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Secondary-Data Analysis: Issues and Examples 周雪光 Stanford University

Secondary-Data Analysis: Issues and Examples 周雪光 Stanford University. Why secondary data?. The role of data in social science research Data availability delimits or expand the horizon of our views, theories, approaches, and knowledge growth French government archive on policing, folklore

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Secondary-Data Analysis: Issues and Examples 周雪光 Stanford University

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  1. Secondary-Data Analysis:Issues and Examples周雪光Stanford University

  2. Why secondary data? • The role of data in social science research • Data availability delimits or expand the horizon of our views, theories, approaches, and knowledge growth • French government archive on policing, folklore • The role of organizations in social stratification • The challenges of collecting first-hand data • What is secondary data? • Data that has been collected (and analyzed) by others • Credibility, and potential for cumulative knowledge • The possibility of further use (reanalysis of data) • GSS, CPS, PSID – hundreds of research articles • New information from the same data, because of new analytical tools, new theoretical perspectives, and new operationalization.

  3. An Example: -- The diffusion of medical innovation • Coleman et al. (1966) • Burt (1987) • Marsden and Pololny (1990) • Strang & Tuma (1993) • Bulte and Lilien (2001)

  4. A variety of secondary data available • Data collected by government agencies • Census, industrial survey, firm survey • Especial survey/study by government agencies • SME firm finance • Employment quality survey • Data collected by other researchers（ICPSR） • Data collected by for-profit databanks （COMPUSTAT，etc.） • Considerations in making data accessible in public domain • The replicatability in scientific research (recent practice in natural science, economics, and sociology) • Accumulation of knowledge • The monopoly of data and knowledge

  5. Issues related to the use of secondary data • An observation：issues are similar to data issues in other types of empirical research • Assessment of data quality • The purpose, information of the data • The population of study, sampling framework and procedures • Methods of data collection, response rate • Data coding and entry • Codebook – questionnaire, coding scheme, etc. • Previous research using the data

  6. The limitation of secondary data-based research • Data quality and representativeness • New organizational forms, new environments • Different research purposes and information • Limitation in available information • Cross-sectional vs. longitudinal data • New topics：EQ，social network， inter-firm contractual relationship

  7. General observations • A large proportion of research is based on secondary data • The issues encountered in using secondary data are similar to data issues in other context • There is a need for a research community for the sharing of secondary data; • Making data available in the public domain • Data evaluation and quality check

  8. Example 1 “Medical Innovation Revisited: Social Contagion versus Marketing Effort” Christophe Van den Bulte Gary L. Lilien AJS 2001 (106)

  9. Medical Innovation ： A dataset’s rich journey • Coleman et al.（1966） • In the mid-1950s, pattern of adopting a new medicine. • The theme: what determines doctors’ adoption decision – uncertainty of new medicine and mechanisms that affect doctors’ decisions. • Social network • Social positions • Research design • Four cities in Illinois • 126 doctors interviewed (total n = 148. • Information: what channel affect a doctor’s adoption decision? • Initial stage, mid-stage, and final stage: what is the most important factor? • Channel: • Salesperson, professional magazine, mailed advertisement, pharmacy magazine, colleague, conference, other.

  10. Subsequent studies • Burt（1987）： • Theme: diffusion mechanisms • Not cohesion, but structural equivalence • Marsden & Podolny （1990），Strang & Tuma （1993） • Statistical models of diffusion • S & T: isomorphic mechanisms • Van den Bulte & Lilien （2001） • Theme：the diffusion mechanisms of innovation • New theory：the role of marketing, not social network • New data collection • Findings: the intensity of marketing wipes out the network effects

  11. Example 2 “Embeddedness in the Making of Financial Capital: How Social Relations and Networks Benefit Firms Seeking Financing” Brian Uzzi ASR 1999 (64)

  12. Research issues • theme：the channel and cost of bank loans • Theoretical: the nonlinear effects of social networks • Embeddedness vs. arms-length social relations: the strength and complementarity of networks • To address problems in quantitative data – “multiplicity” in business transactions.

  13. Research design • Triangle research methods • Theory, quantitative data, and case studies • Secondary data collected by U.S. government • Case study to provide context and details of social network in operation

  14. Characteristics of Interviewees in the Field Research: Relationship Managers (RMs) at Chicago Banks, 1988

  15. Coefficients from the Heckman Selection Regression of Access to Credit and Interest Rate on Loan on Selected Independent Variables: U.S. Nonagricultural Firms, 1989

  16. Social Embeddedness and the Firm’s Cost of Financial Capital: U.S. Nonagricultural Firms, 1989

  17. Summary • The role of data in social science research • Evolving standards and expectations in data quality • The importance of a research community

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