september 25 - steps in a research project.docx

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Department
Political Science
Course
POLS 3180
Professor
Edward Koning
Semester
Fall

Description
th POLS* 3180 – STEPS IN A RESEARCH PROJECT – September 25 /2013 – Lecture VI & VII ____________________________________________________________________________ _________ • Assignment II  write a proposal for a political science research project (you will not have to conduct the actual research) of about 2500 words • Due November 27 /2013 ____________________________________________________________________________ _________ Steps in a Research Project:  formulation of research  theorization and formulation of hypotheses  conceptualization and operationalization  choice of research design  case selection  data collection  data analysis  formulation of conclusions and dissemination of results * none of the steps are independent of each other Research Question:  Find a subject  Consult literature on the subject - peer-reviewed sources - editor-reviewed sources - Anything else: (1) use great caution; (2) mostly useful as a primary source  Establish goal of investigation - exploration - description - explanation - philosophical views guide this choice  Formulate a research question - needs to be as specific as possible ex: Berg and Lune, 2012, p.38: “What is the relationship between college and drinking among American males?”  too vague of a question; no substance; the more specific you are the better - exploration; description; (examples on courselink slides)  what, when, who, how much, how long, what way? - explanation;  why, what are the main reasons, what are the primary drivers? Theorization - “A systematic explanation for the observations that relate to a particular aspect of life” (Babbie & Benaquisito, 2014, p.13)  Hypothesis - research expectation - “Testable propositions about the relations among our research concepts” (Berg and Lune, 2012, p.22) Conceptualization - process of defining all the concepts in your hypotheses - the more specific, the better Operationalization - process of specifying exactly how you want to measure the concepts of interest; process of developing indicators - words (qualitative) or numbers (quantitative)? Research Designs  Four main research designs - study subject in the laboratory  Longitudinal design - compare subject at different points in time  cross-sectional design - compare different subjects at one moment  case study design - study one subject at one moment Case Selection CASE: an instance of an entity that “possesses” the indicator we are interested in SAMPLE: set of cases we actually study two techniques of sampling: (1) Probability Samples - selection of cases from the population based on some type of probability theory - simple random sample and variations - allow for inferential statistics - ** will be discussed in Research Methods II ** (2) Non-Probability Samples - used when population is small or unknown - more common in qualitative methods  three common techniques: (i) convenience sample – sampling cases because they just happen to be around (ii) purposive sample – thought about the selection process; not random; pick the cases that you want ex. Immigrations reps of political parties – would have been irrelevant to talk to everyone in the party when you are only researching immigration (iii) snowball sample – crucial stage, consequential; Data Collection:  acquiring the necessary information to answer your research question  quantitative analysis - surveys, collection of existing statistics  Qualitative - field research - qualitative interviews – in depth discussion with people you are interested in knowing more about; (i.e. research groups) - unobtrusive research – completely detached from what you are studying; (historical analysis and content analysis) **subject of lectures XV-XXII** ________________________________________________ continued: 27-Sept-2013 ________________ Data Analysis:  systematically investigating what the collected information says about the research question - data reduction – weed out the relevant from the irrelevant; find ways to summarize what has been said - data visualization  Tends to be harder in qualitative than in quantitative analysis Conclusions:  reflect on the implications of your findings - evaluate public policy  looking to revise existing theories  suggest research agendas for inconclusive findings Disseminate your conclusions – part one of your conclusion  present research at confer
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