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Research Methods in Practice: Strategies for Description and Causation [Mīkstie vāki]

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  • Formāts: Paperback / softback, 616 pages, height x width: 231x187 mm, weight: 990 g
  • Izdošanas datums: 08-Jun-2010
  • Izdevniecība: SAGE Publications Inc
  • ISBN-10: 1412964679
  • ISBN-13: 9781412964678
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  • Formāts: Paperback / softback, 616 pages, height x width: 231x187 mm, weight: 990 g
  • Izdošanas datums: 08-Jun-2010
  • Izdevniecība: SAGE Publications Inc
  • ISBN-10: 1412964679
  • ISBN-13: 9781412964678
Citas grāmatas par šo tēmu:
In this large scale textbook, an introduction to social and policy research methods, authors Remler (economics, City U. of New York,)and Van Ryzin (psychology, Rutgers U.)take particular care to make distinctions between two main kinds of research: strategies for description and those strategies which seek to reveal causation. The latter they believe to be of special significance in probing social problems and their solutions. Alongside this goal, they cover foundational concepts and tools of research. There is an extensive glossary of terms, objectives at chapters start and exercises at the end, and abundant illustrative graphics throughout. Useful for research design and research methods courses in applied disciplines including public affairs/administration, public policy, urban affairs, education, social work, sociology, public health, and criminal justice. Annotation ©2010 Book News, Inc., Portland, OR (booknews.com) Research Methods in Practice provides an innovative, state-of-the-art introduction to research and analytical methods for postgraduate students. The coverage of the methods and concepts of contemporary research allow researchers and non-researchers alike to truly grasp the logic, and limits, of modern research as it appears in academic journals, government reports, and in the daily news. The textbook emphasizes the critical interpretation and practical application of research findings, and covers many cutting-edge issues and methods not found in other books, including: a more in-depth, contemporary focus on causation the logic and use of control variables with non-experimental data the use of visual path diagrams to better understand both causation and the use of control variables a fuller, more innovative treatment of quasi and natural experiments a focus on data collection for performance measurement a discussion of cutting-edge issues in sampling and survey research (such as the response rate problem in telephone surveys and the emergence of new methods for online surveys) an integrated treatment of qualitative methods that appears throughout the book and emphasizes the integration of qualitative with quantitative methods.
Preface xxiii
Acknowledgments xxvii
About the Authors xxix
PART I: FOUNDATIONS
1(90)
Research in the Real World
3(22)
Do Methods Matter?
3(2)
Good Evidence Comes From Well-Made Research
3(1)
May the Best Methodology Win
4(1)
Research-Savvy People Rule
5(1)
Research, Policy, and Practice
5(2)
Performance Measurement
5(1)
Evaluation Research
6(1)
Evidence-Based Policy and Programs
6(1)
Evidence Can Mislead
7(1)
Misleading Measurements
7(1)
Misleading Samples
7(1)
Misleading Correlations
8(1)
What is Research?
8(4)
Secondary and Primary Research
8(1)
It Comes in Various Shapes and Sizes
9(1)
It's Never Perfect
9(1)
It's Uncertain and Contingent
10(1)
It Aims to Generalize
10(1)
Bits and Pieces of a Puzzle
10(2)
It Involves Competition and Criticism
12(1)
It Can Be Quantitative, Qualitative, or a Mix of Both
12(1)
Formulating Research Questions
12(2)
How the World is---Not How It Should Be
12(1)
Applied and Basic Research
12(1)
Questions We Ideally Would Like to Answer, and Those We Really Can
13(1)
Descriptive and Causal Research
14(2)
Description: What is the World Like?
14(1)
Causation: How Would the World Be Different If Something Changed?
15(1)
Causal Research Needs Qualitative Research
15(1)
Don't Confuse Correlation With Causation
16(1)
Epistemology: Ways of Knowing
16(3)
The Scientific Method
17(1)
Induction and Deduction
17(1)
Proof Requires Fresh Data
18(1)
Truth in Social Science: Controversy and Consensus
19(1)
Approaching Research From Different Angles
19(2)
Consuming Research
20(1)
Commissioning Research
20(1)
Conducting Research
21(1)
Ethics of Research
21(1)
Conclusion: The Road Ahead
22(3)
Theory and Models
25(30)
Fighting Crime in New York City
25(1)
What is a Theory?
26(3)
Theories Tell Causal Stories
26(1)
Theories Explain Variation
27(1)
Theories Generate Testable Hypotheses
27(1)
Theories Take Different Forms in Different Disciplines
28(1)
Where Do Theories Come From?
29(1)
Induction and Deduction
29(1)
Theories, Norms, and Values
29(1)
Modifiable and Nonmodifiable Variables
30(1)
What is a Model?
30(7)
Variables and Relationships
31(1)
Independent and Dependent Variables
31(1)
Causal Mechanisms
32(1)
Direction of a Relationship
33(2)
Naming Variables
35(1)
Models With Multiple Causes
35(1)
Causal and Noncausal Relationships
36(1)
Unit of Analysis
37(1)
Same Theory, Different Unit of Analysis
38(1)
Logic Models
38(6)
Do Smaller Classes Help Kids Learn?
41(1)
Intervening Variables
42(1)
What About Other Causes?
43(1)
Usefulness of a Logic Model
44(1)
Tips for Creating a Logic Model
45(3)
Additional Issues in Theory Building
48(2)
Theories of the Independent Variable
48(1)
Moderators
48(1)
The Aggregation Problem and the Ecological Fallacy
49(1)
Hierarchical (Multilevel) Models and Contextual Variables
50(1)
Theoretical Research
50(1)
Conclusion: Theories Are Practical
50(5)
Qualitative Research
55(36)
Fighting Malaria in Kenya
55(2)
Theory, Causes, and Qualitative Research
56(1)
What is Qualitative Research?
57(4)
Contrasting Qualitative With Quantative Research
57(1)
Small-n Studies and Purposive Sampling
58(1)
Focus on Cases Rather Than Variables
59(1)
Advantages of Qualitative Research
59(1)
Schools of Thought in Qualitative Research
60(1)
Existing Qualitative Data
61(1)
Archival and Other Written Documents
62(1)
Visual Media, Popular Culture, and the Internet
62(1)
Qualitative Interviews
62(5)
Unstructured Interviews
63(1)
Semistructured Interviews
63(2)
Asking Truly Open-Ended Questions
65(1)
The Power of Probes
65(1)
Some Practical Considerations When Doing Interviews
66(1)
Focus Groups
67(4)
What Do People Think of Congestion Pricing?
67(1)
Moderating a Focus Group
67(3)
Why a Focus Group? Why Not Individual Interviews?
70(1)
Telephone and Online Focus Groups
70(1)
Qualitative Observation
71(1)
Participant Observation and Ethnography
71(2)
Why Do the Homeless Refuse Help?
71(1)
Levels on a Participation-Observation Continuum
72(1)
Secret Shopping and Audit Studies
73(1)
Case Study Research
73(2)
Maryland's Gun Violence Act
74(1)
Selecting a Case to Study
75(1)
Qualitative Data Analysis
75(5)
Integration of Analysis and Data Gathering
75(1)
Coding and Content Analysis
76(2)
Qualitative Data Analysis Software
78(2)
The Qualitative-Quantitative Debate
80(5)
A Brief History of the Debate
80(1)
Blurring the Lines: How Qualitative and Quantitative Approaches Overlap
81(1)
A Qualitative-Quantitative Research Cycle
82(2)
Mixed-Methods Research and Triangulation
84(1)
Ethics in Qualitative Research
85(1)
Presenting Qualitative Data
85(1)
Uncovering Sensitive Information
85(1)
Deception in Participant Observation
86(1)
Should Qualitative Research Empower People?
86(1)
Conclusion: Matching Methods to Questions
86(5)
PART II: STRATEGIES FOR DESCRIPTION
91(236)
Measurement
93(46)
The U.S. Poverty Measure
93(1)
What Is Measurement?
93(2)
Measurement in Qualitative Research
94(1)
Performance Measurement
94(1)
Measurement: The Basic Model and a Road Map
95(1)
Conceptualization
95(4)
Defining Can Be Difficult
96(1)
Where Do Conceptualizations Come From?
97(1)
Manifest and Latent Constructs
98(1)
Dimensions
98(1)
Operationalization
99(7)
Birth of the U. S. Poverty Measure
99(2)
Instruments
101(1)
Protocols and Personnel
102(1)
Proxies and Indicators
102(1)
Scales and Indexes
103(3)
Validity
106(2)
Is the U. S. Poverty Measure Valid?
106(1)
Face Validity
106(1)
Conent Validity
107(1)
Valid for What Purpose?
108(1)
Criterion-Related Validity
108(7)
Self-Reported Drug Use: Is It Valid?
108(2)
Does the Measure Predict Behavior?
110(3)
Limitations of Validity Studies
113(2)
Measurement Error
115(3)
Bias
115(1)
Random Error---Noise
116(1)
Bias and Noise in the U. S. Poverty Measure
116(1)
Error Model of Measurement
117(1)
Reliability
118(6)
Why Reliability Matters
118(3)
Many Ways to Tell if a Measure is Reliable
121(3)
Validity and Reliability in Qualitative Research
124(1)
Levels of Measurement
124(8)
Quantitative Measures
125(2)
Categorical Measures
127(1)
Turning Categorical Variables into Quantitative Ones
128(3)
Units of Analysis and Levels of Measurement
131(1)
Measurement in the Real World: Trade-Offs and Choices
132(7)
What Will It Cost?
132(1)
Is It Ethical?
133(1)
How Will it Affect the Quality and Rate of Responding?
133(1)
Validity-Reliablity Trade-Off
133(1)
Use an Established Measure or Invent a New One?
134(1)
Measurement Matters
135(4)
Sampling
139(42)
Gauging the Fallout From Hurricane Katrina
139(1)
Generalizability
140(5)
Population of Interest, Sampling, and Generalizability
141(1)
Are Experiments More Generalizable?
141(1)
Replicating Research and Meta-Analysis
142(1)
Are Relationships More Generalizable? Health and Happiness in Moldova
143(1)
Generalizability of Qualitative Studies
144(1)
Basic Sampling Concepts
145(3)
Population, Sample, and Inference
145(1)
Census Versus Sample
146(2)
Coverage and Nonresponse Bias
148(5)
Sampling Frames and Coverage Issues
148(1)
Nonresponse
148(1)
When Does Nonresponse Cause Bias?
149(2)
When Do Coverage Problems Cause Bias?
151(1)
Ethics of Nonresponse
152(1)
Nonprobability Sampling
153(4)
Voluntary Sampling
153(1)
Convenience Sampling
154(1)
Sampling Online: Open Web Polls and Internet Access Panels
154(2)
Purposive Sampling and Qualitative Research
156(1)
Random (Probability) Sampling
157(3)
The Contribution of Random Sampling
157(1)
Random Sampling Versus Randomized Experiments
158(1)
Simple Random Sampling
158(2)
Sampling Distributions and Statistical Inference
160(2)
Confidence Intervals (Margins of Error)
162(7)
Calculating a Confidence Interval or Margin of Error
163(1)
Interpreting Confidence Intervals (Margins of Error)
163(2)
Sample Size and Sampling Precision
165(1)
Variability and Sampling Precision
166(1)
What a Margin of Error Does Not Tell You
167(1)
Two Meanings of the Word Sample
168(1)
Sampling in Practice
169(6)
Systematic Sampling
169(1)
Stratified Sampling
170(1)
Disproportionate Sampling (Oversampling)
170(1)
Poststratification Weighting
171(1)
Sampling With Probabilities Proportional to Size (PPS)
172(1)
Multistage and Cluster Sampling
172(2)
Design Effects: Complex Survey Sampling Corrections
174(1)
Random Digit Dialing Sampling
174(1)
Sampling and Generalizability: A Summary
175(6)
Secondary Data
181(30)
Tracking the Flu
181(1)
What Are Quantitative Data?
181(2)
Quantitative Data Versus Quanlitative Variables
182(1)
Quantitative Versus Qualitative Research
182(1)
Forms of Quantitative Data
183(4)
Micro, Aggregate, and Multileval Data
183(3)
Time Dimension of Data
186(1)
Where Do Quantitative Data Come From?
187(1)
Administrative Records
187(5)
Adapting Administrative Data for Research
188(2)
Vital Statistics, Crime Reports, and Unemployment Claims
190(1)
Ethics of Administrative Record Data
191(1)
Published Data Tables
192(3)
Where to Find Published Tables
192(1)
Published Time-Series and Panel Data
192(3)
Public Use Microdata
195(11)
Secondary Analysis of Public Use Data: A New Model of Research?
195(1)
Know the Major Surveys in Your Field
195(9)
Accessing and Analyzing Public Use Data
204(1)
Data Archives
204(1)
Ethics of Public Use Microdata
205(1)
Linking Data
206(1)
Some Limitations of Secondary Data
206(1)
Does Data Availability Distort Research?
206(1)
When to Collect Original Data?
207(1)
Conclusion
207(4)
Primary Data Collection: Surveys and Observation
211(30)
Taking the Nation's Economic Pulse
211(1)
When Should You Do a Survey?
212(1)
Do You Know Enough About the Topic?
212(1)
Does the Information Exist Already in Another Source?
212(1)
Can People Tell You What You Want to know?
212(1)
Will People Provide Truthful Answers?
213(1)
Steps in the Survey Research Process
213(3)
Identify the Population and Sampling Strategy
213(1)
Develop a Questionnaire
214(1)
Pretest Questionnaire and Survey Procedures
214(1)
Recruit and Train Interviewers
215(1)
Collect Data
215(1)
Enter and Prepare Data for Analysis
215(1)
Analyze Data and Present Findings
216(1)
Modes of Survey Data Collection
216(8)
Intercept Interview Surveys
216(1)
Household Interview Surveys
217(1)
Telephone Interview Surveys
218(1)
Mail Self-Administered Surveys
219(2)
Group Self-Adminstered Surveys
221(1)
Web or Interview Surveys
221(2)
Establishment (Business or Organization) Surveys
223(1)
Panel or Longitudinal Surveys
224(1)
Crafting a Questionnaire
224(8)
Develop an Outline of Survey Items
224(1)
If You Could Ask Only One or Two Questions
224(1)
Prepare Mock Tables and Charts of Survey Results
225(1)
Look for Prior Surveys on Your Topic
225(1)
Hook Respondents With Your First Few Questions
225(2)
Closed-Ended Versus Open-Ended Questions
227(1)
Some Advice On Question Wording
228(4)
Put Yourself in Your Respondent's Shoes
232(1)
Ethics of Survey Research
232(2)
Informed Consent
232(1)
Pushing for a High Response Rate
232(1)
Overburdening Respondents
233(1)
Protecting privacy and Confidentiality
233(1)
Surveying Minors
233(1)
Making Survey Data Available for Public Use
234(1)
Trained Observation
234(3)
Observing Social Disorder
234(3)
Conclusion
237(4)
Making Sense of the Numbers
241(52)
``Last Weekend I Walked Eight''
241(1)
Units, Rates, and Ratios
242(5)
What Units?
242(1)
Rates or Why Counts Often Mislead
243(1)
Percent Change and Percentage Point Change
244(1)
The Strangeness of Percent Change on the Return Trip
245(1)
Rates of Change and Rates of Change of Rates
245(1)
Odds
246(1)
Prevalence and Incidence
246(1)
Distributions
247(3)
Distribution of a Categorical Variable
247(1)
Distribution of a Quantitative Variable
248(2)
Measures of Center: Mean and Median
250(2)
When to Use Median? When to Use Mean?
251(1)
Measures of Spread and Variation
252(4)
Standard Deviation
253(1)
Pay Attention to the Standard Deviation, Not Just the Mean
253(1)
Standardized (z) Scores
254(1)
Quantiles: Another Way to Measure Spread
255(1)
Conefficient of Variation: A Way to Compare Spread
255(1)
Relationships Between Categorical Variables
256(4)
Cross-Tabulation
256(2)
Relative Risks and Odds Ratios: Another Way to Show Relationships in Categorical Data
258(1)
Adjusted and Standardized Rates: When to Use Them
259(1)
Relationships Between Quantitative Variables: Scatterplots and Correlation
260(2)
Scatterplots
260(1)
Correlation
261(1)
Simple Regression: Best-Fit Straight Line
262(6)
Interpreting the Regression Coefficient (Slope)
264(2)
Can a Regression Coefficient Be Interpreted as a Causal Effect?
266(1)
Changes Versus Levels
267(1)
R-Squared and Residuals: How Well Does the Line Fit the Data?
267(1)
Effect Size and Practical Significance
268(1)
Effect Size
268(1)
Practical Significance
268(1)
Inference and the Standard Error
269(1)
Confidence Intervals
270(2)
Univariate Statistics and Relationships Both Have Confidence Intervals
271(1)
Confidence Intervals Only Reflect Some Sources of Error
271(1)
Significance Tests
272(4)
Falsification and the Logic of Significance Testing
272(1)
Running a Significance Test
273(1)
p Values
274(1)
Chi-Square Test of Cross-Tabs
275(1)
Other Test Statistics
275(1)
Universality of the p Value
275(1)
Statistical Significance, Practical Significance, and Power
276(7)
Combinations of Statistical and Practical Significance
276(3)
Failing to Recognize a Difference: Type II Errors
279(1)
Power
280(1)
Multiple Comparison Corrections
281(1)
The Debate About Significance Testing
281(1)
Sample Size Calculations: Getting the Precision You Want
281(1)
Adjusting Inference for Clustering and Other Complex Sampling
282(1)
Statistical Software
283(1)
Spreadsheets
283(1)
Statistical Packages: SAS, IBM® SPSS®, and Stata
283(1)
Specialzed Modeling and Matrix Language Programs
283(1)
Conclusion: Tools for Description and Causation
283(10)
Making Sense of Multivariate Statistics
293(34)
Multiple Regression: The Basics
293(6)
Multiple Regression for Prediction
295(1)
The Danger (and Necessity) of Out-of-Sample Extrapolation
295(1)
R-Squared and Adjusted R-Squared
296(1)
All Else Held Constant: A Bit More Mathematics
296(1)
Multicollinearity
297(1)
When You Can't Disentangle the Independent Variables
297(1)
How Many Independent Variables Can One Regression Have?
298(1)
Standardized Coefficients: The Relative Importance of Independent Variables
299(1)
Inference for Regression
299(4)
Standard Error of the Coefficient
299(1)
Confidence Intervals in Regression
300(1)
Confidence Interval of a Predicted Value
301(1)
Significance Testing in Regression
301(1)
Influences on Inference in Multiple Regression
302(1)
Categorical Independent Variables
303(4)
Dummy Variables
303(1)
Isn't There a Simpler Way to Estimate Differences in Means?
303(1)
Categorical Varibles With More Than Two Possible Values
304(1)
Interpreting the Coefficient of a Dummy Variable
305(2)
Adjusting Rates and Other Varibles
307(1)
Analysis of Variance (ANOVA)
307(1)
Interactions in Regression
307(3)
How to Use and Interpret Interaction Variables
308(2)
Interactions with Quantitative Variables
310(1)
Always Include Both Main Effects
310(1)
Functional Form and Transformations in Regression
310(3)
How to Fit a Curved Relationship
311(1)
How to Interpret Coefficients When a Variable is Logged
311(1)
The Value of Robustness and Transparency
312(1)
Categorical Variables as Dependent Variables in Regression
313(2)
Linear Probability Model
313(1)
Logistic and Probit Regression
314(1)
Marginal Effects
314(1)
What if the Dependent Variable Has More Than Two Categories?
314(1)
Beware of Unrealistic Underlying Assumptions
315(1)
Which Statistical Methods Can I Use?
315(2)
Other Multivariate Methods
317(8)
Path Analysis
317(1)
Factor Analysis
318(2)
Structural Equation Modeling
320(1)
Multilevel Models
320(2)
Time Series and Forecasting
322(1)
Panel Data Methods
323(1)
Spatial Analysis
324(1)
Limited Dependent Variables
324(1)
Survival Analysis
325(1)
More Multivariate Methods Not Covered
325(1)
Conclusion
325(2)
PART III: STRATEGIES FOR CAUSATION
327(138)
Causation
329(26)
Family Dinners and Teenage Substance Abuse
329(2)
Correlation Is Not Causation
331(1)
Possible Explanations of a Correlation
331(5)
Causation and Reverse Causation
331(1)
Common Causes
332(1)
Bias From a Common Cause
332(2)
Bias From an Unknown or Complex Common Cause
334(1)
Bias From Reverse Causation: Simultaneity Bias
335(1)
Other Examples of Correlation That Imply Causation
335(1)
Causal Mechanisms
336(3)
Chance Correlations and Statistical Significance
337(1)
Arrows, Arrows Everywhere
338(1)
Why Worry About the Correct Causal Model?
339(1)
Evidence of Causation: Some Initial Clues
339(4)
The Cause Happens Before the Effect
340(1)
The Correlation Appears in Many Different Contexts
340(1)
A Plusible Mechanism and Qualitative Evidence
341(1)
There Are No Plausible Alternative Explanations
341(1)
Common Causes Are Accounted for in the Analysis
342(1)
Self-Selection and Endogeneity
343(2)
Self-Selection
343(1)
Endogeneity
344(1)
The Counterfactual Definition of Causation
345(1)
If We Only Had a Time Machine
346(1)
Experimentation and Exogeneity: Making Things Happen
346(6)
Can Exercise Cure Depression?
347(1)
Why Experimentation Beats Passive Observation
347(1)
Exogeneity: Imposing a Change
348(1)
Control: Holding Things Constant
349(1)
Experimentation: A Review of the Basic Steps
350(1)
Comparative Experiments
350(1)
Limited Generalizability of Lab Experiments
351(1)
Ethical Difficulties Are Inherent in Experimentation
351(1)
Experimentation, Policy, and Practice
351(1)
Conclusion: End of Innocence
352(3)
Observational Studies With Control Variables
355(40)
Private Versus Public Schools
355(1)
Observational Studies
355(2)
The Gold Standard for Description---but Not for Causal Estimation
356(1)
Limitations of an Observational Study
357(1)
Control Variables
357(2)
How Control Variables Help Disentangle a Causal Effect
358(1)
How to Choose Control Variables
358(1)
How Did Control Variables Change the Estimate of a Causal Effect?
359(1)
An Empirical Example: Education and Earnings
359(7)
Speculate on Common Causes
360(1)
Look for Differences
361(1)
Stratify by Control Variables
361(1)
How Does Controlling for Aptitude Change the Estimate of the Effect of College?
362(1)
Omitted Variables Bias
363(1)
Interactions
364(1)
A Different Choice of Control Variable
364(1)
More Than One Control Variable at a Time
365(1)
How to Choose Control Variables
366(6)
The Importance of Using Path Diagrams
367(1)
Intervening Variables Should Not Be Used as Controls
368(1)
Complex Common Causes and Unexplained Correlations
369(1)
Causes That Can Be Ignored
369(1)
Choosing Good Control Variables Depends on Your Question
370(1)
Unmeasured Variables and Omitted Variables Bias
370(1)
Proxies
371(1)
Bias in Perspective
372(1)
From Stratification to Multiple Regression
372(7)
Using More Than One (Or Two) Control Variables
372(1)
Control Variables That Are Quantitative
372(1)
Regression: From Description to Causation
373(1)
Multiple Regression: Brief Overview and Interpretation
374(2)
How Multiple Regression Is Like Stratification: A Graphical Illustration
376(1)
Specification: How the Choice of Control Variables Influences Regression Results
377(2)
What About Unmeasured Variables?
379(1)
The Effect of Breastfeeding on Intelligence: Is There a Causal Connection?
379(7)
First Studies
379(1)
Speculate on Common Causes
379(1)
Examine the Relationship Between the Independent Variable of Interest and Potential Common Causes
380(1)
Implement Control Variables Throguh Multiple Regression
380(2)
How to Interpret Multiple Regression Coefficients: Effect of Controls
382(1)
How to Interpret Multiple Regression Coefficents: Effect of Interest
382(2)
Adding and Removing Controls: What Can Be Learned?
384(2)
Technical Complexities
386(1)
Further Topics in Multiple Regression
386(4)
Possible Effects of Adding Control Variables
386(1)
Interactions, Functional Form, and Categorical Dependent Variables
386(1)
The Decision to Focus on One Causal Effect---and the Confusion It Can Cause
387(1)
When Is Low R-Squared a Problem?
388(2)
Software Doesn't Know the Difference, but You Should
390(1)
Control Varibles in Perspective
390(5)
Randomized Field Experiments
395(32)
Time Limits on Welfare
395(1)
Florida's Family Transition Program: A Randomized Field Experiment
396(1)
Random Assignment: Creating Statistical Equivalence
397(5)
Random Assignment in Practice
397(2)
Statistical Equivalence: A Look at the Data
399(1)
Why Random Assignment Is Better Than Matching or Control Varibles
400(2)
Findings: What Happened in Pensacola
402(1)
The Logic of Randomized Experiments: Another Look
402(3)
Statistical Significance of an Experimental Result
404(1)
Generalizability of Randomized Experiments
405(6)
Random Assignment Versus Random Sampling
405(1)
The Limited Settings of Randomized Field Experiments
406(2)
Volunteers and Generalizability
408(1)
The Ideal Study: Random Sampling, Then Random Assignment
409(2)
Generalizability of the Treatment
411(1)
Variations on the Design of Experiments
411(2)
Arms in an Experiment
411(1)
Factors in an Experiment
412(1)
Heterogeneous Treatment Effects
412(1)
Human Artifacts in Experiments
413(4)
Placebo Effect and Blinding
413(1)
Unobtrusive or Nonreactive Measures
414(1)
Contamination
415(1)
Cluster Randomization
415(1)
Demoralization and Rivalry
416(1)
Noncompliance
417(1)
Attrition
417(1)
Analysis of Randomized Experiments
417(4)
Balancing and the Occasional Need for Control Variables
418(1)
Sample Size and Minimal Detectable Effects
418(1)
Intent to Treat Analysis
418(1)
Treatment of the Treated in Moving to opportunity
419(2)
Qualitative Methods and Experiments
421(1)
Conclusion
422(5)
Natural and Quasi Experiments
427(38)
A Casino Benefits the Mental Health of Cherokee Children
427(1)
What Are Natural and Quasi Experiments?
428(8)
Natural Experiments: Taking Advantage of Exogenous Events
428(2)
Quasi Experiments: Evaluating Intentional or Planned Treatments
430(2)
Why Distinguish Quasi Experiments From Natural Experiments?
432(4)
Internal Validity of Natural and Quasi Experiments
436(1)
Exogeneity and Comparability
436(1)
Theory of the Independent Variable
437(1)
Nothing's Perfect
437(1)
Generalizability of Natural and Quasi Experiments
437(1)
Generalizability of the Treatment Effect
438(1)
Types of Natural and Quasi Experimental Studies
438(13)
Before-After Studies
439(1)
Interrupted Time Series
440(2)
Cross-Sectional Comparisons
442(2)
Matching
444(3)
Case-Control Studies
447(2)
Prospective and Retrospective Studies
449(2)
Difference-in-Differences Strategy
451(4)
Do Parental Notification Laws Reduce Teenage Abortions and Births?
451(1)
What Does a Difference-in-Differences Study Assume?
452(2)
Retrospective Pretests and Other Retrospective Variables
454(1)
Difference-in-Differences in a Regression Framework
455(1)
Panel Data for Difference in Differences
455(3)
What Do Panel Difference-in-Differences Studies Assume?
456(1)
Weaknesses of Panel Difference-in-Differeces Studies
457(1)
Instrumental Variables and Regression Discontinuity
458(3)
Instrumental Variables
458(1)
Regression Discontinuity
459(2)
Conclusion
461(4)
Searching for and Creating Exogeneity
461(1)
Estimating Causal Effects in Perspective: A Wrap-Up to Part III
461(4)
PART IV: APPLICATIONS
465(52)
The Politics, Production, and Ethics of Research
467(22)
Risking Your Baby's Health
467(1)
From Research to Policy
468(9)
Rational Model of Policy
468(4)
Pathways of Influence
472(2)
Politics and Other Barriers
474(2)
How Can Research Have More Influence?
476(1)
The Production of Research
477(4)
Who Funds Research?
477(1)
How Time and Cost Shape Research
478(1)
Where is Research Conducted?
479(2)
Research Cultures and Disciplines
481(1)
Ethics of Research
481(5)
Poisoned by New York's Best Restaurants
482(1)
History of Human Subjects Abuses in Research
482(1)
Principles of Ethical Research Emerge
482(1)
The IRB Process
483(1)
Ethical Dilemmas in Research
484(1)
That Ethical State of Mind
485(1)
Conclusion
486(3)
How to Find, Focus, and Present Research
489(28)
Where to Find Research
489(4)
Journals
489(3)
Open-Access and e-Journals
492(1)
Books
492(1)
Attending Conferences and Seminars
492(1)
Reports
493(1)
Working Papers
493(1)
How to Search for Studies
493(5)
Google Scholar
494(1)
Electronic Resources: Indexes, Full-Text Databases, and Aggregators
495(1)
Wikipedia
496(1)
Browsing and Following Citation Trails
497(1)
Bibliographic Citation Software
498(1)
How to Focus Your Own Research Question
498(5)
Different Kinds of Researchers
498(1)
For Those Getting Started: Topics, Questions, and Problems
498(1)
Make Your Question Postitive, Not Normative
499(1)
Know If Your Question is Descriptive or Causal
500(1)
Distinguish the Question You Want to Answer From the Question You Can Answer
500(1)
For the Applied Researcher Given a Policy or Practice Question to Answer
501(1)
For Experienced Researchers: Finding an Important (but Doable) Question
502(1)
How to Write and Present Research
503(12)
The Importance of Rewriting
503(1)
Know Your Audience
503(1)
Organization of a Research Report
504(3)
Writing About Numbers
507(2)
Tables and Figures
509(1)
Tips for Creating Good Tables
509(2)
Tips for Creating Good Figures
511(1)
How to Write About Qualitative Research
511(3)
Presenting: How it is and is Not Like Writing
514(1)
Conclusion
515(2)
Glossary 517(20)
References 537(12)
Index 549
Dahlia K. Remler is Professor at the School of Public Affairs, Baruch College, and the Department of Economics, Graduate Center, both of the City University of New York. She is also a Research Associate at the National Bureau of Economic Research.

Dahlia has been in an unusual mix of disciplinary and interdisciplinary settings. She received a BS in electrical engineering from the University of California at Berkeley, a DPhil in physical chemistry from Oxford Universitywhile a Marshall Scholarand a PhD in economics from Harvard University. During the Clinton administrations health care reform efforts, Dahlia held a fellowship at the Brookings Institution to finish her dissertation on health care cost containment. She then held a postdoctoral research fellowship at Harvard Medical School, followed by assistant professorships at Tulanes and Columbias Schools of Public Health, prior to joining the faculty at Baruch. She enjoys comparing and contrasting how different disciplines see the same issues.

Dahlia has published widely in a variety of areas in health care policy, including health care cost containment, information technology in health care, cigarette tax regressivity, simulation methods for health insurance take-up, and health insurance and health care markets. She has also recently started working on higher education and media issues. Her work has appeared in the Journal of Policy Analysis and Management, Health Affairs, the Quarterly Journal of Economics, the American Journal of Public Health, Medical Care Research and Review, and many other journals. She blogs on health care policy, higher education and other topics at DahliaRemler.com.

Dahlia lives with her husband, Howard, in New York City, where they enjoy the citys theaters, restaurants, and parksand Dahlia enjoys being a complete amateur dancer in some of the citys superb dance studios.

Gregg G. Van Ryzin is Professor at the School of Public Affairs and Administration, Rutgers UniversityNewark. He received his BA in geography from Columbia University and his PhD in psychology from the City University of New York. During his doctoral training, he worked as a planner for a nonprofit housing and community development organization in New York City, and he completed his dissertation on low income housing for the elderly in Detroit. He next worked in Washington, D.C., for ICF Inc. and later Westat, Inc. on surveys and program evaluations for the U.S. Department of Housing and Urban Development and other federal agencies. In 1995, he joined the faculty of the School of Public Affairs, Baruch College, where he directed their Survey Research Unit for 8 years. In that role, he helped develop and direct the New York City Community Health Survey, a large-scale behavioral health survey for the citys health department, and also played a key role in shaping and conducting the citys survey of satisfaction with government services. He has spent time in Madrid, collaborating with researchers there on the analysis of surveys about public attitudes toward Spanish government policy. Gregg has published many scholarly articles on housing and welfare programs, survey and evaluation methods, and public opinion about government services and institutions. His work has appeared in the International Review of Administrative Sciences, the Journal of Policy Analysis and Management, the Journal of Public Administration Research and Theory, the Journal of Urban Affairs, Nonprofit and Voluntary Sector Quarterly, Public Administration Review, Public Management Review, Public Performance and Management Review, Urban Affairs Review, and other journals.

Gregg lives in New York City with his wife, Ada (a history professor at NYU), and their daughters Alina and Lucia. They enjoy life in their Greenwich Village neighborhood, escaping on occasion to Spain, Miami, Maine, Cuba, and other interesting places in the world.