scholarly journals Appropriate Assessment of Neighborhood Effects on Individual Health: Integrating Random and Fixed Effects in Multilevel Logistic Regression

2005 ◽  
Vol 161 (1) ◽  
pp. 81-88 ◽  
Author(s):  
K. Larsen
2021 ◽  
pp. 0013161X2110373
Author(s):  
Benjamin Creed ◽  
Huriya Jabbar ◽  
Michael Scott

Purpose: School choice policies are expected to generate competition leading to improvement in school practices. However, little is known about how competition operates in public education—particularly in charter schools. This paper examines charter-school leaders’ competitive perception formation and the actions taken in response to competition. Research Methods: Using Arizona charter-school leaders’ responses to an original survey, Arizona Department of Education data, and the Common Core of Data, we examined the factors predicting the labeling of a school as a competitor. We estimated fixed effects logistic regression models which examine factors predicting the labeling of competitor schools and of top competitors. We used logistic regression models to understand charter-school leaders’ responses to competition. Findings: We find charter-school leaders in Arizona perceived at least some competition with other schools, and their perceptions vary by urbanicity. While distance between schools mattered generally for labeling a school as a competitor, distance did not factor into labeling “top competitor” schools. Student outcomes did not predict competition between schools, but student demographics were associated with labeling a school a competitor. Charter-school leaders responded to competition through changes in outreach and advertising rather than curriculum and instruction. Competitive responses were related to the respondent school’s quality and the level of perceived competition. Implications for Research and Practice: We found charter-school leaders perceive competition and respond by changing school practices. Responses typically focus on marketing activities over productive responses. The novel state-level analysis allows us to test the effects of local market conditions typically absent in the literature.


2018 ◽  
Author(s):  
Paul D Allison

Standard fixed effects methods presume that effects of variables are symmetric: the effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light (2017) showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this paper, I show that there are several aspects of their method that need improvement. I also develop a data generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.


2020 ◽  
Author(s):  
Lukman Bola Solanke ◽  
Omolayo Bukola Oluwatope ◽  
Yinusa Rasheed Adebayo ◽  
Olaoye James Oyeleye ◽  
Benjamin Bukky Ilesanmi ◽  
...  

Abstract Background The means of transportation available to pregnant women in households may serve either as a driver or deterrent of institutional delivery. However, how household means of transportation associates with place of delivery has been less explored in Nigeria. Methods This study was based on pooled data of 2008-2013 Nigeria Demographic and Health Survey. The study analysed a weighted sample size of 6,540 women. The multilevel logistic regression model was applied using STATA 14. Results The study revealed 37% institutional delivery among women in Nigeria. Women whose household mode of transport were cars were twice more likely to have institutional delivery compared to women who had no viable household means of transportation (AOR=2.044, p<0.01; CI=1.781-2.345). Women who live in communities with high proportions of households with no means of transportation were 12.8% less likely to have institutional delivery (AOR=0.872, p=0.01; CI: 0.788-0.967). Women who live in communities with high proportions of household who owned motorcycle compared to those in communities with low proportion were 31.9% more likely to have institutional delivery (AOR=1.319, p<0.05; CI: 1.071-1.625). Women who live in communities with high proportions of households who owned cars compared to those in communities with low proportion were more than three times more likely to have institutional delivery (AOR=3.146, p<0.01; CI: 2.621-3.777).Conclusion Means of transportation significantly explains choice of place of child delivery in urban Nigeria. A public-private transport support programme to reduce transportation burden among pregnant women is imperative in the country.


2019 ◽  
Vol 5 ◽  
pp. 237802311982644 ◽  
Author(s):  
Paul D. Allison

Standard fixed-effects methods presume that effects of variables are symmetric: The effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this article, I show that there are several aspects of their method that need improvement. I also develop a data-generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.


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