Secondary Data Analysis Essay ⋆ Sociology Essay Examples ⋆ EssayEmpire

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Secondary data analysis is the method of using preexisting data to answer a new research question. Thousands of large-scale data sets are now available for the secondary data analyst. The decennial population census by the US Bureau of the Census is the single most important governmental data source in the United States, but many other data sets are collected by the Census and by other government agencies, including the US Census Bureau’s Current Population Survey and its Survey of Manufactures or the Bureau of Labor Statistics’ Consumer Expenditure Survey. These government data sets typically are quantitative; in fact, the term ”statistics” – state-istics – is derived from this type of data. Surveys conducted by social scientists are another common source of secondary data.

The University of Michigan’s Inter-University Consortium for Political and Social Research (ICPSR) maintains the largest collection of social science data sets in the world: more than 7,400 data sets from 130 countries. Government data, social surveys, political polls, research by international organizations are available online to individuals at the more than 500 colleges and universities around the world that have joined ICPSR. Qualitative datasets are also available at Yale University’s Human Relations Area Files (over 800,000 pages of information on more than 365 different groups studied by anthropologists), at the Murray Research Center at Harvard’s Radcliffe Institute for Advanced Study, and at the University of Southern Maine’s Center for the Study of Lives.

Secondary data analysts face some unique challenges. Secondary data analysis cannot design data collection methods that are best suited to answer their research question; they cannot test and refine their methods on the basis of preliminary feedback from the population to be studied; and they cannot engage in the iterative process of making observations, developing concepts, making more observations, and refining the concepts that is the hallmark of much qualitative methodology. Secondary data analysis inevitably involves a tradeoff between the ease with which the research process can be initiated and the specific hypotheses that can be tested and methods that can be used. Hypotheses or even the research question itself may need to be modified in order to match the analytic possibilities presented by the available data. Secondary analysis of qualitative data must forgo the interaction with research participants that is a hallmark of this method. Data quality must always be evaluated. Government data collection efforts may be limited or incomplete due to limited funding or political considerations. International comparative research must take into account different data collection systems and definitions of key variables used in different countries.

These problems can be lessened by reviewing data features and quality before deciding to develop an analysis of secondary data and then developing analysis plans that maximize the value of the available data. Replicating key analyses with alternative indicators of key concepts, testing for the stability of relationships across theoretically meaningful subsets of the data, and examining findings of comparable studies conducted with other data sets can each strengthen confidence in the findings of a secondary analysis.

Bibliography:

Heaton, J. (2004) Reworking Qualitative Data. Sage, Thousand Oaks, CA.
Riedel, M. (2000) Research Strategies for Secondary Data: A Perspective for Criminology and Criminal Justice. Sage, Thousand Oaks, CA.