Hierarchical Linear Model for Investigating Effect of Built Environment on Bus Transit

2020 ◽  
Vol 146 (2) ◽  
pp. 04020010 ◽  
Author(s):  
Liyuan Zhao ◽  
Shuxian Wang ◽  
Jialing Wei ◽  
Zhong-Ren Peng
Keyword(s):  
Built Environment ◽  
Linear Model ◽  
Hierarchical Linear Model ◽  
Bus Transit

scholarly journals Association between Neighborhood Built Environment and Body Mass Index among Chinese Adults: Hierarchical Linear Model

2019 ◽  
Author(s):  
Mengqi Zhong ◽  
◽  
Yuanyi Shen ◽  
Yifan Yu ◽  
◽  
...  
Keyword(s):  
Body Mass Index ◽  
Built Environment ◽  
Linear Model ◽  
Body Mass ◽  
Hierarchical Linear Model ◽  
The Body ◽  
Mental Condition ◽  
Improvement Strategy ◽  
Independent Variables ◽  
Neighborhood Built Environment

Obesity is becoming a global health problem. With the living standards of residents have improved rapidly in China, the problem of obesity becomes a serious threat to people’s health. Although obesity effected by many factors, the role of the built environment in relation to obesity among population should be taken into consideration. This paper examines the association of built environment and body mass index with the hierarchical linear model, based on the data from 2016 China Labor-force Dynamics Survey (CLDS), which involves 29 provinces in China and investigates 401 villages or communities as well as 14226 families. In this paper, the village or community is used as the basic analysis unit, and the body mass index of the residents is used as the dependent variable, and neighborhood built environment (e.g. density of exercise facilities, square or park and distance to them) is as independent variables, socioeconomic status (e.g. age, gender, education, marital status, income and employment status) and health and exercise characteristics (e.g. self-rated health, average weekly exercise time and frequency) are as control variables. Participants are adults aged 15-65 years (n = 21086; 63.30% rural vs urban). With the independent variables from both individual and residential levels, hierarchical linear model is applied respectively to examine how body mass index is affected. Additionally, samples are classified by age group, urban/rural neighborhood and we figure out which factor mainly effected different groups. We explore that BMI is higher in high- vs. low-facility density neighborhoods but not significantly differ by neighborhood income. Overweight/obesity (BMI >= 25) is lower in high-developed districts. Physical fitness is higher in high-income neighborhoods but unrelates income. We conclude that living in walkable neighborhoods is associated with more physical activity and lower overweight/obesity but not with other benefits. Adults in higher-income neighborhoods have lower BMI and higher mental condition. These findings have important implications for urban planning and the corresponding improvement strategy is proposed

Characteristics of Tables for Disseminating Biobehavioral Results

2017 ◽  
Vol 20 (1) ◽  
pp. 70-76
Author(s):  
Barbara St. Pierre Schneider ◽  
Ed Nagelhout ◽  
Du Feng
Keyword(s):  
Linear Model ◽  
Hierarchical Linear Model ◽  
Editorial Staff ◽  
Research Articles ◽  
Biological Research ◽  
Nursing Research ◽  
Hierarchical Generalized Linear Model ◽  
Review Articles ◽  
Journal Editorial ◽  
The Mean

Background: To report the complexity and richness of study variables within biological nursing research, authors often use tables; however, the ease with which consumers understand, synthesize, evaluate, and build upon findings depends partly upon table design. Objectives: To assess and compare table characteristics within research and review articles published in Biological Research for Nursing and Nursing Research. Method: A total of 10 elements in tables from 48 biobehavioral or biological research or review articles were analyzed. To test six hypotheses, a two-level hierarchical linear model was used for each of the continuous table elements, and a two-level hierarchical generalized linear model was used for each of the categorical table elements. Additionally, the inclusion of probability values in statistical tables was examined. Results: The mean number of tables per article was 3. Tables in research articles were more likely to contain quantitative content, while tables in review articles were more likely to contain both quantitative and qualitative content. Tables in research articles had a greater number of rows, columns, and column-heading levels than tables in review articles. More than one half of statistical tables in research articles had a separate probability column or had probability values within the table, whereas approximately one fourth had probability notes. Conclusions: Authors and journal editorial staff may be generating tables that better depict biobehavioral content than those identified in specific style guidelines. However, authors and journal editorial staff may want to consider table design in terms of audience, including alternative visual displays.

A note on Bayes designs for inference using a hierarchical linear model

Biometrika ◽  
1980 ◽  
Vol 67 (3) ◽  
pp. 613-619 ◽  
Author(s):  
A. F. M. SMITH ◽  
I. VERDINELLI
Keyword(s):  
Linear Model ◽  
Hierarchical Linear Model

scholarly journals Bias factors associated with assessing the validity of university teaching: a hierarchical linear model

2006 ◽  
Vol 14 ◽  
pp. 34
Author(s):  
Raciel Acevedo Alvarez ◽  
Nuria Mairena Rodríguez
Keyword(s):  
Data Analysis ◽  
Linear Model ◽  
Hierarchical Linear Model ◽  
University Teaching ◽  
Teacher Competence ◽  
Quantitative Methodology ◽  
Factors Associated ◽  
Class Environment ◽  
The University ◽  
The City

The present study analyzes the variables that are intrinsically linked with the student, professor and class environment in relation to the university educational evaluation questionnaires. The participants in the study were 374 students with an age mean of 19.9 and 29 professors with an age mean of 36 from 3 different departments at the Universidad de Costa Rica (UCR) at the city of Guanacaste. The hierarchical lineal models were used for the data analysis, a quantitative methodology which facilitates the evaluation of the determinants which affect the results of the study. However, only four of these determinants were associated with the evaluation concerned, class size, enrolment year, department type and forecasted achievement levels. The results obtained from the study demonstrate that these kinds of evaluation are valid despite the results being slightly affected by a range of factors from externalities to teacher competence.

scholarly journals Examining Multilevel Poverty-Causing Factors in Poor Villages: a Hierarchical Spatial Regression Model

2021 ◽  
Author(s):  
Yanhui Wang ◽  
Yuewen Jiang ◽  
Duoduo Yin ◽  
Chenxia Liang ◽  
Fuzhou Duan
Keyword(s):  
Regression Model ◽  
Linear Model ◽  
Goodness Of Fit ◽  
Spatial Regression ◽  
Hierarchical Linear Model ◽  
Central China ◽  
Spatial Effects ◽  
Spatial Regression Model ◽  
Village Level ◽  
The Village

AbstractThe examination of poverty-causing factors and their mechanisms of action in poverty-stricken villages is an important topic associated with poverty reduction issues. Although the individual or background effects of multilevel influencing factors have been considered in some previous studies, the spatial effects of these factors are rarely involved. By considering nested geographic and administrative features and integrating the detection of individual, background, and spatial effects, a bilevel hierarchical spatial linear model (HSLM) is established in this study to identify the multilevel significant factors that cause poverty in poor villages, as well as the mechanisms through which these factors contribute to poverty at both the village and county levels. An experimental test in the region of the Wuling Mountains in central China revealed the following findings. (1) There were significant background and spatial effects in the study area. Moreover, 48.28% of the overall difference in poverty incidence in poor villages resulted from individual effects at the village level. Additionally, 51.72% of the overall difference resulted from background effects at the county level. (2) Poverty-causing factors were observed at different levels, and these factors featured different action mechanisms. Village-level factors accounted for 14.29% of the overall difference in poverty incidence, and there were five significant village-level factors. (3) The hierarchical spatial regression model was found to be superior to the hierarchical linear model in terms of goodness of fit. This study offers technical support and policy guidance for village-level regional development.

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