Predicting Neighborhood Attachment in Germany
(2022) PSYP01 20221Department of Psychology
- Abstract
- Neighborhood attachment is an important and influential concept in environmental psychology. Yet, there is a lack of quantitative research that allows predictions to be made. Additionally, whereas neighborhood attachment is influenced by various variables, most studies only have assessed a few predictors. A quantitative study with cross-sectional design was conducted in order to create a comprehensive predictive model. A sample of 334 German speaking residents was assessed. In an exploratory approach, multiple socio-demographic, socio-relational, architectural and town-planning, functional, and contextual features were examined as predictors of neighborhood attachment in a linear regression model. Whereas most of the basic relationships... (More)
- Neighborhood attachment is an important and influential concept in environmental psychology. Yet, there is a lack of quantitative research that allows predictions to be made. Additionally, whereas neighborhood attachment is influenced by various variables, most studies only have assessed a few predictors. A quantitative study with cross-sectional design was conducted in order to create a comprehensive predictive model. A sample of 334 German speaking residents was assessed. In an exploratory approach, multiple socio-demographic, socio-relational, architectural and town-planning, functional, and contextual features were examined as predictors of neighborhood attachment in a linear regression model. Whereas most of the basic relationships were replicated according to the state of research, only sociability, building aesthetics, stimulating versus boring, length of residence, friends’ propinquity, socio-cultural activities, and homeownership significantly predicted neighborhood attachment. The present study emphasizes both the multiplicity of variables being directly and indirectly related to neighborhood attachment and the assumption that the underlying mechanisms of attachment can only be captured by combining individual and environmental characteristics. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9100439
- author
- Bischoff, Lennart Paul LU
- supervisor
- organization
- course
- PSYP01 20221
- year
- 2022
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Neighborhood attachment, residential environment, environmental psychology, prediction, model
- language
- English
- id
- 9100439
- date added to LUP
- 2022-09-19 15:30:49
- date last changed
- 2022-09-19 15:30:49
@misc{9100439, abstract = {{Neighborhood attachment is an important and influential concept in environmental psychology. Yet, there is a lack of quantitative research that allows predictions to be made. Additionally, whereas neighborhood attachment is influenced by various variables, most studies only have assessed a few predictors. A quantitative study with cross-sectional design was conducted in order to create a comprehensive predictive model. A sample of 334 German speaking residents was assessed. In an exploratory approach, multiple socio-demographic, socio-relational, architectural and town-planning, functional, and contextual features were examined as predictors of neighborhood attachment in a linear regression model. Whereas most of the basic relationships were replicated according to the state of research, only sociability, building aesthetics, stimulating versus boring, length of residence, friends’ propinquity, socio-cultural activities, and homeownership significantly predicted neighborhood attachment. The present study emphasizes both the multiplicity of variables being directly and indirectly related to neighborhood attachment and the assumption that the underlying mechanisms of attachment can only be captured by combining individual and environmental characteristics.}}, author = {{Bischoff, Lennart Paul}}, language = {{eng}}, note = {{Student Paper}}, title = {{Predicting Neighborhood Attachment in Germany}}, year = {{2022}}, }