Foundations of Research

1. Info 271B Lecture 2 Foundations of Research

2. Administrative Stuff • STATA Software • Intercooled vs MP • Readings • Data and Assignments • Course Datasets • Your own data • Easily obtainable data (ICPSR/Roper/GSS/etc) Info 271B

3. Brief Background: Epistemology and Strategies of Inquiry Info 271B

4. Definition of ‘Science’ “an objective, logical , and systematic methodof analysis of phenomena, devised to permit the accumulation of reliable knowledge” (Lastrucci 1963:6)

5. Complications with Observation in Social Science

6. Positivism and Humanism • Positivism • “The truth is out there, we can find it” • If we systematically study humans using a scientific approach, we can learn patterns and commonalities. • Human behavior can be explained in terms of causes and effects. • Humanism • Humans create meaning, thus science is inappropriate for studying humans • Deals with moral questions– right and wrong. • Often embraces subjectivity and unique human experiences Info 271B

7. Rise of “Positivist” Social Science • Experience is the foundation of knowledge • Quality of recorded observation is the key to knowledge August Comte (1798-1857)

8. Logical Empiricism • Again, knowledge is based on experience– but metaphysical explanations of phenomena are incompatible with science. • Established the idea that we can build ‘true’ complex statements if we start with true propositions. • We should only attempt to answer “answerable” questions. • Interestingly, this approach produces a logic of justification– but not of discovery.

9. Quantitative and Qualitative Perspectives • "There's no such thing as qualitative data. Everything is either 1 or 0“ • Fred Kerlinger • "All research ultimately has a qualitative grounding“ • Donald Campbell

10. Different Strategies of Inquiry • Quantitative • Instrument-based questions • Statistical analysis • Surveys, Experiments • Qualitative • Emergent methods • Open-ended questions • Interviews, Case Studies, Ethnographies • Mixed-Methods Approaches • Both quantitative and qualitative methods used Info 271B

11. Why quantitative research? • Standardized methodologies • Statistical techniques are public • Like any science, the methods of research can (and should be) disclosed so that anyone can duplicate your findings • Forces the investigator to think about the measurement of key factors (i.e., variables) and whether they actually measure intended concepts. Info 271B

12. Foundations of Quantitative Research: Variables and Measurement Info 271B

13. Constructs and Variables • Variables • Something we can measure • Concrete measured expressions to which we can assign numeric values • Constructs • Concepts, often complex • Not directly measurable • Also called ‘theoretical variables’ Info 271B

14. Linking Constructs and Variables Success Life Happiness ? ? ? ? Info 271B

15. Conceptual and Operational Definitions • Conceptual Definitions • Abstractions that facilitate understanding • Operational Definitions • How to measure a conceptual variable Info 271B

16. Operationalization Concept: “Emotional State” Info 271B

17. Measurement • How could we operationalize… • Age? • “Intelligence”? • How efficient is interface X? Status AGE INCOME Info 271B

18. Qualitative/Quantitative Measures and Operationalization • Note Bernard’s example (p. 39-40) of parental aspirations and children's career aspirations • What does this kind of example tell us about research design? Info 271B

19. Operationalization • For any operational definition, there are a few important things to keep in mind: • What is the unit of analysis? • Be able to justify your operational definition (i.e., don’t make arbitrary decisions) • This cannot be stressed enough: your entire study and any conclusions you draw can be undone by an insufficient operationalization. Info 271B

20. Measurement: Variables • Independent Variable (X) • Also called predictor variables, or right-hand side variables (RHS) • Those that the researcher manipulates • Attributes or potential causes under investigation in a given study • Dependent Variable (Y) • Also called outcome variable, or left-hand side variables (LHS) X Y y = mx + b Info 271B

21. Types of Variables • Nominal • Categorical • Dichotomous, Binary, Dummy Variables • Qualitative Variables • Ordinal • Rank Variables • Metric • Interval Variables • Ratio Variables Info 271B

22. Nominal Variables • Binary/dichotomous • Example: Gender, event occurred or did not occur, etc. • When coded as 0/1, also called ‘dummy variables’ • Qualitative • Example: State of Residence • Nominal/non-ordered polytomous • Example: Employment Status • 1= Employed • 2= Unemployed • 3= Retired Three New Dummy Variables: Employed (0,1) Unemployed (0,1) Retired (0,1) Info 271B

23. Ordinal Variables • Ordered polytomous • Example: Likert scales • Any ordered, categorical variable where the distance between categories may not be equal and meaningful • Other examples include ordered categories of degree (high, medium, low) Info 271B

24. Metric Variables • Interval • Distance between attributes has meaning • Example: Celsius temperature, “likert-scale” questions • Ratio • Distance between attributes has meaning, and there can be a meaningful zero. • Example: Kelvin temperature, Count variables Info 271B

25. Time spent exercising between time 1 and time 2 Gender (Male =1, Female =2) Ethnic Identity (10 Racial Types) Difference in weight scores between time1 and time 2 Scale 1-5 of attitude about President Owns and iPod or not Info 271B

26. Reliability and Validity Image credit: http://www.documentingexcellence.com Info 271B

27. Testing for Reliability • Reliability: How consistent is the instrument when you use it more than once? • Interobserver reliability • Test-retest reliability Info 271B

28. Determining Validity • Validity: Accuracy of our instrument at measuring the intended concept • Face validity • Content validity • Construct validity • Criterion validity Info 271B

29. Precision and Accuracy • The precisionof an instrument is the magnitude of the smallest unit of measure (e.g., how many decimals in the measurement). • Accuracy of an instrument: How far off your measure is (bias) from the intended target Info 271B

30. Next Week: • Preparing for Research • Defining Problems for Research Info 271B