Model Persamaan Struktural untuk Menganalisis Indikator Kesejahteraan Rumah Tangga

https://doi.org/10.24042/djm.v2i3.4692

Achi Rinaldi

Abstract


Welfare is an important thing that concerns all countries in the world, including Indonesia. Welfare is not only measured materially but also spiritually measured. Materially, welfare is measured by one's wealth, health, nutrition, education, assets, housing, and certain rights in society. While spiritually well-being is measured by perceived happiness. This study discusses the application of the structural equation model to examine the relationship between welfare indicators in Central Java Province. The model-built places education and employment as exogenous latent variables, while objective well-being and subjective well-being are endogenous latent variables. The data used to support the analysis were sourced from BPS, the results of the 2012 KOR National Social Economic Survey and MSBP, with a sample size of 6730 observations. The modeling results obtained by the model are quite feasible to explain the diversity of data, which is indicated by the value of GFI 0.97, AGFI 0.96, RMSEA 0.039, and RMSR 0.072. The analysis shows that education and employment have a direct influence on objective well-being and an indirect effect on subjective well-being. The effect of education on the level of welfare is higher than the effect of work.

Keywords


latent variable; indicator; maximum likelihood; GFI; AGFI; RMSEA; RMSR

Full Text:

PDF

References


Bappenas. (2010). Peraturan Presiden Republik Indonesia No. 5 Tahun 2010 tentang Rencana Pembangunan Jangka Menengah Nasional Tahun 2010-2014, Buku I. Jakarta.

Bollen KA. (1989). Structural Equations with Latent Variables, New York : J. Wiley.

Darmani, J. W., & Renaldi, A. (2018). Analisis Kemampuan Pemecahan Masalah Matematis: Dampak Model Pembelajaran Reciprocal Teaching Dengan Fieldtrip. Desimal: Jurnal Matematika, 1(3), 373-380.

Ferdinand, A. (2000). Structural Equation Modelling dalam Penelitian Managemen. Universitas Diponegoro, Semarang.

Hair JF., Anderson RE., Tatham RL., and Black WC. (1998). Multivariate Data Analysis, Fifth Edition. New Jersey: Prentice Hall, Inc.

Joreskog KG and Sorbon. (1996). LISREL 8 : User’s Reference Guide, Scientific Software International, Inc. Chicago.

World Bank Institute dan BPS. (2002). Dasar-dasar Analisis Kemiskinan, Buku Panduan Basic Poverty Measurement and Diagnostics Course. Jakarta.

Rinaldi, A. (2015). Aplikasi Model Persamaan Struktural Pada Program R (Studi Kasus Data Pengukuran Kecerdasan). Al-Jabar: Jurnal Pendidikan Matematika, 6(1), 1-12.

Rinaldi, A. (2016). Sebaran Generalized Extreme Value (GEV) dan Generalized Pareto (GP) untuk Pendugaan Curah Hujan Ekstrim di Wilayah DKI Jakarta. Al-Jabar: Jurnal Pendidikan Matematika, 7(1), 75-84.

Rinaldi, A., Djuraidah, A., Wigena, A. H., Mangku, I. W., & Gunawan, D. (2018, November). Identification of Extreme Rainfall Pattern Using Extremogram in West Java. In IOP Conference Series: Earth and Environmental Science (Vol. 187, No. 1, p. 012064). IOP Publishing.

Syazali, M., Putra, F., Rinaldi, A., Utami, L., Widayanti, W., Umam, R., & Jermsittiparsert, K. (2019). Partial correlation analysis using multiple linear regression: Impact on business environment of digital marketing interest in the era of industrial revolution 4.0. Management Science Letters, 9(11), 1875-1886.




DOI: https://doi.org/10.24042/djm.v2i3.4692

Article Metrics

Abstract views : 202 | PDF downloads : 141

Refbacks

  • There are currently no refbacks.


Copyright (c) 2019 Desimal: Jurnal Matematika

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

  Creative Commons License
Desimal: Jurnal Matematika is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.