An Empirical Study of Low-income Housing Policy in Small and Medium-sized Areas

Abstract

The purpose of this study is to analyze the housing situation of small and medium-sized cities located in the provinces and to provide housing policy data for low-income households by analyzing empirical data concerned low-income households in small and medium-sized cities. 2,252 data for the year 2017 and 2018 of public assistant recipient households are used, and regression analysis of the amount of income recognition was conducted through curve model estimation. Age, the number of household members, and daily workers' income. The estimation of the second and third degrees shows the largest amount of income is recognized when the number of household members reaches six to seven. Due to the low birth rate and increased number of single or two-person households. The results for the full-time and daily working householders show the relationship directly proportional to the dependent variable with statistical significance. The results indicate that it is necessary to promote different approaching policies. The basic direction of the housing welfare policy would have to be more microscopic than the ongoing one. A granular approach is worth the further research.

Exploring non-linear relationships between perceived interactivity or interface design and acceptance of collaborative web-based learning

“…For this purpose, the researcher moved from the traditional direct effect to more complex non-linear relationships. For analysis, the researcher followed these main statistical procedures: using the percentages to present personal information and technology experiences; conducting independent sample t-test and one-way ANOVA test to outline the statistical differences among participants; and conducting nonlinear regression analysis based on estimated coefficients that will be derived from the best fit curve for the data when using the curve estimation function, this strategy was proposed by (Keum, 2019 ). Also, ANOVA was used to explore the relationship between constructs (in linear and nonlinear regression analysis).…”