In order to characterize the socioeconomic profile of various geographic units, it is common practice to use aggregated indices. However, the process of calculating such indices requires a wide variety of variables from various data sources available concurrently. Using a number of administrative databases for 2001 and 2003, this study examines the question of whether dwelling prices in a given locality can serve as a proxy for its socioeconomic level. Based on statistical and geographic criteria, we developed a Dwelling Price Ranking (DPR) methodology. Our findings show that the DPR can serve as a good approximation for the socioeconomic cluster (SEC) calculated by the Israel Central Bureau of Statistics for years when the required data was available. As opposed to the SEC, the suggested DPR indicator can easily be calculated, thus ensuring a continuum of socioeconomic index series. Both parametric and nonparametric statistical analyses have been carried out in order to examine the additional social, demographic, location, crime and security effects that are exogenous to SEC. Complementary analysis on recently published SEC series for 2006 and 2008 show that our conclusions remain valid. The proposed methodology and the obtained findings may be applicable for different statistical purposes in other countries which possess dwelling transactions data.
In two-group repeated-measures studies, a traditional statistical approach is to base analysis directly on the observed continuous measurements, using either summary measures or a mixed linear model. In some medical studies, however, an alternate approach has been taken: Declare the occurrence of an "event" when the sequence of measurements crosses a prespecified threshold, and compare the groups with respect to time to event using the log-rank test. This approach is appealing to clinicians, but clearly involves a loss of information and therefore statistical efficiency. The aim of this article is to quantify the degree of power loss in the context of the random line model. We also compare the summary measures approach to the random line approach. In regard to the efficiency loss with the survival analysis approach, we find that the loss ranges, depending on the location of the threshold, from moderate to dramatic. Using an optimally weighted log-rank test in place of the standard log-rank test leads to minimal gain in efficiency. In regard to analysis based on the original continuous measurements, for testing the slope a weighted summary measure appears to be the best overall choice, whereas for testing the intercept the maximum likelihood (ML) approach is typically much more efficient than the summary measures approach, although the efficiency of the ML approach can be compromised in studies with a small number of observation timepoints. These results have obvious implications for the choice of study design and analysis.
This paper aims to develop and test a valid and reliable methodology to explore residents' attitudes toward religious composition of their neighborhood, by integrating traditionally used survey data with administrative data, and collaborative Geographic Information System (GIS) techniques. The main research question is whether residents perceive changes in the religious composition of their neighborhood in relation to their personal religiosity. Focusing on the Jewish population, we compared residents' subjective assessment of changes in the religious composition of their neighborhood, obtained from Israel's 2009 Social Survey, with actual changes in the percentage of residents with differing degrees of religiosity. Using a specifically designed methodology, we found that the two groups on the extreme ends of the religious spectrum (ultraorthodox and nonreligious) are the keenest observers of changes in the religious composition within their neighborhoods. Subjective perception of the dynamics of neighborhood religious composition was found to be systematically associated with neighborhood satisfaction, individual traits, dwelling, and neighborhood characteristics. Using spatially dependent analysis, we also examined mutual relationships between the religious composition, both actual and perceived, of census tracts, based on distances between those geographic areas.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.