Exploring Research Ninth Edition Neil J. Salkind University of Kansas Boston Columbus Indianapolis New York City San Francisco Amsterdam Cape Town Dubai London Madrid Milan Munich Paris Montréal Toronto Delhi Mexico City São Paulo Sydney Hong Kong Seoul Singapore Taipei Tokyo VP, Product Development: Dickson Musslewhite Director, Content Strategy and Development: Brita Nordin Editor in Chief: Ashley Dodge Managing Editor: Sutapa Mukherjee Sponsoring Editor: Tanimaa Mehra Program Team Lead: Amber Mackey Program Manager: Carly Czech Editorial Assistant: Casseia Lewis Editorial Project Manager: Kristy Zamagni, Lumina Datamatics, Inc. Asset Development Team: LearningMate Solutions, Ltd. 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Library of Congress Cataloging-in-Publication Data Names: Salkind, Neil J. Title: Exploring research / Neil J. Salkind, University of Kansas. Description: Ninth edition. | Boston : Pearson, [2017] | Includes bibliographical references and index. Identifiers: LCCN 2016005281| ISBN 9780134238418 | ISBN 0134238419 Subjects: LCSH: Psychology—Research—Methodology. | Education—Research—Methodology. Classification: LCC BF76.5 .S24 2017 | DDC 150.72—dc22 LC record available at http://lccn.loc.gov/2016005281 10 9 8 7 6 5 4 3 2 1 Books à la Carte ISBN 13: 978-0-13-423841-8 ISBN 10: 0-13-423841-9 For Sara, Micah, and Ted and my fellow Sharks . . . Happy Laps This page intentionally left blank Contents Preface New to the Edition How This Book Is Organized What’s Special about This Book? A Note to the Instructor How to Use This Book Available Instructor Resources A Big Thanks 1 The Role and Importance of Research ix ix ix x x xi xi xi 1 Say Hello to Research! 1 What Research Is and What It Isn’t 2 A Model of Scientific Inquiry Asking the Question Identifying the Important Factors Formulating a Hypothesis Collecting Relevant Information Testing the Hypothesis Working with the Hypothesis Reconsidering the Theory Asking New Questions 4 5 5 5 6 6 6 7 7 Different Types of Research Nonexperimental Research Experimental Research True Experimental Research Quasi-Experimental Research 7 7 9 10 10 What Research Method to Use When? 11 Basic Research versus Applied Research 11 2 The Research Process Coming to Terms 15 From Problem to Solution 15 The Language of Research 17 All about Variables Dependent Variables Independent Variables The Relationship between Independent and Dependent Variables 17 17 18 Other Important Types of Variables 19 Hypotheses The Null Hypothesis The Research Hypothesis Differences between the Null Hypothesis and the Research Hypothesis What Makes a Good Hypothesis? 20 20 21 Samples and Populations 24 19 22 22 The Concept of Significance 3A Selecting a Problem and Reviewing the Research 24 28 Selecting a Problem 29 Defining Your Interests 30 Ideas, Ideas, Ideas (and What to Do with Them) 31 From Idea to Research Question to Hypothesis 32 Reviewing the Literature 33 Using General Sources Using Secondary Sources Using Primary Sources 34 37 38 Reading and Evaluating Research What Does a Research Article Look Like? Criteria for Judging a Research Study 44 44 45 Using Electronic Tools in Your Research Activities Searching Online The Great Search Engines Using Boolean Operators in a Search More about Google Using Bibliographic Database Programs 46 46 46 48 49 51 Using the Internet: Beyond Searches Research Activities and the Internet A Bit about E-Mail An Introduction to News Groups and RSS Feeds And, Just a Bit about Web Sites Using Social Media in Research 53 53 54 55 57 58 Writing the Literature Review 60 3B The Importance of Practicing Ethics in Research 64 A Bit of History 64 Basic Principles of Ethical Research Protection from Harm Maintenance of Privacy Coercion Informed Consent Confidentiality Debriefing Sharing Benefits 65 66 66 66 67 68 68 69 Ensuring High Ethical Standards The Role of Professional Organizations A Summary of Ethical Guidelines Ethics and Children 69 70 70 70 v vi Ethics Regarding Online Research 4 Sampling and Generalizability 71 73 Populations and Samples 73 Probability Sampling Strategies Simple Random Sampling Systematic Sampling Stratified Sampling Cluster Sampling 74 74 76 77 78 Nonprobability Sampling Strategies Convenience Sampling Quota Sampling 78 78 78 Samples, Sample Size, and Sampling Error How Big Is Big? 79 80 5 Measurement, Reliability, and Validity 83 The Measurement Process 83 Levels of Measurement Nominal Ordinal Interval Ratio Continuous versus Discrete Variables What Is All the Fuss? 83 84 85 85 86 86 87 Reliability and Validity: Why They Are Very, Very Important A Conceptual Definition of Reliability Increasing Reliability How Reliability Is Measured Types of Reliability Establishing Reliability: An Example 87 88 89 90 90 92 Validity A Conceptual Definition of Validity Types of Validity Establishing Validity: An Example 93 93 93 95 The Relationship between Reliability and Validity 96 Closing (and Very Important) Thoughts 96 6 Methods of Measuring Behavior Tests and Their Development Why Use Tests? What Tests Look Like Types of Tests Achievement Tests Multiple-Choice Achievement Items Attitude Tests 100 101 101 102 102 102 103 107 Personality Tests Observational Techniques Techniques for Recording Behavior Questionnaires 7 Data Collection and Descriptive Statistics 109 109 110 111 116 Getting Ready for Data Collection 116 The Data Collection Process Constructing Data Collection Forms Coding Data The Ten Commandments of Data Collection 117 117 119 119 Getting Ready for Data Analysis 120 Descriptive Statistics Distributions of Scores Comparing Distributions of Scores Measures of Central Tendency You and Excel—Computing Measures of Central Tendency 121 121 122 122 124 Measures of Variability The Range The Standard Deviation You and Excel—Computing Measures of Variability 125 125 125 126 Understanding Distributions The Normal (Bell-Shaped) Curve The Mean and the Standard Deviation Standard Scores: Computing and Using z Scores What z Scores Really, Really Mean 126 127 127 129 130 8 Introducing Inferential Statistics 132 Say Hello to Inferential Statistics! How Inference Works The Role of Chance The Central Limit Theorem 132 132 133 133 The Idea of Statistical Significance 135 Tests of Significance How a Test of Significance Works t-Test for Independent Means How to Select the Appropriate Test You and Excel—Computing a t-Value for a Test of Independent Means Using the ToolPak Some Other Tests of Significance Working with More Than One Dependent Variable 136 136 137 139 140 140 142 Significance versus Meaningfulness 143 Meta-Analysis How Meta-Analyses Are Done 144 145 vii 9 Nonexperimental Research Descriptive and Correlational Methods 148 Descriptive Research Survey Research How to Conduct Survey Research 148 149 151 Correlational Research The Relationship between Variables What Correlation Coefficients Look Like Computing the Pearson Correlation Coefficient You and Excel—Computing a Correlation Using the ToolPak Interpreting the Pearson Correlation Coefficient 153 153 153 154 10 156 156 Nonexperimental Research Qualitative Methods 160 Conducting Qualitative Research How Qualitative Research Differs 160 160 Research Sources Documentation Archival Records Physical Artifacts Direct Observation Participant Observation Focus Groups 161 161 161 161 161 162 162 Case Studies Some Advantages of the Case Study Method Some Disadvantages of the Case Study Method 163 163 164 Ethnographies 164 Historical Research Conducting Historical Research The Steps in Historical Research Sources of Historical Data Primary or Secondary Sources: Which Are Best? Authenticity and Accuracy The Limitations of Historical Research 165 165 166 166 167 168 169 Qualitative Research Tools 169 11 Pre- and True Experimental Research Methods 171 Experimental Designs Pre-Experimental Designs True Experimental Designs 172 172 173 Internal and External Validity and Experimental Design Threats to Internal Validity Threats to External Validity 175 175 177 Increasing Internal and External Validity Internal and External Validity: A Trade-Off? Controlling Extraneous Variables Matching Use of Homogeneous Groups Analysis of Covariance 12 177 178 178 179 179 179 Quasi-Experimental Research A Close Cousin to Experimental Research 182 The Quasi-Experimental Method 182 Quasi-Experimental Designs The Nonequivalent Control Group Design The Static Group Comparison Single-Subject Designs Multiple Baseline Designs Evaluating Single-Subject Designs Developmental Research The Utility of Follow-Up Studies The Role of Age in Studying Development 183 183 184 184 186 187 187 189 189 13 Writing a Research Proposal 192 The Format of a Research Proposal Appearance 192 193 Evaluating the Studies You Read Criteria for Judging a Research Study 193 194 Planning the Actual Research Selecting a Dependent Variable 195 195 Reviewing a Test Basic Information General Test Information Design and Appearance Reliability Validity Norms Evaluation 197 197 197 197 197 197 197 197 Selecting a Sample Data Collection and Analysis Selecting an Inferential Statistic Protecting Human Subjects 197 198 199 199 14 Writing a Research Manuscript What a Manuscript Looks Like Title Page Abstract Introduction Method Results Discussion 200 200 200 200 201 201 201 201 viii References Appendices Author Notes Footnotes Table Captions Tables Figure Captions Figures Nuts and Bolts 201 202 202 202 202 202 202 202 202 Appendix A: Fifty Excel Shortcuts for the Mac and Windows Appendix B: Sample Data Set Appendix C: Answers to End-of-Chapter Exercises Bibliography Glossary Credits Index 225 228 233 245 247 252 253 Preface I ’ve been very lucky. I have had the privilege of teaching introductory research methods and have been able to share all that I know and continue to learn about this fascinating topic. This ninth edition of Exploring Research reflects much of what has taken place in my classrooms over those years. This book is intended for upper-level undergraduate students and graduate students in their first research methods course in the social, behavioral, and health sciences fields. These students are the primary audience. But, lately, other disciplines have been introducing research methods courses to their curriculum, such as public policy, government, journalism, and related fields, and students there have been using Exploring Research as well. And, recently, even such fields as American Studies and Ethnomusicology have started incorporating the types of methods we talk about here. Exploring Research is intended to provide an introduction to the important topics in the general area of research methods and to do so in a nonintimidating and informative way. The existence of a ninth edition of Exploring Research means that the audience for a straightforward and unassuming presentation of this material still exists, and I believe that audience is growing. I’m grateful for those who have chosen to use this book. New to the Edition Many of the changes are the result of suggestions from students and faculty. Here are the major changes in this ninth edition. Rather than SPSS, whatever data analysis discussions take place, Excel is the tool of choice. This is because Excel is available almost everywhere including colleges, universities, and other institutions and many users of this book already have it installed on their own computers. I am assuming that even the beginning research methods students have some rudimentary computer and Excel skills. More coverage of ethics because this is becoming increasingly important as a topic that beginning researchers need to know about. There’s more on the history of how ethical practices have progressed as well as a brief coverage of some important case studies. After lots of discussion with faculty who have adopted this book, it was decided that the answers to the end-of-chapter questions should go at the end of the book in a separate appendix (Appendix C) of its own. The online sources for more exploration are increased by about 25% as well. Updated and new coverage of software for dealing with qualitative data and the development and refinement of bibliographies. Inserted after many sections are questions that will help the reader summarize the content in that part of the chapter and serve, if so desired, as a taking-off point for discussion. These Test Yourself questions don’t necessarily have a right or a wrong answer—they are there to help facilitate thinking and discussion about the topic at hand. The material on the use of the Internet for research is updated with more information about conducting research and literature reviews online and including new information on how social media can be used in a research context. Information on previous topics such as e-mail, that were once new to our research endeavors, but are now old hat, has been significantly reduced to allow room for other material such as expanded and updated coverage. Appendix A that provides some tips and tricks for using Excel for data analysis. The last chapter contains information about the use of the latest, sixth, edition of the Publication Manual of the American Psychological Association. How This Book Is Organized Exploring Research is organized into 14 chapters (with a big and little Chapters 3A and 3B, respectively) and three appendices. Chapter 1, The Role and Importance of Research, covers the basics about the scientific method and includes a brief description of the different types of research that are most commonly used in the social and behavioral sciences. Chapter 2, The Research Process: Coming to Terms, focuses on some of the basic terms and concepts in research methods, including variables, samples, populations, hypotheses, and the concept of significance. The first step for any researcher is the selection of a problem, which is what Chapter 3A, Selecting a Problem and Reviewing the Research, is all about. Here, you will learn how to use the library and its vast resources to help you ix x focus your interests and actually turn them into something you want to know more about! You will also be introduced to the use of electronic sources of reference material, such as online searches, and how using the Internet can considerably enhance your research skills. A new Chapter 3B, The Importance of Practicing Ethics in Research, talks about the ethical practices and ethical concerns in research. The content of Chapter 4, Sampling and Generalizability, is critical to understanding the research process. How you select the group of participants and how and when the results of an experiment can be generalized from this group to others are a fundamental premise of all scientific research. In this chapter, you will read all about this process. What is research without measuring outcomes? Not much, I’m afraid. Chapter 5, Measurement, Reliability, and Validity, introduces you to the measurement process and the important concepts of reliability and validity. You need to understand not only the principles of measurement but also the methods used to measure behavior. That is what you will learn in Chapter 6, Methods of Measuring Behavior, which discusses different types of tests and their importance. Once you understand what you want to study and the importance of measuring it, the only thing left to do is to go out and collect data! Chapter 7, Data Collection and Descriptive Statistics, takes you through the process step by step and includes a summary of important descriptive statistics and how they can be used. One of the reasons data are collected is to make inferences from a smaller group of people to a larger one. In Chapter 8, Introducing Inferential Statistics, you will find an introduction to the discipline of the same name and how results based on small groups are inferred to larger ones. Chapter 9, Nonexperimental Research: Descriptive and Correlational Methods, is the first of four chapters that deal with different types of research methods. In this chapter, you will learn about descriptive and correlational methods. Chapter 10, Nonexperimental Research: Qualitative Methods, provides the reader with an introduction to various qualitative tools, including case studies, ethnographies, and historical methods, and talks a bit about the advantages and disadvantages of each. I hope that you find this new chapter helpful and that it will give you another set of tools to answer important and interesting questions. Chapter 11, Pre- and True Experimental Research Methods, and Chapter 12, Quasi-Experimental Research: A Close Cousin to Experimental Research, continue the overview of research methods by introducing you to the different types of research designs that explore the area of cause and effect. Developmental research is discussed in Chapter 12. Chapter 13, Writing a Research Proposal, reviews the steps involved in planning and writing a proposal and includes an extensive set of questions that can be used to evaluate your proposal. If your research methods course does not include the preparation of a proposal as a requirement, this chapter can be used as a stand-alone instructional tool. Exploring Research ends with Chapter 14, Writing a Research Manuscript, a step-by-step discussion of how to prepare a manuscript for submission to a journal for publication using the format prescribed by the sixth edition of Publication Manual of the American Psychological Association. Appendix A is a compilation of Excel tips for use in data analysis. Appendix B contains a sample data set that is used in certain examples throughout the book, and this data set can also be downloaded from www.pearsonhighered.com/irc. Appendix C contains the answers to the exercises found at the end of each chapter. What’s Special about This Book? Several features from previous editions continue to be included in this edition that I hope will help make this book more useful and the learning of the material more interesting. These features have not changed because the feedback from both faculty and students has been so positive. Most chapters begin with a Research Maters entry that illustrates how research in the social and behavioral sciences is conducted using the chapter contents as a focus. You will find notes that highlight important points contained in the text. These can be used for review purposes and help to emphasize especially important points. Those Test Yourself questions mentioned earlier. Last, but not least, is a glossary of important terms found at the end of the book. The terms that you find in the glossary appear in boldface in the text. A Note to the Instructor All teachers tend to use teaching materials in different ways and I tried to complete this edition in such a way that the chapters can be read through in an order different from what is contained in the table of contents. For example, some instructors tell me that they start with Chapter 14 because a central element in their course is writing a research report. Others start with Chapter 4 on sampling and others go right from descriptive statistics to correlational methods. There is, of course, some mention of materials from previous and upcoming chapters throughout, but these are relatively few and will not bear on your students’ access to the information they need to understand the ideas under discussion. Also, if you want to know more about Excel and its application to statistics, you can look at two other books which I have done, published by Sage, including Excel Statistics, Third Edition, and the Excel edition of Statistics xi for People Who (Think They) Hate Statistics, Fourth Edition. And, of course, e-mail me at njs@ku.edu should you have any questions. Finally, you can learn more about supplements that are available for this book by going to www.pearsonhighered.com. How to Use This Book I have tried to write this book so that it is (you guessed it) user friendly. Basically, what I think this means is that you can pick it up, understand what it says, and do what it suggests. One reviewer and user of an earlier edition was put off at first by the easy-going way in which the book is written. My philosophy is that important and interesting ideas and concepts need not be written about in an obtuse and convoluted fashion. Simple is best. You see, your mother was right! Whether you are using this book as the main resource in a research methods course or as a supplemental text, here are some hints on how to go about using the book to make the most out of the experience. Read through the Contents (page vii) so you can get an idea of what is in the book. Take your time and do not try to read too much at one sitting. You will probably be assigned one chapter per week. Although it is not an enormous task to read the 20–30 pages that each chapter contains in one sitting, breaking your reading up by main chapter sections might make things a little easier. Too much too soon leads to fatigue, which in turn leads to frustration, and then no one is happy! Do the exercises at the end of each chapter. They will give you further insight into the materials that you just read and some direct experience with the techniques and topics that were covered. Write down questions you might have in the margins of pages where things seem unclear. When you are able, ask your professor to clarify the information or bring your questions to your study group for discussion. Available Instructor Resources The following resources are available for instructors. These can be downloaded at http://www.pearsonhighered.com/irc.Login required. PowerPoint—provides a core template of the content covered throughout the text. Can easily be expanded for customization with your course. Instructor ’s Manual—includes an overview, set of objectives, important terms and concepts for in-class discussions for each chapter. Test Bank—includes additional questions beyond the chapter-end exercises in multiple choice, and openended—short and essay response—formats. MyTest—an electronic format of the Test Bank to customize in-class tests or quizzes. Visit: http://www.pearsonhighered.com/mytest. A Big Thanks All textbooks have the author’s name on the cover, but no book is ever the work of a single person. Such is also the case with Exploring Research. Many people helped make this book what it is, and they deserve the thanks that I am offering here. Chris Cardone, way back at Macmillan, was the inspiration for this book. She remains the best of editors and a close friend. Special thanks to Kristin Teasdale for her assistance on previous editions. Special thanks also to Doug Bell who worked long and hard to make this edition possible. I take full responsibility for the errors and apologize to those students and faculty who might have used earlier editions of the book and had difficulty because of the mistakes. As many of those screwups (that is exactly the phrase) have been removed as is humanly possible. Finally, as always, words cannot express my gratitude to Leni for her support and love that see projects like this through to the end. And to Sara, Micah and Ted, my deepest admiration and respect as they continue to build professional and personal lives of their own. These people are making the world a better place. So, now it is up to you. Use the book well. Enjoy it and I hope that your learning experience is one filled with new discoveries about your area of interest as well as about your own potential. I would love to hear from you about the book, including what you like and do not like, suggestions for changes, or whatever. You can reach me through snail mail or e-mail. Neil J. Salkind University of Kansas Lawrence, KS 66045 This page intentionally left blank Chapter 1 - truth - Today, more than ever, decisions are evidence based, and what these researchers do is collect evidence that serves as a basis for informed decisions. - - 1 2 Chapter 1 research Research theory - Research is, among other things, an intensive activity that is based on the work of others and generates new ideas to pursue and questions to answer. - - - - research is an activity based on the work of others. - - - - The Role and Importance of Research 3 research generates new questions or is cyclical in nature. The Making of the Atomic Bomb , research is an activity that can be replicated. research is incremental. - good research is generalizable to other settings. - research is an apolitical activity that should be undertaken for the betterment of society. at its best research is based on some logical rationale and tied to theory. - research is doable! - - 4 Chapter 1 - Test Yourself Note: At the end of every major heading in each chapter of Exploring Research, we’ll have a few questions for you that we hope will help you understand the content and guide your studying. - 1. Provide an example of how research is incremental in nature and what advantage is this to both future and past researchers? - 2. Think of an example of how knowledge, in any field of endeavor, can lead to new questions about that, or a related, topic. - - Doing science means following a model that begins with a question and ends with asking new questions. Figure 1.1 The steps in the research process, wherein each step sets the stage for the next. Asking the Question Asking New Questions Identifying the Important Factors Formulating a Hypothesis Reconsidering the Theory Working with the Hypothesis Collecting Relevant Information Testing the Hypothesis The Role and Importance of Research 5 scientific method the effects of using social media on adolescents’ social skills The Wizard of Oz - - - - - - - - hypothesis - if … then 6 Chapter 1 - inferential statistics chance - - test prove - Exploring Research - wrong good - - The Role and Importance of Research 7 - - - - code - - - Nonexperimental research Test Yourself Hypothesis plays a very important role in scientific research, with one of them being the objective testing of a particular question that a scientist might want to ask. What are some of the factors that might get in the way of the scientist remaining objective and what impact might that have on a fair test of the hypothesis of interest? What is the danger of not being aware of these biases? Nonexperimental research examines the relationship between variables, without any attention to cause-and-effect relationships. 8 Chapter 1 Table 1.1 Summary of research methods covered in exploring research. Types of Research Nonexperimental Experimental Descriptive Historical Correlational Qualitative True Experimental QuasiExperimental Purpose Describe the characteristics of an existing phenomenon Relate events that have occurred in the past to current events Examine the relationships between variables To examine human behavior and the social, cultural, and political contexts within which it occurs To test for true cause-and-effect relationships To test for causal relationships without having full control Time frame Current Past Current or past (correlation) Future (prediction) Current or past Current Current or past Degree of control over factors or precision None or low None or low Low to medium Moderate to high High Moderate to high Code words to look for in research articles Describe Interview Review Literature Past Describe Relationship Related to Associated with Predicts Case study Evaluation Ethnography Historical Research Survey Function of Cause of Comparison between Effects of Function of Cause of Comparison between Effects of Example A survey of dating practices of adolescent girls An analysis of Freud’s use of hypnosis as it relates to current psychotherapy practices An investigation that focuses on the relationship between the number of hours of television watching and grade-point average A case study analysis of the effectiveness of policies for educating all children The effect of a preschool language program on the language skills of inner-city children Gender differences in spatial and verbal abilities - content why Reading Assessment - Morbidity and - Mortality Weekly Report DESCRIPTIVE RESEARCH Descriptive research - Descriptive research focuses on events that occur in the present. - The Role and Importance of Research 9 - CORRELATIONAL RESEARCH ical researches historQUALITATIVE RESEARCH Qualitative research - - correlational research - Qualitative research studies phenomena within the social and cultural context in which they occur. - Correlational research examines the relationship between variables. correlation coefficient - - - - thin ideal causes - Experimental research examines the cause-and-effect relationship between variables. 10 Chapter 1 true experimental research method quasi-experimental research preassigned before the experiment begins Quasi-experimental studies also focus on cause and effect, but they use preassigned groups. True experimental research examines direct cause-and-effect relationships. - - create - - - Another phrase for quasi-experimental research is post hoc, or after the fact. - post hoc - - The Role and Importance of Research Test Yourself 11 research designs We have briefly defined and discussed the different research methods that you will learn about later in Exploring Research in much greater detail. For now, answer this question. What determines the research method that a scientist should use to answer a question or test a hypothesis? Which research method described here best lends itself to questions you want answered? - experimental research methods cheat Both basic and applied research are critical parts of studying and understanding a wide range of phenomena. Figure 1.2 Research design cheat sheet. Are you looking for differences between groups? No Are you studying events that primarily occur in the present? Are you studying events that occurred in the past? Yes Historical Research No Yes Descriptive Research Are the participants preassigned to groups? Yes Are you studying the relationship between variables (but not the effects of one on the other)? No Yes No Yes No Correlational Research Time to go back and reconsider the question you are asking Quasi-experimental True experimental Nonexperimental Research Experimental Research 12 Chapter 1 basic research applied research - - Science News Phi Delta Kappan APA Monitor New York Times Magazine Newsweek American Scientist - - Test Yourself Why are both basic and applied research essential to the scientific community as well as to the public community that it serves? What do you think an educated or informed citizen should know about how the research process works? What five questions might he or she be able to answer? Summary Exploring Research Online… Professional Organizations researchese 13 The Role and Importance of Research How Science Works Exercises 1. - a. - 7. - b. - c. 8. 2. Pride and Prejudice 3. hypothesis - theory Clueless 4. - - 9. a. b. c. d. e. 10. 5. a. b. c. d. e. 11. - f. 12. 6. - 14 Chapter 1 13. a. b. - 14. 15. 18. - 16. 17. - 19. 20. - Chapter 2 The Research Process Coming to Terms Research Matters Research matters will introduce you to a research project that touches on the content that’s discussed in the current chapter. The research that we feature is only one example of many that will help show you how actual researchers approach actual problems in doing their work. In this first research work, we hope you’ll pay attention to the introduction of some terms and phrases that may be new to you but you will become more familiar with as you move through the book. You’ll also see how researchers focus on real-world problems and issues in their work. There’s no way to talk about the education of children without talking about the importance of reading. And, it’s not just school books that appear to be important, but reading recreationally as well—you know, those books you really enjoy reading but never seem to be able to find the time? Margaret Kristin Merga from Edith Cowan University in Australia directed the West Australian Study in Adolescent Book Reading where 520 adolescents discussed the quality and quantity of encouragement of recreational reading by their primary school and high school teachers in the past and at present. The theoretical framework that she followed was that social influences such as teachers’ attitudes and practices toward reading have a significant impact on adolescents’ attitudes and values toward reading as well. So what works best for influencing adolescents to recreationally read? Among other factors, such qualities by teachers as showing personal enjoyment of recreational book reading, supporting student’s discussion of such books, and setting expectations that students will read at school and at home. Here’s where a scientist takes her own interest within a theoretical framework and applies that knowledge to a real-world question regarding why adolescents might, and do, read recreationally. A significant question answered in a systematic and comprehensive way. If you want to know more, you can see the original research at … Merga, M.K. (2015). “‘She knows what I like’: Studentgenerated best-practice statements for encouraging recreational book reading in adolescents.” Australian Journal of Education, 59(1): 35–50. From Problem to Solution All you need to do is to identify an interesting question, collect some data, and poof!—instant research! Not quite. The model of scientific inquiry (discussed in Chapter 1) does a nice job of specifying the steps in the research process, but there is quite a bit more to the process than that. At the beginning of this chapter, we will provide a real-life example of how the process actually takes place and how researchers begin with what they see as a problem (to be solved) and end with a solution (or the results) to that problem. Keep in mind, however, that the meanings of the words problem and solution go beyond solving a simple problem of the 2 + 2 = 4 variety. Rather, the questions that researchers ask often reflect a more pressing social concern or economic issue. In addition, the results from a research study often provide the foundation for the next research endeavor. We will look at an interesting study entitled Maternal Employment and Young Adolescents’ Daily Experiences in Single-Mother Families (Duckett and Richards, 1989), which examines the impact of maternal employment on adolescent development. Although the study is almost 40 years old, it continues to effectively illustrate many of the ideas and concepts covered in this chapter. One of the most creative things about this study is the way in which these researchers collected their data. They did not sit down and ask adolescents how they felt about this or that, but instead they tried to get an overall picture of their feelings outside of the laboratory setting. And as you will see, it’s an early use of technology that provides some insight into how people were using new tools (no cell phones then, but pagers) to answer interesting questions. Duckett and Richards studied 436 fifth through ninth graders and their mothers to determine the effects of a combination of issues that continue to receive considerable attention in the media. The general goal of the research (and the problem) was to understand better some of the factors and consequences that surround the large number of working mothers of adolescents. To narrow their investigation, the researchers set out to learn about the general nature of the adolescents’ 15 16 Chapter 2 experiences as a function of having a mother who works, as well as the quality of time that the adolescents spent with their mothers. Given that so many mothers (more than 50% of those with children under 18 years of age) from both single-parent and dual-parent families work outside the home, answers to questions like those posed by this study are becoming increasingly important in the formation of social and economic policies. There are many different ways to answer a question, but often the simplest, most clever research plan is the best one. To obtain their answers, the researchers compared adolescents living with two parents (382, or 88%) with those adolescents who live with only their mother (54, or 12%). However, to reach fully their goal of better understanding the effects of maternal employment, the researchers had to break down the group of children and parents even further into those children whose mothers worked part-time, those children with mothers who worked full-time, and those children with mothers who were unemployed. When the groups were separated on these two factors (family configuration and employment status), the researchers could make a comparison within and between the six groups (all combinations of single-parent and two-parent families, with part-time employed, full-time employed, and unemployed mothers) and get the information they needed to answer the general questions posed. Now comes the really creative part of the study. Duckett and Richards used a method called the experience sampling method previously developed by M. Csikszentmihalyi and R. Larson and published in 1987. In accordance with this method, the adolescents participating in the study would carry electronic beepers. On an unpredictable schedule, they would receive a beep from beep central and would then stop what they were doing and complete a self-report form. They would do this for 1 week. Test Yourself It’s really interesting when new technologies have been adopted by social scientists to help them collect and analyze data. For example, almost all adolescents have cell phones, and the capabilities of these cell phones go way beyond sending and receiving calls; cell phones are, in and of themselves, small computers that have GPS and multimedia capabilities. We’ll discuss technology and the research process later in Exploring Research, but for now, what other new types of technology can you think of that might play a role in completing research? Any ideas as to what the future might bring? What other new technology can you think of that might also play a role in research? A signal telling the participant to stop and complete the form was sent on an average of every 2 hours between 7:30 a.m. and 9:30 p.m., with a total of 49 signals sent for the week for each participant. In the course of 1 week, 49 separate forms were completed, which provided information about how participants felt at any particular moment. For 436 participants at 49 forms each, a total of 21,364 forms were completed, which is a hefty sample of adolescents’ behavior! What was contained on these self-report forms? The adolescents had to report on what the researchers call affect (happy–sad, cheerful–irritable, friendly–angry) and arousal (alert–drowsy, strong–weak, excited–bored). Each of these six items was rated on a scale of 1–7. For example, the participants might indicate a 4, meaning they felt “right in the middle of happy and sad at that moment in time.” These six items could be completed in a short period of time, and an accurate picture of the adolescents’ daily life could then be formed. Adolescents also had to respond to “What were you doing?” and “Whom were you with?” as well as to some questions about their perceptions of their parents’ friendliness and their feelings while they were with their parents. Duckett and Richards had an interesting comparison (single-parent versus dual-parent mothers who are unemployed or employed part-time or full-time) and a goodsized set of reactions from adolescents on which to base their analysis and discussion. To make sense of all this information, the researchers compiled and then applied some statistical tests (you will learn more about these later) to reach their conclusions, including the following: Children of working single mothers benefit in ways other than just in the provision of income. Maternal employment is related to positive parent– child interactions. Children of single mothers employed full-time felt friendliest toward their fathers. This well-designed, straightforward study examined a question that bears on many issues that everyone from schoolteachers to employers needs to have answered. The study involved a more than adequate number of participants and used methods that directly focused on the type of information the researchers wanted. Although they did not answer every question about the relationship between maternal employment and adolescent development, the researchers did provide an important piece to the puzzle of understanding the effects of employment on growing children and changing families. The researchers seemed to take a logical approach of going from a question that has some import for many groups in today’s society and articulating it in such a way that it can be answered in a reasonable and efficient manner. The Research Process The issue of how children are affected by working parents is certainly still an important one, but the results of research, such as that summarized earlier, bring us closer to a solution to some of the questions posed by such work arrangements. To be the kind of researcher you want to be, you need to know the rules of the game (and the lingo) and follow them, as did Duckett and Richards. This knowledge begins with an understanding of some basic vocabulary and ideas. Test Yourself More on technology and research. Think about how these two scientists used technology (in this case beepers) to help them collect data. Now, think of the technology that you use every day for a variety of personal communications and to access information, and see if you can think of a way that those tools could be used in a research setting that focuses on your interests as well as a research setting outside of your interests. The Language of Research Significance levels. Null hypotheses. Independent variables. Factorial designs. Research hypotheses. Samples. Populations. Yikes!—that’s a lot of new terms. But these and other new words and phrases form the basis for much of the communication that takes place in the research world. As with any endeavor, it is difficult to play the game unless you learn the rules. The rules begin here, with a basic understanding of the terminology used by researchers in their everyday activities. The rest of this chapter offers a language lesson of sorts. Once you become familiar with these terms, everything that follows in Exploring Research will be easier to understand and more useful. Each of the terms described and defined here will be used again throughout the book. All about Variables The word variable has several synonyms, such as changeable or unsteady. Our set of rules tells us that a variable is a noun, not an adjective, and represents a class of outcomes that can take on more than one value. For example, hair color is a variable that can take on the values of red, brown, black, blond, blue, magenta, and shockingly bright green and just about any other combination of primary colors as well. Other examples of variables would be height (expressed as short or tall, or 5 feet, 3 inches or 6 feet, 1 inch), weight (expressed as heavy or light, 128 pounds or 150 pounds), age at immunization 17 (expressed as young or old, 6 weeks or 18 months), number of words remembered, time off work, political party affiliation, favorite type of M&Ms™, and so on. The one thing all these traits, characteristics, or preferences have in common is that the variable (such as political party affiliation) can take on any one of several values, such as Republican, Democrat, or Independent. However, the more precisely that a variable is measured, the more useful the measurement is. For example, knowing that Rachael is taller than Gregory is useful, but knowing that Rachael is 5 feet, 11 inches and Gregory is 5 feet, 7 inches is even more useful. Interestingly, variables that might go by the same name can take on different values. You could measure height in inches (60) or in rank (the tallest), for example— or be defined differently, depending on a host of factors, such as the purpose of the research or the characteristics of the participants. For example, consider the variable called intelligence. For one researcher, the definition might be scores on the Stanford–Binet Intelligence Test, whereas for another it might be scores on the Kaufman Assessment Battery. For Howard Gardner (1983), who believes in the existence of multiple intelligences, the definition might be performance in mathematics, music, or some physical activity. All of these variables represent the same general construct of intelligence, albeit assessed in different ways. Variables are used for different purposes as well. For example, a variable such as average number of days hospitalized following surgery might be used as a measure of recovery from surgery. But, this same variable might be used to equalize initial differences in patients when the question becomes, “How much post-operative pain did patients experience?” Statistically removing (or controlling for) how long they stayed in the hospital after their surgery is a fancy and very cool technique for taking differences in length of hospital stay out of the equation. The following paragraphs describe several types of variables, and Table 2.1 summarizes these types and what they do. Dependent Variables A dependent variable represents the measure that reflects the outcomes of a research study. For example, if you measure the difference between two groups of adults on how well they can remember a set of 10 single digits after a 5-hour period, the number of digits remembered is the dependent variable. Another example: If you are looking at the effect of parental involvement in school on children’s grades, the grades that the children received would be considered a dependent variable. The dependent variable is that which is examined as the outcome of an experiment or a research project. 18 Chapter 2 Table 2.1 Different types of variables. Type of Variable Other Terms You Might See Definition Dependent A variable that is measured to see whether the treatment or manipulation of the independent variable had an effect Outcome variable Results variable Criterion variable Independent A variable that is manipulated to examine its impact on a dependent variable Treatment variable Factor Predictor variable Control A variable that is related to the dependent variable, the influence of which needs to be removed Restricting variable Extraneous A variable that is related to the dependent variable or independent variable that is not part of the experiment Threatening variable Moderator A variable that is related to the dependent variable or independent variable and has an impact on the dependent variable Interacting variable Think of a dependent variable as the outcome that may depend on the experimental treatment or on what the researcher changes or manipulates. Independent Variables An independent variable represents the treatments or conditions that the researcher has either direct or indirect control over to test their effects on a particular outcome. An independent variable is also known as a treatment variable—it is within this context that the term is most often used. An independent variable is manipulated in the course of an experiment to understand the effects of this manipulation on the dependent variable. The independent variable is that which is manipulated or changed to examine its effect upon the dependent variable. For example, you might want to test the effectiveness of three different reading programs on children’s reading skills. This design is illustrated in Figure 2.1. Method A includes tutoring, Method B includes tutoring and rewards, and Method C includes neither tutoring nor rewards (these kids just spend some time with the teacher). In this example, the method of reading instruction is manipulated, and it is the independent variable. The outcome or dependent variable could be reading scores. This experiment includes three levels of one independent variable (method of teaching) and one dependent variable (reading score). The direct and indirect distinction has to do with whether the researcher actually creates the levels (such as Method A, Method B, or Method C) or the levels are already naturally occurring and cannot be manipulated directly but can only be tested, such as differences in gender (we cannot very well assign that trait to people) or age groupings (we cannot make people younger or older). So, what if you wanted to investigate whether there is a difference between males and females in their mathematics scores on some standardized test? In this example, the independent variable is gender (male or female), and the outcome or dependent variable is the mathematics score. Or, you could look at the effects of the number of hours of weekly television-watching time (less than 25 hours for group A or 25 or more hours for group B) on language skills. Here, the amount of time watching television is the independent variable, and the level of language skills is the dependent variable. The general rule to follow is that when the researcher is manipulating anything or assigning participants to groups based on some characteristic, such as age or ethnicity or treatment, that variable is the independent variable. When researchers look to some outcome to determine whether the grouping had an effect, they look to the dependent variable. In some cases, when researchers are not interested in looking at the effects of one thing on another, but only in how variables may be related, there are no independent Figure 2.1 Research designs can take on many different configurations. Here, the researcher is examining the effects of three different methods or levels of teaching reading on reading scores. Note that in the last method neither treatment is implemented, making it the control condition. Method of Teaching Reading (Independent Variable) Method A (with tutoring) Method B (with tutoring and rewards) Method C (no tutoring and no rewards) Average Reading Score Average Reading Score Average Reading Score One independent variable with three levels One dependent variable The Research Process variables. For example, if you are interested only in the relationship between the amount of time a father spends with his children and his job performance, nothing is manipulated, and, in a sense (but not everyone agrees), there are no variables that are independent of one another nor are there variables that are dependent upon others. Independent variables must take on at least two levels or values (because they are variables) and variables, by definition, vary. For example, if a researcher were studying the effects of gender differences (the independent variable) on language development (the dependent variable), the independent variable would have two levels, male and female. Similarly, if a researcher were investigating age differences in stress for people aged 30–39 years, 40–49 years, and 50–59 years, then the independent variable would be age, and it would have three levels. What happens if you have more than one independent variable like we just described? Look at Figure 2.2, which represents a factorial design wherein gender, age, and social class are independent variables. Factorial designs are experiments that include more than one independent variable. Here are two levels of gender (male and female), three levels of age (3, 5, and 7 years), and three levels of social class (high, medium, and low), accounting for a 2 by 3 by 3 design for a total of 18 separate combinations of treatment conditions, or cells, of levels of independent variables. You can see that, as independent variables are added to a research design, the total number of cells increases rapidly. Here’s the key in understanding this way of noting variables and their levels. If you see something like this … 3*4 you can rest assured there are the same number of independent variables as there are numerals separated by the “*” which stand for times just as in simple multiplication. You can ignore the value of the number. So, for a 3 * 4, there are two independent variables (one for the “3” and one for the “4”). For each of these independent variables, the value of the number represents the number of levels. So, for this example, there are two independent variables, one having 3 levels and the other having 4. And, the total number of separate conditions? That’s right, it’s 12 since 3 * 4 = 12. The Relationship between Independent and Dependent Variables This is really important and sure to be a question on your next test or quiz. The best independent variable is one that is independent of any other variable that is being used in the same study. In this way, the independent variable can contribute the maximum amount of understanding beyond what other independent variables can offer. When variables compete to explain the effects, it is sometimes called confounding. The best dependent variable is one that is sensitive to changes in the different levels of each independent variable; otherwise, even if the treatment had an effect, you would never know it. Test Yourself Go back to the Duckett and Richards study and define what the independent and dependent variables are. According to the last paragraph in this section, why are the two independent variables a good choice? Other Important Types of Variables Independent and dependent variables are the two kinds of variables that you will deal with most often throughout Exploring Research. However, there are other variables that are important for you to know about as well, because an understanding of what they are and how they fit into the Figure 2.2 Many experiments in the social and behavior sciences use more than one independent variable. In this particular example, there are three independent variables: two (what else?) levels of gender, three levels of age, and three levels of social class. Social Class Gender Male Female High 19 Age (years) 3 5 Med. Low High Med. Low High 7 Med. Low 20 Chapter 2 research process is essential for you to be an intelligent consumer and to have a good foundation as a beginning producer of research. The following are other types of variables that you should be familiar with (see Table 2.1). A control variable is a variable that has a potential influence on the dependent variable; consequently, the influence must be removed or controlled. For example, if you are interested in examining the relationship between reading speed and reading comprehension, you may want to control for differences in intelligence, because intelligence is related both to reading speed and to reading comprehension. Intelligence must be held constant for you to get a good idea of the nature of the relationship between the variables of interest. An extraneous variable is a variable that has an unpredictable impact upon the dependent variable. For example, if you are interested in examining the effects of television watching on achievement, you might find that the type of television programs watched is an extraneous variable that might affect achievement. Such programs as Discovery, Nova, and Sesame Street might have a positive impact on achievement, whereas other programs might have a negative impact. A moderator variable is a variable that is related to the variables of interest (such as the dependent and independent variable), masking the true relationship between the independent and dependent variable. For example, if you are examining the relationship between crime rate and ice cream consumption, you need to include temperature because it moderates that relationship. Otherwise, your conclusions will be inaccurate. Hypotheses In Chapter 1, a hypothesis was defined as an educated guess. Although a hypothesis reflects many other things, perhaps its most important role is to reflect the general problem statement or the question that was the motivation for undertaking the research study. That is why taking care and time with that initial question is so important. Such consideration can guide you through the creation of a hypothesis, which in turn helps you to determine the types of techniques you will use to test the hypothesis and answer the original question. The “I wonder …” stage becomes the problem statement stage, which then leads to the study’s hypothesis. Here is an example of each of these. The Stage An Example “I wonder” It seems to me that several things could be done to help our employees lower their high absentee rate. Talking with some of them tells me that they are concerned about after-school care for their children. I wonder what would happen if a program were started right here in the factory to provide child supervision and activities? The Stage An Example The hypothesis Parents who enroll their children in after-school programs will miss fewer days of work in 1 year and will have a more positive attitude toward work as measured by the Attitude Toward Work (ATW) survey than parents who do not enroll their children in such programs. A good hypothesis provides a transition from a problem statement or question into a form that is more amenable to testing using the research methods we are discussing. The following sections describe the two types of hypotheses—the null hypothesis and the research hypothesis—and how they are used, as well as what makes a good hypothesis. The Null Hypothesis A null hypothesis is an interesting little creature. If it could talk, it would say something like, “I represent no relationship between the variables that you are studying.” In other words, null hypotheses are statements of equality such as: There will be no difference in the average score of ninth graders and the average score of 12th graders on the ABC memory test. There is no relationship between personality type and job success. There is no difference in voting patterns as a function of political party. The brand of ice cream preferred is independent of the buyer’s age, gender, and income. The null hypothesis is a statement of equality. A null hypothesis, such as the ones described here, would be represented by the following equation: Ho : m9 = m12 where: Ho = the symbol for the null hypothesis m9 = the symbol (the Greek letter mu) for the theoretical average for the population of ninth graders m12 = the symbol (the Greek letter mu) for the theoretical average for the population of 12th graders. The four null hypotheses listed earlier all have in common a statement of two or more things being equal or unrelated to each other. What are the basic purposes of the null hypothesis? The null hypothesis acts as both a starting point and a benchmark against which the actual outcomes of a study will be measured. Let’s examine each of these purposes. The Research Process First, the null hypothesis acts as a starting point because it is the state of affairs that is accepted as true in the absence of other information. For example, let’s look at the first null hypothesis stated earlier in the list: There will be no difference in the average score of ninth graders and the average score of 12th graders on the ABC memory test. Given no other knowledge of 9th and 12th graders’ memory skills, you have no reason to believe there will be differences between the two groups. You might speculate as to why one group might outperform another, but if you have no evidence a priori (before the fact), then what choice do you have but to assume that they are equal? This lack of a relationship, unless proved otherwise, is a hallmark of the method being discussed. In other words, until you prove that there is a difference, you have to assume that there is no difference. Furthermore, if there are any differences between these two groups, you have to assume that the differences are due to the most attractive explanation for differences between any groups on any variable: chance! That’s right; given no other information, chance is always the most likely explanation for differences between two groups. And what is chance? It is the random variability introduced as a function of the individuals participating as well as many unforeseen factors. For example, you could take a group of soccer players and a group of football players and compare their running speeds. But who is to know whether some soccer players practice more, or if some football players are stronger, or if both groups are receiving additional training? Furthermore, perhaps the way their speed is being measured leaves room for chance; a faulty stopwatch or a windy day can contribute to differences unrelated to true running speed. As good researchers, our job is to eliminate chance as a factor and to evaluate other factors that might contribute to group differences, such as those that are identified as independent variables. The second purpose of the null hypothesis is to provide a benchmark against which observed outcomes can be compared to determine whether these differences are caused by chance or by some other factor. The null hypothesis helps to define a range within which any observed differences between groups can be attributed to chance (which is the contention of the null hypothesis) or whether they are due to something other than chance (which perhaps would be the result of the manipulation of the independent variable). Most correlational, quasi-experimental, and experimental studies have an implied null hypothesis; historical and descriptive studies may not. For example, if you are interested in the growth of immunization during the last 70 years (historical) or how people feel about school 21 vouchers (descriptive), then you are probably not concerned with positing a null hypothesis. The Research Hypothesis Whereas a null hypothesis is a statement of no relationship between variables, a research hypothesis is a definite statement of the relationship between two variables. For example, for each of the null hypotheses stated earlier, there is a corresponding research hypothesis. Notice that I said a and not the corresponding research hypothesis, because there can certainly be more than one research hypothesis for any one null hypothesis. Here are some research hypotheses that correspond with the null hypotheses mentioned earlier: The average score of ninth graders is different from the average score of 12th graders on the ABC memory test. There is a relationship between personality type and job success. Voting patterns are a function of political party. The brand of ice cream preferred is related to the buyer’s age, gender, and income. Research hypotheses are statements of inequality. Each of these four research hypotheses has one thing in common: They are all statements of inequality. Unlike the null hypothesis, these research hypotheses posit a relationship between variables, not an equality. The nature of this inequality can take two different forms: directional and nondirectional. If the research hypothesis posits no direction to the inequality (such as different from), then the research hypothesis is a nondirectional research hypothesis. If the research hypothesis posits a direction to the inequality (such as more than or less than), then the research hypothesis is a directional research hypothesis. A nondirectional research hypothesis reflects a difference between groups, but the direction of the difference is not specified. For example, the research hypothesis The average score of ninth graders is different from the average score of 12th graders on the ABC memory test is nondirectional in that the direction of the difference between the two groups is not specified. The hypothesis states only that there is a difference and says nothing about the direction of that difference. It is a research hypothesis because a difference is hypothesized, but the nature of the difference is not specified. THE NONDIRECTIONAL RESEARCH HYPOTHESIS 22 Chapter 2 A nondirectional research hypothesis such as the one described here would be represented by the following equation: H1 : X9 Z X12 where: H1 = the symbol for null hypothesis X12 = the average memory score for 12th graders Z = the inequality symbol or the not equal symbol X12 = the average memory score for ninth graders A directional research hypothesis reflects a difference between groups, and the direction of the difference is specified. For example, the research hypothesis The average score of 12th graders is greater than the average score of ninth graders on the ABC memory test is directional, because the direction of the difference between the two groups is specified— one group’s score is hypothesized to be greater than the other. Directional hypotheses can take one of the following forms or really, any statement of inequality that shows direction: THE DIRECTIONAL RESEARCH HYPOTHESIS A is greater than B (or A 7 B) B is greater than A (or B 7 A) These both represent inequalities. A directional research hypothesis, such as the one described earlier wherein 12th graders are hypothesized to score better than ninth graders, would be represented by the following equation: H1 : X12 7 X9 where: H1 = the symbol for (the first of possible) research hypothesis X12 = the average memory score for 12th graders 7 = the greater-than sign Differences between the Null Hypothesis and the Research Hypothesis Other than the fact that the null hypothesis represents an equality and the research hypothesis represents an inequality, there are several important differences between these two types of hypotheses. First, the null hypothesis states that there is no relationship between variables (an equality), whereas the research hypothesis states that there is a relationship (an inequality). Second, null hypotheses always refer to the population, whereas research hypotheses always refer to the sample. As you will read later in this chapter, researchers select a sample of participants from a much larger population. It is too expensive, and often impossible, to work with the entire population and thus directly test the null hypothesis. Third, because the entire population cannot be directly tested (again, it is impractical, uneconomical, and often impossible), you can never really say that there is actually no difference between groups (or an inequality) on a specified dependent variable (if you accept the null hypothesis). Rather, you have to infer it (indirectly) from the results of the test of the research hypothesis, which is based on the sample. Hence, the null hypothesis is indirectly tested, whereas the research hypothesis is directly tested. Fourth, null hypotheses are always stated using Greek symbols (such as m or mu for the average), whereas research hypotheses are always stated using Roman symbols (such as X for the average), as illustrated just a few pages ago. Finally, because you cannot directly test the null hypothesis (remember that you rarely will have access to the total population), it is an implied hypothesis. The research hypothesis, on the other hand, is explicit. It is for this reason that you rarely see null hypotheses stated in research reports, whereas you almost always see the research hypothesis. X9 = the average memory score for ninth graders What is the purpose of the research hypothesis? It is this hypothesis that is tested directly as one step in the research process. The results of this test are compared with what you expect by chance alone (reflecting the null hypothesis) to see which of the two explanations is the more attractive one for observed differences between groups. But do beware of one thing. Beginning researchers often start out to prove a research hypothesis. As good scientists, we are not to be swayed by our own too personal beliefs and prejudices. Rather than setting out to prove anything, we set out to test the hypothesis. What Makes a Good Hypothesis? Hypotheses are educated guesses. Some guesses are better than others right from the start. I cannot stress enough how important it is to ask the question you want answered and to keep in mind that any hypothesis you present is a direct extension of the original question you asked. This question will reflect your own personal interests as well as previous research. Good hypotheses are declarative in nature and posit a very clear and unambiguous relationship between variables. The Research Process With that in mind, here are some criteria you might use to decide whether a hypothesis you read in a research report or the ones you formulate are acceptable. Let’s use an example of a study that examines the effects of after-school child-care programs for employees who work late on the parents’ adjustment to work. The following is a well-written hypothesis: Parents who enroll their children in after-school programs will miss fewer days of work in one year and will have a more positive attitude toward work as measured by the Attitude Toward Work (ATW) Survey than parents who do not enroll their children in such programs. Here are the criteria we want to evaluate if a hypothesis is good. 1. A good hypothesis is stated in declarative form and not as a question. Hypotheses are most effective when they make a clear and forceful statement. 2. A good hypothesis posits an expected relationship between variables. The example hypothesis clearly describes the relationship between after-school child care, the parents’ attitude, and the absentee rate. These variables are being tested to determine whether one (enrollment in the after-school program) has an effect upon the others (absentee rate and attitude). Notice the word expected in the second criterion? Defining an expected relationship is intended to prevent the fishing-trip approach (sometimes called the shotgun approach) which may be tempting to take but is not very productive. In the fishing-trip approach, you throw out your line and pull in anything that bites. You collect data on as many things as you can, regardless of your interest or even whether collecting the data is a reasonable part of the investigation. Or, put another way, you load up the guns and blast away at anything that moves. You are bound to hit something. The problem is that you may not want what you hit and, worse, you may miss what you want to hit—even worse (if possible), you may not know what you hit! Good researchers do not want just anything they can catch or shoot—they want specific results. To get such results, researchers must formulate their opening questions and hypotheses in a manner that is clear, forceful, and easily understood. 3. Hypotheses ref lect the theory or literature upon which they are based. As you read in Chapter 1, the accomplishments of scientists can rarely be attributed to only their hard work. Their accomplishments also are due to the work of many other researchers who have come before them and laid a framework for later explorations. A good hypothesis reflects this; it has a substantive link to existing literature and theory. 23 In the previous example, let’s assume that the literature indicates that parents who know their children are being cared for in a structured environment can be more productive at work. Knowledge of this would allow a researcher to hypothesize that an after-school program would provide parents the security they are looking for, which in turn allows them to concentrate on work rather than on awaiting a phone call to find out whether Max or Sophie got home safely. 4. A hypothesis should be brief and to the point. Your hypothesis should describe the relationship between variables in a declarative form and be as succinct (to the point) as possible. The more succinct the statement, the easier it will be for others (such as your master’s thesis committee members) to read your research and understand exactly what you are hypothesizing and what the important variables are. In fact, when people read and evaluate research (as you will learn more about later in this chapter), the first thing many of them do is read the hypotheses so they can get a good idea of the general purpose of the research and how things will be done. A good hypothesis defines both these things. 5. Good hypotheses are testable hypotheses. This means that you can actually carry out the intent of the question reflected in the hypothesis. You can see from the sample hypothesis that the important comparison is between parents who have enrolled their child in an after-school program with those who have not. Then, such things as attitude and number of workdays missed will be measured. These are both reasonable objectives. Attitude is measured by the ATW Survey (a fictitious title, but you get the idea), and absenteeism (the number of days away from work) is an easily recorded and unambiguous measure. Think how much harder things would be if the hypothesis were stated as Parents who enroll their children in after-school care feel better about their jobs. Although you might get the same message, the results might be more difficult to interpret given the ambiguous nature of words such as feel better. In sum, complete and well-written hypotheses should be stated in declarative form, posit a relationship between variables, reflect a theory or a body of literature upon which they are based, be brief and to the point, and be testable. When a hypothesis meets each of these five criteria, then it is good enough to continue with a study that will accurately test the general question from which the hypothesis was derived. 24 Chapter 2 Test Yourself Hypotheses are absolutely critical to the scientific process, and we reviewed several reasons why and reviewed the hypothesis’ relationship to chance. What is that relationship and in general why is it important to the scientific process? Samples and Populations As a good scientist, you would like to be able to say that if Method A is better than Method B, this is true forever and always and for all people. Indeed, if you do enough research on the relative merits of Methods A and B and test enough people, you may someday be able to say that, but it is unlikely. Too much money and too much time (all those people!) are required to do all that research. Our goal is to select a sample from a population that most closely matches the characteristics of that population. However, given the constraints of limited time and limited research funds which almost all scientists live with, the next best strategy is to take a portion of a larger group of participants and do the research with that smaller group. In this context, the larger group is referred to as a population, and the smaller group selected from a population is referred to as a sample. Samples should be selected from populations in such a way that you maximize the likelihood that the sample represents the population as best as possible. The goal is to have the sample resemble the population as much as possible. The most important implication of ensuring similarity between the two is that, once the research is finished, the results based on the sample can be generalized to the population. When the sample does represent the population, the results of the study are said to be generalizable or to have generalizability. The various types of sampling procedures are discussed in Chapter 4. Test Yourself It’s important that samples be representative of the populations from which they came. Provide an example of a population and a sample from that population. How would you know that the sample is representative? The Concept of Significance There is probably no term or concept that represents more confusion for the beginning student than that of statistical significance. This term is explained in detail in Chapter 8, but it is important to be exposed to the term early in Exploring Research because it is a basic and major component of understanding the research process. Significance is a measure of how much risk we are willing to take when reaching a conclusion about the relationship between variables. At the beginning of this chapter, you read a simple overview of a study wherein two researchers examined the differences between adolescents whose mothers work and adolescents whose mothers do not (as well as family status, but for this example let’s stick with the employed and not employed groups). Let’s modify the meaning of differences to include the adjective significant. Here, significant differences are the differences observed between adolescents of mothers who work and of those who do not that are due to some influence and do not appear just by chance. In this example, that influence is whether the mothers work. Let’s assume that other factors that might account for any differences were controlled for. Thus, the only thing left to account for the differences between adolescents is whether or not the mothers work. Right? Yes. Finished? Not quite. Because the world and you and I and the research process are not perfect, one must allow for some leeway. In other words, you need to be able to say that, although you are pretty sure the difference between the two groups of adolescents is due to the mothers’ working, you cannot be absolutely, 100%, positively, unequivocally, indisputably (get the picture?) sure. Why? There are many different reasons. For example, you could just be wrong (horrors!). Maybe during this one experiment, differences were not due to the group the adolescents were in but to some other factor that was inadvertently not accounted for, such as out-of-home experiences. What if the people in one group were mostly adolescent boys and reacted quite differently than the people in the other group, mostly adolescent girls? If you are a good researcher and do your homework, such differences between groups are unlikely outcomes, but possible ones nonetheless. This factor (gender) and others certainly could have an impact on the outcome or dependent variable and, in turn, have an impact on the final results and the conclusion you reach. So, what to do? In most scientific endeavors that involve proposing hypotheses and examining differences between groups, there is bound to be a certain amount of error that simply cannot be controlled. The Research Process Significance level is the risk associated with not being 100% confident that the difference is caused by what you think and may be due to some unforeseen factor. If you see that a study resulted in significant findings at the .05 level (it looks like this in journal articles and scientific reports p < .05), the translation is that a chance of less than 1 in 20 (or .05 or 5%) exists that any differences found between the groups were not due to the hypothesized reason (the independent variable in the case of a comparison between two groups) but to some other unknown reason or reasons. This number is actually an indirect measure of chance. As you will see in Chapter 8, new data analysis computer programs have gone a step further and rather than defining a range of probability (such as less than .05 or less than 5%), they assign a specific probability (such as .042 or 4.2%). As a good scientist, your job is to reduce this likelihood as much as possible by accounting for all the competing reasons, other than the one you are testing, for any differences that you observed. Because this is possible in 25 theory only and you cannot fully eliminate the likelihood of other factors, you account for these other factors by assigning them a level of probability and report your results with that caveat. So even if you are quite sure that your findings reflect the truth, the good scientist is neither so arrogant nor so confident that he or she cannot admit there is a chance of error. The probability that error may occur is what we mean by significance. We get into a much more detailed discussion of this in Chapter 8. Test Yourself You’re going to see the word significance a lot in Exploring Research and learn a good deal more about it. What is the relationship between a significant finding and the likelihood that the finding is due to chance? Summary That wraps up some vocabulary and provides you with a basic knowledge for understanding most of the important terms used in the research process, terms that you will see and use throughout the rest of Exploring Research. Being familiar with these terms will provide a foundation for a better understanding during subsequent chapters. If you are unsure about the meaning of a certain term, refer back to this chapter for a refresher course or consult the glossary at the end of the book. Online… How to do Great Research Research 101 That’s exactly the name of the site at http://greatresearch .org/ bought to us by Nick Feamster and Alex Gray from Georgia Tech. Not only will you learn about putting research ideas into practice but much of the site is devoted to the practical side of being (and succeeding as) a graduate student. Lots of good advice here. The University of Washington Libraries has created a tutorial to help you start your research available at http://www.lib.washington.edu. From there, click on “Research Guides” and then select your discipline such as Psychology, Education or Science. Exercises 1. In the following examples, identify the independent and dependent variable(s): a. Two groups of children were given different types of physical fitness programs to determine whether the programs had an effect on their strength. b. A group of 100 heavy smokers was divided into five groups, and each group participated in a different smoking-cessation program. After 6 months of program participation, the number of cigarettes each participant smoked each day was counted. c. Brands A and B differ in the way the packing is colored and the industrial designers are interested to see if there is an impact on number of units purchased. d. One group of teenage drivers was given a lecture on using seatbelts and other was shown pictures of accident victims who did not wear seatbelts and the variable of interest was safe driving practices. 26 Chapter 2 2. For the following situations name at least one independent variable (and the levels of that variable) and one dependent variable. a. A research project where the topic of interest is achievement. b. A research project where the topic of interest is voting preferences in the presidential election. c. A research project where the topic is recovery rate in a drug and alcohol rehabilitation program. 3. What is another name for outcome variable? Provide five examples dependent variables. 4. Why is the null hypothesis always a statement of equality? Why can the research hypothesis take on many different forms? 5. Write the null and research hypotheses for the following description of a research study: A group of middle-aged men was asked to complete a questionnaire on their attitudes toward work and family. Each of these men is married and has at least two children. Another group of men with no children also completed the same survey. 6. Write the null and a directional research hypothesis for the following description of a research study: A pediatrician was comparing the effects of an early intervention program during children’s first 3 years of life and the impact that program might have on academic achievement on grade school competency tests. 7. Name two advantages of having a hypothesis that is linked to existing literature and theory. 8. Why is having a hypothesis with an expected outcome better practice than a fishing-trip approach? 9. No one would argue that defining variables clearly and in an unambiguous manner is critical to good research. With that in mind, work as a group and define the following variables. Keep track of how different people’s definitions reflect their personal views of what the variable represents, and note how easy it is to define some variables and how difficult it is to define others. a. Intelligence b. Height c. Social skills d. Age e. Aggressiveness f. Conservatism g. Alcohol consumption h. Street smarts i. Personality Be sure to note that even those variables that appear to be easy to define (e.g., height) can take on different meanings and definitions (tall, 5 feet 1 inch, awesome) as well. 10. What is statistical significance and why is it important? 11. A researcher spent 5 years on a project, and the majority of the findings were not significant. How can the lack of significant results still make an important contribution to the field? 12. A researcher interested in the use of energy in Howard County households makes survey phone calls to every 10th household listed in the county phonebook. In this example, assuming every household contacted participates in the survey: a. What is the sample? b. What is the population? 13. Indicate which of the following are variables and which are constants: a. Lew’s hair color b. Age in years c. Number of windows in your residence d. Color of the late-model car parked in front of the building e. What time it is right now f. Number of possible correct answers on this week’s quiz g. Number of signers of the Declaration of Independence h. Name of the fifth girl in the third row i. Today’s date j. Number of words remembered on a memory test 14. The principal of an elementary school wants to know how well her 600 students like the school. She hires a researcher, who gives a satisfaction with school survey to three different third-grade classes. a. How could the use of a sample like this threaten the value of the study’s outcomes? b. How might the flawed sample affect the usefulness of the results? 15. Two researchers complete projects looking at the relationship between the amount of time studied and performance on a science test. Researcher A’s results state that “John performed better than Lisa, who performed better than Drew.” Researcher B’s results state that “John earned a 97%, Lisa earned a 96%, and Drew earned a 71%.” Between Researcher A and Researcher B, who has the best way of measuring the dependent variable of test performance? Why? 16. A researcher from Louisiana hypothesizes that people living out in the country display fewer avoidance strategies under stress than do people living in large cities. To test his hypothesis, he completes research with a group of participants from New Orleans and a group of participants from Church Point, Louisiana, and finds support for his hypothesis. However, he later learns that most of the participants from New Orleans were there during Hurricane Katrina. In this The Research Process example, the experience of Hurricane Katrina is considered what type of variable? 17. Go to the library and locate three journal articles in your area of interest which are experimental in nature (where groups are compared). Do the following: a. Identify the independent and dependent variables. b. For each dependent variable, specify how it is going to be measured and whether it is clearly defined. 27 c. For each independent variable, identify the number of levels of that variable. What other independent variables would you find of interest to study? 18. What makes a good hypothesis? 19. Why is the concept of significance important? 20. What is the difference between statistical significance and meaningfulness? Chapter 3A Selecting a Problem and Reviewing the Research So here you are, in the early part of a course that focuses on research methods, and now you have to come up with a problem that you are supposed to be interested in! You are probably so anxious about learning the material contained in your professor’s lectures and what is in this volume that you barely have time to think about anything else. If you stop for a moment and let your mind explore some of the issues in the behavioral and social sciences that have piqued your interest, you will surely find something that you want to know more about. That is what the research process is all about—finding out more about something that is, in part, at least, partially known. Research Matters There’s no question that the ethical dimensions of providing health care deserve a great deal of attention, given the rapidly changing role that technology and advancements in knowledge currently play. And, as with any topic as a focus of research, starting with a good basis for the review of existing literature and studies is critical. Among the thousands of ideas to pursue, it’s easy to see why ethics was selected in the following summary, given its importance. Younjae Oh and Chris Gastmans from the Catholic University of Leuven, Belgium, note how nurses are often confronted with ethical dilemmas in their everyday nursing work and as a result of these dilemmas, they experience moral distress. This review of literature focused on 19 articles published between January 1984 and December 2011 and revealed that many nurses do experience what the authors term moral distress. For our purposes here in Exploring Research, what this chapter does is illustrate how to take a variety of different studies and apply quantitative tools to better understanding a particular topic and, of course, using the review to formulate additional questions to ask in the future. If you want to know more, you can see the original research at . . . Oh, Y., & Gastmans, C. (2015). “Moral Distress Experienced by Nurses: A Quantitative Literature Review. Nursing Ethics, 22: 15–31. Once you select an area of interest, you are only part of the way there. Next comes the statement of this interest in the form of a research question followed by a formal hypothesis. Then, it is on to reviewing the literature, a sort of fancy phrase that sounds like you will be very busy! A literature review involves library time online or actually being there, note taking, and organizational skills (and of course writing), but it provides a perspective on your question that you cannot get without knowing what other work has been done as well as what new work needs to be done. But hold on a minute! How is someone supposed to have a broad enough understanding of the field and spew forth well-formed hypotheses before the literature is reviewed and then become familiar with what is out there? As poet John Ciardi wrote, therein “lies the rub.” The traditional philosophers and historians of science would have us believe that the sequence of events leading up to a review of what has been done before (as revealed in the literature) is as shown in Figure 3A.1a. This sequence of steps is fine in theory, but as you will discover, the actual process does not go exactly in the manner shown in the figure. The research question and research hypothesis are more an outgrowth of an interaction between the scientist’s original idea and an ongoing, thorough review of the literature (good scientists are always reading), as you can see in Figure 3A.1b. This means that once you formulate a hypothesis, it is not carved in stone but can be altered to fit what the review of the literature may reflect, as well as any change in ideas you may have. Remember, almost all of our work “stands on the shoulder of giants.” For example, you might be interested in how working adults manage their time when they are enrolled in graduate programs. That’s the kernel of the idea you want to investigate. A research question might ask what the effects of enrollment in graduate school and full-time work are on personal relationships and personal growth. For a Figure 3A.1a From idea to literature review with a research hypothesis along the way. Idea 28 Research Question Research Hypothesis Literature Review Selecting a Problem and Reviewing the Research Figure 3A.1b From idea and literature review to a research hypothesis. Idea Research Question Literature Review Research Hypothesis hypothesis, you might predict that those adults enrolled in school and who work full time and who participate in a time management support group have more meaningful personal relationships than those who do not. Use the results of previous studies to fine-tune your research ideas and hypotheses. You might consider the hypothesis to be finished at this point, but in reality your ongoing review of the literature and your changing ideas about the relationship between the variables will influence the direction your research will take. For example, suppose the findings of a similar previous study prompt you to add an interesting dimension (such as whether the employer subsidizes the cost of tuition) to your study, because the addition is consistent with the intent of your study. You should not have to restrict your creative thinking or your efforts to help you understand the effects of these factors just because you have already formulated a hypothesis and completed a literature review. Indeed, the reason for completing the review is to see what new directions your work might take. The literature review and the idea play off one another to help you form a relevant, conceptually sound research question and research hypothesis. In sum, you will almost always find that your first shot at a hypothesis will need revision, given the content of the literature that you review. Remember, it is your idea that you will pursue. The way in which you execute it as a research study will be determined by the way in which you state the research question and the way in which you test the research hypothesis. It is doubtful that a review of the relevant literature would not shed some light on this matter. This chapter begins with some pointers on selecting a problem worth studying, and then the focus moves to a description of the tools and the steps involved in preparing a review of the literature. 29 Selecting a Problem People go to undergraduate and graduate school for a variety of reasons, including preparing for a career, the potential financial advantages of higher education, and even expanding their personal horizons and experiencing the sheer joy of learning (what a radical thought!). Many of you are in this specific course for one or more of these reasons. Select a problem which genuinely interests you. The great commonality between your course work and activities is your exposure to a wealth of information, which you would not otherwise experience. That is the primary purpose of taking the time to select a research problem that makes sense to you and that interests you, while at the same time makes a contribution to your specific discipline. The selection of the area in which to work on is extremely important for two reasons. First, research takes a great deal of time and energy, and you want to be sure that the area you select interests you. You will work so hard throughout this project that continuing to work on it, even if it’s the most interesting project, may at times become overwhelming. Just think of what it would be like if you were not interested in the topic! Second, the area you select is only the first step in the research process. If this goes well, the remaining steps, which are neither more nor less important, also have at least a decent chance of going well. Just as there are many different ways to go about selecting a research problem, there are also some potential hazards. To start you off on the right foot, the following b...
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