Typical Steps in Quantitative Social Science Research

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

9. 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.

14. 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).

16. 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

17. 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?

18. 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.

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

20. 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