SSRC Eurasia Quantitative Methods Webinar Grantwriting for Quantitative

SSRC Eurasia Quantitative Methods Webinar Grantwriting for Quantitative Research Professor Jane Zavisca January 25, 2013 University of Arizona janez@u. arizona. edu

Developing a Quantitative Research Agenda �Possible but limited without funding �Why do you need funding? � To buy data: new fieldwork or secondary data � To buy equipment: hardware/software � To buy time: research assistance, course releases � To get/keep a job that values external grants

Start small and build up �Be realistic: big grants go to people with proven track records �Be ambitious: apply for leveraging grants to support a longer-term agenda

My trajectory �Grants as graduate student � Fulbright-Hays to support qualitative/historical research ($15, 000) � NSF dissertation improvement grant (plus internal supplement) to support modest survey in one city ($20, 000) �Grants as assistant professor � SSRC postdoctoral fellowship to support pilot qualitative research for new study ($20, 000) � NCEEER grant to pay for qualitative data collection and to purchase quantitative data ($32, 000)

�Grants as associate professor. Co-PI with Ted Gerber, full professor at University of Wisconsin � NSF grant to support a large retrospective survey in Russia, to test hypotheses drived from my NCEEER/SSRC-supported research ($250 k) � Mystery grant to support four-country, longitudinal survey, building on the NSF work ($3. 5 million). [Funder has not yet announced award publicly]

Types of quantitative research that warrant external funding �Collect original survey data � Costly, time-consuming risky � Tailor design to your research agenda �Piggyback on existing surveys � Cheaper, logistically simpler � Constrained in length and content �New uses for existing data � Combine existing sources in new ways � Use old data to answer new questions

Types of funders � Area studies � NCEEER, SSRC � International/comparative � Fulbright. studies Some fellowships suffice for small scale surveys � Defense/Homeland Security. Minerva Research Initiative, DRTA, DARPA � Basic social science � NSF � NIH � Topic-based � Spencer funding Foundation (education) � Macarthur Foundation (various topics)

General advice on proposal writing � SSRC: The art of writing proposals � http: //www. ssrc. org/publications/view/7 A 9 CB 4 F 4 -815 F- DE 11 -BD 80 -001 CC 477 EC 70/ � Great general advice � NIH: Writing a great grant application � http: //www. niaid. nih. gov/researchfunding/qa/Pages/writi ng. aspx#hypo � Geared toward quantitative proposals � Other � Ask resources successful grant-writers for copies of their proposals � If you are at an R 1 university, take advantage of internal resources for proposal development (workshops, editing, leveraging funds)

Do your homework �Review existing data and literature: to generate hypotheses, and demonstrate novelty of your proposal �Start writing very early – at least 3 months before deadline (your institution’s deadline may differ from sponsor). �Line up collaborators and solicit letters of support

Research the funder: �What is the purpose of a given grant, and are you eligible? �What are the selection criteria? � Look for instructions given to reviewers in addition to instructions for applicants. � Structure your proposal around the criteria �Who are the reviewers, and how are they selected? �What are the funder’s broader priorities? �Who are the funder’s own funders? What are their priorities?

Pitching quantitative research to Eurasian studies audiences �Appeal to funder’s priorities � NCEEER is funded by US State Department, which has recently called for more quantitative work on the region. �Appeal to novelty/systematicity. Majority of field research in the region is qualitative � Build on qualitative area expertise to develop quantitative hypotheses �

Pitching quantitative research to Eurasian studies audiences �Demonstrate your regional knowledge and commitment � Don’t frame only as a theoretical case � Signal commitment to further study in the region � Regional languages: if you don’t know them, explain why you don’t need them for this particular project. �Don’t � Use assume any knowledge of statistics flow charts to depict models � Signal your sophistication to those who do with parenthetical citations, footnotes, appendices

Pitching Eurasian context to non-disciplinary audiences � Why should the funder care about your research if they don’t care about your region? � Theoretical advantages � Quasi-natural experiment due to rapid social change � Cross-national variation within comparable contexts � Logistical advantages � Relatively low cost compared to Western contexts � More stable/accessible than other semi-authoritarian regions of the world � OR: …convince them to care about the region � Security issues � Humanitarian concerns � Resources/environment

Questions any effective proposal should answer �What is the core question? �Why is it worth answering? �How do you plan to answer it? �What is new about your approach? �What exactly will you do with the time and money provided? �What will you produce? �Why should you be the one to do this research? �Why should the funder pay for it?

Writing an effective quantitative proposal �Effective quantitative proposals are more a function of research design than statistics �There should be a tight fit between research questions, concepts, measures, hypotheses, and models

�At least half of proposal should details the nuts and bolts of what you will do: � what data you will collect (if applicable), how you will ensure its quality, and how you will analyze it. � Justify your specific design choices with reference back to your research questions, theory, and hypotheses

Specifics about survey design �Instrument � Proposed design measures—and what concepts they will measure � Survey medium (e. g. face-to-face, computerassisted, web) � Plans for pretests and pilot tests �Sampling: � Generalizability: how is sample related to population of interest? � Sample size (actual and effective) � Sampling strategy

Specifics about survey design �Data collection � Personnel � Field procedures � Quality control �Analytical techniques � Class of statistical models � Examples of models to be tested �Data management plan � Confidentiality, quality, archiving, sharing � Making your data public gives value-added for funder

Be careful what you wish for �Is the budget realistic? �Is the timeline realistic? �Do you have adequate institutional support beyond what the grant can provide? �Do you really want to do the proposed research?