Research Design Explained

by Mitchell, Mark L.; Jolley, Janina M.

• ISBN13: 9780155075054
• ISBN10: 0155075055
• Additional ISBN(s): 9780155028289, 9780534617318
• Edition: 4th
• Format: Hardcover
• Copyright: 2000-08-01
• Publisher: Cengage Learning
• More Book Details
• Purchase Benefits

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