Analysis of Multiple Data Sets in Outdoor Recreation Research: Introduction to the Special Issue

Abstract

Outdoor recreation research has evolved and matured over the past several decades. The modern era of outdoor recreation research can be traced to the studies supporting the Outdoor Recreation Resources Review Commission (ORRRC) in the early 1960s. Since that time, the scientific and professional literature has expanded dramatically and several trends in outdoor recreation research are evident. First, research has evolved from primarily descriptive empirically based studies of visitor characteristics and use patterns to more theoretically based analytical studies of visitor behavior and the underlying meanings of outdoor recreation. Second, the research-based literature in outdoor recreation has been synthesized in a number of conceptual/organizational frameworks (e.g., the Recreation Opportunity Spectrum, carrying capacity frameworks) that are useful for integrating multiple studies and guiding further research and management. Third, the synergistic effects of an accumulating body of research have developed a strong theoretical understanding of important issues in outdoor recreation including crowding, conflict, motivations and benefits, norms, substitutability, specialization, and sense of place. Fourth, issues addressed in outdoor recreation research continue to evolve to meet societal interests and needs including the relationship between outdoor recreation and race, ethnicity, and gender; the appropriate roles of fees in outdoor recreation; and the characteristics and impacts of new forms of outdoor recreation such as mountain biking. Analysis of multiple data sets offers another approach for outdoor recreation research to evolve and mature. Recreation research has been traditionally characterized by cross-sectional surveys of recreation participants and, occasionally, other populations. The resulting data tend to be isolated in space and time and often lack the ability to support theoretical development and broad generalization. As multiple data sets have been generated using widely accepted and reasonably comparable study methods, new integrative analytical approaches have become possible and potentially productive. Examples of these approaches include meta-analysis, comparative analysis, time series analysis, and