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Foundations of Research

Foundations of Research. Info 271B Lecture 2 Coye Cheshire. Administrative Stuff. Software Intercooled vs. small stata Stata MP Readings Data and Assignments Course Datasets Your own data Easily obtainable data (ICPSR/Roper/GSS/etc).

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Foundations of Research

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  1. Foundations of Research Info 271B Lecture 2 Coye Cheshire

  2. Administrative Stuff • Software • Intercooled vs. small stata • Stata 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. 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

  5. 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

  6. 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) Info 271B

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

  8. 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

  9. Linking Constructs and Variables Being Nice Life Happiness ? ? ? ? Info 271B

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

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

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

  13. 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

  14. 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) Info 271B

  15. 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

  16. Time spent playing Game X Observed ‘violent acts’ Over time Y Info 271B

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

  18. Nominal Variables • Binary/dichotomous • Example: Gender, event occurred or did not occur, etc. • When coded as 0/1, also called ‘dummy variables’ • 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

  19. Ordinal Variables • Ordered polytomous • Example: Likert scales • Any ordered, categorical variable where the distance between categories may not be equal and meaningful Info 271B

  20. 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

  21. 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 Presidential Candidate Owns and iPod or not Info 271B

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

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