1. Seminar on Research Methods: Introduction to Quantitative Methods Instructor: Coye Cheshire Lecture 1: The Elements of Research

2. About your instructors: Coye Cheshire Office 305A Office 305A Office Hours Tues and Thurs 3-4pm Office Hours Tues and Thurs 3-4pm Class Location Change: 202 Yuri Takhteyev Hal Varian (guest lecturer for some topics)

3. Course Website http://sims.berkeley.edu/courses/is296a-4/s06/

4. Course Design Part lecture, part skills development One major topic per week One major topic per week Some time devoted to working with statistical software packages Some time devoted to working with statistical software packages Two major course sections Research Methodology (weeks 1-6) Research Methodology (weeks 1-6) Quantitative Methods (weeks 7-15) Quantitative Methods (weeks 7-15)

5. Course Readings Readings: Links to online readings on course website Links to online readings on course website List of recommended readings also on course website List of recommended readings also on course website

6. Statistical Software My class examples will use SPSS and STATA SIMS lab has SPSS; you are not required to purchase a statistical package for this class. If you are interested, both STATA and SPSS have grad versions (cheaper)… or you could rent SPSS software through www.e-academy.com www.e-academy.com www.e-academy.com You can purchase a one-year or perpetual STATA license with the grad plan: http://stata.com/order/new/edu/gradplans/gp-campus.html http://stata.com/order/new/edu/gradplans/gp-campus.html http://stata.com/order/new/edu/gradplans/gp-campus.html SPSS can be purchased through the Scholar’s Workstation: https://www.tsw.berkeley.edu/ https://www.tsw.berkeley.edu/ https://www.tsw.berkeley.edu/

7. Software and Computers I encourage you to bring your laptop to class. I will devote some class time in many sessions to working with statistical software. I encourage you to sit with anyone who has a statistical software package when we begin to use it in class.

8. Course Assignments Three assignments First assignment First assignment Exercise on research methodology (20%) Second assignment Second assignment Using a statistical software package to do some basic statistical tests on an existing dataset (20%) Third assignment Third assignment Group project: 4-6 person teams (60%) Find and work with dataset Find and work with dataset Short paper (5-8 pages), short class presentation Short paper (5-8 pages), short class presentation

9. Final Presentations Last day of class (May 8 th ) One paper turned in for each group Contribution breakdown for each group member (includes paper and presentation)

10. Course Topics Defining and justifying research problems Theory and Measurement (causation, validity, reliability Secondary data analysis Experimental design

11. Course Topics (continued) Descriptive univariate statistics Bivariate statistics Exploratory data analysis Analysis of variance (ANOVA) General linear model (linear regression)

12. Course Topics (continued) Regression for discrete outcomes (logistic regression) Advanced topics Social Network Analysis Social Network Analysis Time Series Forecasting Time Series Forecasting

13. Overall Course Goals You will have an understanding of research method terminology. You will have good knowledge of common research methods used in quantitative research (surveys, experiments) You will understand basic univariate and bivariate statistics, and have an introductory knowledge of common mulitivariate statistics You will be able to use a general purpose statistical package to conduct univariate, bivariate, and multivariate statistics

14. Class Survey We will email you a link to a short survey for use in this class. Please fill it out this week.

15. Today’s Introductory Lecture The Elements of Research: Research Design Process and Common Terminology

16. Why quantitative research? Standardized methodologies Methods are public Methods are public Theoretically, anyone should be able to duplicate your findings Theoretically, anyone should be able to duplicate your findings Forces the investigator to think about the measurement of key factors (i.e., variables)

17. A Primer for Thinking About Research Three general questions when thinking about designing research (Creswell 2003): What knowledge claims are being made by the researcher? What knowledge claims are being made by the researcher? What strategies of inquiry will inform procedures? What strategies of inquiry will inform procedures? What methods of data collection and analysis will be used? What methods of data collection and analysis will be used?

18. Knowledge Claims Positivism/Post-positivism Often starts with theory; deductive Often starts with theory; deductiveConstructivism Often does not start with theory; inductive Often does not start with theory; inductiveAdvocacy/Participatory Literally advocates action in a specific area Literally advocates action in a specific areaPragmatism The ‘problem’ is the key issue; specific methods chosen based on the nature of the problem(s) The ‘problem’ is the key issue; specific methods chosen based on the nature of the problem(s)

19. Positivism Determinism Determinism Empirical observation Empirical observation and measurement and measurementConstructivism Social and historical Social and historical construction construction Theory generation Theory generation Advocacy/Participatory Political Political Change-oriented Change-orientedPragmatism Problem-centered Problem-centered Real-world practice Real-world practice (Creswell 2003)

20. Strategies of Inquiry Qualitative Ethnographies Ethnographies Case studies Case studies Narrative research Narrative researchQuantitative Surveys Surveys Experiments Experiments

21. Research Methods Qualitative Instrument-based questions Instrument-based questions Statistical analysis Statistical analysisQuantitative Emergent methods Emergent methods Open-ended questions Open-ended questions Interviews Interviews Mixed-Methods Approaches Both quantitative and qualitative methods used Both quantitative and qualitative methods used

22. Knowledge Claims Strategies of Inquiry Methods Qualitative Quantitative Mixed Methods Questions Data collection Data analysis Elements of Inquiry Approaches to Research Design Process Adapted from (Creswell 2003)

23. What are the Elements of Research? Common terminology for constructing testable hypotheses Terms and relationships between terms are useful for theoretical and applied research All research, regardless of tradition, uses similar concepts for building testable statements and measuring results

24. Constructs and Variables Constructs Concepts, often complex Concepts, often complex Not directly measurable Not directly measurable Also called ‘theoretical variables’ Also called ‘theoretical variables’Variables Something we can measure Something we can measure Concrete measured expressions to which we can assign numeric values Concrete measured expressions to which we can assign numeric values

25. An example theoretical model Socioeconomic Status Academic Ability Academic Achievement

26. Theoretical Model with Variables Socioeconomic Status Academic Ability Academic Achievement Math skills Language skills Income Job Prestige Grades Level of Schooling attained

27. Causation and Causal Paths Direct causal paths Reciprocal causation Indirect causation XYXYXZY

28. Propositions and Hypotheses Propositions link concepts together with specific relationships Hypotheses link variables together with specific relationships Video GamesViolence Time spent playing Game X Observed ‘violent acts’ Over time Y

29. Hypothesis “hypothesis statements contain two or more variables that are measurable or potentially measurable and that specify how the variables are related” (Kerlinger 1986) “hypothesis statements contain two or more variables that are measurable or potentially measurable and that specify how the variables are related” (Kerlinger 1986)

30. Measurement Variable: A characteristic of the participants or a situation in a given study that has different values in that study. A characteristic of the participants or a situation in a given study that has different values in that study. Operational Definition: Describes or defines a variable in terms of the operations used to produce it or techniques used to measure it. Describes or defines a variable in terms of the operations used to produce it or techniques used to measure it.

31. Measurement Example operationalizations: Age Age Guess, based on how old a person looks. Ask to look at person’s drivers license. Ask people their age. Ask for actual number of years Ask for actual number of years Ask between categories (18-25, 26-33, 34-41, 42+) Ask between categories (18-25, 26-33, 34-41, 42+)

32. Operationalization For any operational definition, there are a few important things to keep in mind: What is the unit of analysis? What is the unit of analysis? Be able to justify your operational definition (i.e., don’t make arbitrary decisions) Be able to justify your operational definition (i.e., don’t make arbitrary decisions) Try to be consistent about level of analysis unless this is part of your theory and/or research question. Try to be consistent about level of analysis unless this is part of your theory and/or research question.

33. Measurement: Variables Independent Variable Also called predictor variables, or right-hand side variables (RHS) Also called predictor variables, or right-hand side variables (RHS) Those that the researcher manipulates Those that the researcher manipulates Attributes or potential causes under investigation in a given study Attributes or potential causes under investigation in a given study Dependent Variable Also called outcome variable, or left-hand side variables (LHS) Also called outcome variable, or left-hand side variables (LHS)

34. Time spent playing Game X Observed ‘violent acts’ Over time Y

35. Types of Variables CategoricalOrdinalMetric

36. Categorical Variables Binary/dichotomous Binary/dichotomous Example: Student versus non-student Nominal/non-ordered polytomous Nominal/non-ordered polytomous Example: Ethnicity

37. Ordinal Variables Ordered polytomous Ordered polytomous Example: Likert scales 1=Strongly Agree, 2=Agree, 4=Undecided, 5=Disagree, 6=Strongly Disagree 1=Strongly Agree, 2=Agree, 4=Undecided, 5=Disagree, 6=Strongly Disagree

38. Metric Variables Interval Interval Distance between attributes has meaning Example: Fahrenheit temperature Ratio Ratio Distance between attributes has meaning, and there can be a meaningful zero. Example: Count variables

39. Time spent playing Game X Observed ‘violent acts’ Over time Y Gender Scale 1-5 of attitude About the President Race or Ethnicity Uses Internet or not

40. Next Class: Research Questions: What is a good ‘research problem’ and how is it justified? How do we turn these questions into testable hypotheses?