Getting the big picture in community science: methods that capture context.
Community science has a rich tradition of using theories and research designs that are consistent with its core value of contextualism. However, a survey of empirical articles published in the American Journal of Community Psychology shows that community scientists utilize a narrow range of statistical tools that are not well suited to assess contextual data. Multilevel modeling, geographic information systems (GIS), social network analysis, and cluster analysis are recommended as useful tools to address contextual questions in community science. An argument for increased methodological consilience is presented, where community scientists are encouraged to adopt statistical methodology that is capable of modeling a greater proportion of the data than is typical with traditional methods.
Luke
Douglas A
DA
Saint Louis University School of Public Health, Saint Louis, MO 63104, USA dluke@slu.edu
D016000:Cluster Analysis; D003156:Community Mental Health Services / Q000458:organization & administration*; D003159:Community-Institutional Relations; D006801:Humans; D015994:Incidence; D033183:Interdisciplinary Communication; D007400:Interprofessional Relations; D015233:Models, Statistical; D012586:Science / Q000379:methods*; D012944:Social Support; D019966:Substance-Related Disorders / Q000453:epidemiology / Q000517:prevention & control; D014481:United States