Quality of Life Survey of South East Queensland Residents

The Quality of Life survey was conducted amongst households in the Brisbane and South East Queensland region to collect subjective comments about urban quality of life issues. The study was also carried out for the purposes of garnering information about the context of spatial behaviour. Key variabl...

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Bibliographische Detailangaben
1. VerfasserIn: Stimson, Robert J. (VerfasserIn)
Beteiligte: Western, John S.
Medienart: Elektronisch Forschungsdaten Statistik
Sprache:Englisch
Veröffentlicht: [Brisbane] ADA Dataverse 2018
In:Jahr: 2018
Ausgabe:version 2.0
Online-Zugang: Volltext (Resolving-System)
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Zusammenfassung:The Quality of Life survey was conducted amongst households in the Brisbane and South East Queensland region to collect subjective comments about urban quality of life issues. The study was also carried out for the purposes of garnering information about the context of spatial behaviour. Key variables include measures assessing satisfaction with the quality of life in the region (in regard to climate, lifestyle etc); and Likert scales for statements pertaining to various social, political and economic concerns in the region (availability of public transport, the conduct of local and state governments, economic growth etc). Localised neighbourhood concerns (crime, housing availability) are also addressed; so too are levels of political capital; and social capital in the forms of civic engagement and social trust. Respondents' daily activities are enquired into, the dynamics of how they travel from home to work and back; along with the nature of their daily activities. The types of leisure activities and satisfaction with these are also measured; along with general satisfaction with personal life aspects (employment situation, housing, health etc). Respondents were also asked about their internal emotional state/s. Background variables cover employment status and job satisfaction, partner's employment status, level of education, household type, age, gender, marital status and income. The dataset was checked to ensure that the skip patterns were consistent across variables. Out of range codes were also investigated, and rectified where possible. Missing values were assigned according to the type of missing data (skip, no response, cannot determine response etc). Where possible string variables were condensed into numeric variables.
DOI:10.26193/VDYZYR