apa style simplified writing in psychology education nursing and sociology

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APA style simplified : writing in psychology, education, nursing, and sociology

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• Machine generated contents note: 1. Writing Professionally
• How Does Professional Writing Differ From Other Kinds of Writing?
• Using APA Style
• Making a Credible Argument
• Different Types of Communication
• Effective Communication
• Establishing Good Writing Habits
• Ethical Writing
• 2. Elements of Style
• Recognizing the Importance of Good Grammar and Style
• Choosing Effective Wording
• Using Inclusive and Appropriate Language
• Deciding on the Use of Technical Language
• Avoiding Common Problems
• Specific Word Use
• 3. Introduction Section
• Introducing the Topic
• Different Approaches to Starting the Introduction
• How to Begin
• Reviewing the Literature
• Introducing Your Research: Generating a Hypothesis
• 4. Method Section
• Participants and Subjects
• Materials and Apparatus
• 5. Communicating Statistics
• Why Do We Use Statistics?
• What Point Are You Trying to Make?
• Understanding Your Numbers
• Helping Readers Understand Your Statistics
• Differentiating Results and Interpretations
• 6. Results Section
• Your Hypothesis
• Deciding What to Present
• APA Style and Presentation of Your Results
• Creating Tables
• Creating Figures
• Connection Between the Text and the Tables and Figures
• Difference Between Results and Discussion Sections
• Some Final Points About Presenting Results
• 7. Discussion Section
• Summarizing Your Results
• Connecting Different Aspects of Your Results
• Dealing With Nonsignificant Results
• Comparing Your Results With Those of Others
• Stating the Importance and Implications of Your Results
• Acknowledging the Limitations of Your Study
• 8. References: Citations in the Text and the Reference List
• Citing References in the Text
• Order of Citations in the Reference List
• Using Your Word-Processing Program to Create the Citation
• Examples of How Different Types of References Should Be Laid Out
• 9. Final Touches: The Abstract and Formatting Details
• Formatting Your Manuscript
• 10. Creating Poster Presentations
• Differentiating Visual and Written Communication
• Reducing the Amount of Information
• Visual Style
• Your Behavior: The Ethic of a Poster Session
• Creating Your Poster Using PowerPoint®
• 11. Giving Oral Presentations
• Difference Between Oral and Written English
• Adapting APA Style to Oral Presentations
• Preparing for Your Talk
• Creating Graphics for Your Presentation
• Giving the Presentation
• 12. Sharing Your Work Electronically
• New Capabilities in Electronic Sharing
• Using a Word Processor to Create Manuscripts for the Internet
• Advantages of Internet Publishing Software
• Publishing Your Poster on the Web
• Uploading Your Manuscript to the Internet
• Revisiting the Concept of Plagiarism.
• Add to Refworks
• Request through ILL
• Find Related Articles
• Search in Google Scholar
• Borrowing Info
• View in Legacy Catalog

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APA Style Simplified: Writing in Psychology, Education, Nursing, and Sociology Hardcover – 20 April 2012

• Demonstrates how to write objective scientific research papers using interesting prose
• Incorporates guidelines from the 6 th edition of the APA publication manual
• Explores how to develop ideas, connect them to what others have written, and express them clearly
• Discusses the differences between written, oral, and poster presentations and offers instructions for applying APA style to each
• ISBN-10 0470672323
• ISBN-13 978-0470672327
• Edition 1st
• Publisher Wiley-Blackwell
• Publication date 20 April 2012
• Language English
• Dimensions 16.15 x 1.64 x 22.8 cm
• Print length 216 pages
• See all details

Product description

''As with previous editions Browne et al have provided a comprehensive, contemporary and focussed A2 textbook which directly applies to the AQA specification. Via attention to the demands of the specification and reference to exam style questions the book is thoroughly usable and gives the reader clarity of purpose concerning the material. A balance is achieved between classic Sociology and up to date material, giving students the opportunity to recognise the relevance and importance of sociological investigations in today's society.'' David Morton, King Edward VI College, Stourbridge and examiner for AQA

''This is an magnificent text. It won’t just be another text on A2 Sociology for students but will serve as the leader in the area. It has key concepts but with inspirational examples. It’s the best textbook I have ever read for AQA A2 for Sociology.'' Jason L. Powell, Coventry University

''This is an outstanding addition to the range of resources now available to sociology students. Ken Brown and his colleagues represent the very best in sociology teaching and this text, now in its long-awaited second edition, should be on the shelves of all aspiring sociology students and their teachers.'' Dr Gerry Czerniawski, University of East London, A-level examiner and National Teaching Fellow

About the Author

Bernard C. (Barney) Beins , Ph.D., is professor and chair of psychology at Ithaca College. He is author of about 150 articles, chapters, books, and pedagogical material. He recently received the Charles L. Brewer Distinguished Teaching of Psychology Award from the American Psychological Foundation. He is author with his daughter Agatha of Effective Writing in Psychology: Papers, Posters, and Presentations , 2nd Edition (Wiley-Blackwell, 2012) and Research Methods: A Tool for Life (2008). He is a Fellow of the American Psychological Association, the Association for Psychological Science, and the Eastern Psychological Association.

Product details

• Publisher ‏ : ‎ Wiley-Blackwell; 1st edition (20 April 2012)
• Language ‏ : ‎ English
• Hardcover ‏ : ‎ 216 pages
• ISBN-10 ‏ : ‎ 0470672323
• ISBN-13 ‏ : ‎ 978-0470672327
• Dimensions ‏ : ‎ 16.15 x 1.64 x 22.8 cm

About the author

Bernard beins.

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Apa style simplified: writing in psychology, education, nursing, and sociology - softcover, beins, bernard c..

• 3.78 3.78 out of 5 stars 9 ratings by Goodreads

• About this title
• About this edition
• Demonstrates how to write objective scientific research papers using interesting prose
• Incorporates guidelines from the 6th edition of the APA publication manual
• Explores how to develop ideas, connect them to what others have written, and express them clearly
• Discusses the differences between written, oral, and poster presentations and offers instructions for applying APA style to each

"synopsis" may belong to another edition of this title.

About the Author

Bernard C. (Barney) Beins , Ph.D., is professor and chair of psychology at Ithaca College. He is author of about 150 articles, chapters, books, and pedagogical material. He recently received the Charles L. Brewer Distinguished Teaching of Psychology Award from the American Psychological Foundation. He is author with his daughter Agatha of Effective Writing in Psychology: Papers, Posters, and Presentations, 2nd Edition (Wiley-Blackwell, 2012) and Research Methods: A Tool for Life (2008). He is a Fellow of the American Psychological Association, the Association for Psychological Science, and the Eastern Psychological Association.

''As with previous editions Browne et al have provided a comprehensive, contemporary and focussed A2 textbook which directly applies to the AQA specification. Via attention to the demands of the specification and reference to exam style questions the book is thoroughly usable and gives the reader clarity of purpose concerning the material. A balance is achieved between classic Sociology and up to date material, giving students the opportunity to recognise the relevance and importance of sociological investigations in today's society.''David Morton, King Edward VI College, Stourbridge and examiner for AQA

''This is an magnificent text. It won't just be another text on A2 Sociology for students but will serve as the leader in the area. It has key concepts but with inspirational examples. It's the best textbook I have ever read for AQA A2 for Sociology.''Jason L. Powell, Coventry University

''This is an outstanding addition to the range of resources now available to sociology students. Ken Brown and his colleagues represent the very best in sociology teaching and this text, now in its long-awaited second edition, should be on the shelves of all aspiring sociology students and their teachers.''Dr Gerry Czerniawski, University of East London, A-level examiner and National Teaching Fellow

"About this title" may belong to another edition of this title.

• Publisher Wiley-Blackwell
• Publication date 2012
• ISBN 10  0470671238
• ISBN 13  9780470671238
• Binding Paperback
• Edition number 1
• Number of pages 216

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The Introduction Section 37 repeatedly across generations, so beginning the Introduction with a quotation can put your own issue into context. Students … will listen to a lecture, hearing the words and understanding their logical structure and their meaning and, as best they can, … memorize their notes.… But the content does not become part of their own individual system of thought, enriching and widening it. … The student and the content of the lectures remain strangers to each other, except that each student has become the owner of a collection of statements made by somebody else. (From Ku, Dittmar, & Banerjee, 2011, p. 1) Among many people, a quotation by Sigmund Freud will generate interest. In an article on Freudian repression, Boag (2006) took advantage of such reader interest. Freud once wrote, “the essence of repression lies simply in turning something away, and keeping it at a distance, from the conscious” [italics in original]. (Boag, 2006, p. 74) Describing Common Occurrences Another tactic to generate interest in your ideas is to describe a common experience to which the reader can relate. A description of gender-based discrimination in hiring as the focus of an opening paragraph serves as a good example. For traditionally male jobs, … women are less likely to be hired than men. They are also paid less, given less authority, and promoted less often. … Conversely, male applicants are discriminated against for jobs that are considered feminine. (Uhlmann & Cohen, 2005, p. 474) In composing your Introduction, you should ask yourself whether you would enjoy reading it. If you wouldn’t enjoy your own writing, it is pretty certain that nobody else will. As a rule, readers are not going to be captivated by writing that begins with names of authors they don’t know, and they won’t care about the date an article was published (e.g., “In 1999, Dunning and Kruger found that …”). Instead of starting with this type of mundane fact, you should try to entice your reader with your opening statement. The adage that you only get to make a first impression once certainly holds for your Introduction.

38 The Introduction Section How to Begin After establishing the opening premise, authors of a journal article are likely to include references to books, journal articles, and conference presentations. In the first part of the Introduction, there will be little detail about actual research projects. Instead, the references will be related to the overall question being addressed in the paper you are reading. For example, in a research article about true and false confessions in the legal system (Russano, Meissner, Narchet, & Kassin, 2005), the authors began their Introduction with a very broad set of ideas: ● goal of police interrog ation; ● power of a confession on jury decisions; ● value of confession in avoiding trials; ● prevalence of false confessions; ● psychological processes at work in police interrogations. The authors addressed all of these elements in three paragraphs, citing 11  references. Two points are important here as they relate to your Introduction section. First, in a few paragraphs, you are not going to be able to include the details of the research you mention. You have to limit your presentation to the most important, global issues in that research. Second, you have to make it clear how the ideas interrelate and where they are going. You don’t want to give your reader the impression that you are talking about unrelated facts and ideas, merely listing one study after another. Reviewing the Literature No matter how novel your research idea, it is very likely that somebody has already addressed a similar question. You can conduct systematic literature searches to identify research related to yours. Depending on your topic, you might locate many more articles that you could possibly read. You should not try to read everything. Your literature review does not need to be absolutely exhaustive, but it should be illustrative. That is, you should read enough background material so that you can discuss

The Introduction Section 39 the  research and theory, giving a reasonably complete account of our knowledge of the topic. Your treatment of the topic should address any controversies in the area. What are the sources of disagreement among scholars and researchers? You can use selective examples that represent what investigators have found. You might have your opinion regarding the subject you are studying, but stating your opinion is not the purpose of the Introduction. Rather, in this section you are supposed to present ideas that are based on data and theory, including conflicting views of different researchers. You should avoid simply listing a number of studies, describing each one as if it were unrelated to the others. The task is to make it easy for the reader to understand how all of the studies interrelate. Reasons for Reviewing the Literature What could happen if you do not locate relevant references? One possibility is that you might plan and carry out a study that somebody has already done, maybe better than you did. There are few rewards for simple replication of an existing study (Beins, 2009, pp. 77–79). A second reason for perusing the existing literature related to your topic is that a reader will want to know where your idea fits in with other research. Science proceeds one small step at a time; we accumulate knowledge from one project to the next. Your research is one of those small steps. Failure to cite previous research might also lead the reader to question your expertise in the area. A third reason for becoming familiar with the research in your area is that you might spot limitations in early work that you could remedy. Every research project answers some questions but leaves other questions unanswered. Finally, an advantage associated with a thorough literature search is that you may be able to adapt others’ methodologies, their materials and apparatus, and their statistical analysis for your research. Creating a sound methodology on your own is difficult because there are many details you have to consider. It is completely legitimate to use techniques that others have developed. You just need to make sure to credit them for their ideas. Generally, you do not present great detail about methodology in the Introduction, but if methodological details are important in how you set up your study, you might want to introduce them here very briefly.

40 The Introduction Section Clarifying Terms in the Research Researchers in every discipline have developed their own language. There are terms with specific meanings to a researcher that might have a different meaning to a layperson. For instance, schizophrenia is a diagnostic label relating to people with inappropriate affect and behavior, but to the general public, schizophrenia often means a split personality (which a practitioner would call dissociative identity disorder). Similarly, terms may not reflect the same concepts in different domains. For instance, professionals who see the abbreviation SSRI could interpret it differently from one another. Within the realm of treatment for medical disorders, those initials stand for selective serotonin reuptake inhibitors, whereas in studies of emotional intelligence, the initials refer to the Schutte self-report inventory (Schutte et al., 1998), and in sociology and social work, people might recognize it as Duke University’s Social Science Research Institute. In your Introduction, you can let your reader know how you are using important terms. Sometimes you might be discussing a relatively obscure concept that is generally known within a limited domain. Your reader might need help understanding the concept. If you are not making use of any unusual terms or definitions, you probably don’t need to worry unduly that your audience will not follow your argument, but you can let your reader know about potential confusions here. In the same vein, you can use your Introduction to discuss the operational definitions that previous researchers have used, particularly if there is disagreement among professionals about how to measure a construct. For instance, Braveman et al. (2011) noted that a discussion of health care disparities associated with gender, race or ethnicity, disability, and other social factors requires that professionals adopt a clear operational definition of disparity – what it means and what it is based on. Introducing Your Research: Generating a Hypothesis In many cases, your hypothesis will be based on what a theory predicts or what other researchers have discovered. Your hypothesis will carry more weight if you present a logical argument based on existing knowledge. As an example of how to generate a hypothesis, consider how a research team studying terror management theory (TMT) introduced their ideas (Goldenberg, Pyszczynski, McCoy, Greenberg, & Solomon, 1999). They

The Introduction Section 41 noted that TMT deals with people’s feelings of mortality—that is, knowledge that we will die some day—and how we cope with those feelings. Based on previous ideas, they created this train of logic. 1. Rank (1936) posited that neurotic people are highly conscious of their mortality. 2. Eysenck (1971) speculated that neuroticism is related to discomfort with sex. 3. Becker (1973) suggested that thoughts of sex seem to be associated with thoughts of death. 4. Rank (1932) and Becker (1973) hypothesized that if the spiritual or romantic aspects of sex are emphasized, the connection to death is removed. Based on these ideas, the research team generated a hypothesis regarding the tendency to link sex and death among people with high levels of neuroticism. In this research, the logic of their hypothesis was clear and orderly. Previous psychologists had provided the building blocks for the hypotheses; these researchers connected the ideas. Idea 1 People with high levels of neuroticism associate the physical act of sex and death. Idea 2 People with high levels of neuroticism do not connect romantic or spiritual ideas of sex with death. Hypothesis: If people with high levels of neuroticism are induced to think about the physical aspect of sex, they will mentally transform neutral stimuli into death-related responses. On the other hand, if they are primed to think about the romantic aspect of sex, they will be less likely to transform neutral stimuli into death-related responses. This is an interesting hypothesis, but how would you go about testing it? The researchers created a clever research task in which participants completed words when given word fragments (e.g., COFF_ _). These words could be completed to create either death-related words or more neutral words. Thus, COFF_ _ could be rendered either as coffin or coffee. The researchers’ hypothesis was supported. Neurotic individuals tended to form death-related words twice as often when given a prime about the physical aspect of sex than about the spiritual aspect of sex.

42 The Introduction Section The researchers’ study included other variables, but the example illustrates how they developed this particular hypothesis. They had reviewed the research and theoretical literature on this topic and used a logical connection between ideas as a basis to predict the outcome of their study. Goldenberg et al.’s (1999) study in turn led to further research. Dietz, Albowicz, and Beins (2011) reasoned that humor involving sexual themes might stimulate thoughts of death in people scoring high in a test of neuroticism compared to those scoring low. On the other hand, Strick, Holland, van Baaren, and van Knippenberg, 2009) argued that humor protects people from anxiety. So sexual humor might not affect participants high in neuroticism any differently than such humor affects people low in neuroticism. Dietz et al. presented participants with sexually themed jokes followed by word fragments that could be completed with either death-related words or neutral words. They found that sexually themed jokes led to significantly higher generation of death-related words, providing support for TMT and reducing confidence that sexual humor serves the purpose of minimizing the anxiety of high-neurotic participants. In your research project, your hypotheses should be based on what previous research has documented, just as Goldenberg et al. used previous ideas and Dietz et al. used Goldenberg et al.’s ideas. Your hypothesis will be more credible if it follows logically from established ideas. Using intuition or hunches is not particularly helpful because people may consider the same ideas but generate very different intuitions. Finally, you should spell out the implications of your study. In their study of TMT, Goldenberg et al. essentially said, “This is what we expect to find (i.e., their hypothesis) and this is what it will mean regarding support for terror management theory (the implication).” In your writing, you should also give the reader a preview of what it will mean if your data support your hypothesis. At the end of your Introduction, the topic of your study should be obvious, the current state of knowledge about this topic should be apparent, your expectations should be unambiguous, and the potential impact of the study should be defined. Obviously, there is no simple formula for accomplishing all of this, but a well-constructed Introduction will contain all of these elements to some degree. Thus, the reader will know where your ideas came from and where you are going to take them.

APA Style Simplified: Writing in Psychology, Education, Nursing, and Sociology, First Edition. Bernard C. Beins. © 2012 John Wiley & Sons, Inc. Published 2012 by John Wiley & Sons, Inc. 4 The Method Section Research is formalized curiosity. It is poking and prying with a purpose. Zora Neal Hurston It doesn’t matter how beautiful your theory is, it doesn’t matter how smart you are—if it doesn’t agree with experiment, it’s wrong. Richard Feynman Language lovers like the following sentence: Time flies like an arrow, but fruit flies like a banana. At first, the beginning and the ending don’t seem to go together, but if you puzzle through it, you can get its meaning. When you write a paper, your introduction and your conclusions should go together, but your reader should not have to work hard to figure out the connection. Your Method section shows how the ideas in the beginning are linked to the ideas at the end. This chapter will highlight the points that you should include in the description of your methodology. These points link the more general or abstract concepts in your Introduction to the specific means you used to investigate the concepts critical to your research. The Method section provides your reader with a detailed picture of exactly what you did. It is oriented more toward technical details and much less toward the subject matter of your study, and it serves two basic functions. First, it allows readers to evaluate how well your methodology answers your research question and leads to your conclusions. Second, the description of your methodology provides other researchers with the information they need to be able to replicate your study.

44 The Method Section As such, you should offer the reader sufficient detail to permit comprehension and reproduction of your research. It seems that providing such a description should be fairly easy, but, in reality, you have to make quite a number of choices about what to include and what to omit. If you give too little detail, your reader will not have sufficient knowledge to assess your study or to replicate it. If you give too much detail, you will lose your reader in boring and meaningless facts. When you write your Method section, keep your readership in mind. You have planned and conducted your study, so you know just about everything important about it. However, what you are writing may be the first exposure that the readers have had to your topic, so concepts that are completely clear to you may be largely unknown to others. Similarly, the methodology for your study may be obvious to you because you may have tested many participants. It will not be so clear to a naive reader. A good strategy is to regard your audience as consisting of people who are intelligent but uninformed. Your writing should call upon their intelligence as you teach them about your research. Participants and Subjects Human Participants Much behavioral research entails studying college students (Plous, 1996; Thomas & Blackman, 1992). These students have some generally predictable characteristics: They tend to be young, often first-year students; White; middle class or higher, socioeconomically speaking; female; intelligent and well educated; cooperative; and responsible. However, even though most research involves people, some psychologists study nonhuman animals. Historically, people (or indeed nonhuman animals) were referred to as subjects. Then, a couple of decades ago, the authors of the APA’s Publication Manual decided that because people are actively engaged in a behavioral or cognitive research session, the term participant was more descriptive. Although researchers generally complied with this decision in their writing, it was not unusual to hear people speaking about subjects, and some people (e.g., Roediger, 2004) were quite unhappy about the new terminology. The people at the APA must have been paying attention because the current version of the Publication Manual specifies that the word subject is appropriate (American Psychological Association, 2010). So in your writing, either subject or participant is acceptable in APA style. Your instructor may

The Method Section 45 have a preference, however, and you should describe the people in your research accordingly. The difference in designation relates to the fact that a research situation is also a social situation. Those who volunteer to take part in research interact with experimenters and have expectations about how they should act. In addition, experimenters can communicate subtle expectations. Consequently, the argument was made that people who volunteer for research are more than just subjects of study; they are active participants because they help create the dynamics of the experimental situation. When people are going about their business without knowing that a researcher is studying them, like in a purely observational study, the people are truly a subject of study. The researcher’s expectations and perspectives might affect the type of observations made, but the researcher’s act of observing will not affect the behavior of the person being observed. Likewise, researchers generally believe that experimental animals do not interact with the experimenter in the same sense that humans do. The reality is that an experimenter’s expectations can affect a rat’s behavior (Rosenthal & Fode, 1963; Burhnam, cited in Rosnow & Rosenthal, 1993). Nonetheless, it is common in APA style to refer to nonhumans as subjects. Whether you intend to study people, rats, pigeons, mealworms, or some other species, it is important to inform the reader of the characteristics of those organisms. There are particular characteristics of your participants that you should communicate with your reader. Table 4.1 presents some of the general participant characteristics that you need to provide. This listing is not exhaustive, and you can tailor the information you include to meet the specific needs of your own study. If your research question involves comparing people in different age groups, you want to emphasize the ages of people in the groups you compare. If your research entails a discussion of cultural differences, you need to tell which cultures you are studying, including enough detail about your participants so the reader is confident that your comparisons are meaningful. As the Publication Manual notes, even if the characteristics of your participants (e.g., sex, age, ethnicity) are not part of your data analysis, providing that information may help the reader understand the implications and limitations of your research better (American Psychological Association, 2010, p. 30). Other information that you should report is the strategies by which you recruited your participants and the number of participants who completed the study or who dropped out. These elements can be important in

Table 4.1 Demographics of Research Participants Characteristic What to report Age Older children, adolescents, and adults ● Average age of sample, in years ● Range and/or standard deviation of age Young children ● Age in months ● Range and/or standard deviation Sex Number of female and male participants (man and woman, boy and girl are nouns; male and female are typically used as adjectives, although if the range of ages is considerable, APA style permits the use of male and female as nouns.) Ethnicity General designations ● White ● Black (or African-American) ● Indian (or Native American) ● Hispanic (or Latino/Latina) ● Asian Examples of subgroups if different cultures are a focus of the research Hispanic: ● Mexican-American ● Mexican ● Puerto Rican ● Colombian Asian ● Chinese ● Japanese ● Thai ● Vietnamese ● Indian Recruitment method Nonprobability samples ● Convenience samples (e.g., solicitation in psychology classes) ● Notices posted in public spaces, newspapers, etc. ● Purposive (judgmental) sampling ● Chain-referral sampling Probability samples ● Simple random sampling (for which you specify your population) ● Stratified random sampling Inducement to participate Extra credit in class Possibility of winning a prize in a raffle for all participants Money (including amount) No inducement

The Method Section 47 understanding your results because a random sample of people from a population may act differently from a convenience sample. For example, in studies of bulimia, samples that consist of people referred to the researchers by doctors show more severe symptoms than do samples of people suffering from bulimia in the general community (Fairburn, Welch, Norman, O’Connor, & Doll, 1996). In addition, people who are persistent enough to complete the study may be very different from those who are not. Thus, the conclusions you draw based on your research may have a lot to do with your sample. The particular characteristics you should include will depend on the research question you are asking. Box 4.1 gives some examples of participant descriptions that require varying levels of detail. For instance, Wimer and Beins (2008) studied the effect of misleading information on participants’ ratings of jokes. The intent was not to investigate cultural or social factors, so a fairly broad characteristic of participants sufficed. On the other hand, Vorauer and Sakamoto (2006) studied formation of friendships across ethnic groups. In order for a reader to understand the nature of their research topic, the investigators provided information about ethnicity, but they did not give information about the ages of participants. Although it is common for researchers to identify the ages, Vorauer and Sakamoto simply referred to “introductory psychology students” (p. 327). The implication here is that they were traditional college-aged students, about 18–22 years old. Sometimes, the population of participants is quite specialized and merits significant detail. This was the case for the research of Lann-Wolcott, Medvene, and Williams (2011), who studied caregivers in nursing homes and provided significant details about the people who participated in the project. Similarly, Grandey, Fisk, and Steiner (2005) investigated cultural differences in employees in the workplace. So they presented detail that far exceeds that of laboratory research, including types of jobs in which people were engaged, how long they had been employed, how many hours they worked per week, and nationality. When research involves cultural considerations, researchers often include very detailed information about participants. For example, Abu-Bader, Tirmazi, & Ross-Sheriff, 2011) provided extensive detail about participant characteristics in their work. Their study, which is categorized as gerontological social work, involved elderly people who were members of a minority group, so issues of age, ethnicity, and length of time in the United States were all important.

Box 4.1 Participant Descriptions From Journal Articles Involving People General Characteristics for Research That Does Not Study Social or Cultural Variables Ninety undergraduate students are tested in this study. The participants include 61 women and 29 men whose ages ranged from 17 to 23 years (M = 18.9, s = 1.2). They volunteered in order to receive extra credit in psychology classes. (Wimer & Beins, 2008, p. 352) More Specific Characteristics for Research That Studies Social or Cultural Variables One hundred and twelve introductory psychology students (56 same-sex pairs) participated in the study in exchange for partial course credit. There were 22 White-White pairs, 19 White-Chinese pairs, and 15 Chinese-Chinese pairs. The ratio of male to female pairs was approximately the same across the three pair types …. Students were assigned to pairs on the basis of scheduling convenience …. (Vorauer & Sakamoto, 2006, p. 327) Highly Specific Characteristics Required for Understanding the Research Residents’ ages ranged from 69 to 97 years (M = 82.9, SD = 8.2). Of those residents, there was one African-American male, four Caucasian males, and 15 Caucasian females. Participants’ functional status was obtained using the Activities of Daily Living Scale with scores ranging from 7 to 52 (M = 29.6, SD = 10.5), with higher scores indicating greater dependency of residents on caregivers. The Minimum Data Set Cognition Scale (MDS-COGS) ranged from 4 to 9 (M = 6.4, SD = 1.4), indicating that participants were in the moderate stage of dementia (see Table 1). (Lann-Wolcott et al., 2011, p. 92) Description of Recruitment of Participants in a Research Project The study was conducted among staff members of 11 nursing homes of a supra-regional organization for residential elderly care located in a federal state in Germany. Participants were recruited through announcements at staff meetings and memos sent by the managers of the homes. A total of 379 out of 557 employees followed the invitation to participate in the study. Participation was voluntary, could be refused and was not motivated by any extrinsic incentives. All participants were geriatric nurses or other nursing personnel, representing 68 percent of the target group of the study. (Schmidt & Diestel, 2011, p. 317)

The Method Section 49 Participants in the study were recruited from four mosques and Islamic centers in the Washington DC metropolitan area (Washington, Maryland, and northern Virginia). The sample comprised 70 participants; 44 were men (70%), 56 were married (81%), 31 were Asians (44%), and 28 had graduate degrees (40%). Their ages ranged between 50 and 92 with a mean age of 63 years (SD = 10.2). They had been in the US between 5 and 53 years with a mean of 26 years (SD = 10.6). They self-identified as elderly. Table 1 describes the sample characteristics in more detail. (Abu-Bader, Tirmazi, & RossSheriff, 2011, p. 433) Sometimes, it can be important to be aware of specific characteristics of participants. In educational research, for instance, ages of participants can be important. Also, if there are cultural components to the research, it is a good idea to present the ethnicity or country of origin of people involved in the study. A total of 577 teenagers from four different schools participated in the study. There were 132 Year 9 pupils from the United Kingdom (mean age = 13.87 years, SD = 0.34 years; 75 girls) and 189 from Hong Kong (mean age = 13.96 years, SD = 0.70 years; 77 girls), 125 teenagers attending Year 12 in the United Kingdom (mean age = 16.94 years, SD = 0.50 years; 68 girls), and 131 in Hong Kong (mean age = 17.47 years, SD = 0.65 years; 73 girls). (Ku, Dittmar, & Banerjee, 2011) Another element of describing participants involves their motivation for participating. Sometimes, people receive money for participation in research; sometimes, they simply do it because they think it is a valuable activity in and of itself. Schmidt and Diestel (2011) investigated the strains and job demands of nurses by using a questionnaire. In addition to the characteristics of people in their sample, they provided information about recruitment and participation. You should include participant characteristics that relate to your research hypotheses. If you are studying visual memory, you should include characteristics such as age, sex, education level, whether your participants have a background in visual arts, any visual impairments, and so on; in most studies of this type, it is probably not critical to indicate cultural backgrounds of your participants. On the other hand, if you are studying cross-cultural issues, ethnicity would be a critical variable to report. The information that appears in journal articles that you read can provide guidance.

50 The Method Section Confidentiality of Participants In your description of research participants you are supposed to provide detailed information. But, in virtually any project you complete, you need to keep participant identities anonymous and confidential. Data are anonymous when nobody can link a particular piece of data to a single individual; data are confidential when nobody outside the research project has access to information that could link participants to the study or to their data. When you present participant information in the Method section, you are legally and ethically bound not to divulge details that will reveal the identities of your participants. Most psychological research is benign, but some studies do pose risks. You must observe extra caution if your research involves sensitive issues. Revealing behaviors of participants in such research to people who are not part of the research project would be unethical. Nonhuman Subjects In some medical and some psychological research, projects may involve nonhuman animals. In such research, you identify the animals in the subsection of the Method labeled Subjects. You need to specify the type of organism (genus and species), the age, where you obtained them, and their physiological condition. Behavior that holds true for one species may not generalize to another, although sometimes you can predict behaviors from one type of animal to another. For instance, in some cases, nonhuman animals such as cockroaches make decisions like humans (Warren, 1965). But you cannot count on such generalizability. The fact that behaviors across species may or may not be similar in a given environment necessitates that you offer information on the species you used. Some examples of how researchers have described the animals they have used in their studies appear in Box 4.2. As you can see, the authors have presented considerable detail about the animals. Attrition Sometimes the humans or animals you are studying do not finish your study, particularly in projects involving repeated testing sessions. People may not return; in some studies involving patients, they may leave a hospital or they may have died. Animals similarly may become ill or die. You should report the attrition rate in the Method section and speculate in the Discussion section how it might have affected your data.

The Method Section 51 Box 4.2 Examples of Descriptions of Subjects in Research Involving Nonhuman Animals Rats Male Wistar rats from Pasteur Institute (Iran), weighing 180–230 g at the time of surgery, were used. The animals were housed four per cage, in a room under a 12 hr light–12 hr dark cycle (lights on 07:00 h) and controlled temperature (23 ± 1°C). Animals had access to food and water ad libitum and were allowed to adapt to the laboratory conditions for at least 1 week before surgery. Rats were handled about 3 min each day prior to behavioral testing. All experiments were performed between 9:00 and 13:00 hr and each rat was tested only once. Eight animals were used in each group of experiments. (Jafari-Sabet, 2006, p. 121) Monkeys We studied 32 mated, but nonbreeding, adult cottontop tamarins from a captive colony at the University of Wisconsin-Madison Psychology Department. We housed all colony animals socially throughout their lives, either in family groups or in mated pairs. The tamarins ranged in age from 1 to 6 years old and had all been paired for at least 6 months prior to the beginning of the study. Tamarin pairs live in cages measuring 160 × 93 × 263 cm, furnished with natural tree branches, ropes, acrylic or polycarbonate sheeted nestboxes and various toys for environmental enrichment. We fed the colony three times daily from food platforms at least 1 m above the floor. Water was available ad libitum. For further details on colony husbandry refer to Ginther et al. (2001). Testing occurred either between 10:00 and 11:30 hr, before the main feed, or between 15:00 and 16:00 hr, before a high-protein snack. (Moscovice & Snowdon, 2006, p. 935) Pigeons The subjects were 16 female Carneau pigeons about 1 year old, originally purchased from Palmetto Pigeon Plant. They had previously participated in another autoshaping experiment that had used different stimuli. The assignment of pigeons to groups in the present experiment was random with respect to their previous treatments. They were housed in pairs and maintained at 80% of their free-feeding weights. (Rescorla, 2006, p. 139)

52 The Method Section The problem with attrition, sometimes called subject mortality, is that the subjects or participants who leave your study may be quite different from those who remain. In some cases, researchers can assess the degree of similarity between those who stay and those who leave. When those who drop out are generally similar to those who remain, the outcome of the study may not differ much from what would have occurred without attrition (e.g., LaGreca, Silverman, Vernberg, & Prinstein, 1996). Unfortunately, in many cases, researchers cannot evaluate how attrition has affected their studies. Thus, if you lose participants over the course of your research, you need to specify the attrition in detail. Materials and Apparatus When you conduct a study, you may need stimulus materials or other implements. If you are studying learning, your participants must have something to learn. If you survey your participants, you need a questionnaire of some kind. If you are observing people for the presence of a behavior, you will need a behavior checklist. If you are performing a surgery on a rat, you will need surgical instruments. In preparing your manuscript, you need to tell the reader about materials (e.g., questionnaires, stimulus words) and apparatus (e.g., devices to record data, surgical implements) that you used. You do not need to detail ordinary instruments you might use, such as stopwatches and computers, beyond mentioning that you used them. For specialized or unusual equipment, you should produce a clear and complete description of what you used. For example, Kolovelonis, Goudas, and Dermitzaki (2011) use a dart-throwing task in a physical education class to assess whether progress in a task differed when students recorded their own scores. (It did.) These authors provided detailed information about the dart-throwing setting because they recognized that other educational researchers would not be familiar with their setup. A lot of behavioral research with people involves little apparatus that merits detailed description, but materials appear in virtually all humanoriented studies. The difference in designation between materials and apparatus is that materials are often printed on paper or displayed on a computer screen. Apparatus refers to instruments used by the researcher. Another way to differentiate them is that, if you drop an apparatus, it breaks; if you drop materials, they simply scatter on the floor.

The Method Section 53 In general, if researchers are likely to be familiar with your materials and apparatus, you need only mention them. But if you created your own materials, you should give a very detailed depiction of them. If you are using relatively unknown materials or apparatus created by others, you should provide a description of them and indicate to the reader where to obtain them. If you used personality inventories or questionnaires, it is a good idea to indicate levels of reliability reported by previous researchers. Some examples of descriptions of materials from published journal articles are given in Box 4.3 and apparatus in Box 4.4. You can see the extent of the description of materials provided by AbuSeileek (2011) in educational research on learning English as a second language. The materials were not as straightforward as those used by Schatzman and McQueen (2006) and merited a very detailed presentation. Readers often do not get as interested in this aspect of the methodology as they do in the hypotheses and theories, but knowing about the materials and apparatus can be critical in a reader’s decision about whether the research is worthwhile. Box 4.3 Examples of Details of Materials in Published Research Articles Description of Stimulus Materials Seven reading passages of about 400 words each were used. [Texts were listed here.] … The texts were read by a pilot study of 32 first-year first-semester students and three raters. Two of the raters were English language EFL instructors and the third was a native speaker who is a specialist in applied linguistics. Students in the pilot study and the three raters read the texts to decide difficulty level (unfamiliar words to the students), interest and text suitability. The texts were selected based on the following criteria: 1) The textbook was written by a team of six professors who are specialists in English language and applied linguistics, and it is revised regularly. 2) It has been used by students in the University of Jordan, the most prominent university in Jordan, since 1999 in general language courses, and many other universities and institutions have been using it.

54 The Method Section 3) It contained some of the words that were not known for the students in the pilot study. A total of 175 words were selected by the three raters as difficult; most of them (151) were also listed by the authors as not familiar to the students. However, some of these items were deleted because they were known by the majority of the students in the pilot study, so only 147 of them were glossed. 4) Each gloss contained a synonym of 1-word, or a definition of 2–7 words. The number of glossed vocabulary items in each category from 1–7 words was twenty-one; each of the seven reading passages contained twenty-one glossed vocabulary items, distributed on three glossed vocabulary items of each category. 5) The majority of the students in the pilot study (4.59 on a five-point Likert scale) and the raters categorized the texts as interesting. They (4.78 on a five-point Likert scale) also agreed that it is not possible to understand the texts without the glossed items as the task could not be carried out without understanding them, and (4.69 on a five-point Likert scale) additional vocabulary items do not cause comprehension difficulty for participants. 6) Each text consisted of about 400 words, a number which is suitable for beginners such as the sample of this study. (AbuSeileek, 2011, p. 1283) Twenty line drawings of nonsense objects were randomly selected from a database of nonobjects (see Fig. 1 for examples). Ten consonant-vowel-consonant (CVC) Dutch nonwords (e.g., bap) were selected as monosyllabic novel words. Ten bisyllabic novel words were constructed by adding a second syllable to each of these monosyllables (e.g., baptoe). These items are listed in Table 1. The nonsense-object pictures were randomly assigned to the novel words. (Shatzman & McQueen, 2006, p. 373) Description of Questionnaires Marsh’s (1990) Self-Description Questionnaire (SDQII) is designed to measure self-concept in adolescents. Three scales, each containing 10 items, were used in this study: the general school scale (academic self-concept), the general self scale (global self-esteem), and the emotional stability scale. The coefficient alpha estimate of reliability of scores on each of the SDQII scales has a median of .87 ….

The Method Section 55 Box 4.4 Examples of Details of Apparatus in Published Research Articles Many behavioral journal articles do not involve apparatus, but they do involve materials. Researchers are likely to use different types of apparatus in studies involving children and nonhuman animals, and in those involving perceptual processes. Much human-oriented research involves only materials. Research with Humans The dart-throwing skill was selected because it was a novel task, preventing the effect of students’ previous learning experiences (Zimmerman & Kitsantas, 1997). A dartboard and three darts, with steel tip, metal barrel, and plastic shaft and flights were used. The dartboard, made from papier-mâché, had a diameter of 40 cm and consisted of ten concentric circles. The central circle had 4 cm diameter and a value of ten points and each of the succeeding nine concentric circles increased the semi-diameter by 2 cm, diminishing its value by one point respectively. To control for the effect of students’ height, the dartboard was fixed on a tripod and its height was adjusted so [that] the central circle [was] at each students’ eye level. (Kolovelonis et al., 2011, p. 358) The apparatus consisted of a wooden display box (106 cm high × 101 cm wide × 35 cm deep) that was mounted 76 cm above the room floor.… Each infant sat on a parent’s lap and faced an opening (41 × 95 cm) in the front of the apparatus; the opening was hidden by a curtain that was raised at the start of the trial. An experimenter introduced his or her right hand (in a yellow rubber glove) into the apparatus through a curtained window (36 × 43 cm) in the back wall. (Wang & Baillargeon, 2005, p. 545) To measure test anxiety, Sarason’s (1972) 37-item Test Anxiety Scale (TAS), with test-retest reliability at least .80 (Spielberger, 1976) was adapted. It incorporated Sarason’s later (1984) work that differentiated the TAS into four components—test-irrelevant thinking, worry, tension, and bodily reactions. (Matters & Burnett, 2003, pp. 243–244)

56 The Method Section Research with Nonhumans The monkeys were tested by using the Language Research Center’s Computerized Test System (described in Rumbaugh, Richardson, Washburn, Savage-Rumbaugh, & Hopkins, 1989; Washburn & Rumbaugh, 1992) that consisted of a PC, a digital joystick, a color monitor, and a pellet dispenser. Monkeys could manipulate the joystick through the mesh of their home cages, producing isomorphic movements of a computer-graphic cursor on the screen. Contacting appropriate computer-generated stimuli with the cursor resulted in the delivery of a 94-mg fruit-flavored chow pellet (Bioserve, Frenchtown, NJ) by using a Gerbrands 5120 dispenser interfaced to the computer with a relay box and output board (PIO-12 and ERA-01; Keithley Instruments, Cleveland, OH). (Beran, Smith, Redford, & Washburn, 2006, p. 112) Procedure A thorough and detailed description of the actual behaviors of participants and of researchers is critical for complete understanding of research. The trick is to include the important material and to omit the details that are irrelevant to the outcome of the study. You should specify the sequence of steps associated with the data collection, including what the researcher does and what the participants do. This information is likely to merge the actual procedures with the materials and apparatus because it is hard to say what the participants were doing without indicating what they were doing it with. There are some fairly standard elements in the procedure. They include: ● variables that are manipulated and measured, including independent and dependent variables; ● any conditions or groups that you intend to compare; ● how participants are assigned to, or placed in, groups; ● the role of the researcher in the session; ● the directions that participants received; ● the activities in which the participants engaged.

The Method Section 57 Table 4.2 Common Elements in the Design Subsection Element of design subsection Possible examples Design Experiment with (manipulated) variables ● Quasi-experiment (measured or categorical variables) ● Mixed design (experimental and quasi-experimental variables) ● Correlational design ● Observational study ● Archival study Variables ● Independent variables ● Dependent variables ● Matched variables ● Extraneous or confounding variables Assignment of participants ● Random assignment ● Systematic assignment ● Assignment by pre-existing characteristics (e.g., sex, age) In essence, you should present only detail that relates to the data you collect. So if your participants are learning something, you describe the nature of the stimuli that they are learning. The nonsense words that Shatzman and McQueen (2006) created for their research, as described in Box 4.3, might produce very different learning than actual words. As a rule, you do not need to report verbatim the directions to the participants. A summary will suffice unless there is something unusual about the instructions. If your instructions are part of a manipulation, you might want to report exactly how you instructed the participants. You should ask yourself if somebody could conduct your research in all its important aspects from the information you provide. If the answer is no, you should provide more detail. Finally, sometimes writers include a statement with the procedure that participants provided informed consent. When you write your own procedure section, you can determine whether to include how you obtained informed consent. Strictly speaking, it is not part of the data collection process, so you can logically argue that it does not belong in this subsection.

58 The Method Section However, for purposes of establishing that the researcher followed ethical guidelines, writers sometimes discuss how they obtained informed consent during the study. Design The final, common subsection regarding methodology offers a statement of the design of your study. The reader will benefit from a clear statement of your independent variables (IVs), the groups that constitute the IVs, and your dependent variables. You also specify here whether you tested your participants more than once (repeated measures) or only once (independent measures). If your design is not yet apparent to the reader before this subsection, you have one final chance to indicate how many variables are of interest in this research and how you set up your study. Table 4.2 identifies common elements that appear in the design subsection. The guidelines presented in this chapter are not fixed rules. If it makes more sense for your presentation to identify your variables in the Procedure subsection, you may not need to include a Design subsection. The important point is that the reader should be able to find information critical to understanding your research.

APA Style Simplified: Writing in Psychology, Education, Nursing, and Sociology, First Edition. Bernard C. Beins. © 2012 John Wiley & Sons, Inc. Published 2012 by John Wiley & Sons, Inc. 5 Communicating Statistics If you want to inspire confidence, give plenty of statistics. It does not matter that they should be accurate, or even intelligible, as long as there [are] enough of them. Lewis Carroll When you can measure what you are speaking about and express it in numbers, you know something about it. Lord Kelvin If you can’t fall asleep, you might try counting sheep. As everybody knows, numbers can put your mind into a stupor that is every bit as deep as sleep. When you are reading and you see a set of numbers, does your mind go numb? If so, you know exactly what you want to avoid in your writing. Your own numbers should support your arguments, but most of the time people are going to be more interested in the words you write. Statistics have a reputation that, to most people, is more negative than positive. People try to avoid them because statistics involve numbers, because statistics can be hard to understand, and because statistics can be deceptive. We too often forget that, when we use them properly, statistics can be highly informative. In the end, they are merely tools that people can use well or poorly. The problem is that too many of us are so intimidated by them that we fail to evaluate them critically. We need to remember that people use statistics just as they use words—to communicate a message. A good speaker can

60 Communicating Statistics persuade an audience with well-chosen words. Politicians are very adept at creating messages to convince us that they are looking out for our welfare. It is not the fault of the words when the politicians end up lining their own pockets at taxpayers’ expense. The problem lies with the failure of people to evaluate critically what the politicians are saying. The same dynamic occurs with statistics. If people are willing to accept statistics at face value, there is no limit to the chicanery that others can perpetrate with numbers. However, as McGrath (2011) has said, “If you think it is easy to lie with statistics, you should see how easy it is to lie without them” (p. 5). Fortunately, when social researchers use statistics to bolster their arguments, they are generally not trying to deceive. Rather, they are using statistics as a tool to help them understand behavior and to let them generate logical and believable conclusions. After drawing their inferences, researchers then use statistics to convince others of the validity of those conclusions. In your own work, effective communication requires that you know your audience. If you are writing for a statistically sophisticated readership, you can probably assume that people reading your material will possess the technical information necessary to understand the statistics you have used. So a complete and thorough presentation of those statistics would be entirely appropriate. On the other hand, if your audience is less sophisticated about research and statistical techniques, presenting technical jargon will probably only confuse and distract them from the message that you are trying to convey. When you write, it may be very helpful for you to imagine what a reader will think when reading your words. This strategy is useful when you decide how you want to frame your message. Why Do We Use Statistics? The human sciences are empirical disciplines, so we gather information to answer our questions. Most of the time, this information goes by the name data and most of the time it involves numbers. If we study a large group of people (or rats, pigeons, etc.), we generate a lot of information and we have to make sense of it. The most common way of making a large amount of data comprehensible so we are not swamped with too much information is to find the average score. There are several distinct averages, each computed

Communicating Statistics 61 differently. The common averages are the mean, the median, and the mode. The most frequently used is the mean, which is the technical term for the score you obtain by adding all your numbers and dividing by the number of scores you added. In everyday language, most people use the word average to reflect the mean. Sometimes we also want to find out whether the scores are bunched together or spread apart, so we find the standard deviation. Together, these two statistics give us a sense of the typical score (the average) and how far from the typical score we can expect other scores to be (the standard deviation). A second use of statistics is to allow us to evaluate the similarities and differences between groups. So if we have, say, 100 measurements from one group and 100 measurements from another group, we can compute two numbers for each group, the mean (the average score) and the standard deviation (an indicator of the spread of the scores), to find out if the two groups are similar or different. There are other ways to assess the relations among the numbers, but they all have the same goal of allowing us to put measurements in context or to create a perspective that helps us think about what they mean. A third use of statistics is to draw inferences about whether we should believe that our numbers are reliable. That is, if we gather our data from one group of people, can we expect the same result if we repeat our research or take the same measurements from a different group of people? The purpose of these inferential statistics is to establish a level of confidence that what holds true for the groups we measured will be true for different groups as well. If you keep these three general uses in mind when you read about statistics, you will have an easier time understanding what a writer is trying to convey. Similarly, if you make clear to your reader why you are using a particular statistic, you will be helping your reader to understand the points you are making. What Point Are You Trying to Make? Using numbers to make a point is not much different from using words to make the same point. In both cases, you introduce an assertion that you subsequently support with logical argument. The difference between statistics and words is that statistics involve a shorthand way of making your case.

62 Communicating Statistics To use one of the simplest types of statistics, the average, you are able to tell the reader what is typical. An average simply depicts what you can generally expect. To cite an average, however, hides a significant amount of background information. Specifically, when you give an average, you are relying on the reader to understand that you had a group of measurements and that you used some kind of arithmetic or numerical action to come up with a single number to represent the entire group. In addition, if you are communicating with a lay audience, you might want to point out that nobody may actually have scored precisely at the mean, even if a lot of scores might be close to it. Furthermore, there are different types of averages, so your reader should know that there are various ways to come up with the average. So when you say that “The average is X”, you are only giving the final step in a sequence. To make your point effectively, it is important not to let the reader lose sight of that point or why each statement you make relates to it. Burying readers in a bunch of numbers and symbols whose relevance they do not understand is a good way to divert their attention from your message, reducing its impact. So how can you make sure your readers know where you are taking them? You must give them sufficient detail to allow them to assemble a complete picture. This means writing a verbal statement of your point, presenting relevant statistics, and creating a verbal statement of what the numbers mean. Presenting only the numbers poses a risk that readers will not connect them to your main point; presenting only words poses the risk that readers will not be able to evaluate the cogency of your argument. For each statistic that you provide, you need to include four elements for the benefit of the reader. ● a statement of the point you are addressing; ● a verbal summary of your statistic; ● the numerical presentation of statistical information; ● how the numbers relate to your point. Your language should be as simple as possible, while allowing you to make your point. Some writers, particularly beginning writers, believe that using complex terms and difficult vocabulary will lead to more credible prose. The reverse is be more likely. If readers do not understand the point you are trying to make, your writing will be entirely ineffective.

Communicating Statistics 63 Understanding Your Numbers Numbers, by themselves, are not particularly interesting or informative. As researchers use numbers, those numbers take on meaning only when they link to an idea. We use our statistics as tools to help us understand behavior. As such, the numbers should take second place to your ideas when you write. So you should strive to make your prose complete and comprehensible even without the numerical information. The statistics reinforce your writing in much the same way that quotations support writing in the humanities. In both cases, the writer creates an argument and then buttresses it with numbers (in the sciences) or with quotations (in the humanities). Table 5.1 provides two examples of how the technical information complements the verbal presentation but is not necessary for understanding the ideas. Naturally, the more information you include, the greater the understanding that your reader will gain. But you should be able to communicate the main points of your research without reliance on any statistical presentation whatsoever. Table 5.2 illustrates frequently asked questions that behavioral researchers pose in their studies and the statistical tests that are appropriate for those questions. If you ask whether the averages of different groups are comparable and if your statistical test tells you that the answer to the question is “no,” you have a significant effect. Basically, significant means reliable; a significant difference means that the difference is reliable—you can count on it happening again. So t-tests and F-tests let you know if the difference between groups is likely to appear again if you do your research a second time. Similarly, if scores are paired so that knowing something about one member of the pair helps you predict the second, there is a significant relation between the two variables. Again, significant means reliable. So if you were to conduct your study a second time, you could reasonably expect to see a similar pattern in the data. The term significant is a technical term, one that psychologists would probably change if they could. It means something very different in statistics and in everyday life. The reason for this usage is that one of the most influential statisticians in history, Ronald Fisher, noted that a statistical result could signify something, therefore it was significant. However, that usage has not survived to the present (Salsburg, 2002). In a casual conversation, if somebody tells you a significant fact, it is probably an important fact. But if your results are significant, they may or may not be important; they are simply reliable—that is, likely to recur if you replicate your original study.

64 Communicating Statistics As an example, if standardized aptitude test (SAT) scores increase nationwide by only a few points from one year to the next, that difference is going to be statistically significant because huge sample sizes make it easy to spot small, but reliable, differences. The difference is significant in a statistical sense, but it is not particularly significant in a real-world sense. This is why psychologists have recently started reporting a statistic called an effect size. The effect size can tell you if an experimental manipulation produces a large, medium, or small effect. The larger the effect size, the more meaningful it is in a real-world sense. Table 5.1 Examples of Text With and Without Statistical Information Each version should communicate the same basic information; the numbers should not be necessary for the reader to understand your message. Statistical information included Statistical information excluded Participants rated how offensive they found a set of jokes. The average rating was highest for jokes victimizing women (M = 3.6), with jokes victimizing men (M = 2.1) and neutral jokes (M = 2.3) showing lower levels of perceived offensiveness. This effect was significant, F(2, 160) = 91.782, p < .001. (Beins et al., 2005) Participants rated how offensive they found a set of jokes. The average rating was highest for jokes victimizing women, with jokes victimizing men and neutral jokes showing lower levels of perceived offensiveness. Participants in the different groups expected jokes that varied in humor level. People who believed that they would read very funny jokes found the jokes the funniest (M = 4.8), followed by participants in the neutral, no-information condition (M = 4.0), and then by participants who expected very unfunny jokes, who found the jokes least funny (M = 3.2). When we led participants to expect either horrible jokes or hysterical jokes, the ratings did not differ significantly from the neutral group (M = 3.5 and 3.9, respectively), F(4, 85) = 8.59, p < 001. (Wimer & Beins, 2000) Participants in the different groups expected jokes that varied in humor level. People who believed that they would read very funny jokes found the jokes the funniest, followed by participants in the neutral, no-information condition, and then by participants who expected very unfunny jokes, who found the jokes less funny. When we led participants to expect either horrible jokes or hysterical jokes, the ratings did not differ significantly from the neutral group.

Communicating Statistics 65 Helping Readers Understand Your Statistics A number of authors (e.g., Best, 2001, 2004; Huff, 1954; Tufte, 1983) have perused the research literature and the popular media and have found examples of poor statistical communication. Fortunately, we do not need to be victims of poor statistics. Huff (1954) suggested that experts at statistical deception know how to mislead people with numbers, so it would be a good idea for the rest of us to know about these tricks. Best (2001) stressed that the cure for the so-called “bad statistic” is a recognition that we must evaluate numbers. One of the most effective strategies to avoid giving your reader the wrong message with your numbers is to keep your presentation as simple as possible. As the information becomes more complicated, you need to include more statistics, but your goal should be to minimize the likelihood that your information will distract the reader from the point you want to make. Tufte (1983) has coined the term chartjunk to describe elements that appear in graphs that distract the reader from understanding the content in the graph. He suggested that, if you create a figure, you should maximize the Table 5.2 Common Research Questions and the Traditional Statistical Tests Associated With Them Each type of test has variations that are appropriate for different circumstances. You can refer to statistics and research methods books for details on appropriate use of these tests. Question Statistical test Do two groups have the same averages?* t-test, z-test Do three or more groups have the same averages? Analysis of variance (F-test) When two scores are paired, are the two scores independent? Or does knowing one score give you a clue about the other score, reflecting that the scores are dependent and related to one another? Correlation (ρ, r, Φ) For paired scores, if you know the score of one member of the pair, can you predict the second score at above-chance levels? Regression analysis If you have multiple categories into which different observations can fall, does the number of observations in each group match your expectations? Chi-square (χ2 ) *You can use the analysis of variance with two groups, but for historical reasons, researchers generally use the t-test.

66 Communicating Statistics amount of relevant information and minimize anything that would distract the reader. For example, differentiating the bars in a bar chart or a histogram is more effective when you use darkness of shading rather than thin lines that produce a distracting, moiré effect like the one in Figure 5.1 (a). A useful principle that Tufte identified is the maximization of the datato-ink ratio. That is, graphs become more useful when the amount of data grows and the amount of ink decreases. Another way of saying this is that Condition 1 Condition 2 Condition 1 Condition 2 (a) (b) Group A Group B Group C Group D Group E Group A Group B Group C Group D Group E 60 50 40 30 20 10 0 60 50 40 30 20 10 0 Figure 5.1 An illustration of (a) a graph with uninformative gridlines that clutter the figure and bars with a moiré effect that Tufte (1983) asserts will distract the reader, and (b) a graph with a less cluttered, more easily comprehensible format.

Communicating Statistics 67 you will communicate more effectively if you present the critical information and as little else as possible in a graph. Sometimes you can miscommunicate with words, too. Best (2001) identified what he described as “The Worst Social Statistic Ever.” He pointed out that a graduate student had made the claim, based on research by the Children’s Defense Fund (1994), that “[e]very year since 1950, the number of American children gunned down has doubled” (pp. 1–2). This statistic is patently impossible because if a single child had died by gunfire in 1950, the number of children in the United States felled by gunshot in the year 2000 would have exceeded one quadrillion, obviously an impossible figure. The message here is that you need to be very careful in selecting your wording. The original claim by the Children’s Defense Fund about gunshot deaths was that “[t]he number of American children killed each year by guns has doubled since 1950” (Children’s Defense Fund, 1994, p. x). This claim is very different from the misstatement by the graduate student. You need to ensure that your wording accurately represents the data. Another common problem is presenting data out of context. For instance, Best (2004) pointed out that during a news broadcast in 2001, CBS anchor Dan Rather commented on the “epidemic” of school shootings after a student in California had killed two students and wounded several others. Obviously, deaths of children are tragic; multiple, violent deaths are worse. But the truth is that the rate of school-based violence had decreased by around 50% in the previous decade. Rather’s comments were based on the California shooting and a few other well-publicized tragedies rather than on the mass of data that showed the schools were safer than a decade before and pretty safe compared with nonschool locations. This incident exemplifies the concept of missing data and relates to the need to understand the context in which numbers exist. In another instance, Daniel Okrent, who worked for the New York Times, noted that health workers and activists had criticized the administration of President Ronald Reagan for scant funding for AIDS research. An official in the Reagan administration, Gary Bauer, asserted that AIDS funding had increased during Reagan’s tenure as president. In assessing Bauer’s claim, Okrent explained that AIDS had not existed as a public health issue prior to the Reagan administration, so there would have been no need for funding. Obviously, comparing contemporary funding with a time when no money was needed does not help resolve arguments. Bauer’s statistics were misleading, whether or not his failure to present the whole picture was intentional (Gross & Miller, 2006).

68 Communicating Statistics Obviously, a few examples here cannot exhaust the ways that somebody can present a distorted message with statistics. But these examples can alert you to the need to be cautious in presenting your statistics and in assessing the statistics that others present to you. Differentiating Results and Interpretations When you analyze your data, the results provide a description of what has happened. You conclude that the groups you compared are significantly different or they are not. Or the relation between two variables is significant or nonsignificant. Or the number of observations in different categories matches your expectations or it does not. If statistical results tell you what happened, it is now up to you to figure out why things occurred as they did. This is the realm of interpretation. In an APA-formatted paper, the description of what happened belongs in the Results section. The interpretation and speculation goes in the Discussion section. Your statistics support your description of what happened. At some point, though, you need to figure out why the data emerged as they did. In your discussion, you are using your knowledge of the area, your creativity, and your insights and intuitions to speculate regarding what it all means. For just about any data set you generate, there will be more than one feasible interpretation. Your job is to generate the most plausible interpretation based on your statistics and on what previous researchers have discovered. For questions that are complex, there will always be explanations that differ from yours. Further research will resolve the issue of whether your interpretation or some other interpretation is most reasonable.

APA Style Simplified: Writing in Psychology, Education, Nursing, and Sociology, First Edition. Bernard C. Beins. © 2012 John Wiley & Sons, Inc. Published 2012 by John Wiley & Sons, Inc. 6 The Results Section Without data, all you are is just another person with an opinion. Unknown Statistics may be defined as “a body of methods for making wise decisions in the face of uncertainty.” W. A. Wallis Everybody likes a good story. An interesting plot will leave you satisfied at the end, particularly if there are surprises along the way. In the story of your research, your hypothesis is the plot, and your results provide the surprises. In this chapter, you will learn how to present your results so the reader can see whether your data support the ideas you presented in your Introduction section. Your Results section also prepares your reader for your conclusions, which appear in the Discussion section and which is the subject of the next chapter. You can’t just make things up, though. Each element in your story should be interesting, and each provides a glimpse of the truth. In your Results section, your interesting story comes through your words; the sense and truth of your words come through your statistics. Your ideas are the most critical aspect of the paper you are writing; the statistics are merely tools that provide support for your ideas. If you keep this relation between ideas and statistics in mind, you will find it easier to communicate with your reader.

70 The Results Section Creating an outline or an idea map can be very sound because it forces you to think of your main ideas. If you have generated a reasonably complete outline or idea map, you will have a good sense of your main points and how you will support them. Both of these approaches take a top-down approach (Salovey, 2000), which means that you are beginning with an overall sense of where your ideas are going. If you simply line up your statistics one after another, they will have as much meaning (and interest) as listings in a phone book. You may have noticed that I have not focused very much on statistics or numbers so far. The reason is that your first responsibility in the Results section is to convey the ideas of what happened in your study. The numbers can wait. If you understand the purpose of the statistics from your data analysis, you can tell the reader in words what the numbers have revealed. The words give the idea; the numbers, which come later, support your idea and provide an element of precision to your presentation. Your Hypothesis In your Introduction, you will have presented your hypotheses. Normally you talk about your hypothesis in the Results section in the same order as you offered them in the Introduction. A good example of a research report that describes support for a single hypothesis involved testosterone level and aggression (Klinesmith, Kasser, & McAndrew, 2006). The investigators noted that previous researchers had discovered that insults or challenges to status can be associated with increases in testosterone levels in males. So Klinesmith et al. hypothesized that the presence of a gun (compared with a child’s toy) would increase testosterone levels in men. The researchers created a task in which participants handled for 15 minutes either a child’s toy or a pellet gun that resembled an automatic handgun. The investigators measured testosterone level before and after the 15-minute period. The data confirmed their hypothesis. As they reported, Our first hypothesis was confirmed: Subjects who interacted with the handgun showed a greater increase in testosterone from Time 1 to Time 2 than did those who interacted with the children’s game. Thus, interacting with the gun increased testosterone levels. (Klinesmith et al., 2006, pp. 569–70) This verbal presentation of the results is clear and straightforward. After reading it, you know what happened. The researchers mentioned that they

The Results Section 71 tested a hypothesis, how they tested it, the pattern of results, and the fact that the data confirmed their hypothesis. You don’t need statistics to understand their point. In a research report, though, a reader expects the technical, statistical information that supports the researchers’ conclusion. Here is how those researchers included the statistical information: Our first hypothesis was confirmed: Subjects who interacted with the handgun showed a greater increase in testosterone from Time 1 to Time 2 (mean change = 62.05 pg/ml, SD = 48.86) than did those who interacted with the children’s game (mean change = 0.68 pg/ml, SD = 28.57), t(28) = −4.20, prep = .99, d = 1.53. Thus, interacting with the gun increased testosterone levels. (Klinesmith et al., 2006, pp. 569–570) As you can see, the technical information supports the verbal statement, but you don’t need the statistics to understand the authors’ point. When you create your own Results section, you should try to make your point using words. Then insert the technical part. This advice is a little simplistic, but as a strategy, starting with ideas instead of numbers is a good idea. In addition, you should begin your presentation of the data with descriptive statistics. The authors who studied testosterone level presented the means and standard deviations, then the inferential statistic, a t-test. There are numerous ways to present your data. Examples of how authors present descriptive statistics appear in Table 6.1. Deciding What to Present The main point of the Results section is to let the reader know what happened. Start this section with the most interesting, important, and surprising results. You should present as much detail as required in order to inform the reader adequately. Generally, theoretical and empirical reports are meant for a readership that is versed in professional research techniques. You can expect the reader to know the difference between experimental and nonexperimental methods, the uses of various statistical tests, the norms for hypothesis and significance testing, and so forth. When you present your data and statistics, you don’t need to belabor the obvious. For example, all competent researchers know that t-tests tell us whether two groups differ significantly. So don’t bother explaining that fact to the reader.

72 The Results Section Table 6.1 Examples of Presentations of Descriptive Statistics Type of presentation Example Means in the text with standard deviations presented in parentheses Research: Infants’ reactions to sweet and bitter tasting substances The proportion of leg kicks occurring after post-tasting was similar whether the object had tasted sweet or bitter. The mean for the sweet condition was 0.584 (sd = 0.165), and for the bitter condition it was 0.604 (sd = 0.223). (From Rader & Vaughn, 2000, p. 537) Means and standard deviations presented in parentheses Research: Social work students’ perception of field work experiences Burnout or strain-related feelings associated with the field experience were reported “rarely,” using the 1 {never) to 7 {always) metric (M = 3.30, SD = .97). Similarly, satisfaction with the current field experience (M = 3.84, median = 4.0) fell closer to the “satisfied” (4) than “neither satisfied nor dissatisfied” (3) marker on the 1–5 satisfaction scale. Perceived efficacy (M = 7.67, SD = 1.46) was rated somewhat higher than an average or “fair” amount. Finally, the students rated themselves slightly more than moderately prepared (M = 5.84) on a1 {not at all prepared) to 10 {extremely prepared) scale of preparedness for entering the current field placement. (From Kanno & Koeske, 2010, p. 29) Means referred to in the text but placed in a table (or figure) Research: Student performance in statistics as a function of teaching emphasis As Table 1 indicates, the results show nearly equal means in the traditional-emphasis class and in both moderateemphasis classes but notably better scores in the highemphasis class. (From Beins, 1993, p. 162) Unless a statistical approach is fairly obscure, you should expect the reader to know what you are talking about. Similarly, if you use standard data analysis software such as SPSS® or Minitab®, you don’t need to mention it specifically. Reporting Significant and Nonsignificant Results Authors tend to devote space in their manuscripts to statistically significant effects and to downplay nonsignificant effects. Significant results

The Results Section 73 suggest that something interesting took place, so it is not surprising for the researcher to detail it. If your data analysis reveals an unexpected, significant result, it is probably worth mentioning, but you should remember that significant effects are sometimes accidental and might not really signify anything. If you see an effect that you cannot explain, mention it, but do not develop a convoluted account of the result that is pure guesswork. Just as researchers attend to significant effects, they minimize attention devoted to nonsignificant results. Such results let us know that something did not happen, but not why it didn’t happen. Most of the time, when results are not significant, it does not pay to discuss them at great length. The problem with nonsignificance is that you don’t know if the results turned out the way they did because there is no real effect or because your methodology obscured an effect that is real. Incidentally, when your statistical analysis fails to result in a significant effect, the norm is to report it is as nonsignificant. Psychologists will recognize this wording in its statistical context. You should not refer to that effect as insignificant. If you hypothesized that there would be a significant effect and it turned out not to be, it would be nonsignificant statistically but it would not be insignificant. After all, the results differed from what you thought would happen—and that would be significant in the normal English meaning of that word. In recent years, psychologists have recommended that researchers rely on more than merely tests of significance. Recommendations for the statistics that should appear in journal articles include effect sizes and confidence intervals (American Psychological Association, 2010; Wilkinson and Task Force on Statistical Inference, 1999). These new approaches are gradually appearing in the professional literature as investigators become more familiar with them. The nature of statistical reporting is changing, as McGrath (2011) has noted, but we do not yet know what standards will ultimately prevail. For instance, the Association for Psychological Science (APS) recommended several years ago that authors use a statistic called prep, the probability that an effect will replicate instead of the traditional p-value, but articles in APS journals no longer include this statistic. As you write your Results section, it is probably safe to err on the side of more complete information rather than less. Thus, presenting p-values will satisfy traditionalists, and presenting effect sizes and confidence intervals will satisfy those who embrace the emerging conventions.

74 The Results Section Marginally Significant Effects Researchers typically adopt the Type I error rate of 5%; that is, the likelihood of mistakenly concluding that there is a real effect is 5%. For decades, authors claimed significance and rejected the null hypothesis when p ≤ .05 and did not reject the null when p > .05. The analogy researchers often used involved pregnancy: you either are or you aren’t. You can’t be highly pregnant or slightly pregnant or nearly pregnant. Some investigators are more comfortable with the idea that you should not ignore a possibly interesting effect, even if p > .05. For example, why should a p-value of .050 be so much more interesting than a p-value of .051? Further, there is no logical or scientific rationale for choosing the typical significance levels of .05 and .01; it is merely tradition. In studies with low power (i.e., small sample sizes combined with small effect sizes), it may be more reasonable to adopt significance levels of .20 or .30 (Winer, Brown, & Michels, 1991). Authors have called potentially interesting effects that do not attain conventional levels of significance marginally significant when .05 < p < .10. Some writers (e.g., Salovey, 2000) have suggested avoiding such qualifying statements, preferring a simple statement that, for example, p = .06. A reader would know that such a value is not significant and can evaluate it appropriately. If you are writing about your results and an effect is not quite at the level of significance but may be important, let the reader know about it and declare that you are discussing the result with caution. APA Style and Presentation of Your Results APA-style presentation of results is not difficult to implement, but there are a lot of details that you have to keep in mind. The presentation style for numbers, results of statistical tests, and creation of tables and figures is quite technical. Table 6.2 gives the format for statistical presentation of commonly used tests. One notable change in APA style in the current publication manual is that authors should not routinely use the symbols for less than (<)  or greater than (>) in presenting probability values. Rather, with the ubiquity of computerized data analysis, authors should present exact probability values. As the table indicates, the standard format is to present the statistical test and degrees of freedom, the computed value, the probability value, an effect size statistic, and the confidence interval for the statistic.

The Results Section 75 Table 6.2 Format for Presenting Commonly Used Inferential Statistics Statistic Example Generic Statistic (degrees of freedom) = value of statistic, p = significance level, effect size, confidence interval [in square brackets] t-testa t(186) = 3.51, p < .001, d = .52, CI [0.30, 1.06] t(60) = 3.23, p = .002, d = .82, CI [0.27, 1.15] F-test (ANOVA) F(2, 33) = 5.25, p = .01, MSE = 2.95, η2 = .24 Correlation r(107) = .13, p = .18 Chi-squareb χ2 (1, N = 46) = 1.39, p = .24 (using the Greek letter χ) X2 (1, N = 46) = 1.39, p = .24 (using the italicized Roman letter X) Note: APA style says that, normally, you should report test values to two decimal places; probability levels can go to three decimal places, but when a p-value is less than .001 (e.g., p = .0005), you may see it reported as p < .001. Also, insert a space between each element in the statistical presentation. The spaces make it easier to read. a The exact probability value is appropriate unless the value to three decimal places is .000. When that occurs, use p < .001. b When reporting a chi-square value, indicate the degrees of freedom followed by the sample size in the parentheses. You can use either the Greek letter chi (χ) or the uppercase letter X in italics. Some common abbreviations that are standard for referring to various measurements appear in Table 6.3. In addition, standard symbols in presenting the results of statistical tests are in Table 6.4. A general rule is that if the measurement is written using Roman letters (e.g., t, F, M), it appears in italics; if the measurement is written using Greek letters (e.g., α, β, μ), it is not italicized. You may occasionally see text underlined in a typed or wordprocessed manuscript; underlining is the old-fashioned manner of indicating that the text should be in italics. Old typewriters were incapable of generating italics, boldface, or any other nonstandard form of print. The need to represent italics by underlining has disappeared because you can insert italics with a word processor. Remember that the focus of the Results section is to present ideas. You are trying to convey to the reader a complete sense of what happened. If you compared two groups to see which one had the higher mean score, your first job is to tell the reader that the average score in one group is different from the average score in the other group. If you are correlating variables, you need to tell the reader that as the scores on one variable go up (or down), the scores on the second variable go up (or down). Most research

76 The Results Section Table 6.3 Some Common Symbols and Abbreviations Used in Measurement Symbol* What it represents f Frequency f e Expected frequency H0 Null hypothesis Ha or H1 Alternate or research hypothesis M Mean Mdn Median N Total sample size n Size of subsample SD Standard deviation z Standardized score α Probability of Type I error β Probability of Type II error *Symbols appearing in Roman letters (e.g., a, b, c) are italicized; symbols appearing in Greek letters (e.g., α, β, γ) are not italicized. If the symbol or abbreviation has a subscript or superscript, it is not italicized. Table 6.4 Some Common Symbols Used in Presenting the Results of Statistical Tests Symbol* What it represents ANOVA Analysis of variance ANCOVA Analysis of covariance d Cohen’s measure of effect size d′ Measure of sensitivity (psychophysics) df Degrees of freedom MANOVA Multiple analysis of variance MS Mean square MSE Mean square error p Probability; probability of success in binomial trial prep Probability that an effect will replicate r Pearson product-moment correlation r2 Coefficient of determination rpb Point-biserial correlation rs Spearman’s correlation R Multiple correlation

The Results Section 77 that you will encounter deals with those two issues: do averages differ and are variables correlated? It should not be difficult to tell that to the reader. So what is the difficulty in presenting results? One issue is expressing the technical details in appropriate format. This is relatively easy because all you have to do is to insert them into your prose in a meaningful place and follow the prescribed format. A second issue is taking your understanding of what the numbers have told you and converting that information into coherent and comprehensible English. This requires more thought. Creating Tables Tables are an effective way to present a lot of information in a small space. When you have many data, it might be difficult to include them all in the text without overwhelming or boring the reader. The most difficult part of creating tables is generating a format that makes the purpose of your data transparent—that is, clear and accessible. The first task here is to decide whether you need a table (or a figure) for your results. If you are comparing two or three groups, you would not use a table because the table would take up too much space relative to the information you are presenting. Previous editions of the APA Publication Manual (American Psychological Association, 2001) pointed out that tables and figures were expensive because a person had to take extra time to format it so that it would fit into a journal article appropriately. Thus, authors were Table 6.4 (cont’d) Symbol* What it represents R2 Square of multiple correlation SEM Standard error of measurement; standard error of the mean SS Sum of squares t t-test value η2 Eta-squared (measure of effect size) Φ Phi (measure of association) χ2 Chi-square value ω2 Omega squared (measure of effect size) ^ Caret (reflects an estimated value when used above a Greek letter) *Symbols appearing in Roman letters (e.g., a, b, c) are italicized; symbols appearing in Greek letters (e.g., α, β, γ) are not italicized. If the symbol or abbreviation has a subscript or superscript, it is not italicized.

78 The Results Section cautioned to use tables only when there was a lot of information, as with research involving multiple variables that, if presented in prose format, would constitute a string of numbers that were hard for the reader to process. The most recent version of the publication manual (American Psychological Association, 2010) takes a more liberal attitude toward using tables and figures. The basic guidelines for the use of tables and figures are as follows: Use a sentence with three numbers or fewer. Use a table for four to 20 numbers. Use a graph for more than 20 numbers. As an example, consider research in which my students and I collected data about the way that women and men respond to offensive jokes that victimized either women, men, or neither gender in particular. After analyzing the results, we would communicate which groups differed in their ratings of the jokes. As such, we could (but should not) indicate the mean ratings on a scale of 1 (not funny) to 7 (very funny) using the following dense, text-dependent passage: There was a significant interaction between sex of joke victim and sex of rater. Women rated male-victimizing jokes as funniest (M = 3.6, SD = 1.2) followed by no-victim jokes (M = 3.4, SD = 1.0), then by female-victimizing jokes (M = 2.7, SD = 1.2). Men rated male-victimizing jokes (M = 3.2, SD = 0.7) and no-victim jokes (M = 3.2, SD = 0.9) comparably, and femalevictimizing jokes funniest (M = 3.9, SD = 1.2), F(2, 160) = 26.66, p < .001, prep > .986. (Data from Beins et al., 2005) This presentation is factually accurate, but who would want to wade through the string of numbers? Besides, it is difficult to make comparisons across groups based on this presentation. If these were your data, you could create either a table or a figure to depict them. Figure 6.1 provides the structure for a table that would represent the data more coherently. In many published articles, the means appear next to the standard deviations, with the latter in parentheses, as illustrated in Figure 6.1. If, for some reason, you wanted to discuss differences between means separately from differences in standard deviations, you could present them in different places in the table, as shown in Figure 6.2. Figures 6.1 and 6.2 both demonstrate how you can depict numbers showing differences among means. Researchers use other types of data, however, so you may need tables with somewhat different formats. For instance, if you

The Results Section 79 want to present correlational data, the layout of your table will not be quite the same as when you present averages. If you measured several different variables and computed the correlations among them, a table like Figure 6.3 would illustrate the relations among the variables clearly. You can also create a table with only one column, but with rows as shown in Figure 6.3. You would then use the space bar to create the spacing you desire. As you can see in Figures 6.1 and 6.3, you can put notes at the bottom of a table if it helps the reader understand the contents of the table. You insert a horizontal line and put any notes below the line. You may insert more than one note. General notes with a wide scope come first, followed by more specific notes (e.g., referring to individual groups), then probability notes related to significance levels. When you create your table, you can simply type it, using the space bar to creating the spacing you want. Or you can use your word processor to create the table; this approach is often the most efficient and easiest. Table 6.5 shows how you can use Word© to format the table. Table 6.6 shows the final result. Do not use vertical lines. Insert a horizontal line under the variable/condition. Put the table number on the first line, the table title in italics on the second line, and a horizontal rule (i.e., an underline) below the title. Capitalize each main word in the title. If you use notes, put general notes first, then specific notes, then probability values. If you include a note, separate the note from the rest of the table with a horizontal line. Align the decimal points so they are underneath each other in the column. Insert a horizontal line at the bottom of the table. You can present overall group means in the margins. You can indicate which groups differ significantly by using a common subscript for groups that do not differ and different subscripts for groups that do differ. Table 1 Means (and Standard Deviations) of Ratings of Joke According to Sex of Joke Victim and Sex of Participant. Marginal Means in Parentheses Reflect Overall Group Means Note. Different subscripts in a row indicate significant difference, p <.01. Sex of participant Female (3.3) (3.4) (3.3) 2.7a 3.4b 3.6 (1.0) (3.2) b (1.2) (1.2) 3.9 (3.4)(0.9) c 3.2d 3.2d (1.2) (0.8) Female Male Neutral Victim of joke Male Figure 6.1 Illustration of a basic table of data in APA style.

80 The Results Section Put the table number on the first line, the table title in italics on the second line, and a horizontal rule below the title. Capitalize each main word in the title. Table 1 Mean rating Sex of Victim of joke participant Female Underline headings that indicate conditions that go together, in this case, means go with means and standard deviations go with standard deviations. Male Female Male Place a horizontal rule below the labels for conditions and variables. 2.7 3.6 3.4 1.2 1.2 1.0 3.9 3.2 3.2 1.2 0.8 0.9 Align the decimal points so they are underneath each other in the column. Neutral Female Male Neutral Standard deviation Means and Standard Deviations of Ratings of Joke According to Sex of Joke Victim and Sex of Participant Victim of joke Figure 6.2 Example of a table in which different measurements (means and standard deviations) appear separately. This format is fairly rare in published articles. Figure 6.3 Example of a table of correlations in APA style. Put the table number on the first line, the table title in italics on the second line, and a horizontal rule below the title. Capitalize each main word in the title. Sometimes authors will label, rather than number, the variables. So the numbers would be replaced in this example by personality characteristics. Table 1 1 – –.34** .06 .03 .13 – – – .01 1. –.29** 2. 3. 4. If you use notes, put general notes first, then specific notes, then probability values. If you include a note, separate the note from the rest of the table with a horizontal line. Normally, you present correlations to two decimal places. Use asterisks to indicate level of significance. 2 4 Align the decimal points so they are underneath each other in the column. Characteristic 3 Neuroticism Extraversion Openness Agreeableness Note. N = 109 **p < .01 Intercorrelations Between Personality Characteristics

Table 6.5 Creating a Table with Word® Step 1 Type the table number on the first line, then type the table title in italics on the second line. Step 2 Make a 5 × 5 table by selecting Insert in the toolbar at the top of the Word page. (For different tables, you might not use a 5 × 5 table. It depends on your research design.) Step 3 Leave the upper left cell empty. In the first column, second row, indicate the first variable name (Sex of participant) and, below the variable name, the conditions for that variable (female and male), each in its own row. This is often the variable with fewer conditions. Step 4 On the top line, first column, type the second variable name (Joke victim). Step 5 • Highlight the top row of cells. • Right-click on the mouse and select Merge cells. • Center the variable name. Step 6 On the second line, type the conditions of the second variable name, one per column. (In this case, Female, Male, Neutral.) This is often the variable with more conditions. Now that you have established the structure of the table, enter the data. Step 7 To erase all lines in the table: • Right-click on the mouse and select Borders and Shading. • Choose None. The lines in the table will disappear. Step 8 To insert a line under the title of the table: • Highlight cells in the top row of the table. • Right-click on the mouse and choose Borders and Shading. • Click on the icon on the right that shows a line at the top of the cell. • Click on OK. Step 9 To insert a line under the row listing the variables (Sex of participant: female, male, neutral): • Highlight the row listing the variables. • Right-click on the mouse and choose Borders and Shading. • Click on the icon on the right that shows a line at the bottom of the cell. • Click on OK. Step 10 To insert a line at the bottom of the data: • Highlight cells in the bottom row of the table • Right-click on the mouse and choose Borders and Shading. • Click on the icon on the right that shows a line at the bottom of the cell. • Click on OK. Step 11 To enter any notes below the bottom line: • Position the cursor below the table and type the notes. Note: To create subscripts as you see in the completed table, click on the small arrow in the Font tab on the top of the Word page, then select subscript. When you finish typing the subscript, click on the Font arrow again and remove the check mark from the subscript box.

82 The Results Section Creating Figures Tables summarize large numbers of data efficiently and precisely. Sometimes, though, visual presentations using figures can be helpful. In a graph, it is not always possible to discern the exact values of the data, so using figures may entail sacrificing some precision. Figure 6.4 shows the standard elements of a graph that depicts comparisons among multiple groups. Figure 6.5 illustrates the results of a study in which participants’ moods were manipulated prior to their rating a set of jokes (data from Cronin, Fazio, & Beins, 1998). It would be possible (but not desirable because of the density of the information) to present the results in text as follows: Participants showed greater mirth in the elevated mood condition; that is, they exhibited more laughing, smiling, and outward signs of humor. In contrast, the participants showed nearly equal levels of mirth in the depressed and in the neutral conditions. For women in the depressed mood condition, M = 0.64 (SD = 0.86); for men in the depressed mood condition, M = 0.86 (SD = 1.08). For women in the elevated and neutral conditions, M = 0.66 (SD = 0.65) and M = 0.67 (SD = 0.68), respectively. For men in the elevated and neutral conditions, M = 1.70 (SD = 1.64) and M = 1.22 (SD = 1.35), respectively. I hope that it has become clear to you that simply listing a series of numbers can be an accurate way to present data but not a particularly effective way to generate understanding on the part of the reader. It is just too hard to keep all the numbers in mind to see how they relate to one another. Table 6.6 The Result: Means (and Standard Deviations) of Ratings of Jokes According to Sex of Joke Victim and Sex of Participant. Marginal Means in Parentheses Reflect Overall Group Means Joke victim Sex of participant Female Male Neutral Female 2.7a (1.2) 3.6b (1.2) 3.4b (1.0) 3.2 Male 3.9c (1.2) 3.2d (0.7) 3.2d (0.9) 3.4 3.3 3.4 3.3 Note: Different subscripts in a row indicate significant differences, ps < .01.

LEGEND that identifies additional independent variables ERROR BARS Y-AXIS X-AXIS X-AXIS LABEL 0 0.5 1 1.5 2 2.5 3 Depressed Elated Figure 1. The mean mirth score (e.g., degree of laughing and smiling) as a function of induced moon and sex of the participant. Error bars reflects 95% confidence intervals around the mean. Neutral Mood Mean mirth scores Y-AXIS LABEL, identifying the dependent variable and the type of measurement Measurement of dependent variable with tick marks to help the reader discern the values. It normally begins at zero. Conditions of independent variable FIGURE CAPTION describing the contents of the figure and explaining anything that needs clarification for the reader Female Male Figure 6.4 Graph with labels of common elements of the graph. This is a bar graph, but the same general principles hold for any graph, such as line graphs, showing comparisons across groups. Line graphs are appropriate when the variable on the X-axis is quantitative. In your manuscript, the figure caption appears below the figure. The figure caption should include any explanatory information needed to understand the graph. In preparing a manuscript, you would place the caption and the figure on their own separate page, one caption and figure to a page. 1.8 Type the Y-axis label vertically. The lowest value on the Y-axis is normally zero. 1.6 1.4 1.2 1 0.8 0.6 0.2 Depressed Place the legend inside the graph. Make the lines different for each variable. The variable with the greater number of conditions (type of mood in this example) normally appears on the X-axis. Mood Elated Neutral Mean mirth scores 0 0.4 Male Female Figure 6.5 A line graph showing the extent of mirth expressions like laughing and smiling as a function of mood of research participants. Note that the Y-axis starts with the value of zero.

84 The Results Section If you refer to Figures 6.5 and 6.6, you will see that the pattern of data looks somewhat different, even though both of these figures depict the same information. One of the important considerations in creating graphs is that you can alter their appearance greatly by changing the scale of the X- and Y-axes. The data don’t change, but their appearance does. The data in Figure 6.5 are based on the same data as Figure 6.6, but the scale on the Y-axis has changed. In Figure 6.5, the Y-axis begins with a value of zero whereas Figure 6.6 begins 0.5, and it is lengthened vertically. These changes in the structure of the graph lead to a very noticeable change in appearance. If your Y-axis begins at a number greater than zero, small differences can be magnified. Further, if you extend the Y-axis so that it is quite long compared with the X-axis, the differences between groups will look big; if Figure 6.6 Line graph in APA style. Normally, the Y-axis begins with zero. When it does not, the results may be deceptive. In this graph, the data are the same as those in Figure 6.5, but the differences across conditions on the X-axis seem greater because of the change of the scale of the Y-axis. Figures do not generally use notes the way tables do. 1.7 1.5 1.1 0.9 0.7 0.5 1.3 When the Y-axis does not begin at zero, differences among groups appear exaggerated. Compare the appearance of this graph with that of Figure 6.4 . The data are the same, but the differences between means look quite different. Depressed Mood Elated Neutral Mean mirth scores Male Female

The Results Section 85 you compress the Y-axis so that it is quite short compared with the X-axis, the differences between groups will look small. The appearance of the graph should match the accurate message you want to convey. You need to make sure that you are not deceiving your reader by portraying the data so that they lead to an inaccurate conclusion. The figures described so far have related to comparison of separate groups, which lends itself to bar and line graphs. If your research involves correlational data, however, your data will probably lend themselves to scatter diagrams. The principles involved in creating a scatter diagram are similar to those for bar charts and line graphs. An example of a scatter diagram appears in Figure 6.7. One final type of figure to be discussed here is the frequency histogram. It looks like a bar graph, but its main function is to illustrate how many observations fall into different categories. In the histogram shown in Figure 6.8, you can see how many students thought they had unhealthy lifestyles (low ratings) versus healthy lifestyles (high ratings) based on the results of The lowest value on the Y- axis does not have to be zero in a scatter diagram. Type the Y-axis label vertically. 140 120 100 80 60 Although correlational analysis does not really involve independent and dependent variables, the variable you want to use to make predictions goes on the X-axis, like an independent variable. 40 40 50 Average state temperature at state capital Teen pregnancy rate per 1,000 60 70 80 Figure 6.7 Scatter diagram in APA style showing the relation between temperature and teen birth rates in the states in the US and example of a figure caption. A scatter diagram is appropriate for showing the pattern of individual cases measured on two different variables. In this example, there is a significant positive correlation, reflecting the fact that as the temperature increases, so does the teen pregnancy rate. Because this relation is correlational, however, you cannot draw any conclusions about cause and effect.

86 The Results Section LEGEND, showing additional useful information Place the legend inside the graph. Y-AXIS 70 60 50 40 30 20 10 0 0 2 4 6 8 ERROR BARS Mean = 3.8 Std.Dev. = 1 .417 N = 274 X-AXIS X-AXIS LABEL The different categories or measurement conditions Type the Y-axis label vertically. Frequency of ratings Measurement of the dependent variable with tick marks to help the reader discern the values The lowest value on the Y-axis is normally zero. Rating of healthiness of lifestyle If the values on the X-axis are on a continuum, the bars should touch one another. If the categories are discrete and qualitatively different like sex, political affiliation, ethnicity, etc., the bars should not touch. Figure 6.8 Illustration of a frequency histogram in which the categories fall on a continuum. In this case, the categories are ratings that progress from 1 to 7. In such a histogram, the bars should touch one another. Figure 6.9 Illustration of a frequency histogram with discrete (nonquantitative) categories on the X-axis. When the categories are discrete, the bars should not touch. 30 Type the Y-axis label vertically. The lowest value on the Y-axis is normally zero. 25 20 15 10 Number of states with policy State policy regarding sex education 0 No mandate Stress abstinence Figure1. The number of states with policies that are silent regarding content of sex education programs, that mandate stress abstinence, or that simply mandate some coverage of abstinence in sex education programs. Cover abstinence Put the different categories into which observations can fall on the X-axis. These categories on the X-axis are discrete and qualitatively different so the bars should not touch. 5

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This is a compact but comprehensive guide to writing clearly and effectively in APA style.

• Demonstrates how to write objective scientific research papers using interesting prose
• Incorporates guidelines from the 6th edition of the APA publication manual
• Explores how to develop ideas, connect them to what others have written, and express them clearly
• Discusses the differences between written, oral, and poster presentations and offers instructions for applying APA style to each

Year:2012Edition:1Publisher:Wiley-BlackwellLanguage:englishPages:216ISBN 10:0470672323ISBN 13:9780470672327File:PDF, 3.32 MB

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How to Cite in APA Format (7th edition): Citation Style Guide

Table of Contents

What is APA 7th Edition ?    

The American Psychological Association (APA) introduced the latest edition of the APA Style Manual (i.e., APA 7 th edition ) in 2019. APA Style has its origins in 1929 , when a group of academics and professionals decided to develop a set of guidelines that would standardize scientific writing. The earliest form was a seven-page guide published in the Psychological Bulletin . The first formal edition of the APA Publication Manual was published in 1952, with subsequent editions appearing in 1974, 1983, 1994, 2001, 2009, and most recently, in 2019.  

Over the years, the APA style guide has evolved considerably to meet the changing needs of a wide range of disciplines, from social and behavioral sciences to health care, natural sciences, and humanities. APA Style is widely used by academic journals and books.  

As APA 7 th edition is the current and most updated version, students and researchers should familiarize themselves with the style guidelines, including the reference and citation styles.  

Who Should Use APA 7th Edition ?    

The aim of style guides such as the APA Style Guide is to simplify the work of editors and make it easier for readers to understand a text by ensuring a uniform format for a given publication. For example, the APA Style Guide will contain guidelines that lay down the APA reference format and APA style citation for authors to follow.  

The APA Style Guide can be a valuable reference when writing and formatting academic papers, irrespective of discipline. However, this style is primarily used by professionals, researchers, and students in the social sciences (e.g., sociology, psychology, anthropology), behavioral sciences, education, business, and nursing.  

If you are a student, you may consult with an instructor to check what style your discipline uses before using APA Style in your work. Most importantly, be sure to check the guidelines of your university or the journal you plan to publish in to ensure APA 7 th edition is the correct style to use.   

APA In-text Citations    

Researchers rely on citations and references to give credit to original sources, support their arguments, and guide readers to additional information for further study. Let us now look at how to cite sources in APA format. The formatting requirements of APA 7th edition citation for referring to secondary sources in your text are as follows.  

The APA citation format includes the author’s last name and the year of publication. When referring to a particular part of a source, the page number may be included, e.g., “(Rawat et al., 2018, p. 115).”  

APA style citation in the text may be parenthetical or narrative.  

In parenthetical citation, the author’s name and the year of publication are placed in parentheses within the text. This style is used at the end of a sentence. See below for an example:  

• Gender, age, education, and income determine a person’s risk and adaptive capacities (Birkmann et al., 2022).  

In narrative citation, the author’s name appears as a part of the sentence, followed by the publication year in parentheses. See below for an example:  

• Birkman et al. (2022) note that gender, age, education, and income determine a person’s risk and adaptive capacities.

Table 1: How to cite sources in APA format when there are multiple authors and organizations as authors  

*First time with an abbreviation: (World Health Organization [WHO], 2016); all subsequent citations: (WHO, 2016)  

Table 2: How to cite sources in APA format when some information is missing  

  APA Reference Format    

After APA citations , let us now look at the APA reference format. As an academic, you might use a wide array of source types. According to the APA style guide , each type is formatted in a certain manner. The most commonly used sources are journal articles, books, and dissertations. However, you occasionally might need to cite webpages, podcasts, and news article. How would all these appear in an APA 7 reference page ? You will find your answers in Table 3!  

Table 3: How to format various sources in the APA reference list  

Formatting the APA Paper    

Papers formatted according to APA 7th edition typically contain the following elements:  

Title page with the title (bold, centered), running head and page number in the header, author names and affiliations, and author notes (ORCID ID, conflicts of interest, etc.); see below:  

Next comes the abstract . It should be double spaced and use consistent font. The keywords appear below the abstract, with an indent.  

For the main text, margins are 1 inch on all four side s. T he text is double spaced . T he font used should be consistent . H eadings are used to separate sections , and d etails on heading levels are as follows:  

Source: https://apastyle.apa.org/style-grammar-guidelines/paper-format/headings

Finally, the APA 7 reference page contains the references to all sources used in the paper. References are double-spaced and use hanging indents :  

Style guides in academic writing serve to standardize formatting, citations, and language use across scholarly works. They provide readers with cues they can use to follow the text more efficiently and to locate information of interest to them. In other words, it avoids distraction of unfamiliar or non-uniform formatting.  

In this piece about APA 7th edition , we have explained in detail the nuances of APA citations , including how to cite in APA format and how to format the APA 7 reference page . A brief primer on formatting a manuscript in line with APA 7th edition is also provided.  

Frequently Asked Questions    

• What are the major changes from APA 6th to APA 7th Edition ?  

The updates from APA 6th to APA 7th Edition reflect a commitment to improving clarity, inclusivity, and usability in scholarly writing. The salient differences from APA 6 th edition are as follows:  

• Title page format: The title page has been updated for professionals. The author note includes information such as ORCID IDs and conflict of interest disclosures.
• Running head: The running head format has been simplified for professional authors.
• Font flexibility : There is greater flexibility in font specifications to enhance accessibility.
• Bias-free language guidelines: The guidelines for bias-free language have been updated to promote inclusivity and respect when writing about various identities.  
• Reference formatting: In APA 7th edition , the number of authors included in a reference entry has changed to allow up to 20 authors before using an ellipsis. The presentation of DOIs and URLs has been standardized.  
• In-text citations: In-text citations for works with three or more authors have been simplified to include only the first author’s name followed by “et al.”
• Accessibility: APA 7th edition emphasizes accessibility for users with disabilities, ensuring that guidelines support various modalities, including screen readers.  
• How do I cite a journal article in APA 7th Edition ?  

According to APA 7 th edition , a reference would appear as below:  

Author, A. A. (Year of Publication). Title of the article. Title of the Journal, volume number (issue number), pages. https://doi.org/xx.xxx/yyyy .  

The in-text citation would include the author name(s) and year of publication, e.g., (Andriolatou, 2007).   

• How should multiple authors be cited in APA 7th Edition?  

APA 7 citation style would vary depending on the number of authors and whether you are citing the source parenthetically or narratively:  

Single author, parenthetical: (Bik, 2020)  

Single author, narrative: Bik (2020) reported that…  

Two authors, parenthetical: (Bik & Urs, 2019)  

Two authors, narrative: As demonstrated by Bik and Urs (2019), …  

Three or more authors, parenthetical: (Bik et al., 2023)  

Three or more authors, narrative: Bik et al. (2023) have published…  

• How do I format in-text citations for a direct quote?  

When someone else’s words are copied verbatim in your paper, it refers to a direct quote.   

For APA 7 citation of short direct quotes (<40 words), use quotation marks around the quote and cite the author, year, and page number:  

Another way to look at it is how people “get a broader range of experience than they would on a feature” (Catmull, 2014, p. 209).  

For APA 7 citation of longer quotes, or block quotes, the sentence preceding the quote ends in a colon and is followed by the quoted text. No quotation marks are used. The quote is indented and cited without a period at the end (see the figure below).   

Figure: How to cite block quotes in APA citations

To conclude  

Over the years, the APA style guide has evolved significantly. The current version, APA 7th edition , has expanded in both scope and size to accommodate the needs of wide-ranging fields and to address changing times. Despite these changes, the core intention—to provide clear, consistent guidelines for effective communication—remains at the heart of APA Style .   

For busy researchers, not only is it difficult to remember the different elements of citations and references and the subtle differences in formatting, but it also a tedious task to manually format in-text citations and reference lists and ensure consistency between them. In addition, you might need to format (and even re-format) your manuscript according to different journals, which might follow styles different from APA 7th edition (MLA, Chicago, AMA, etc.)! But there is no need to worry…help is at hand!

Citation generators are online tools that format references in different styles. There are a number of citation generators available, and Paperpal has launched its new citation generator , which stands out from the others in many ways. As a part of Paperpal’s writing workflow, you can easily find and cite sources accurately in a matter of seconds.

This free citation generator supports the updated recent versions of 10,000+ styles, including APA 7th edition . Therefore, it can serve as an APA 7th edition citation generator if you need one! What’s more, you don’t need to switch between multiple tools to search, save, or format your citations. You can rely Paperpal’s citation generation feature for help with citing sources accurately and consistently in your writing. Explore Paperpal for free now!  

Paperpal is a comprehensive AI writing toolkit that helps students and researchers achieve 2x the writing in half the time. It leverages 21+ years of STM experience and insights from millions of research articles to provide in-depth academic writing, language editing, and submission readiness support to help you write better, faster.  

Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed.  Try for free or  upgrade to Paperpal Prime  starting at US$19 a month  to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

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