URBP 204A QUANTITATIVE METHODS I Social Research Lecture I

1. URBP 204A QUANTITATIVE METHODS ISocial Research Lecture I Gregory Newmark San Jose State University (This lecture accords with Chapters 1,2,3,5 of Earl Babbie’s The Practice of Social Research)

2. How do we know anything? • Observation – our experienced knowledge • “Ouch, this fire is hot!” • “Pigeons can fly.” • Agreement – our accepted knowledge • “Wait 15 minutes after eating before swimming.” • “Red is a primary color.”

3. Errors of Casual Observation • Inaccurate observations • “What color are my pants?” • Overgeneralization • “All women are left-handed” • Selective Observation • “This driver is slow, probably an old person” • Illogical Reasoning • “The exception that proves the rule . . .”

4. Guarding against those Errors • Inaccurate observations • Deliberate, structured measurement • Overgeneralization • Committing to a sufficient sample of observations • Replicating the experiment • Selective Observation • Research design structures observations • Illogical Reasoning • Conscious use of logic • Peer review

5. Views on Reality • Pre-Modern • “We see things as they actually are.” • Modern • “We see things subjectively, but there is an objective truth out there.” • Post-Modern • “We see things subjectively and that is truth.”

6. Scientific Knowledge • Logic • Theory • Observation • Data Collection • Data Analysis

7. What is Research? • “a process through which we attempt to achieve systematically and with the support of data the answers to a question, the resolution of a problem, or a greater understanding of a phenomenon” (Leedy 1997)

8. A Dialectic of Explanation • Idiographic Explanation • Explains a single case with excruciating detail • “Here are the 23 reasons I personally chose SJSU” • Specific explanation • Nomothetic Explanation • Explains a class of cases with economical detail • “Here are the 4 top reasons students choose SJSU” • General explanation

9. A Dialectic of Theory • Inductive Theory • Theorizes from specific cases to general pattern • “San Jose is sunny. Oakland is sunny. LA is sunny. Therefore, cities in California are sunny.” • Deductive Theory • Theorizes from general pattern to specific cases • “Cities in California are sunny. San Jose is in California. Therefore San Jose must be sunny.”

11. A Dialectic of Data • Qualitative Data • Non-numeric information • “The adult Martian is tall!” • Quantitative Data • Numeric information • “The adult Martian is 5’8”.”

12. A Dialectic of Focus • Macrotheory • Deals with large aggregate entities of society • “How do economic classes interact?” • Microtheory • Deals with intimate level of individuals • “How do panhandlers and pedestrians interact?”

13. Theories are logical explanations • Theories prevent our being deceived by flukes • “In theory, everyone rides the bus, so if the first four passengers are women, I won’t assume that all passengers will be.” • Theories make sense of observed patterns in a way that can suggest other possibilities • “In theory, if we understand why people take the bus, we can design a policy to support that use.” • Theories shape and direct research efforts • “In theory, faster travel times encourage transit. Let’s look into that!”

14. Rational Objectivity Reconsidered • Experience is inescapably unique • Individual experience of ‘subjectivity’ • Humans seek agreement on what’s real • Social pursuit of ‘objectivity’ • Ideas that hold up to inter-subjective scrutiny are considered real • Problem: Scrutiny can be skewed by culture • E.g. Ignoring experiences of subaltern groups

15. Hypothesis • A testable expectation about empirical reality derived from theory • “If the theory is correct, then x will be observed” • Must be disconfirmable • Examples: • Theory: Crime is inversely related to income • Hypothesis: A lower income school district will report more crimes per capita than a higher income one

16. Ethical Issues in Social Research • Voluntary participation • Deception – at times research purpose concealed • No harm to participants • Anonymity versus confidentiality • Institutional review boards (IRB) • Honest analysis and reporting • Use appropriate analytical procedures • Disclose problems and negative results

17. Moving from Theory to Research • Conceptualization • Specification of abstract terms in research • Operationalization • Development of working (operational) definitions • Specification of procedure (operations) for measuring a variable • Measurement • Deliberate empirical observations to describe phenomena in terms of variable attributes

18. Measurement • Attributes • Characteristic or quality of something • “ This animal is female.” • Variables • Logical sets of attributes • Exhaustive – every observation can be classified • Mutually exclusive – every observation classified once • “Female is an attribute that composes gender.”

19. Levels of Measurement • Nominal Measures • Exhaustive and mutually exclusive only • E.g. birthplace, gender, religious affiliation • Ordinal Measures • Exhaustive and mutually exclusive • Capable of being ranked in order • E.g. social class, conservatism, level of satisfaction

20. Levels of Measurement • Interval Measures • Exhaustive and mutually exclusive • Capable of being ranked in order • Distance separating attributes has fixed meaning • E.g. Temperature (C or F), credit score, GRE • Ratio Measures • Exhaustive and mutually exclusive • Capable of being ranked in order • Distance separating attributes has fixed meaning • Based on a true zero point • E.g. Temperature (K), height (ft), income ($)

21. Levels of Measurement • Certain analytical techniques require certain levels of measurement • “Calculate the class’s average height (ft).” • “Calculate the class’s average birthplace.” • Measures taken at one level can be recoded into lower levels of measurement • “Recode heights (ft) into three ordinal categories.” • “Recode birthplaces into three ordinal categories.” • Level of measurement determined by analytical uses you have planned • If you are not sure how you might use the data, aim for the highest level of measurement

22. Criteria of Measurement Quality Precision (versus Accuracy) A B

23. Criteria of Measurement Quality • Precision (versus Accuracy) • Reliability • Definition: same technique yields same result • Methods: Test-retest, Split-half, pre-established • Research workers are not always reliable • Validity • Definition: accurately measures the concept it is intended to measure

24. Types of Validity • Face Validity • Indicator seems reasonable “on its face” • Grievances as a measure of worker morale • Criterion-related Validity • Indicator relates to some external criterion • SAT scores as a predictor of college GPA • Construct Validity • Indicator relates to other variables as expected within a system of theoretical relationships • Comparing marriage satisfaction to marriage fidelity • Content Validity • Indicator covers the range of meanings associated within a concept • Planning licensing exam covers all the planning skills

25. Reliability vs. Validity • More reliable: Quantitative, nomothetic • More valid: Qualitative, idiographic