TY - JOUR
T1 - Structural equation modeling with many variables
T2 - A systematic review of issues and developments
AU - Deng, Lifang
AU - Yang, Miao
AU - Marcoulides, Katerina M.
N1 - Publisher Copyright:
© 2018 Deng, Yang and Marcoulides.
PY - 2018/4/25
Y1 - 2018/4/25
N2 - Survey data in social, behavioral, and health sciences often contain many variables (p). Structural equation modeling (SEM) is commonly used to analyze such data. With a sufficient number of participants (N), SEM enables researchers to easily set up and reliably test hypothetical relationships among theoretical constructs as well as those between the constructs and their observed indicators. However, SEM analyses with small N or large p have been shown to be problematic. This article reviews issues and solutions for SEM with small N, especially when p is large. The topics addressed include methods for parameter estimation, test statistics for overall model evaluation, and reliable standard errors for evaluating the significance of parameter estimates. Previous recommendations on required sample size N are also examined together with more recent developments. In particular, the requirement for N with conventional methods can be a lot more than expected, whereas new advances and developments can reduce the requirement for N substantially. The issues and developments for SEM with many variables described in this article not only let applied researchers be aware of the cutting edge methodology for SEM with big data as characterized by a large p but also highlight the challenges that methodologists need to face in further investigation.
AB - Survey data in social, behavioral, and health sciences often contain many variables (p). Structural equation modeling (SEM) is commonly used to analyze such data. With a sufficient number of participants (N), SEM enables researchers to easily set up and reliably test hypothetical relationships among theoretical constructs as well as those between the constructs and their observed indicators. However, SEM analyses with small N or large p have been shown to be problematic. This article reviews issues and solutions for SEM with small N, especially when p is large. The topics addressed include methods for parameter estimation, test statistics for overall model evaluation, and reliable standard errors for evaluating the significance of parameter estimates. Previous recommendations on required sample size N are also examined together with more recent developments. In particular, the requirement for N with conventional methods can be a lot more than expected, whereas new advances and developments can reduce the requirement for N substantially. The issues and developments for SEM with many variables described in this article not only let applied researchers be aware of the cutting edge methodology for SEM with big data as characterized by a large p but also highlight the challenges that methodologists need to face in further investigation.
KW - Parameter estimates
KW - Small sample size
KW - Stand errors
KW - Structural equation modeling
KW - Test statistics
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U2 - 10.3389/fpsyg.2018.00580
DO - 10.3389/fpsyg.2018.00580
M3 - Review article
C2 - 29755388
AN - SCOPUS:85046071016
SN - 1664-1078
VL - 9
JO - Frontiers in Psychology
JF - Frontiers in Psychology
IS - APR
M1 - 580
ER -