Academic journal article
By Sales, Esther; Lichtenwalter, Sara; Fevola, Antonio
Journal of Social Work Education , Vol. 42, No. 3
RESEARCH TRAINING for social work practitioners and scholars has made noteworthy progress over the years, including the emergence of a variety of social work-specific textbooks covering general research methodology (e.g., Grinnell, 2001; Monette, Sullivan, & DeJong, 2002; Royse, 2004; Rubin & Babbie, 2005; Yegidis & Weinbach, 1996), as well as texts focusing on such specialized areas as evaluation research (e.g., Royse, Thyer, Padgett, & Logan, 2001; Weinbach, 2005) and qualitative research (e.g., Padgett, 1998; Reissman, 1994; Sherman & Reid, 1994). Amidst this burgeoning of educational resources for social work research training, the methodology of secondary data analysis has received lesser attention.
Secondary analysis (SA), which is most generally defined as the re-analysis of existing data sets, has great potential in social work for several reasons. First, many social problems of concern to our field, such as social and economic inequalities, health problems, and criminal justice, have been the focus of well-funded research studies that offer high quality data sources for social work investigators. Second, computer technology has advanced rapidly to provide compact forms of data storage and transmission, Web-based access to data archives, and a growing ability to analyze some data sets online. Third, most human service settings and governmental agencies now have in place computerized information systems that may be sources of data for examining service patterns. Finally, student research training that encourages the use of these expanding data archives may be more transferable to students' post-educational work settings, resulting in greater research engagement after program completion.
This article reviews the state of the art of secondary analysis. It gives particular attention to emphasis on SA in social work research texts, relevance to social work problems, and implications for social work education. Although this research strategy has been either acknowledged or employed by many social work researchers over the years and has contributed to our understanding of social problems, secondary analysis has engendered remarkably little discussion in the social work literature. This article attempts to overcome this gap. We begin by reviewing the general background and emergence of SA, followed by a consideration of its strengths and weaknesses, and conclude with a discussion of its implications for social work research and education. Whenever possible, our discussion draws from social work sources. However, because such sources are scarce, we also draw on the broader (but still surprisingly limited) literature on SA and reflect on its relevance for social work.
Emergence of Secondary Analysis
Secondary analysis has been recognized as a method for examining research questions for more than a century. Many view Durkheim's 1897 analysis of suicide (Durkheim, 1951) as a classic early example of SA (Hakim, 1982; Krysik, 2001); however, it was not until 1972 that Herbert Hyman's book, Secondary Analysis of Sample Surveys, appeared, marking a rather belated watershed for both legitimizing and delineating this research methodology (Kiecolt & Nathan, 1985). Overall, the general dearth of relevant general publications regarding SA is striking. Apart from the Hyman (1972) and Kiecolt & Nathan (1985) texts, we could locate only a few other books on this method, including two by British researchers in the 1980s (Dale, Arber, & Proctor, 1988; Hakim, 1982), and a 1993 monograph by Stewart.
Reflecting this low profile, the usage of SA by researchers has grown surprisingly slowly over the intervening years. Despite Hyman's early assertion that "secondary analysis ... can be a viable and large-scale enterprise" (p.vii) for further utilizing existing survey data, no groundswell has emerged. Thirteen years after Hyman's optimistic prediction, Kiecolt and Nathan (1985) were still viewing it as an emerging technique. …