Analyzing the impact of sport infrastructure on sport participation using geo-coded data: Evidence from multi-level models

Sport policies aiming at increasing mass participation and club participation have stressed the importance of sport infrastructure. Previous research has mainly analyzed the influence of individual factors (age, income, etc.) on sport participation. Although a few studies have dealt with the impact of sport facilities on sport participation, some methodological shortcomings can be observed regarding the integration of sport infrastructure into the research design. Oftentimes, subjective measures of infrastructure are employed, leading to biased results, for example inactive people have a worse perception of the actual supply of facilities. In fact it is important to measure the available sport infrastructure objectively using a quantitative approach and integrate it into statistical models. Therefore, the purpose of this study is to analyze the impact of individual and infrastructure variables on sport participation in general and in sport clubs using geo-coded data following a multi-level design. For this purpose, both primary data (individual level) and secondary data (infrastructure level) were collected in the city of Munich, Germany. A telephone survey of the resident population was carried out (n=11,175) and secondary data on the available sport infrastructure in Munich were collected. Both datasets were geo-coded using Gauss–Krueger coordinates and integrated into multi-level analyses. The multi-level models show that swimming pools are of particular importance for sport participation in general and sport fields for participation in sport clubs. Challenges and implications for a more holistic modeling of sport participation including infrastructure variables are discussed.