Secondary Analysis in Entrepreneurship: An Introduction to Databases and Data Management
介绍创业研究中二次分析的方法，包括数据库类型、10个相关数据集、数据管理基础及常见问题，帮助研究者利用现有数据开展个体层面的创业分析。
Low-barrier-to-entry (LBE) is a term derived from Northwestern University's Denise Rousseau's efforts (1987) to describe research methods that rely on inexpensive or free-to-the-researcher techniques. Examples of LBE approaches for entrepreneurship research have been outlined before (Katz 1989); but more details on LBE methods are commonly needed because many of the approaches are not easy to use. This article focuses on one of the most daunting of these approaches: secondary analysis. There are two major reasons why secondary analysis can be difficult: 1. There is a knowledge explosion and growing unfamiliarity with existing holdings. For example, the 1989 listing of the archives of the Interuniversity Consortium for Political and Social Research (ICPSR) is more than 500 pages long and grows by about 15 percent per year. Also, many studies of potential utility to entrepreneurship researchers are not readily apparent from the archive listing description. As the ICPSR develops better cross-referencing methods, these problems may lessen, but they will still remain substantial. 2. Secondary analysis poses problems that are different from those experienced by researchers accustomed to gathering their own data. For example, the self-employed have not been looked at as a separate group in many national studies. Often, the low error rates associated with these national surveys are based on years of detection and corrections of errors in the data set. However, since the self-employed are not studied as often as those who earn wages or salaries, the error rates for the data on the self-employed are usually higher. Another unique problem is researcher unfamiliarity with the feel of the population, survey, or data set. In doing original research, the researcher usually has detailed first-hand knowledge about the people, the questions, and the results of the study. Such information i s invaluable for intuitively identifying errors, anomalies, and paradoxes in the data. To be maximally effective when using secondary data, researchers must develop techniques for improving their feel for the study and develop supplementary techniques for error detection and correction of their big picture perspective. This article will introduce the process of secondary analysis, paying particular attention to databases that are potentially useful for the individual-level analysis of entrepreneurship issues.(1) It also will introduce types of data archives, identify 10 relevant data sets available from one of the largest archives, and explain the fundamentals of data management for archival data sets and the typical problems in secondary analyses. An evaluation of the potential of secondary analysis for the field of entrepreneurship will also be offered. THE NATURE AND AVAILABILITY OF ARCHIVAL HOLDINGS The underlying theory for this article comes from a number of sources. The philosophy behind LBE research comes from Katz (1989). The specific model for data preparation comes from Geda (1987), while the problem identification and resolution approach comes from Katz (in press). Overall, the underlying approach reflects the methods for secondary analysis pioneered at the Institute for Social Research at the University of Michigan. There are three major sources for secondary analysis data sets: noncommercial archives, commercial sources, and other researchers. Noncommercial archives are the major source used by academic researchers. Kiecolt and Nathan (1985) list 12 noncommercial archives in the United States, all of which are located on college campuses or in government agencies. Data costs range from free, for ICPSR holdings sought by member schools, to $460 per tape from the National Technical Information Service. …