1 / 30

Foundations of Research

Info 271B Lecture 2. Foundations of Research. Administrative Stuff. STATA Software Intercooled vs MP Readings Data and Assignments Course Datasets Your own data Easily obtainable data (ICPSR/Roper/GSS/etc). Brief Background: Epistemology and Strategies of Inquiry.

damita
Télécharger la présentation

Foundations of Research

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related