1. URBP 204A QUANTITATIVE METHODS I Survey Research II
Gregory Newmark
San Jose State University
(This lecture is based on Chapters 4 & 6 of Earl Babbie’s
The Practice of Social Research, 10th Edition.
All uncredited cartoons are from CAUSEweb.org by J.B. Landers.)

2. Research Purposes
Exploration
Description
Explanation 2

3. Research Purposes Exploration – to ‘get to know’ a new topic Examples
“What is transit oriented development?”
“What is equity planning?”
Purposes
To satisfy researcher’s curiosity
To test the feasibility of more extensive study
To develop methods to be employed in future study
Benefits and Drawbacks
Breaks new ground and yields new insights
Seldom provides satisfactory answers 3

4. Research Purposes Description – to describe observations Examples
“What are the characteristics of TOD in California?”
“How does this equity planning program work?”
Purposes
To describe situations and events
To chronicle activities
Benefits and Drawbacks
Answers questions of what, where, when, and how
Does not answer why 4

5. Research Purposes Explanation – to explain phenomena Examples Purposes
“Why are TODs not more successful in California?”
“Why does this equity planning program work so well?”
Purposes
To explain things
To understand why something occurred
Benefits and Drawbacks
Causality 5

6. Explanation
How do you know if your explanation is credible? 6

7. Nomothetic Causality Real Criteria Correlation Time Order
A relationship exists between variables
“Housing values are negatively related to commute length”
Time Order
Cause precedes effect
“Gender impacts political opinions”
Non-Spurious (Non Coincidental)
Effect can not be explained by a third variable
“Height is not explained by Zodiac sign” 7

8. Nomothetic Causality Why is correlation not enough for causality here?8

9. Nomothetic Causality
Does this cartoon pass the causality criteria? 9

10. Nomothetic Causality False Criteria Complete Causation
Nomothetic explanations are usually incomplete
“Three common factors for driving a Prius are . . .”
Exceptional Cases
Nomothetic explanations are not voided by exceptions
“I am an environmentalist, but I don’t drive a Prius.”
Majority of Cases
Nomothetic explanations still valid even if effect only occurs in the minority of cases
“Most environmentalists don’t drive a Prius.” 10

11. Nomothetic Causality Necessary Causes Sufficient Causes
Represents a condition that must be present for the effect to follow
“You need to be female to get pregnant”
“You need to go to college to get a college degree”
Sufficient Causes
Represents a condition that guarantees effect will follow
“Skipping the final exam will result in automatic failure”
“Participating in our survey will get you a t-shirt” 11

12. Nomothetic Causality Unit of Analysis What or whom are being studied
Individuals, groups, organizations, social artifacts, etc.
Typically, but not always, the unit of observation
For example, a study of couples might observe both partners separately
Data from individual units can be aggregated
“32 percent of our sample is college educated”
“This neighborhood predominantly does not vote” 12

13. Nomothetic Causality Faulty Reasoning regarding Units of Analysis
Ecological Fallacy
Drawing conclusions about individual based on observation of groups
“Younger neighborhoods support recycling, therefore younger people are more likely to support recycling”
Reductionism
Explaining complex phenomena with narrow concepts
Oversimplification
“What is the single cause of the American Revolution?”
“Economists see everything in economic terms.” 13

14. Nomothetic Causality
Ecological Fallacy 14

15. Nomothetic Causality
Reductionism 15

16. Time Dimension of Research
Cross-Sectional Studies
Observations are all made at one point in time
E.g. our FWBT survey
Longitudinal Studies
Observations are extended over time
Trend Studies track a characteristic
Cohort Studies track a subpopulation
Panel Studies track the same people (the panel)
Problem of panel attrition (dropping out of the study) 16

17. The Research Proposal Problem/ objective of research
Relevance of research
Literature review
Research question
Hypothesis
Variables of interest
Research method/s
Types of data to be collected - methods, sources
Plan for data analysis
Outline of research report - main chapters, sub-chapters
References/ Bibliography
Schedule
Budget 17

18. Composite Measures
Some concepts are not easily represented by a single variable
Particularly attitudes and orientations
E.g. religiosity, alienation, prejudice, etc
Composite measures can help
Can combine information from several variables
Can expand the range of variation
Can be useful for data analysis 18

19. Indexes vs. Scales Both are ordinal measures of variables
Both are composite measures of variables
Indexes are the simple accumulation of scores
“We award a point for each task completed and tally those scores to create an index value”
“Dow Jones Industrial Index”
Scales assign scores to patterns of responses
Consider differences in intensity of the response
Have some sort of logical or empirical order
“We assess the health risk to humans of a mix of pollutants and then award scores on a scale from least to most harmful.” 19

20. Indexes vs. Scales
Determine an index measure which would be contradicted by a scale measure.
For example, if you were to index conservative politicians by votes on conservative bills, you might incorrectly consider a politician who voted for several moderately conservative bills as more conservative than a politician who did not vote for those bills because they were not conservative enough. 20

21. Index Construction Item selection
What should we include in our index?
Face validity, Unidimensionality, Specificity, Variance
Examination of empirical relationships
Are variables related to each other? Generally useful.
No relation – probably should not include one
Too close a relation – probably should not include one
Index Validation
Internal Validation (Item analysis)
Each item should have independent contribution
Should not be perfectly correlated with other another item
External Validation
Index should correlate with other presumed indicators of variable
“People who score strongly on the index should score strongly on other related measures.” 21

22. Scale Construction Bogardus Social Distance Scale Guttman Scale
Determines the willingness of people to participate in social relations of varying degrees of closeness with other kinds of people
(Least extreme)
1. Are you willing to permit immigrants to live in your country?
2. Are you willing to permit immigrants to live in your community?
3. Are you willing to permit immigrants to live in your neighborhood?
4. Are you willing to permit immigrants to live next door to you?
5. Would you permit your child to marry an immigrant?
(Most extreme)
E.g., agreement with item 3 implies agreement with items 1 and 2.
Guttman Scale
More general case than that above
Binary (Yes/No) responses to a set of questions can be ranked, so that some one answering yes to a more difficult question will also answer yes to an easier one.
A coefficient of reproducibility is used as few Guttman Scales are perfect 22

23. Scale Construction Thurstone Scale Likert Scale Semantic Differential
Measures intensity structure among indicators by use of ‘judges’ to assign scores
Likert Scale
Uses standardized response categories in survey questionnaires to determine the relative intensity of different items
Semantic Differential
Two opposing possibilities are given and the respondent selects their gradation between those poles 23

24. Scale Construction
Likert Scale 24

25. Scale Construction
Semantic Differential Scale 25

26. Typology
Classification of observations with respect to two or more attributes.
Typology of Housing
High Quality
Low Quality
Pre-War Construction
Post War Construction 26