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Your journal entry is not for reciting facts, it is a self-reflection on what you have learned and how you plan to use or have used the information. Use concepts from the text as must as possible to guide your journal writing. Keep entries to a minimum of 300 words unless otherwise instructed. You may add an interesting quote or relatable cartoon. Reference the text for concepts cited. You are required to complete a journal entry every week. The journal entry is for you and me. You do NOT respond to any peers. Grades will be given based on content and word count.
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On page 47, in the book it discusses how to begin your research.One of the most important factors in research is to focus on a topic that you are passionate about.If you had to pick a research topic; what would it be and why? Share your passion with me on this topic.Practical Research
G LO BA L E D I T I O N
Paul D. Leedy
Late of American University
Jeanne Ellis Ormrod
University of Northern Colorado (Emerita)
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Authorized adaptation from the United States edition, entitled Practical Research: Planning and Design, 11th edition,
ISBN 978-0-13-374132-2, by Paul D. Leedy and Jeanne Ellis Ormrod, published by Pearson Education © 2016.
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NEW TO THE ELEVENTH EDITION
Every year brings exciting new strategies in research methodologies, making any updated edition of Practical Research a joy to write. With this eleventh edition, the book has been revised
in numerous ways. As always, every page has been revisited—every word, in fact—and many
minor changes have been made to tighten the prose or enhance its clarity. Also, discussions of
technology-based strategies have been updated to reflect not only new software options but also
the increasing technological sophistication of most of our readers.
Probably the two most noteworthy changes in this edition are the addition of a new chapter and a reorganization of some of the other chapters. In response to reviewers’ requests, the
tenth edition’s chapter “Qualitative Research” has been expanded into two chapters, “Qualitative Research Methods” and “Analyzing Qualitative Data.” Discussions of quantitative research
methods now precede (rather than follow) discussions of qualitative methodologies, and the
chapter on analyzing quantitative data now immediately follows the two chapters on quantitative methodologies.
Other significant changes in the eleventh edition are these:
■ Chapter 1. Revision of Figure 1.1 and accompanying text to include seven (rather than six)
steps in order to better align with discussions that follow in the chapter; new section on
philosophical underpinnings of various methodologies; new discussion of quantitative vs.
qualitative vs. mixed-methods research (moved from its previous location in Chapter 4); discussion of the iterative nature of research; expansion of Table 1.1; revision of the guidelines
for using word processing software to focus on features that readers may not routinely use in
their day-to-day writing.
■ Chapter 2. Introduction of the idea of a priori hypotheses (to distinguish them from
hypotheses that researchers might form midway through a study); new discussion about
identifying the limitations (as well as delimitations) of a proposed study.
■ Chapter 3. Elimination of outdated sections “Using Indexes and Abstracts” and “Locating Relevant Government Documents,” with electronically based strategies in those
sections being incorporated into the sections “Using Online Databases” and “Surfing the
Internet”; relocation of the discussion of database creation to the Practical Application
“Planning a Literature Search.”
■ Chapter 4. Better balance between discussions of quantitative and qualitative
approaches; addition of design-based research to what is now Table 4.2 (previously
■ Chapter 6 (formerly Chapter 8). New discussion of rubrics; omission of a random numbers table (because such tables are widely available on the Internet); expanded discussion
of possible biases in descriptive research; new Guidelines feature (“Identifying Possible
Sampling Bias in Questionnaire Research”); new Checklist feature (“Identifying Potential Sources of Bias in a Descriptive Study”).
P re fa c e
■ Chapter 7 (formerly Chapter 9). New section on possible biases in quantitative re-
search; new Checklist (“Identifying Potential Sources of Bias and Potential Threats to
External Validity in an Experimental, Quasi-Experimental, or Ex Post Facto Study”).
■ Chapter 8 (formerly Chapter 11). New example (regarding a cancer prognosis) as an
illustration of the limitations of a median as a predictor; addition of the five-number
summary as a possible indicator of variability in ordinal data.
■ Chapter 9 (formerly Chapter 6). Focus now on general design, planning, and data collection in qualitative research, with data analysis being moved to the new Chapter 11;
new section on validity and reliability; expanded discussion of how cultural differences
can influence interviews; relocation of the extensive example in international relations
(formerly in the chapter “Descriptive Research”) to this chapter, where it is more appropriately placed.
■ Chapter 10 (formerly Chapter 7). Expanded discussion of possible biases in primary
and secondary sources; updated and expanded list of online databases.
■ Chapter 11 (new chapter). Greatly expanded discussion of qualitative data analysis;
new Checklist (“Pinning Down the Data Analysis in a Qualitative Study”); new Sample Dissertation (by Society for Research in Child Development award winner Christy
■ Chapter 12 (formerly Chapter 10). Expanded discussion of mixed-methods designs,
with a new fifth category, multiphase iterative designs; new Conceptual Analysis Exercise
(“Identifying Mixed-Methods Research Designs”); new section on sampling; expanded
discussion of data analysis strategies; new Practical Application section discussing helpful software for analyzing mixed-methods data; new section on systematic reviews.
■ Chapter 13 (formerly Chapter 12). Better balance between quantitative and qualitative
research reports; reorganization and revision of the section “Essential Elements of a Research
Report” (formerly titled “Planning a Research Report”); updated discussion of APA style for
electronic resources; new Guidelines feature (“Writing a Clear, Coherent Report”).
THE PURPOSE OF THIS BOOK
Practical Research: Planning and Design is a broad-spectrum, cross-disciplinary book suitable for
a wide variety of courses in basic research methodology. Many basic concepts and strategies in
research transcend the boundaries of specific academic areas, and such concepts and strategies are
at the heart of this book. To some degree, certainly, research methods do vary from one subject
area to another: A biologist might gather data by looking through a microscope, a historian by
examining written documents from an earlier time period, and a psychologist by administering certain tests or systematically observing people’s behavior. Otherwise, the basic approach to
research is the same. Regardless of the discipline, the researcher identifies a question in need of
an answer, collects data potentially relevant to the answer, analyzes and interprets the data, and
draws conclusions that the data seem to warrant.
Students in the social sciences, the natural sciences, education, medicine, business administration, landscape architecture, and other academic disciplines have used this text as a guide
to the successful completion of their research projects. Practical Research guides students from
problem selection to completed research report with many concrete examples and practical,
how-to suggestions. Students come to understand that research needs planning and design, and
they discover how they can effectively and professionally conduct their own research projects.
Essentially, this is a do-it-yourself, understand-it-yourself manual. From that standpoint, it can
be a guide for students who are left largely to their own resources in carrying out their research
projects. The book, supplemented by occasional counseling by an academic advisor, can guide
the student to the completion of a successful research project.
P re fa c e
LEARNING ABOUT THE RESEARCH PROCESS IS AN
ESSENTIAL COMPONENT OF ACADEMIC TRAINING
All too often, students mistakenly believe that conducting research involves nothing more than
amassing a large number of facts and incorporating them into a lengthy, footnoted paper. They
reach the threshold of a master’s thesis or doctoral dissertation only to learn that simply assembling previously known information is insufficient and unacceptable. Instead, they must do
something radically different: They must answer a question that has never been answered before
and, in the process, must discover something that no one else has ever discovered. Something has
gone tragically wrong in the education of students who have, for so many years of their schooling, entirely misunderstood the true nature of research.
Research has one end: the discovery of some sort of “truth.” Its purpose is to learn what
has never before been known; to ask a significant question for which no conclusive answer has
previously been found; and, by collecting and interpreting relevant data, to find an answer to
Learning about and doing research are of value far beyond that of merely satisfying a program requirement. Research methods and their application to real-world problems are skills
that will serve you for the rest of your life. The world is full of problems that beg for solutions;
consequently, it is full of research activity! The media continually bring us news of previously
unknown biological and physical phenomena, life-saving medical interventions, and groundbreaking technological innovations—all the outcomes of research. Research is not an academic
banality; it is a vital and dynamic force that is indispensable to the health and well-being of
Planet Earth and its human and nonhuman inhabitants.
More immediate, however, is the need to apply research methodology to those lesser daily
problems that nonetheless demand a thoughtful resolution. Those who have learned how to analyze problems systematically and dispassionately will live with greater confidence and success
than those who have shortsightedly dismissed research as nothing more than a necessary hurdle
on the way to a degree. Given the advantages that a researcher’s viewpoint provides, considering an academic research requirement as annoying and irrelevant to one’s education is simply an
Many students have found Practical Research quite helpful in their efforts both to understand
the nature of the research process and to complete their research projects. Its simplification of research concepts and its readability make it especially suitable for those undergraduate and graduate students who are introduced, perhaps for the first time, to genuine research methodology.
We hope we have convinced you that a course on research methodology is not a temporary
hurdle on the way to a degree but, instead, an unparalleled opportunity to learn how you might
better tackle any problem for which you do not have a ready solution. In a few years you will undoubtedly look back on your research methods course as one of the most rewarding and practical
courses in your entire educational experience.
Pearson would like to thank the following people for their work on the Global Edition:
Amita Agarwal, S.K. Government College, Sikar
Priyanka Pandey, London School of Economics
No man is an iland, entire of it selfe; every man is a
peece of the Continent, a part of the maine . . .
So wrote John Donne, the great dean of St. Paul’s Cathedral in the 17th century. And so do we
authors write in the 21st century.
Those who have had a part in the making of this book, known and unknown, friends and
colleagues, gentle critics and able editors—all—are far too many to salute individually. Those
of you who have written in journals and textbooks about research methods and strategies, the
generations of graduate and undergraduate students whom we authors have taught and who have
also taught us, the kindly letters and e-mail messages that so many of you have written to describe how this book has helped you in your own research endeavors—to all of you, I extend my
acknowledgment and appreciation wherever you may be. You have had the greater part in bringing this book through its previous ten editions. I am especially grateful to the reviewers of the
eleventh edition, who recently offered many good suggestions for strengthening the book so that
it can better assist novice researchers in the 21st century: Brian Belland, Utah State University;
Robert Hayden, Michigan State University; Walter Nekrosius, Wright State University; Lloyd
Rieber, University of Georgia; and Susan Twombly, University of Kansas.
I am also indebted to the students whose research proposals, doctoral dissertations, and master’s
theses have enabled me to illustrate some of the research and writing strategies described in the
book. In particular, I extend my gratitude to Rosenna Bakari, Arthur Benton, Jennifer Chandler,
Kay Corbett, Dinah Jackson, Ginny Kinnick, Laura Lara-Brady, Peter Leavenworth, Christy Leung,
Matthew McKenzie, Kimberly Mitchell, Richard Ormrod, Luis Ramirez, Janie Shaklee, Nancy
Thrailkill, and Debby Zambo. Pete Leavenworth and Matt McKenzie gave me their time as well as
their research reports, and their recommendations for the chapter on historical research were superb.
Equally important is to say “Thank you, thank you, thank you” to many folks at Pearson and
S4Carlisle who have been key players in bringing this book to fruition. In particular, I extend
my deepest gratitude to Gail Gottfried, who has lined up helpful multimedia supplements to
the book and, in general, has been a regular and reliable sounding board and source of support
throughout my writing endeavors in recent years. Thanks also to Lauren Carlson and Mary Tindle,
both of whom have expertly coordinated what has become an ever-evolving and increasingly
complex textbook-production process in the electronic age. A shout-out to Chris Feldman, whose
close attention to nitty-gritty details during copy edits has consistently warmed the cockles of
my obsessive-compulsive heart. And several people have worked diligently outside my range
of sight to make the whole project come together; hearty thanks to Kate Wadsworth for the
interactive quizzes and end-of-chapter activities, as well as to Carrie Mollette, Caroline Fenton,
and Caitlin Griscom for the many behind-the-scenes contributions I can only begin to fathom.
Finally, I must thank our editor, Kevin Davis, for his guidance throughout this and preceding editions. Throughout its many editions, Kevin has shared Paul’s and my vision for the book
and struck the ever-so-important balance between providing guidance to help us improve it
while also trusting our instincts about how best to explain and illustrate the complex, multifaceted nature of research planning and design.
No author is an island, entire of itself. Paul and I have had many hands guiding our pens and many
minds adding richness and depth to our thoughts. All of you have been exceedingly helpful, all of you
have been “a peece of the Continent, a part of the maine.” For that, I offer my humble and hearty thanks.
Jeanne Ellis Ormrod
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Focusing Your Research Efforts
The Problem: The Heart of the Research Process . . . . . . . 45
Review of the Related Literature . . . . . . . . . . . . . . . . . . . . 70
Planning Your Research Project . . . . . . . . . . . . . . . . . . . . 92
Writing the Research Proposal . . . . . . . . . . . . . . . . . . . . 134
Descriptive Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
Experimental, Quasi-Experimental, and Ex Post
Facto Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
Analyzing Quantitative Data . . . . . . . . . . . . . . . . . . . . . . 229
Qualitative Research Methods . . . . . . . . . . . . . . . . . . . . 269
Historical Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296
Analyzing Qualitative Data . . . . . . . . . . . . . . . . . . . . . . . 309
The Nature and Tools of Research . . . . . . . . . . . . . . . . . . 19
Mixed-Methods Designs . . . . . . . . . . . . . . . . . . . . . . . . . 329
Planning and Preparing a Final Research Report . . . . . 347
Appendix A Using a Spreadsheet: Microsoft Excel . . . . . . . . . . . . . . . . . . . 372
Appendix B Using SPSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379
Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396
The Nature and Tools of Research . . . . . . . . . . . . . . . . . . . . . 19
What Research Is Not 19
What Research Is 20
Philosophical Assumptions Underlying Research Methodologies 25
Tools of Research 26
The Library and Its Resources 27
Computer Technology 27
PRACTICAL APPLICATION: Communicating Effectively
Through Writing 31
G uidelines : Writing to Communicate 32
G uidelines : Using the Tools in Word Processing Software 33
The Human Mind 35
Critical Thinking 35
Deductive Logic 36
Inductive Reasoning 37
Scientific Method 38
Theory Building 39
Collaboration with Other Minds
Reflections on Noteworthy Research 40
Exploring Research in Your Field 42
PRACTICAL APPLICATION: Identifying Important Tools
in Your Discipline 42
C hecklist : Interviewing an Expert Researcher 43
For Further Reading 43
C on te nts
Focusing Your Research Efforts
The Problem: The Heart of the Research Process . . . . . . . . . 45
Finding Research Projects
PRACTICAL APPLICATION: Identifying and Describing the Research
G uidelines : Choosing an Appropriate Problem 47
G uidelines : Stating the Research Problem 49
C hecklist : Evaluating the Research Problem 53
Dividing the Research Problem into Subproblems 54
Subproblems Versus Pseudo-Subproblems 54
Characteristics of Subproblems 55
Identifying Subproblems 55
Taking a Paper-and-Pencil Approach 55
Using Brainstorming (Mind Mapping) Software
Every Problem Needs Further Delineation 57
Stating Hypotheses 57
Distinguishing Between Research Hypotheses and Null Hypotheses
in Quantitative Research 58
Identifying the Variables Under Investigation
CONCEPTUAL ANALYSIS EXERCISE: Identifying Independent,
Dependent, Mediating, and Moderating Variables 60
Defining Terms 61
Stating Assumptions 62
Identifying Delimitations and Limitations 62
Importance of the Study 63
Writing the First Chapter or Section of a Research Proposal 63
PRACTICAL APPLICATION: Writing the First Section of a Proposal 64
C hecklist : Evaluating Your Proposed Research Project 65
PRACTICAL APPLICATION: Reappraising a Proposed Research
G uidelines : Fine-Tuning Your Research Problem 66
For Further Reading 67
Answers to the Conceptual Analysis Exercise “Identifying Independent,
Dependent, Mediating, and Moderating Variables” 68
Review of the Related Literature . . . . . . . . . . . . . . . . . . . . . . 70
Understanding the Role of the Literature Review
Strategies for Locating Related Literature 71
Using the Library Catalog 71
C o n te nts
Using Online Databases 74
Consulting with Reference Librarians 76
Surfing the Internet 77
Using Citations and Reference Lists of Those Who Have Gone Before You
PRACTICAL APPLICATION: Planning a Literature Search 78
G uidelines : Using Your Library Time Efficiently 80
PRACTICAL APPLICATION: Evaluating the Research of Others 83
C hecklist : Evaluating a Research Article 83
Knowing When to Quit 84
Organizing and Synthesizing the Literature into a Cohesive Review 85
PRACTICAL APPLICATION: Writing the Literature Review 85
G uidelines : Writing a Clear and Cohesive Literature Review 85
A Sample Literature Review 88
For Further Reading 91
Planning Your Research Project . . . . . . . . . . . . . . . . . . . . . . . 92
Planning a General Approach 93
Research Planning Versus Research Methodology 93
The Nature and Role of Data in Research 94
Data Are Transient and Ever Changing 94
Primary Data Versus Secondary Data 94
Planning for Data Collection 95
Linking Data and Research Methodology 97
Comparing Quantitative and Qualitative Methodologies 98
Combining Quantitative and Qualitative Designs
PRACTICAL APPLICATION: Choosing a General Research Approach 100
G uidelines : Deciding Whether to Use a Quantitative
or Qualitative Approach 101
Considering the Validity of Your Method 103
Internal Validity 103
External Validity 105
Validity in Qualitative Research 106
Identifying Measurement Strategies 106
Defining Measurement 107
Measuring Insubstantial Phenomena: An Example 108
Types of Measurement Scales 110
Nominal Scales 110
Ordinal Scales 111
Interval Scales 111
Ratio Scales 112
CONCEPTUAL ANALYSIS EXERCISE: Identifying Scales
of Measurement 113
C on te nts
Validity and Reliability in Measurement 114
Validity of Measurement Instruments 114
Reliability of Measurement Instruments 116
Enhancing the Reliability and Validity of a Measurement Instrument 117
CONCEPTUAL ANALYSIS EXERCISE: Identifying Problems
with Validity and Reliability in Measurement 118
Ethical Issues in Research 120
Protection from Harm 120
Voluntary and Informed Participation 121
Right to Privacy 123
Honesty with Professional Colleagues 123
Internal Review Boards 124
Professional Codes of Ethics 124
PRACTICAL APPLICATION: Planning an Ethical Research Study 125
C hecklist : Determining Whether Your Proposed Study Is Ethically
Critically Scrutinizing Your Overall Plan 126
PRACTICAL APPLICATION: Judging the Feasibility
of a Research Project 126
C hecklist : Determining Whether a Proposed Research Project
Is Realistic and Practical 126
When You Can’t Anticipate Everything in Advance: The Value of a Pilot Study 128
PRACTICAL APPLICATION: Developing a Plan of Attack 128
Using Project Management Software and Electronic Planners 130
Keeping an Optimistic and Task-Oriented Outlook 130
For Further Reading 131
Answers to the Conceptual Analysis Exercise “Identifying Scales of
Answers to the Conceptual Analysis Exercise “Identifying Problems with
Validity and Reliability in Measurement” 133
Writing the Research Proposal . . . . . . . . . . . . . . . . . . . . . . . 134
Characteristics of a Proposal 135
A Proposal Is a Straightforward Document 135
A Proposal Is Not a Literary Production 136
A Proposal Is Clearly Organized 136
Organizing and Writing a Research Proposal
Formatting Headings and Subheadings 137
PRACTICAL APPLICATION: Writing Your Proposal
G uidelines : Writing the First Draft 138
G uidelines : Revising Your Proposal 143
PRACTICAL APPLICATION: Strengthening Your Proposal 147
C hecklist : Evaluating an Early Draft of a Research Proposal 148
C o n te nts
Final Thoughts About Proposal Writing 148
A Sample Research Proposal 149
For Further Reading 153
Descriptive Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
Descriptive Research Designs
Observation Studies 154
Correlational Research 155
A Caution about Interpreting Correlational Results 157
Developmental Designs 157
Survey Research 159
Face-to-Face and Telephone Interviews 160
Planning for Data Collection in a Descriptive Study 161
PRACTICAL APPLICATION: Using Checklists, Rating Scales,
and Rubrics 161
PRACTICAL APPLICATION: Computerizing Observations
PRACTICAL APPLICATION: Planning and Conducting Interviews
in a Quantitative Study 165
G uidelines : Conducting Interviews in a Quantitative Study 165
PRACTICAL APPLICATION: Constructing and Administering a
G uidelines : Constructing a Questionnaire 166
G uidelines : Using Technology to Facilitate Questionnaire Administration
and Data Analysis 170
G uidelines : Maximizing Your Return Rate for a Questionnaire 171
PRACTICAL APPLICATION: Using the Internet to Collect Data
for a Descriptive Study 175
Choosing a Sample in a Descriptive Study 176
Sampling Designs 177
Probability Sampling 177
Nonprobability Sampling 182
Sampling in Surveys of Very Large Populations
PRACTICAL APPLICATION: Identifying a Sufficient Sample Size
PRACTICAL APPLICATION: Analyzing the Population in
a Descriptive Study 185
C hecklist : Analyzing Characteristics of the Population Being
Common Sources of Bias in Descriptive Studies 186
Sampling Bias 186
C on te nts
Instrumentation Bias 187
Response Bias 188
Researcher Bias 188
PRACTICAL APPLICATION: Acknowledging the Probable Presence
of Bias in Descriptive Research 188
G uidelines : Identifying Possible Sampling Bias in Questionnaire
C hecklist : Identifying Potential Sources of Bias in a Descriptive
Interpreting Data in Descriptive Research 190
Some Final Suggestions 191
A Sample Dissertation 191
For Further Reading 195
Experimental, Quasi-Experimental, and Ex Post
Facto Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
The Importance of Control 197
Controlling for Confounding Variables 198
Overview of Experimental, Quasi-Experimental, and Ex Post Facto Designs 202
Pre-Experimental Designs 203
Design 1: One-Shot Experimental Case Study 203
Design 2: One-Group Pretest–Posttest Design 203
Design 3: Static Group Comparison 204
True Experimental Designs 204
Design 4: Pretest–Posttest Control-Group Design 204
Design 5: Solomon Four-Group Design 205
Design 6: Posttest-Only Control-Group Design 205
Design 7: Within-Subjects Design 206
Quasi-Experimental Designs 207
Design 8: Nonrandomized Control-Group Pretest–Posttest Design 207
Design 9: Simple Time-Series Design 208
Design 10: Control-Group Time-Series Design 208
Design 11: Reversal Time-Series Design 208
Design 12: Alternating-Treatments Design 209
Design 13: Multiple-Baseline Design 209
Using Designs 11, 12, and 13 in Single-Subject Studies 211
Ex Post Facto Designs 212
Design 14: Simple Ex Post Facto Design 213
Factorial Designs 213
Design 15: Two-Factor Experimental Design 213
Design 16: Combined Experimental and Ex Post Facto Design 214
CONCEPTUAL ANALYSIS EXERCISE: Identifying Quantitative
Research Designs 218
C o n te nts
PRACTICAL APPLICATION: Determining Possible Cause-and-Effect
C hecklist : Looking for Confounding Variables 219
Conducting Experiments on the Internet 221
Testing Your Hypotheses, and Beyond 222
PRACTICAL APPLICATION: Acknowledging the Probable Presence
of Bias in Experimental Research 222
C hecklist : Identifying Potential Sources of Bias and Potential Threats
to External Validity in an Experimental, Quasi-Experimental,
or Ex Post Facto Study 223
A Sample Dissertation 224
For Further Reading 228
Answers to the Conceptual Analysis Exercise “Identifying Quantitative
Research Designs” 228
Analyzing Quantitative Data . . . . . . . . . . . . . . . . . . . . . . . . 229
Exploring and Organizing a Data Set 229
Organizing Data to Make Them Easier to Think About and Interpret 231
Using Computer Spreadsheets to Organize and Analyze Data 233
Choosing Appropriate Statistics 235
Functions of Statistics 235
Statistics as Estimates of Population Parameters
Considering the Nature of the Data 237
Single-Group Versus Multi-Group Data 237
Continuous Versus Discrete Variables 237
Nominal, Ordinal, Interval, and Ratio Data 237
Normal and Non-Normal Distributions 238
Choosing between Parametric and Nonparametric Statistics 240
Descriptive Statistics 241
Measures of Central Tendency 241
Curves Determine Means 242
Measures of Central Tendency as Predictors 244
Measures of Variability: Dispersion and Deviation 244
How Great Is the Spread? 245
Using the Mean and Standard Deviation to Calculate Standard Scores 247
Keeping Measures of Central Tendency and Variability in Perspective 249
Measures of Association: Correlation 249
How Validity and Reliability Affect Correlation Coefficients 251
A Reminder About Correlation 252
Inferential Statistics 252
Estimating Population Parameters 252
An Example: Estimating a Population Mean 253
Point Versus Interval Estimates 254
C on te nts
Testing Hypotheses 255
Making Errors in Hypothesis Testing 256
Another Look at Statistical Hypotheses Versus Research Hypotheses 258
Examples of Statistical Techniques for Testing Hypotheses 258
Using Statistical Software Packages 260
Interpreting the Data 261
PRACTICAL APPLICATION: Analyzing and Interpreting Data in a
Quantitative Study 263
C hecklist : Choosing Statistical Procedures 263
A Sample Dissertation 264
For Further Reading 267
Qualitative Research Methods . . . . . . . . . . . . . . . . . . . . . . 269
Research Problems and Methodology Choice in Qualitative Research 270
Potential Advantages of a Qualitative Approach 271
Qualitative Research Designs 271
Case Study 271
Phenomenological Study 273
Grounded Theory Study 274
Content Analysis 275
CONCEPTUAL ANALYSIS EXERCISE: Choosing a Qualitative Research
Collecting Data in Qualitative Research 277
PRACTICAL APPLICATION: Addressing Validity and Reliability Issues in
Qualitative Data Collection 278
PRACTICAL APPLICATION: Selecting an Appropriate Sample for a
Qualitative Study 279
PRACTICAL APPLICATION: Making Observations in a Qualitative
PRACTICAL APPLICATION: Planning and Conducting Interviews in a
Qualitative Study 281
G uidelines : Conducting a Productive Interview 282
An Example in International Relations 286
Using Technology to Facilitate Collection of Interview Data
Criteria for Evaluating Qualitative Research 287
PRACTICAL APPLICATION: Planning the Logistics of a Qualitative Study 288
C hecklist : Pinning Down the Methodology of a Qualitative Study 289
C o n te nts
A Sample Dissertation 290
For Further Reading 294
Answers to the Conceptual Analysis Exercise “Choosing a Qualitative
Research Design” 295
Historical Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296
Data Sources in Historical Research 296
Collecting Historical Records 300
Online Databases for Historical Events 300
PRACTICAL APPLICATION: Handling Historical Data
Evaluating and Interpreting Historical Data 301
External Evidence 301
Internal Evidence 301
Psychological or Conceptual Historical Research 303
Searching for Roots 303
PRACTICAL APPLICATION: Historical Research Writing 303
G uidelines : Writing the Historical Research Report 303
A Sample Dissertation 304
For Further Reading 308
Analyzing Qualitative Data . . . . . . . . . . . . . . . . . . . . . . . . . 309
Qualitative Analysis Strategies 310
General Strategies for Organizing and Analyzing Qualitative Data 310
Creswell’s Data Analysis Spiral 315
An Example: Data Analysis in a Grounded Theory Study 315
An Example: Data Analysis in a Content Analysis Study 317
PRACTICAL APPLICATION: Using Computer Databases to Facilitate
Data Organization and Analysis 318
Acknowledging the Role of Researcher-as-Instrument in Qualitative
PRACTICAL APPLICATION: Planning Data Analysis for a Qualitative
C hecklist : Pinning Down the Data Analysis in a Qualitative
PRACTICAL APPLICATION: Observing How Experienced Researchers
Have Conducted Qualitative Research 322
C hecklist : Evaluating a Qualitative Study 322
A Sample Dissertation 323
For Further Reading 328
C on te nts
Mixed-Methods Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329
When Mixed-Methods Designs Are Most Useful and Appropriate 330
Common Mixed-Methods Designs 330
Convergent Designs 331
Embedded Designs 331
Exploratory Designs 331
Explanatory Designs 331
Multiphase Iterative Designs 331
Common Symbolic Notations for Mixed-Methods Designs 332
CONCEPTUAL ANALYSIS EXERCISE: Identifying Mixed-Methods
Research Designs 333
Planning a Mixed-Methods Study 334
Identifying Research Questions and Hypotheses 334
Conducting the Literature Review 335
Choosing One or More Appropriate Samples 335
Addressing Validity Concerns 336
Special Ethical Considerations in Mixed-Methods Research 337
Analyzing and Interpreting Mixed-Methods Data 337
PRACTICAL APPLICATION: Using Computer Software to Facilitate MixedMethods Data Analysis 339
PRACTICAL APPLICATION: Deciding Whether to Use a Mixed-Methods
C hecklist : Pinning Down the Logistics and Feasibility
of a Mixed-Methods Study 339
Systematic Reviews of Qualitative and Mixed-Methods Studies 340
A Sample Dissertation 342
For Further Reading 346
Answers to the Conceptual Analysis Exercise “Identifying Mixed-Methods
Research Designs” 346
Planning and Preparing a Final Research Report . . . . . . . 347
Getting Started 347
Surfing the Internet for Writing Assistance 348
Learn by Looking 348
Essential Elements of a Research Report 349
Explanation of the Research Problem 349
Description of Methods 350
Description of the Data and Data Analyses 350
C o n te nts
Interpretation of the Data 351
Identification of Possible Weaknesses of the Study 353
Summary and Connections to a Broader Context 353
Maintaining Your Academic Integrity 353
Front Matter and End Matter 354
Preliminary Pages 354
Endnotes and Footnotes 355
Reference List 355
Appendix Content 358
Organizing a Research Report 358
Writing—and Finishing!—A Report 360
PRACTICAL APPLICATION: Writing Your Final Report 360
G uidelines : Writing a Clear, Coherent Report 361
PRACTICAL APPLICATION: Developing a Writing Schedule 362
G uidelines : Pinning Down and Adhering to a Workable Schedule
PRACTICAL APPLICATION: Critiquing a Final Research Report 364
C hecklist : Criteria for Critiquing a Research Report 364
Beyond the Unpublished Research Report: Presenting and Publishing 366
Conference Presentations 366
PRACTICAL APPLICATION: Presenting Your Research at a Professional
G uidelines : Presenting an Effective Paper or Poster 367
Journal Articles 368
Sharing Authorship 369
Responding to Reviewers’ Critiques 369
A Closing Thought 370
For Further Reading 370
Appendix A Using a Spreadsheet: Microsoft Excel
Using Excel to Keep Track of Literature Resources 372
Using Excel to Record and Recode Data 374
Reorganizing Data in Excel 377
Using Excel to Perform Simple Statistical Analyses 377
Appendix B Using SPSS
Creating a Data Set 379
Computing Basic Descriptive Statistics 381
Computing Inferential Statistics 382
The Nature and Tools
In virtually every subject area, our collective knowledge about the world is
incomplete: Certain questions remain unanswered, and certain problems remain
unsolved. Systematic research provides many powerful tools—not only physical
tools but also mental and social tools—that can help us discover possible answers
and identify possible solutions.
1.1 Distinguish between (a) common
uses of the term research that reflect
misconceptions about what research
involves and (b) the true nature of
research in academic settings.
1.2 Describe the cyclical, iterative nature
of research, including the steps that
a genuine research project involves.
1.3 Distinguish among positivism,
postpositivism, constructivism, and
pragmatism/realism as philosophical
underpinnings of a research project.
1.4 Identify examples of how six general
research tools can play significant
roles in a research project: (a) the library and its resources, (b) computer
technology, (c) measurement,
(d) statistics, (e) language, and
(f) the human mind.
1.5 Describe steps you might take to
explore research in your field.
In everyday speech, the word research is often used loosely to refer to a variety of activities. In
some situations the word connotes simply finding a piece of information or taking notes and
then writing a so-called “research paper.” In other situations it refers to the act of informing oneself about what one does not know, perhaps by rummaging through available sources to locate a
few tidbits of information. Such uses of the term can create considerable confusion for university
students, who must learn to use it in a narrower, more precise sense.
Yet when used in its true sense—as a systematic process that leads to new knowledge and
understandings—the word research can suggest a mystical activity that is somehow removed from
everyday life. Many people imagine researchers to be aloof individuals who seclude themselves in laboratories, scholarly libraries, or the ivory towers of large universities. In fact, research is often a practical enterprise that—given appropriate tools—any rational, conscientious individual can conduct. In
this chapter we lay out the nature of true research and describe the general tools that make it possible.
WHAT RESEARCH IS NOT
Following are three statements that describe what research is not. Accompanying each statement
is an example that illustrates a common misconception about research.
1. Research is not merely gathering information. A sixth grader comes home from school
and tells her parents, “The teacher sent us to the library today to do research, and I learned a lot
C h a p ter 1 The N atu re and Tool s of Re se arch
about black holes.” For this student, research means going to the library to find a few facts. This
might be information discovery, or it might be learning reference skills. But it certainly is not, as the
teacher labeled it, research.
2. Research is not merely rummaging around for hard-to-locate information. The house
across the street is for sale. You consider buying it and call your realtor to find out how much
someone else might pay you for your current home. “I’ll have to do some research to determine
the fair market value of your property,” the realtor tells you. What the realtor calls doing “some
research” means, of course, reviewing information about recent sales of properties comparable
to yours; this information will help the realtor zero in on a reasonable asking price for your own
home. Such an activity involves little more than searching through various files or websites to
discover what the realtor previously did not know. Rummaging—whether through records in
one’s own office, at a library, or on the Internet—is not research. It is more accurately called an
exercise in self-enlightenment.
3. Research is not merely transporting facts from one location to another. A college student reads several articles about the mysterious Dark Lady in William Shakespeare’s sonnets and
then writes a “research paper” describing various scholars’ suggestions of who the lady might
have been. Although the student does, indeed, go through certain activities associated with
formal research—such as collecting information, organizing it in a certain way for presentation
to others, supporting statements with documentation, and referencing statements properly—
these activities do not add up to true research. The student has missed the essence of research:
the interpretation of data. Nowhere in the paper does the student say, in effect, “These facts
I have gathered seem to indicate such-and-such about the Dark Lady.” Nowhere does the student
interpret and draw conclusions from the facts. This student is approaching genuine research;
however, the mere compilation of facts, presented with reference citations and arranged in a
logical sequence—no matter how polished and appealing the format—misses genuine research
by a hair. Such activity might more realistically be called fact transcription, fact documentation, fact
organization, or fact summarization.
Going a little further, this student would have traveled from one world to another: from
the world of mere transportation of facts to the world of interpretation of facts. The difference
between the two worlds is the distinction between transference of information and genuine
research—a distinction that is critical for novice researchers to understand.
WHAT RESEARCH IS
Research is a systematic process of collecting, analyzing, and interpreting information—data—
in order to increase our understanding of a phenomenon about which we are interested or concerned.1 People often use a systematic approach when they collect and interpret information to
solve the small problems of daily living. Here, however, we focus on formal research, research in
which we intentionally set out to enhance our understanding of a phenomenon and expect to
communicate what we discover to the larger scientific community.
Although research projects vary in complexity and duration, in general research involves
seven distinct steps, shown in Figure 1.1. We now look at each of these steps more closely.
1. The researcher begins with a problem—an unanswered question. Everywhere
we look, we see things that cause us to wonder, to speculate, to ask questions. And by asking questions, we strike a spark that ignites a chain reaction leading to the research process.
Some people in academia use the term research more broadly to include deriving new equations or abstract principles from
existing equations or principles through a sequence of mathematically logical and valid steps. Such an activity can be quite
intellectually challenging, of course, and is often at the heart of doctoral dissertations and scholarly journal articles in mathematics, physics, and related disciplines. In this book, however, we use the term research more narrowly to refer to empirical
research—research that involves the collection and analysis of new data.
Wh a t R e se arch Is
FI GU R E 1. 1 ■
The Research Cycle
The researcher begins
with a problem—an
The researcher interprets
the meaning of the data
as they relate to the
problem and its
The researcher collects, organizes,
and analyzes data related to
the problem and its subproblems.
The researcher clearly and
specifically articulates the
goal of the research endeavor.
The researcher often divides
the principal problem into more
The researcher develops a specific
plan for addressing the problem
and its subproblems.
The researcher identifies
hypotheses and assumptions
that underlie the research effort.
An inquisitive mind is the beginning impetus for research; as one popular tabloid puts it, “Inquiring minds want to know!”
Look around you. Consider unresolved situations that evoke these questions: What is suchand-such a situation like? Why does such-and-such a phenomenon occur? What does it all
mean? With questions like these, research begins.
2. The researcher clearly and specifically articulates the goal of the research endeavor.
A clear, unambiguous statement of the problem one will address is critical. This statement is an
exercise in intellectual honesty: The ultimate goal of the research must be set forth in a grammatically complete sentence that specifically and precisely answers the question, “What problem
do you intend to solve?” When you describe your objective in clear, concrete terms, you have a
good idea of what you need to accomplish and can direct your efforts accordingly.
3. The researcher often divides the principal problem into more manageable subproblems.
From a design standpoint, it is often helpful to break a main research problem into several subproblems that, when solved, can resolve the main problem.
Breaking down principal problems into small, easily solvable subproblems is a strategy
we use in everyday living. For example, suppose you want to drive from your hometown to
a town many miles or kilometers away. Your principal goal is to get from one location to the
C h a p ter 1 The N atu re and Tool s of Re se arch
other as expeditiously as possible. You soon realize, however, that the problem involves several
How do I get from Town A to Town B?
1. What route appears to be the most direct one?
2. Is the most direct one also the quickest one? If not, what route
might take the least amount of time?
3. Which is more important to me: minimizing my travel time or
minimizing my energy consumption?
4. At what critical junctions in my chosen route must I turn right
What seems like a single question can be divided into several smaller questions that must be
addressed before the principal question can be resolved.
So it is with most research problems. By closely inspecting the principal problem, the researcher often uncovers important subproblems. By addressing each of the subproblems, the
researcher can more easily address the main problem. If a researcher doesn’t take the time or
trouble to isolate the lesser problems within the major problem, the overall research project can
become cumbersome and difficult to manage.
Identifying and clearly articulating the problem and its subproblems are the essential starting
points for formal research. Accordingly, we discuss these processes in depth in Chapter 2.
4. The researcher identifies hypotheses and assumptions that underlie the research
effort. Having stated the problem and its attendant subproblems, the researcher sometimes
forms one or more hypotheses about what he or she may discover. A hypothesis is a logical
supposition, a reasonable guess, an educated conjecture. It provides a tentative explanation for a
phenomenon under investigation. It may direct your thinking to possible sources of information
that will aid in resolving one or more subproblems and, as a result, may also help you resolve the
principal research problem.
Hypotheses are certainly not unique to research. In your everyday life, if something happens, you immediately try to account for its cause by making some reasonable conjectures. For
example, imagine that you come home after dark, open your front door, and reach inside for the
switch that turns on a nearby table lamp. Your fingers find the switch. You flip it. No light. At
this point, you identify several hypotheses regarding the lamp’s failure:
Hypothesis 1: A recent storm has disrupted your access to electrical power.
Hypothesis 2: The bulb has burned out.
Hypothesis 3: The lamp isn’t securely plugged into the wall outlet.
Hypothesis 4: The wire from the lamp to the wall outlet is defective.
Hypothesis 5: You forgot to pay your electric bill.
Each of these hypotheses hints at a strategy for acquiring information that may resolve the
nonfunctioning-lamp problem. For instance, to test Hypothesis 1, you might look outside to
see whether your neighbors have lights, and to test Hypothesis 2, you might replace the current
light bulb with a new one.
Hypotheses in a research project are as tentative as those for a nonfunctioning table lamp. For
example, a biologist might speculate that certain human-made chemical compounds increase
the frequency of birth defects in frogs. A psychologist might speculate that certain personality
traits lead people to show predominantly liberal or conservative voting patterns. A marketing
researcher might speculate that humor in a television commercial will capture viewers’ attention
and thereby will increase the odds that viewers buy the advertised product. Notice the word
speculate in all of these examples. Good researchers always begin a project with open minds about
what they may—or may not—discover in their data.
Hypotheses—predictions—are an essential ingredient in certain kinds of research, especially experimental research (see Chapter 7). To a lesser degree, they might guide other forms
Wh a t R e se arch Is
of research as well, but they are intentionally not identified in the early stages of some kinds of
qualitative research (e.g., see the discussion of grounded theory studies in Chapter 9).
Whereas a hypothesis involves a prediction that may or may not be supported by the data,
an assumption is a condition that is taken for granted, without which the research project
would be pointless. Careful researchers—certainly those conducting research in an academic
environment—set forth a statement of their assumptions as the bedrock upon which their study
rests. For example, imagine that your problem is to investigate whether students learn the unique
grammatical structures of a language more quickly by studying only one foreign language at a
time or by studying two foreign languages concurrently. What assumptions would underlie such
a problem? At a minimum, you must assume that
• The teachers used in the study are competent to teach the language or languages in question and have mastered the grammatical structures of the language(s) they are teaching.
• The students taking part in the research are capable of mastering the unique grammatical
structures of any language(s) they are studying.
• The languages selected for the study have sufficiently different grammatical structures that
students might reasonably learn to distinguish between them.
Aside from such basic ideas as these, however, careful researchers state their assumptions, so that
other people inspecting the research project can evaluate it in accordance with their own assumptions. For the beginning researcher, it is better to be overly explicit than to take too much for
5. The researcher develops a specific plan for addressing the problem and its subproblems.
Research is not a blind excursion into the unknown, with the hope that the data necessary to
address the research problem will magically emerge. It is, instead, a carefully planned itinerary
of the route you intend to take in order to reach your final destination—your research goal. Consider the title of this text: Practical Research: Planning and Design. The last three words—Planning
and Design—are especially important ones. Researchers plan their overall research design and
specific research methods in a purposeful way so that they can acquire data relevant to their
research problem and subproblems. Depending on the research question, different designs and
methods are more or less appropriate.
In the formative stages of a research project, much can be decided: Are any existing data
directly relevant to the research problem? If so, where are they, and are you likely to have access
to them? If the needed data don’t currently exist, how might you generate them? And later, after
you have acquired the data you need, what will you do with them?2 Such questions merely hint
at the fact that planning and design cannot be postponed. Each of the questions just listed—and
many more—must have an answer early in the research process. In Chapter 4, we discuss several
general issues related to research planning. Then, beginning in Chapter 6, we describe strategies
related to various research methodologies.
6. The researcher collects, organizes, and analyzes data related to the problem and its
subproblems. After a researcher has isolated the problem, divided it into appropriate subproblems, identified hypotheses and assumptions, and chosen a suitable design and methodology,
the next step is to collect whatever data might be relevant to the problem and to organize and
analyze them in meaningful ways.
The data collected in research studies take one of two general forms. Quantitative research
involves looking at amounts, or quantities, of one or more variables of interest. A quantitative researcher typically tries to measure variables in some numerical way, perhaps by using
As should be apparent in the questions posed in this paragraph, we are using the word data as a plural noun; for instance,
we ask “Where are the data?” rather than “Where is the data?” Contrary to popular usage of the term as a singular noun, data
(which has its origins in Latin) refers to two or more pieces of information. A single piece of information is known as a datum,
or sometimes as a data point.
C h a p ter 1 The N atu re and Tool s of Re se arch
commonly accepted measures of the physical world (e.g., rulers, thermometers, oscilloscopes) or
carefully designed measures of psychological characteristics or behaviors (e.g., tests, questionnaires, rating scales).
In contrast, qualitative research involves looking at characteristics, or qualities, that cannot
be entirely reduced to numerical values. A qualitative researcher typically aims to examine the
many nuances and complexities of a particular phenomenon. You are most likely to see qualitative research in studies of complex human situations (e.g., people’s in-depth perspectives about a
particular issue, the behaviors and values of a particular cultural group) or complex human creations (e.g., television commercials, works of art). Qualitative research is not limited to research
problems involving human beings, however. For instance, some biologists study, in a distinctly
qualitative manner, the complex social behaviors of other animal species; Dian Fossey’s work
with gorillas and Jane Goodall’s studies of chimpanzees are two well-known examples (e.g., see
Fossey, 1983; Goodall, 1986).
The two kinds of data—quantitative and qualitative—often require distinctly different research methods and data analysis strategies. Accordingly, three of the book’s subsequent chapters
focus predominantly on quantitative techniques (see Chapters 6, 7, and 8) and three others focus
largely on qualitative techniques (see Chapters 9, 10, and 11). Nevertheless, we urge you not to
think of the quantitative–qualitative distinction as a mutually exclusive, it-has-to-be-one-thing-orthe-other dichotomy. Many researchers collect both quantitative and qualitative data in a single
research project—an approach sometimes known as mixed-methods research (see Chapter 12).
Good researchers tend to be eclectic researchers who draw from diverse methodologies and data
sources in order to best address their research problems and questions (e.g., see Gorard, 2010;
Onwuegbuzie & Leech, 2005).
7. The researcher interprets the meaning of the data as they relate to the problem and its
subproblems. Quantitative and qualitative data are, in and of themselves, only data—nothing
more. The significance of the data depends on how the researcher extracts meaning from them.
In research, uninterpreted data are worthless: They can never help us answer the questions we
Yet researchers must recognize and come to terms with the subjective and dynamic nature
of interpretation. Consider, for example, the many books written on the assassination of U.S.
President John F. Kennedy. Different historians have studied the same events: One may interpret
them one way, and another may arrive at a very different conclusion. Which one is right? Perhaps
they both are; perhaps neither is. Both may have merely posed new problems for other historians
to try to resolve. Different minds often find different meanings in the same set of facts.
Once we believed that clocks measured time and that yardsticks measured space. In one sense,
they still do. We further assumed that time and space were two different entities. Then along
came Einstein’s theory of relativity, and time and space became locked into one concept: the
time–space continuum. What’s the difference between the old perspective and the new one? It’s
the way we think about, or interpret, the same information. The realities of time and space have
not changed; the way we interpret them has.
Data demand interpretation. But no rule, formula, or algorithm can lead the researcher unerringly to a correct interpretation. Interpretation is inevitably a somewhat subjective process that
depends on the researcher’s hypotheses, assumptions, and logical reasoning processes.
Now think about how we began this chapter. We suggested that certain activities cannot
accurately be called research. At this point you can understand why. None of those activities
demands that the researcher draw any conclusions or make any interpretations of the data.
We must emphasize two important points related to the seven-step process just described.
First, the process is iterative: A researcher sometimes needs to move back and forth between
two or more steps along the way. For example, while developing a specific plan for a project
(Step 5), a researcher might realize that a genuine resolution of the research problem requires
addressing a subproblem not previously identified (Step 3). And while interpreting the collected data (Step 7), a researcher may decide that additional data are needed to fully resolve
the problem (Step 6).
P h i l o sophi cal Assu m pti ons U nde rl yi ng Re se arch Me thodo logies
Second, the process is cyclical. The final step in the process depicted in Figure 1.1—
interpretation of the data—is not really the final step at all. Only rarely is a research project a one-shot effort that completely resolves a problem. For instance, even with the best
of data, hypotheses in a research project are rarely proved or disproved—and thus research
questions are rarely answered—beyond a shadow of a doubt. Instead, hypotheses are either
supported or not supported by the data. If the data are consistent with a particular hypothesis,
the researcher can make a case that the hypothesis probably has some merit and should be
taken seriously. In contrast, if the data run contrary to a hypothesis, the researcher rejects the
hypothesis and turns to other hypotheses as being more likely explanations of the phenomenon in question. In either case, one or more additional, follow-up studies are called for.
Ultimately, then, most research studies don’t bring total closure to a research problem.
There is no obvious end point—no point at which a researcher can say “Voila! I’ve completely
answered the question about which I’m concerned.” Instead, research typically involves a cycle—
or more accurately, a helix (spiral)—in which one study spawns additional, follow-up studies. In
exploring a topic, one comes across additional problems that need resolving, and so the process
must begin anew. Research begets more research.
To view research in this way is to invest it with a dynamic quality that is its true nature—a
far cry from the conventional view, which sees research as a one-time undertaking that is static,
self-contained, an end in itself. Here we see another difference between true research and the
nonexamples of research presented earlier in the chapter. Every researcher soon learns that genuine research is likely to yield as many problems as it resolves. Such is the nature of the acquisition
PHILOSOPHICAL ASSUMPTIONS UNDERLYING
Let’s return to Step 4 in the research process: The researcher identifies hypotheses and assumptions
that underlie the research effort. The assumptions underlying a research project are sometimes so
seemingly self-evident that a researcher may think it unnecessary to mention them. In fact, the
researcher may not even be consciously aware of them! For example, two general assumptions
underlie many research studies:
■ The phenomenon under investigation is somewhat lawful and predictable; it is not com-
prised of completely random events.
■ Cause-and-effect relationships can account for certain patterns observed in the
But are such assumptions justified? Is the world a lawful place, with some things definitely causing or influencing others? Or are definitive laws and cause-and-effect relationships nothing more
than figments of our fertile human imaginations?
As we consider such questions, it is helpful to distinguish among different philosophical orientations3 that point researchers in somewhat different directions in their quests to make sense of
our physical, social, and psychological worlds. Historically, a good deal of research in the natural
sciences has been driven by a perspective known as positivism. Positivists believe that, with appropriate measurement tools, scientists can objectively uncover absolute, undeniable truths about
cause-and-effect relationships within the physical world and human experience.
In the social sciences, most researchers have been less self-assured and more tentative,
especially within the past few decades. Some social scientists take a perspective known as
postpositivism, believing that true objectivity in seeking absolute truths can be an elusive
goal. Although researchers might strive for objectivity in their collection and interpretation
Some writers use terms such as worldviews, epistemologies, or paradigms instead of the term philosophical orientations.
C h a p ter 1 The N atu re and Tool s of Re se arch
of data, they inevitably bring certain biases to their investigations—perhaps biases regarding
the best ways to measure certain variables or the most logical inferences to draw from patterns
within the data. From a postpositivist perspective, progress toward genuine understandings
of physical, social, and psychological phenomena tends to be gradual and probabilistic. For
example, recall the earlier discussion of hypotheses being either supported or not supported by
data. Postpositivists don’t say, “I’ve just proven such-and-such.” Rather, they’re more likely to
say, “This increases the probability that such-and-such is true.”
Still other researchers have abandoned any idea that absolute truths are somewhere “out
there” in the world, waiting to be discovered. In this perspective, known as constructivism, the
“realities” researchers identify are nothing more than human creations that can be helpful in finding subjective meanings within the data collected. Constructivists not only acknowledge that
they bring certain biases to their research endeavors but also try to be as upfront as possible about
these biases. The emphasis on subjectivity and bias—rather than objectivity—applies to the
phenomena that constructivist researchers study as well. By and large, constructivists focus their
inquiries on people’s perceptions and interpretations of various phenomena, including individuals’
behaviors, group processes, and cultural practices.
Many of the quantitative methodologies described in this book have postpositivist, probabilistic underpinnings—a fact that becomes especially evident in the discussion of statistics in
Chapter 8. In contrast, some qualitative methodologies have a distinctly constructivist bent,
with a focus on ascertaining people’s beliefs about truth, rather than trying to pin down absolute,
objective truths that might not exist at all.
Yet once again we urge you not to think of quantitative research and qualitative research
as reflecting a mutually exclusive, either-this-or-that dichotomy. For instance, some quantitative
researchers approach a research problem from a constructivist framework, and some qualitative
researchers tend to think in a postpositivist manner. Many researchers acknowledge both that
(a) absolute truths regarding various phenomena may actually exist—even if they are exceedingly difficult to discover—and (b) human beings’ self-constructed beliefs about those phenomena are legitimate objects of study in their own right. You might see the labels pragmatism
and realism used in reference to such a philosophical orientation (e.g., see R. B. Johnson &
Onwuegbuzie, 2004; Maxwell & Mittapalli, 2010).
TOOLS OF RESEARCH
Every professional needs specialized tools in order to work effectively. Without hammer and
saw, the carpenter is out of business; without scalpel or forceps, the surgeon cannot practice.
Researchers, likewise, have their own set of tools to carry out their plans.
The tools that researchers use to achieve their research goals can vary considerably depending
on the discipline. A microbiologist needs a microscope and culture media; an attorney needs a
library of legal decisions and statute law. By and large, we do not discuss such discipline-specific
tools in this book. Rather, our concern here is with general tools of research that the great majority of researchers of all disciplines need in order to collect data and derive meaningful conclusions.
We should be careful not to equate the tools of research with the methodology of research. A
research tool is a specific mechanism or strategy the researcher uses to collect, manipulate, or
interpret data. The research methodology is the general approach the researcher takes in carrying out the research project; to some extent, this approach dictates the particular tools the
Confusion between the tool and the research method is immediately recognizable. Such
phrases as “library research” and “statistical research” are telltale signs and largely meaningless
terms. They suggest a failure to understand the nature of formal research, as well as a failure to
differentiate between tool and method. The library is merely a place for locating or discovering
certain data that will be analyzed and interpreted at some point in the research process. Likewise,
statistics merely provide ways to summarize and analyze data, thereby allowing us to see patterns
within the data more clearly.
To ol s o f Re se arch
Six general tools of research are these:
1. The library and its resources
2. Computer technology
6. The human mind
In the following sections, we look more closely at each of these general tools.
The Library and Its Resources
Historically, many literate human societies used libraries to assemble and store their collective
knowledge. For example, in the seventh century B.C., the ancient Assyrians’ Library of Nineveh
contained 20,000 to 30,000 tablets, and in the second century A.D., the Romans’ Library of Celsus
in Ephesus housed more than 12,000 papyrus scrolls and, in later years, parchment books as well.4
Until the past few decades, libraries were primarily repositories of concrete, physical representations of knowledge—clay tablets, scrolls, manuscripts, books, journals, films, and the like.
For the most part, any society’s collective knowledge expanded rather slowly and could seemingly be contained within masonry walls. But by the latter half of the 20th century, people’s
knowledge about their physical and social worlds began to increase many times over, and at the
present time it continues to increase at an astounding rate. In response, libraries have evolved
in important ways. First, they have made use of many emerging technologies (e.g., microforms,
CDs, DVDs, online databases) to store information in more compact forms. Second, they have
provided increasingly fast and efficient means of locating and accessing information on virtually any topic. And third, many of them have made catalogs of their holdings available on the
Internet. The libraries of today—especially university libraries—extend far beyond their local,
We explore efficient use of a library and its resources in depth in Chapter 3. For now, we
simply want to stress that the library is—and must be—one of the most valuable tools in any
As a research tool, the personal computer is now commonplace. Personal computers have become
increasingly compact and portable—first in the form of laptops and more recently in the forms
of iPads, other tablet computers, and smartphones. In addition, computer software packages and
applications have become increasingly user friendly, such that novice researchers can easily take
advantage of them. But like any tool—no matter how powerful—computer technology has its
limitations. Yes, computers can certainly calculate, compare, search, retrieve, sort, and organize
data more efficiently and accurately than you can. But in their present stage of development,
they depend largely on people to give them directions about what to do.
A computer is not a miracle worker—it cannot do your thinking for you. It can, however, be
a fast and faithful assistant. When told exactly what to do, it is one of the researcher’s best friends.
Table 1.1 provides suggestions for how you might use computer technology as a research tool.
Especially when conducting quantitative research, a researcher needs a systematic way of measuring the phenomena under investigation. Some common, everyday measurement instruments—
rulers, scales, stopwatches—can occasionally be helpful for measuring easily observable variables,
Many academic scholars would instead say “seventh century BCE” and “second century CE” in this sentence, referring to the
more religiously neutral terms Before Common Era and Common Era. However, we suspect that some of our readers are unfamiliar
with these terms, hence our use of the more traditional ones.
C h a p ter 1 The N atu re and Tool s of Re se arch
TAB L E 1 . 1 ■ The Computer as a Research Tool
Part of the Study
Relevant Technological Support Tools
Planning the study
Study implementation and
Brainstorming assistance—software used to help generate and organize ideas related to the
research problem, research strategies, or both.
● Outlining assistance—software used to help structure various aspects of the study and focus
● Project management assistance—software used to schedule and coordinate varied tasks that
must occur in a timely manner.
● Budget assistance—spreadsheet software used to help in outlining, estimating, and monitoring
the potential costs involved in the research effort.
Literature identification assistance—online databases used to help identify relevant research
studies to be considered during the formative stages of the research endeavor.
● Communication assistance—computer technology used to communicate with other researchers who are pursuing similar topics (e.g., e-mail, Skype, electronic bulletin boards, list servers).
● Writing assistance—software used to facilitate the writing, editing, formatting, and citation
management of the literature review.
Materials production assistance—software used to develop instructional materials, visual
displays, simulations, or other stimuli to be used in experimental interventions.
● Experimental control assistance—software used to physically control the effects of specific
variables and to minimize the influence of potentially confounding variables.
● Survey distribution assistance—databases and word processing software used in combination
to send specific communications to a targeted population.
● Online data collection assistance—websites used to conduct surveys and certain other types
of studies on the Internet.
● Data collection assistance—software used to take field notes or to monitor specific types
of responses given by participants in a study.
Organizational assistance—software used to assemble, categorize, code, integrate, and search
potentially huge data sets (such as qualitative interview data or open-ended responses to
● Conceptual assistance—software used to write and store ongoing reflections about data
or to construct theories that integrate research findings.
● Statistical assistance—statistical and spreadsheet software packages used to categorize
and analyze various types of data sets.
● Graphic production assistance—software used to depict data in graphic form to facilitate
Analysis and interpretation
Communication assistance—telecommunication software used to distribute and discuss
research findings and initial interpretations with colleagues and to receive their comments
● Writing and editing assistance—word processing software used to write and edit successive
drafts of the final report.
● Dissemination assistance—desktop publishing software and poster creation software used
to produce professional-looking documents and posters that can be displayed or distributed
at conferences and elsewhere.
● Presentation graphics assistance—presentation software used to create static and animated
slides for conference presentations.
● Networking assistance—blogs, social networking sites, and other Internet-based mechanisms
used to communicate one’s findings to a wider audience and to generate discussion for
follow-up studies by others in the field.
such as length, weight, or time. But in most cases, a researcher needs one or more specialized
instruments. For example, an astronomer might need a high-powered telescope to detect patterns of light in the night sky, and a neurophysiologist might need a magnetic resonance imaging (MRI) machine to detect and measure neural activity in the brain.
In quantitative research, social and psychological phenomena require measurement as well,
even though they have no concrete, easily observable basis in the physical world. For example, an
economist might use the Dow-Jones Industrial Average or NASDAQ index to track economic
growth over time, a sociologist might use a questionnaire to assess people’s attitudes about
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marriage and divorce, and an educational researcher might use an achievement test to measure
the extent to which school children have learned something. Finding or developing appropriate measurement instruments for social and psychological phenomena can sometimes be quite a
challenge. Thus, we explore measurement strategies in some depth when we discuss the research
planning process in Chapter 4.
Statistics tend to be more useful in some academic disciplines than in others. For instance,
researchers use them quite often in such fields as psychology, medicine, and business; they use
statistics less frequently in such fields as history, musicology, and literature.
Statistics have two principal functions: to help a researcher (a) describe quantitative data
and (b) draw inferences from these data. Descriptive statistics summarize the general nature of
the data obtained—for instance, how certain measured characteristics appear to be “on average,”
how much variability exists within a data set, and how closely two or more characteristics are
associated with one another. In contrast, inferential statistics help the researcher make decisions about the data. For example, they might help a researcher decide whether the differences
observed between two experimental groups are large enough to be attributed to the differing
experimental interventions rather than to a once-in-a-blue-moon fluke. Both of these functions
of statistics ultimately involve summarizing the data in some way.
In the process of summarizing data, statistical analyses often create entities that have no
counterpart in reality. Let’s take a simple example: Four students have part-time jobs on campus. One student works 24 hours a week in the library, a second works 22 hours a week in the
campus bookstore, a third works 12 hours a week in the parking lot, and the fourth works
16 hours a week in the cafeteria. One way of summarizing the students’ work hours is to calculate the arithmetic mean.5 By doing so, we find that the students work, “on average,” 18.5 hours
a week. Although we have learned something about these four students and their working hours,
to some extent we have learned a myth: None of these students has worked exactly 18.5 hours a
week. That figure represents absolutely no fact in the real world.
If statistics offer only an unreality, then why use them? Why create myth out of hard,
demonstrable data? The answer lies in the nature of the human mind. Human beings can cognitively think about only a very limited amount of information at any single point in time.6
Statistics help condense an overwhelming body of data into an amount of information that the
mind can more readily comprehend and deal with. In the process, they can help a researcher
detect patterns and relationships in the data that might otherwise go unnoticed. More generally,
statistics help the human mind comprehend disparate data as an organized whole.
Any researcher who uses statistics must remember that calculating statistical values is not—
and must not be—the final step in a research endeavor. The ultimate question in research is,
What do the data indicate? Statistics yield information about data, but conscientious researchers are
not satisfied until they determine the meaning of this information.
Although a book such as this one cannot provide all of the nitty-gritty details of statistical
analysis, we give you an overview of potentially useful statistical techniques in Chapter 8.
One of humankind’s greatest achievements is language. Not only does it allow us to communicate with one another but it also enables us to think more effectively. People can often think
more clearly and efficiently about a topic when they can represent their thoughts in their heads
with specific words and phrases.
When the word arithmetic is used as an adjective, as it is here, it is pronounced with emphasis on the third syllable
If you have some background in human memory and cognition, you may realize that we are talking about the limited capacity
of working memory here (e.g., see Cowan, 2010; G. A. Miller, 1956).
C h a p ter 1 The N atu re and Tool s of Re se arch
For example, imagine that you’re driving along a country road. In a field to your left, you
see an object with the following characteristics:
■ Black and white in color, in a splotchy pattern
■ Covered with a short, bristly substance
■ Appended at one end by something similar in appearance to a paintbrush
■ Appended at the other end by a lumpy thing with four smaller things coming out of its
top (two soft and floppy; two hard, curved, and pointed)
■ Held up from the ground by four spindly sticks, two at each end
Unless you have spent most of your life living under a rock, you would almost certainly identify
this object as a cow.
Words—even those as simple as cow—and the concepts that the words represent enhance our
thinking in several ways (J. E. Ormrod, 2012; also see Jaccard & Jacoby, 2010):
1. Words reduce the world’s complexity. Classifying similar objects and events into categories and assigning specific words to those categories can make our experiences easier
to make sense of. For instance, it’s much easier to think to yourself, “I see a herd of cows,”
than to think, “There is a brown object, covered with bristly stuff, appended by a paintbrush and a lumpy thing, and held up by four sticks. Ah, yes, and I also see a black-andwhite spotted object, covered with bristly stuff, appended by a paintbrush and a lumpy
thing, and held up by four sticks. And over there is a brown-and-white object . . . .”
2. Words allow abstraction of the environment. An object that has bristly stuff, a
paintbrush at one end, a lumpy thing at the other, and four spindly sticks at the bottom
is a concrete entity. The concept cow, however, is more abstract: It connotes such characteristics as female, supplier of milk, and, to the farmer or rancher, economic asset. Concepts
and the labels associated with them allow us to think about our experiences without
necessarily having to consider all of their discrete, concrete characteristics.
3. Words enhance the power of thought. When you are thinking about an object covered
with bristly stuff, appended by a paintbrush and a lumpy thing, held up by four sticks,
and so on, you can think of little else (as mentioned earlier, human beings can think about
only a very limited amount of information at any one time). In contrast, when you simply
think cow, you can easily think about other ideas at the same time and perhaps form connections and interrelationships among them in ways you hadn’t previously considered.
4. Words facilitate generalization and inference drawing in new situations. When
we learn a new concept, we associate certain characteristics with it. Then, when we encounter a new instance of the concept, we can draw on our knowledge of associated characteristics to make assumptions and inferences about the new instance. For instance, if
you see a herd of cattle as you drive through the countryside, you can infer that you are
passing through either dairy or beef country, depending on whether you see large udders hanging down between two of the spindly sticks.
Just as cow helps us categorize certain experiences into a single idea, so, too, does the terminology of your discipline help you interpret and understand your observations. The words tempo,
timbre, and perfect pitch are useful to the musicologist. Such terms as central business district, folded
mountain, and distance to k have special meaning for the geographer. The terms lesson plan, portfolio,
and charter school communicate a great deal to the educator. Learning the specialized terminology
of your field is indispensable to conducting a research study, grounding it in prior theories and
research, and communicating your results to others.
Two outward manifestations of language usage are also helpful to the researcher: (a) knowing two or more languages and (b) writing one’s thoughts either on paper or in electronic form.
The Benefits of Knowing Two or More Languages
It should go without saying that
not all important research is reported in a researcher’s native tongue. Accordingly, many doctoral
programs require that students demonstrate reading competency in one or two foreign languages
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in addition to their own language. The choice of these languages is usually linked to the area of
The language requirement is a reasonable one. Research is and always has been a worldwide
endeavor. For example, researchers in Japan have made gigantic strides in electronics and robotics. And two of the most influential theorists in child development today—Jean Piaget and Lev
Vygotsky—wrote in French and Russian, respectively. Many new discoveries are first reported in
a researcher’s native language.
Knowing two or more languages has a second benefit as well: Words in a second language
may capture the meaning of certain phenomenon in ways that one’s native tongue may not. For
example, the German word Gestalt—which roughly means “organized whole”—has no direct
equivalent in English. Thus, many English-speaking psychologists use this word when describing the nature of human perception, because people often perceive organized patterns and
structures in visual data that, in the objective physical world, are not organized. Likewise, the
Zulu word ubuntu defies an easy translation into English. This word—which reflects the belief
that people become fully human largely through regularly caring for others and contributing
to the common good—can help anthropologists and other social scientists capture a cultural
worldview quite different from the more self-centered perspective so prevalent in mainstream
The Importance of Writing
To be generally accessible to the larger scientific community
and ultimately to society as a whole, all research must eventually be presented as a written
document—a research report—either on paper or in electronic form. A basic requirement for
writing such a report is the ability to use language in a clear, coherent manner.
Although a good deal of conventional wisdom tells us that clear thinking precedes clear writing, in fact writing can be a productive form of thinking in and of itself. When you write your
ideas down on paper, you do several things:
■ You must identify the specific ideas you do and do not know about your topic.
■ You must clarify and organize your thoughts sufficiently to communicate them to your
■ You may detect gaps and logical flaws in your thinking.
Perhaps it isn’t surprising, then, that writing about a topic actually enhances the writer’s understanding of the topic (e.g., Kellogg, 1994; Shanahan, 2004).
If you wait until all your thoughts are clear before you start writing, you may never begin.
Thus, we recommend that you start writing parts of your research proposal or report as soon as
possible. Begin with a title and a purpose statement for your study. Commit your title to paper;
keep it in plain sight as you focus your ideas. Although you may very well change the title later
as your research proceeds, creating a working title in the early stages can provide both focus and
direction. And when you can draft a clear and concise statement that begins, “The purpose of this
study is . . .,” you are well on your way to planning a focused research study.
PRACTICAL APPLICATION Communicating Effectively
In our own experiences, we authors have found that most students have a great deal to learn
about what good writing entails. Yet we also know that with effort, practice, mentoring, and
regular feedback, students can learn to write more effectively. Subsequent chapters present specific strategies for writing literature reviews (Chapter 3), research proposals (Chapter 5), and
research reports (Chapter 13). Here we offer general strategies for writing in ways that can
help you clearly communicate your ideas and reasoning to others. We also offer suggestions for
making the best use of word processing software.
C h a p ter 1 The N atu re and Tool s of Re se arch
GUIDELINES Writing to Communicate
The following guidelines are based on techniques often seen in effective writing. Furthermore,
such techniques have consistently been shown to facilitate readers’ comprehension of what people have written (e.g., see J. E. Ormrod, 2012).
1. Be specific and precise. Precision is of utmost importance in all aspects of a research
endeavor, including writing. Choose your words and phrases carefully so that you communicate
your exact meaning, not some vague approximation. Many books and online resources offer suggestions for writing clear, concise sentences and combining them into unified and coherent paragraphs (e.g., see the sources in the “For Further Reading” list at the end of the chapter).
2. Continually keep in mind your primary objective in writing your paper, and focus
your discussion accordingly. All too often, novice researchers try to include everything they
have learned—both from their literature review and from their data analysis—in their research
reports. But ultimately, everything you say should relate either directly or indirectly to your research problem. If you can’t think of how something relates, leave it out! You will undoubtedly
have enough things to write about as it is.
3. Provide an overview of what you will be talking about in upcoming pages. Your
readers can more effectively read your work when they know what to expect as they read. Providing an overview of what topics you will discuss and in what order—and possibly also showing
how the various topics interrelate—is known as an advance organizer. As an example, Dinah
Jackson, a doctoral student in educational psychology, was interested in the possible effects of
self-questioning—asking oneself questions about material one is studying—on college students’
note taking. Jackson began her dissertation’s “Review of the Literature” with the following
The first part of this review will examine the theories, frameworks, and experimental research
behind the research on adjunct questioning. Part two will investigate the transition of adjunct
questioning to self-generated questioning. Specific models of self-generated questioning will
be explored, starting with the historical research on question position [and progressing] to
the more contemporary research on individual differences in self-questioning. Part three will
explore some basic research on note taking and tie note taking theory with the research
on self-generated questioning. (Jackson, 1996, p. 17)
4. Organize your ideas into general and more specific categories, and use headings and
subheadings to guide your readers through your discussion of these categories. We authors
have read many student research reports that seem to wander aimlessly and unpredictably from
one thought to another, without any obvious organizational structure directing the flow of ideas.
Using headings and subheadings is one simple way to provide an organizational structure for
your writing and to make that structure crystal clear to others.
5. Use concrete examples to make abstract ideas more understandable. There’s a fine line
between being abstract and being vague. Even as scholars who have worked in our respective academic disciplines for many years, we authors still find that we can more easily understand something when the writer gives us a concrete example to illustrate an abstract idea. As an example,
we return to Jackson’s dissertation on self-questioning and class note taking. Jackson made the
point that how a researcher evaluates, or codes, the content of students’ class notes will affect what
the researcher discovers about those notes. More specifically, she argued that only a superficial
coding scheme (e.g., counting the number of main ideas included in notes) would fail to capture
the true quality of the notes. She clarified her point with a concrete example:
For example, while listening to the same lecture, Student A may record only an outline of the
lecture, whereas Student B may record an outline, examples, definitions, and mnemonics. If a
researcher only considered the number of main ideas that students included in their notes,
then both sets of notes might be considered equivalent, despite the fact that the two sets differ
considerably in the type of material recorded. (Jackson, 1996, p. 9)
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6. Use figures and tables to help you more effectively present or organize your ideas
and findings. Although the bulk of your research proposal or report will almost certainly
be prose, in many cases it might be helpful to present some information in figure or table
form. For example, as you read this book, look at the variety of mechanisms we use to accompany our prose, including art, diagrams, graphs, and summarizing tables. We hope
you will agree that these mechanisms help you understand and organize some of the ideas
7. At the conclusion of a chapter or major section, summarize what you have said. You
will probably be presenting a great deal of information in any research proposal or report that
you write. Summarizing what you have said in preceding paragraphs or pages helps your readers
identify the things that are, in your mind, the most important things for them to remember. For
example, in a dissertation that examined children’s beliefs about the mental processes involved
in reading, Debby Zambo summarized a lengthy discussion about the children’s understanding
of what it means to pay attention:
In sum, the students understand attention to be a mental process. They know their attention
is inconsistent and affected by emotions and interest. They also realize that the right level of
material, amount of information, and length of time helps their attention. The stillness of reading
is difficult for some of the students but calming for others, and they appear to know this, and
to know when reading will be difficult and when it will be calming. This idea is contrary to what
has been written in the literature about struggling readers. (Zambo, 2003, p. 68)
8. Anticipate that you will almost certainly have to write multiple drafts. All too often, we authors have had students submit research proposals, theses, or dissertations with the
assumption that they have finished their task. Such students have invariably been disappointed—
sometimes even outraged—when we have asked them to revise their work, usually several
times. The need to write multiple drafts applies not only to novice researchers but to experienced scholars as well. For instance, we would hate to count the number of times this book
has undergone revision—certainly far more often than the label “eleventh edition” indicates!
Multiple revisions enable you to reflect on and critically evaluate your own writing, revise and
refocus awkward passages, get feedback from peers and advisors who can point out where a
manuscript has gaps or lacks clarity, and in other ways ensure that the final version is as clear
and precise as possible.
9. Fastidiously check to be sure that your final draft uses appropriate grammar and
punctuation, and check your spelling. Appropriate grammar, punctuation, and spelling are
not just bothersome formalities. On the contrary, they help you better communicate your meanings. For example, a colon announces that what follows it explains the immediately preceding
statement; a semicolon communicates that a sentence includes two independent clauses (as the
semicolon in this sentence does!).
Correct grammar, punctuation, and spelling are important for another reason as well: They
communicate to others that you are a careful and disciplined scholar whose thoughts and work
are worth reading about. If, instead, you mispel menny of yur words—as we our doing in this
sentance—your reeders may quikly discredit you as a sloppy resercher who shuldn’t be taken
Many style manuals, such as those in the “For Further Reading” list at the end of this chapter,
have sections dealing with correct punctuation and grammar. In addition, dictionaries and word
processing spell-check functions can obviously assist you in your spelling.
Using the Tools in Word Processing Software
Most of our readers know the basics of using word processing software—for instance, how to
“copy,” “paste,” and “save”; how to choose a particular font and font size; and how to format text
as italicized, underlined, or boldface. Following are specific features and tools that you may not
C h a p ter 1 The N atu re and Tool s of Re se arch
have routinely used in previous writing projects but that can be quite useful in writing research
An “outlining” feature lets you create bullets and subbullets to organize
your thoughts. (In Microsoft Word, you can find this tool under the “View” pull-down
menu at the top of the screen.)
■ Setting headers and footers. A “header” is a line or two at the top of the page that appears on …
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