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Typical Steps in Quantitative Social Science Research

Typical Steps in Quantitative Social Science Research. State general nature of problem, question, relationship Review previous studies Create and state “stylized facts” Develop testable hypotheses Discover or develop data to be used to test hypotheses Size & nature of the sample

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Typical Steps in Quantitative Social Science Research

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  1. Typical Steps in Quantitative Social Science Research • State general nature of problem, question, relationship • Review previous studies • Create and state “stylized facts” • Develop testable hypotheses • Discover or develop data to be used to test hypotheses • Size & nature of the sample • Perform appropriate (statistical) tests • Describe results • Draw conclusions

  2. Two examples with two very different models • Econometric model of effect of Internet use on scholarly productivity • Financial model to estimate costs of removing minority preferences in broadcast licensing

  3. Traditional Bibliometric Model of Scholarly Productivity Socio-economic factors Institutionalfactors Funding Research output Citation: Noam Kaminer, Yale M. Braunstein: "Bibliometric Analysis of the Impact of Internet Use on Scholarly Productivity." JASIS 49(8): 720-732 (1998)

  4. Revised Bibliometric Model of Scholarly Productivity Socio-economic factors Institutional factors Funding Research output Computer skills/literacy Computer use / Internet use

  5. Revised Bibliometric Model of Scholarly Productivity Socio-economic factors Institutional factors Funding Research output Computer skills/literacy Computer use / Internet use Major issues: - What sample/universe to use? - How to measure inputs? - How to measure outputs? - Functional form? (linear?, additive?)

  6. List of Selected Processes

  7. Our main hypothesis in this research is that scholars' Internet-use data add explanatory power to models of scholarly productivity. The formal null and alternate hypotheses are: • H0: Internet use data does not add explanatory power to the Traditional Publication Model. • H1: Internet use data adds explanatory power to the Traditional Publication Model. • We used multiple regressions to estimate the four models listed in Figure 5. Since we are interested in whether a set of one or more Internet-use variables “improves” the explanatory power of the traditional model, the appropriate measure is the F-statistic from a comparison of the restricted (traditional) model with the unrestricted (new) model.

  8. The exact equation, as estimated in Equation 1, is: PUBAV = 17.1 – 0.313 AGE + 0.197 PHD-AGE + 0.0860 RL – 0.0794 PIM1 + 19.6 (Login-FTP principal component) + 2.99 (Kermit principal component).

  9. Two examples with two very different models • Econometric model of effect of Internet use on scholarly productivity • Financial model to estimate costs of removing minority preferences in broadcast licensing

  10. Motivation • FCC was leader in creating “equal opportunity” requirements in employment and ownership via its regulatory authority • Many (most? / all?) of the provisions have disappeared • But poor data and no valid theoretical analysis • Common response: why collect data if we don’t know what to do with it?

  11. Financial Model of “Typical” Radio Broadcaster • Create financial model • Use data from large “chain” broadcaster to calibrate model • Define base case Citation: Yale M. Braunstein: "The FCC’s Financial Qualification Requirements: Economic Evaluation of a Barrier to Entry of Minority Broadcasters,” Fed. Comm. Law J., V. 53, No. 1, pp. 70-90.

  12. What have been major legislative, judicial, or regulatory actions? • Review case law • Review other articles, testimony, etc. • Develop ways to operationalize the changes

  13. Scenario Major Changes Valuation(per station) Change from Base Case Base case --- $4.45 million --- 1 Increased cost of L-T Debt $4.00 million ($0.45 million) 2 No entry varies ($4.45 million per station “lost”) 3 No minority tax certificate (a) $2.97 million(b) $2.92 million (a) ($1.22 million)(b) ($1.09 million) Key findings • Excel-based financial model useful for this sort of analysis • Already widely used in consulting, acquisitions, etc., but not often in scholarly work

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