Random Effect Meta-Analytic Structural Equation Modeling (MASEM) Estimation Using The Method Of Moment: Case Study On The Poverty In The Island Of Java

Abstract

Meta-Analytic Structural Equation Modeling (MASEM) is divided into two types according to the homogeneity of the effect size, the fixed-effects MASEM, and the random-effects MASEM. The random effects of MASEM are heterogeneous or there are variations in the effects of the study population, thus implying both between-study variance and within-study variance. Accordingly, the process of estimating the MASEM random-effects parameters involves an additional process, the estimation of the variance between studies. The initial process of MASEM parameters was estimated with generalized least squares (GLS) and then estimated the variance between studies. The iterative EM algorithm is used to estimate the variances between studies of random effects MASEM GLS but this method may not achieve convergence. So, a non-iterative procedure was developed to estimate the variance between studies using the moment method. Data on poverty on the island of Java are likely to differ. Poverty data, if collected from a different population in between province, will be more accurate and in line with targets. The poverty model with the economy, human resources, and health as the right exogenous variables on the island of Java. The results of applied random-effect MASEM GLS with the method of moment approach to estimate the variance between studies on a poverty model show that the coefficient of each economic, health and human resources variable on poverty is 0.4942, -0.1106, and -0.2843. When human resources increase along with health, people’s creativity and productivity also increase, it affects the decline in the value of economic indicators or is equivalent to the increase in the economy of the community so that poverty decreases.