Abstract
Determining sample size requirements for structural equation modeling (SEM) is a challenge often faced by investigators, peer reviewers, and grant writers. Recent years have seen a large increase in SEMs in the behavioral science literature, but consideration of sample size requirements for applied SEMs often relies on outdated rules-of-thumb. This study used Monte Carlo data simulation techniques to evaluate sample size requirements for common applied SEMs. Across a series of simulations, we systematically varied key model properties, including number of indicators and factors, magnitude of factor loadings and path coefficients, and amount of missing data. We investigated how changes in these parameters affected sample size requirements with respect to statistical power, bias in the parameter estimates, and overall solution propriety. Results revealed a range of sample size requirements (i.e., from 30 to 460 cases), meaningful patterns of association between parameters and sample size, and highlight the limitations of commonly cited rules-of-thumb. The broad "lessons learned" for determining SEM sample size requirements are discussed.
Highlights
The earliest socialization in human beings, a process whereby family and society are built, takes place through communication
The aim of this study was to analyze the psychometric properties of the Spanish version of the Family Communication Scale using a random sample
A factorial solution of two constructs was found with χ2 test=9.466
Summary
Olson (1982), adaptada y validada en España por Sanz et al (2002). El instrumento, en su versión española, muestra un coeficiente de consistencia interna —alfa de Cronbach— de .88 y una correlación test-retest e intraclase de .88. Luego de conocer la cantidad de constructos que contiene el instrumento, se aplicó el análisis factorial confirmatorio para evaluar si los ítems correlacionaban adecuadamente con los constructos, además del nivel de relación entre dichos constructos, la magnitud de los errores de medida y el ajuste global del modelo especificado a los datos muestrales. Posteriormente, se generó una segunda solución factorial, esta vez con dos constructos (Tabla 3, Solución B), en donde se encontró que estos dos suman el 60.1 % de la varianza, con un único reactivo con valor de comunidad inferior a .50 (CF_5) y con pesos factoriales que se diferencian entre los dos constructos (a excepción de CF_8 y CF_7), sin embargo, en ambos constructos se encontraron valores alfa superiores a .80. Tabla 2 Medidas de resumen, correlación ítem-total, coeficiente alfa si se elimina el ítem y coeficiente MSA de todos los ítems de la escala
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