás ◽  
Melchor Gutiérrez
Keyword(s):  
University Students ◽  
Latent Variables ◽  
Autonomy Support ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Psychological Needs ◽  
Basic Psychological Needs ◽  
Academic Satisfaction ◽  
Mediational Model ◽  
Estudiantes Universitarios

La literatura especializada ofrece evidencias de que en todo el mundo las tasas de deserción universitaria son elevadas, generando inconvenientes para los propios estudiantes, para la institución a la que pertenecen y para la sociedad en general. Los determinantes del abandono de los estudios son diversos, considerando uno de los más importantes la satisfacción de los estudiantes con su entorno educativo. La satisfacción académica de los estudiantes depende en gran medida del clima motivacional del aula y de la satisfacción de las necesidades psicológicas básicas, fundamento de la teoría de la autodeterminación. En el marco teórico de la motivación autodeterminada y de la psicología positiva, el objetivo de este trabajo es analizar la capacidad predictiva del apoyo a la autonomía por los profesores sobre la satisfacción académica de los alumnos, mediado por la satisfacción de las necesidades psicológicas básicas de los estudiantes. Los participantes son 752 estudiantes universitarios dominicanos. Instrumentos: Percepción de Apoyo a la Autonomía por los Profesores, Satisfacción Necesidades de las Psicológicas Básicas de los Estudiantes, y Conectividad Académica. Los datos se analizan a través de dos Modelos de Ecuaciones Estructurales con variables latentes, uno con mediación total y otro con mediación parcial. Los resultados muestran que el apoyo a la autonomía se relaciona positivamente con la satisfacción de las necesidades psicológicas básicas; las necesidades básicas se relacionan positivamente con la satisfacción académica; y también aparece un efecto positivo y directo del apoyo a la autonomía por los profesores sobre la satisfacción académica de los estudiantes universitarios. The specialized literature offers evidence that university dropout rates are high throughout the world, creating problems for the students themselves, for the institution to which they belong, and for society in general. The determining factors of dropout are diverse, considering the student satisfaction with their educational environment as one of the most important ones. There is also evidence that the students’ satisfaction with their academic environment depends to a large extent on the classroom motivational climate and the satisfaction of basic psychological needs, main elements of the self-determination theory. In the theoretical framework of self-determined motivation and positive psychology, the objective of this paper is to analyze the predictive capacity of teachers’ autonomy support on students’ academic satisfaction, mediated by the satisfaction of the students’ basic psychological needs. Participants are 752 Dominican university students. Instruments: Perceived Teachers’ Autonomy Support, Students’ Basic Psychological Needs Scale, and Academic Connectedness Scale. The data has been analyzed through two Structural Equation Models with latent variables, a total mediational model and a partial mediational model. The results show that support for autonomy is positively related to the satisfaction of basic psychological needs; that basic needs are positively related to academic satisfaction; and that there is also a positive and direct effect of autonomy support by teachers on university students’ academic satisfaction.

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