Research Design Explained,9780155075054

Research Design Explained

by ;
Edition: 4th
ISBN13: 9780155075054
ISBN10: 0155075055
Format: Hardcover
Pub. Date: 2000-08-01
Publisher(s): Cengage Learning
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Summary

Celebrated for its easy-to-grasp, student-oriented approach, this book puts research design into a practical context that teaches students how to use theory and gives novices the practical advice they need. The book encourages students to value, read, and conduct ethical research. The authors introduce ethical issues in Chapter 1 and discuss ethics throughout the book. It includes an entire chapter on how to read research.

Table of Contents

Preface iii
Psychology and Science
1(46)
Overview
2(1)
Why Psychology Uses the Scientific Approach
2(15)
The Characteristics of Science
2(6)
The Characteristics of Psychology
8(7)
The Importance of Science to Psychology
15(2)
Questions About Applying Techniques From Physical Sciences to Psychology
17(16)
Internal Validity Questions: Did the Treatment Cause a Change in Behavior?
18(3)
Construct Validity Questions: Making the Leap From the Physical World to the Mental World?
21(5)
External Validity Questions: Can the Results Be Generalized?
26(1)
Ethical Questions: Should the Study Be Conducted?
27(5)
Conclusions About the Questions That Researchers Face
32(1)
Why You Should Understand Research Design
33(5)
To Understand Psychology
34(1)
To Read Research
34(1)
To Evaluate Research
34(1)
To Protect Yourself From ``Quacks''
35(1)
To Be a Better Thinker
35(1)
To Be Scientifically Literate
36(1)
To Increase Your Marketability
37(1)
To Do Your Own Research
37(1)
Concluding Remarks
38(1)
Summary
39(2)
Key Terms
41(1)
Exercises
42(5)
Generating and Refining Research Hypotheses
47(24)
Overview
48(1)
Generating Research Ideas From Common Sense
48(2)
Generating Research Ideas From Previous Research
50(4)
Specific Strategies
51(3)
Conclusions About Generating Research Ideas From Previous Research
54(1)
Converting an Idea Into a Research Hypothesis
54(10)
Make It Testable
55(1)
Make It Supportable
56(1)
Be Sure to Have a Rationale: How Theory Can Help
56(1)
Demonstrate Its Relevance: Theory Versus Trivia
57(1)
Refine It: 10 Time-Tested Tips
58(5)
Make Sure That Testing the Hypothesis Is Both Practical and Ethical
63(1)
Changing Unethical and Impractical Ideas Into Research Hypotheses
64(3)
Make Variables More General
65(1)
Use Smaller Scale Models of the Situation
66(1)
Carefully Screen Potential Participants
66(1)
Use ``Moderate'' Manipulations
66(1)
Do Not Manipulate Variables
67(1)
Concluding Remarks
67(1)
Summary
67(1)
Key Terms
68(1)
Exercises
69(2)
Measuring and Manipulating Variables: Reliability And Validity
71(49)
Overview
72(1)
Choosing a Behavior to Measure
73(1)
Errors in Measuring Behavior
73(13)
Overview of Types of Errors
74(2)
Two Types of Observer Errors
76(4)
Errors in Administering the Measure
80(1)
Errors Due to the Participant
81(4)
Summary of Types of Measurement Error
85(1)
Reliability: The (Relative) Absence of Random Error
86(11)
The Importance of Being Reliable: Reliability as a Prerequisite to Validity
86(1)
Using Test-Retest Reliability to Assess Overall Reliability: To What Degree Is a Measure ``Random Error Free''?
86(2)
Identifying (and Then Dealing With) the Main Source of a Measure's Reliability Problems
88(8)
Conclusions About Reliability
96(1)
Beyond Reliability: Establishing Construct Validity
97(10)
Content Validity: Does Your Test Have the Right Stuff?
99(1)
Internal Consistency Revisited: Evidence That You Are Measuring One Characteristic
99(2)
Convergent Validation Strategies: Statistical Evidence That You Are Measuring the Right Construct
101(2)
Discriminant Validation Strategies: Showing That You Are Not Measuring the Wrong Construct
103(3)
Summary of Construct Validity
106(1)
Manipulating Variables
107(7)
Common Threats to a Manipulation's Validity
108(1)
Evidence Used to Argue for a Manipulation's Construct Validity
109(1)
Tradeoffs Among Three Common Types of Manipulations
110(3)
Manipulating Variables: Conclusions
113(1)
Concluding Remarks
114(1)
Summary
114(1)
Key Terms
115(2)
Exercises
117(3)
Beyond Reliability and Validity: Choosing the Best Measure for Your Study
120(23)
Overview
121(1)
Sensitivity: Will the Measure Be Able to Detect the Differences You Need to Detect?
122(6)
Achieving The Necessary Level of Sensitivity
122(5)
Sensitivity: Conclusions
127(1)
Scales of Measurement: Will the Measure Allow You to Make the Kinds of Comparisons You Need to Make?
128(10)
The Different Scales of Measurement
128(4)
Why Our Numbers Do Not Always Measure Up
132(1)
Which Level of Measurement Do You Need?
133(4)
Conclusions About Scales of Measurement
137(1)
Ethical and Practical Considerations
138(1)
Concluding Remarks
139(1)
Summary
139(1)
Key Terms
140(1)
Exercises
141(2)
Internal Validity
143(32)
Overview
144(1)
Two-Group Designs
145(12)
Why We Never Have Identical Groups
145(11)
Conclusions About Two-Group Designs
156(1)
Problems With the Pretest-Posttest Design
157(7)
Three Reasons Participants May Change Between Pretest and Posttest
158(3)
How Measurement Changes May Cause Scores to Change Between Pretest and Pretest
161(3)
Conclusions About Trying to Keep Everything Except the Treatment Constant
164(1)
Ruling out Extraneous Variables
165(3)
Accounting for Extraneous Variables
166(1)
Identifying Extraneous Variables
167(1)
The Relationship Between Internal and External Validity
168(2)
Concluding Remarks
170(1)
Summary
170(1)
Key Terms
171(1)
Exercises
172(3)
The Simple Experiment
175(48)
Overview
176(1)
Basic Logic and Terminology
177(13)
Experimental Hypothesis: The Treatment Has an Effect
177(3)
Null Hypothesis: The Treatment Does Not Have an Effect
180(1)
Conclusions About Experimental and Null Hypotheses
181(1)
Manipulating the Independent Variable
181(1)
Experimental and Control Groups: Similar, but Treated Differently
182(1)
The Value of Independence: Why Control and Experimental Groups Shouldn't Really Be ``Groups''
182(2)
The Value of Assignment (Manipulating the Treatment)
184(2)
Collecting the Dependent Variable
186(1)
The Statistical Significance Decision: Deciding Whether to Declare That a Difference Is Not a Coincidence
186(1)
Statistically Significant Results: Declaring That the Treatment Has a Reliable Effect
186(2)
Null Results: Why We Can't Draw Conclusions From Nonsignificant Results
188(2)
Summary of the ``Ideal'' Simple Experiment
190(1)
Errors in Determining Whether Results Are Statistically Significant
190(3)
Type 1 Errors: ``Crying Wolf''
190(2)
Type 2 Errors: ``Failing to Announce the Wolf''
192(1)
The Need to Prevent Type 2 Errors: Why You Want the Power to Find Significant Differences
193(1)
Statistics and the Design of the Simple Experiment
193(5)
Power and the Design of the Simple Experiment
194(3)
Conclusions About How Statistical Considerations Impact Design Decisions
197(1)
Nonstatistical Considerations and the Design of the Simple Experiment
198(3)
External Validity Versus Power
198(1)
Construct Validity Versus Power
199(2)
Ethics Versus Power
201(1)
Analyzing Data From the Simple Experiment: Basic Logic
201(10)
Estimating What You Want to Know: Your Means Are Sample Means
202(2)
Why We Must Do More Than Subtract the Means From Each Other
204(1)
How Random Error Affects Data From the Simple Experiment
205(2)
When Is a Difference Too Big to Be Due to Random Error?
207(4)
Analyzing the Results of the Simple Experiment: The t-Test
211(2)
Using the r-Table
211(1)
Assumptions of the t-Test
212(1)
Questions Raised by Results
213(2)
Questions Raised by Nonsignificant Results
214(1)
Questions Raised by Significant Results
214(1)
Concluding Remarks
215(1)
Summary
215(2)
Key Terms
217(3)
Exercises
220(3)
Expanding the Simple Experiment: The Multiple-Group Experiment
223(36)
Overview
124(100)
The Advantages of Using More Than Two Values of an Independent Variable
224(15)
Comparing More Than Two Kinds of Treatments
224(2)
Comparing Two Kinds of Treatments With No Treatment
226(1)
Comparing More Than Two Levels (Amounts) of an Independent Variable to Increase External Validity
226(7)
Using Multiple Levels to Improve Construct Validity
233(6)
Analysis of Multiple-Group Experiments
239(13)
Analyzing the Multiple-Group Experiment: An Intuitive Overview
240(2)
A Closer Look ac the Analysis of a Multiple-Group Experiment
242(10)
Concluding Remarks
252(1)
Summary
253(1)
Key Terms
254(2)
Exercises
256(3)
Expanding the Simple Experiment: Factorial Designs
259(56)
Overview
260(1)
The 2 x 2 Factorial Design
260(12)
How One Experiment Can Do as Much as Two
261(1)
How One Experiment Can Do More Than Two
262(8)
Example of Questions Answered by the 2 x 2 Factorial Experiment
270(2)
Potential Results of a 2 x 2 Factorial Experiment
272(14)
A Main Effect and No Interaction
273(5)
Two Main Effects and No Interaction
278(2)
Two Main Effects and an Interaction
280(1)
Interaction Without Main Effects
281(2)
One Main Effect and an Interaction
283(2)
No Main Affects and No Interaction
285(1)
Analyzing the Results From a 2 x 2 Experiment
286(14)
What Degrees of Freedom Tell You
286(2)
Interpreting the Results of an ANOVA Table
288(12)
Putting the 2 x 2 to Work
300(5)
Adding a Replication Factor to Increase Generalizability
301(1)
Using an Interaction to Find an Exception to the Rule: Looking at a Potential Moderating Factor
302(1)
Using Interactions to Create New Rules
303(2)
The Hybrid Design: A Factorial Design That Allows You to Study Nonexperimental Variables
305(4)
Increasing Generalizability
307(1)
Studying Effects of Similarity
308(1)
Finding an Exception to the Rule
308(1)
Concluding Remarks
309(1)
Summary
309(2)
Key Terms
311(1)
Exercises
312(3)
Within-Subjects Designs
315(40)
Overview
316(1)
The Matched-Pairs Design
317(6)
Procedure
317(1)
Considerations in Using Matched-Pairs Designs
317(5)
Analysis of Data
322(1)
Summary of the Matched-Pairs Design
322(1)
Within-Subjects (Repeated Measures) Designs
323(8)
Considerations in Using Within-Subjects Designs
323(5)
Dealing With Order Effects
328(3)
Randomized Within-Subjects Designs
331(2)
Procedure
331(1)
Analysis of Data
332(1)
Summary of Randomized Within-Subjects Designs
333(1)
Counterbalanced Within-Subjects Designs
333(12)
Procedure
334(1)
Advantages and Disadvantages of Counterbalancing
334(10)
Conclusions About Counterbalanced Within-Subjects Designs
344(1)
Choosing Designs
345(5)
Choosing Designs: The Two-Conditions Case
345(1)
Choosing Designs: When You Have More Than One Independent Variable
346(4)
Concluding Remarks
350(1)
Summary
350(1)
Key Terms
351(2)
Exercises
353(2)
Reading and Evaluating Research
355(28)
Overview
356(1)
Reading for Understanding
356(12)
Choosing an Article
356(1)
Reading the Abstract
357(1)
Reading the Introduction
357(4)
Reading the Method Section
361(2)
Reading the Results Section
363(4)
Reading the Discussion
367(1)
Developing Research Ideas From Existing Research
368(12)
The Direct Replication
368(5)
The Systematic Replication
373(4)
The Conceptual Replication
377(1)
The Value of Replications
378(1)
Extending Research
379(1)
Concluding Remarks
380(1)
Summary
380(1)
Key Terms
381(1)
Exercises
382(1)
Single-N Designs And Quasi-Experiments
383(43)
Overview
384(1)
Inferring Causality in Randomized Experiments
384(2)
Establishing Covariation
384(1)
Establishing Temporal Precedence
385(1)
Battling Spuriousness
385(1)
Single-n Designs
386(14)
Keeping Nontreatment Factors Constant: The A-B Design
387(4)
Variations on the A-B Design
391(5)
Evaluation of Single-n Designs
396(3)
Conclusions About Single-n Designs
399(1)
Quasi-Experiments
400(20)
The Problem: Accounting for Nontreatment Factors
401(5)
Time-Series Designs
406(8)
The Nonequivalent Control-Group Design
414(5)
Conclusions About Quasi-Experimental Designs
419(1)
Concluding Remarks
420(1)
Summary
420(2)
Key Terms
422(3)
Exercises
425(1)
Introduction to Descriptive Methods
426(42)
Overview
427(1)
Uses and Limitations of Descriptive Methods
427(5)
Descriptive Research and Causality
428(2)
Description for Description's Sake
430(1)
Description for Prediction's Sake
431(1)
Why We Need Science to Describe Behavior
432(3)
We Need Scientific Measurement
432(1)
We Need Systematic, Scientific Record-Keeping
433(1)
We Need Objective Ways to Determine If Variables Are Related
433(1)
We Need Scientific Methods to Generalize From Experience
434(1)
Conclusions About the Need for Descriptive Research
435(1)
Sources of Data
435(8)
Data You Previously Collected
435(2)
Archival Data
437(3)
Observation
440(2)
Tests
442(1)
Describing Data From Correlational Studies
443(10)
Graphing Data
444(3)
Correlation Coefficients
447(6)
Summary of Describing Correlational Data
453(1)
Making Inferences From Correlational Data
453(10)
Analyses Based on Correlation Coefficients
454(2)
Analyses Not Involving Correlation Coefficients
456(4)
Interpreting Significant Results
460(2)
Interpreting Null Results
462(1)
Concluding Remarks
463(1)
Summary
463(2)
Key Terms
465(1)
Exercises
466(2)
Survey Research
468(43)
Overview
469(1)
Questions to Ask Before Doing Survey Research
470(7)
What Is Your Hypothesis?
470(4)
Can Self Report Provide Accurate Answers?
474(2)
To Whom Will Your Results Apply?
476(1)
Conclusions About the Advantages and Disadvantages of Survey Research
477(1)
The Advantages and Disadvantages of Different Survey Instruments
477(6)
Written Instruments
477(3)
Interviews
480(3)
Planning a Survey
483(16)
Deciding on a Research Question
483(1)
Choosing the Format of Your Questions
484(4)
Choosing the Format of Your Survey
488(1)
Editing Questions: Nine Mistakes to Avoid
489(3)
Sequencing Questions
492(2)
Putting the Final Touches on Your Survey Instrument
494(1)
Choosing a Sampling Strategy
495(4)
Administering the Survey
499(1)
Analyzing Survey Data
500(6)
Summarizing Data
500(3)
Using Inferential Statistics
503(3)
Concluding Remarks
506(1)
Summary
506(1)
Key Terms
507(3)
Exercises
510(1)
Putting It All Together: Writing Research Proposals and Reports
511
Overview
512(1)
Aids to Developing Your Idea
512(2)
The Research Journal
512(1)
The Research Proposal
513(1)
Writing the Research Proposal
514(21)
General Strategies for Writing the Introduction
514(4)
Specific Strategies for Writing Introduction Sections for Different Types of Studies
518(6)
Writing the Method Section
524(3)
Writing the Results Section
527(1)
Writing the Discussion Section
528(2)
Putting on the Front and Back
530(5)
Writing the Research Report
535(5)
What Stays the Same of Changes Very Little
535(1)
Writing the Results Section
535(4)
Writing the Discussion Section
539(1)
Concluding Remarks
540(1)
Summary
540(1)
Key Terms
541
Appendix A Ethics A-2
Appendix B Searching the Literature (Electronically and the Old-Fashioned Way) B-1
Appendix C Conducting a Study C-1
Appendix D Sample Research Paper D-1
Appendix E Statistics and Random Numbers Tables E-1
Appendix F Introduction to Statistics F-1
Glossary G-1
References R-1
Credits CR-1
Index I-1

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