Research Proposal

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APPENDIX A Communicating Your Research Findings

Research Reports and Poster Presentations

For scientists in all fields, the research process is not complete until results have been reported in a peer-reviewed journal. Students are also required to report their research findings, but the primary audience is usually the instructor. It is rare for undergraduate students to try to have their research findings published, as the rejection rates of many peer-reviewed journals in psychology and related fields are daunting (70%, 80%, or even higher). Even professional researchers have experienced rejection, and they do not lightly encourage students to strike out on their own without the skilled guidance of an experienced hand. If, however, you are that rare individual encouraged by your instructor to submit a paper to a journal, then your instructor will also caution you about the precise style required for journal submissions. The standard of most (but not all) psychology journals in the United States is the Publication Manual of the American Psychological Association (hereafter called the APA manual), published by the American Psychological Association in 2001. The APA manual also has a Web site that you can visit to see what is new in APA style (www.apastyle.org).

The purpose of this appendix is not to show you how to prepare a journal manu- script, but to show you how to communicate your research findings in a written report for your instructor and, if applicable, in a poster presentation for a wider audience. It is be- coming increasingly common for students to present their research results in poster pre- sentations as well as in written reports. If you plan to present a poster at a local or regional meeting, it is important to familiarize yourself with any special requirements or guidelines of the sponsoring organization. The guidelines and tips that we list in this appendix were borrowed from Rosnow and Rosnow’s Writing Papers in Psychology: A Student Guide to Research Reports, Literature Reviews, Proposals, Posters, and Handouts (2006). Although these guidelines are in the spirit of the APA manual, there are certain departures from strict APA style, such as an appendix for your raw data and calculations. The reason for this dif- ference is that you are writing a paper for your instructor to evaluate and grade, and the needs of instructors are different from journal editors’ requirements.

Getting Organized

Before we turn to the procedure of writing a report, we discuss the most difficult step for many students: getting started. Clearly, it would be advantageous to begin early to ensure that your task will not be rushed and that you will have ample time to revise

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and polish your work well before the due date. One reason that students have trouble getting going is that they are unclear about the assignment. Thus, before you do any- thing else, make sure you know what is expected of you. You can talk with other stu- dents to get their impressions, but that approach may stress you out even more. The best person for you to consult is the instructor, teaching assistant, or grader to make sure that you are on the right track.

Besides knowing the form of the final report, keep the following questions in mind as you get organized:

• When is the final report due? • How will it be graded? • Will there be an opportunity to obtain feedback as the project progresses? • Is there a specified length for the final report? • Are intermediate drafts or outlines required, and when are they due? • Are sample reports available to provide a further idea of what is expected?

Questions about due dates are especially important because missing deadlines (just like unexcused absences on a job) is a sure way to elicit disapproval. If you are some- one who has a hard time meeting deadlines, remember that instructors have heard all the excuses. Try keeping a pocket calendar of self-imposed “deadlines” and checking it frequently to see what your tasks are over the next several days. If that approach fails, try posting scheduled dates and appointments over your mirror or desk, or anywhere else you routinely look.

To help you keep on schedule, you can jot down both self-imposed and assigned dates, such as

• Completion of preliminary literature search for proposal • Completion of proposal for research • Completion of ethics review • Implementation of data collection • Completion of data collection • Completion of data analysis • Completion of an outline for first draft • Completion of first draft • Completion of revised draft(s) • Completion of final typed manuscript (and poster and handout, if required)

Sample Research Report

Exhibit A.1 shows what a research report submitted as a course requirement looks like. It is a good idea to study this annotated report before you continue. We will focus on each of the following eight parts and provide you with simple guidelines and tips:

Title page

Abstract

Introduction

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Method

Results

Discussion

References

End material (e.g., tables, figures, appendixes)

Title Page

We turn now to the structure and form of your research report, beginning with the title page. Notice that the page number in the upper-right corner is accompanied (on every page) by the words “Biasing Effects.” These words are called page headers, and their purpose is to make it easy for the instructor or grader to identify each manuscript page if any pages become separated. The APA rule is that page headers should consist of two or three words from the title. The other kinds of information shown on the title page of Mary’s sample paper are her name (the byline), the number and name of the course or sequence for which the paper is being submitted, the name of the instructor, and the date the paper will be submitted.

Abstract

Although the abstract (or summary) appears on page 2, it is written after you have completed the rest of your paper, as it is a distillation of the important points covered in the body of your report. It tells the reader what your research is about in one suc- cinct paragraph. In the sample report, Mary gives a synopsis of the background of her research, her hypothesis, the way she tested it, the results, and a very brief description of the way her discussion section will proceed. The APA rule is that abstracts not ex- ceed 960 characters and spaces (approximately 120 words), but instructors are usually more lenient about the length of abstracts in student reports.

Remember that the purpose of the abstract is to let the instructor quickly anticipate what to expect in your report. With that objective in mind, here are some questions to guide you when planning your abstract:

• What was the problem that I studied or the objective of my study? • What principal method did I use (a laboratory experiment, a survey question-

naire, judges as raters, etc.)? • Who were the research participants (i.e., what were their pertinent characteristics)? • What were the major results? • What primary conclusions and implications appear in the discussion section?

Introduction

The introduction (the first section after the abstract page) has no lead (as the other sec- tions do) but begins by repeating the full title of the paper (not the student’s name, how- ever). This section emphasizes linking ideas to past research and should lead into your

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APPENDIX A Communicating Your Research Findings 371

Biasing Effects 1

Biasing Effects of Knowledge of Drug-Testing

Results on Bail Judgments

Mary Jones

(e-mail address or other contact information)

(Number and Name of Course)

Instructor: Professor Rind

(Date the Research Report Is Submitted)

The title is succinct, yet adequately descriptive.

The student’s name and contact infor- mation ap- pear below the title.

Insert course and instructor and the date the paper is turned in.

Pages are numbered consecutively, beginning with the title page, and contain a short heading.

Exhibit A.1 Mary Jones’s Research Report

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Biasing Effects 2

Abstract

This simulation experiment was inspired by legal arguments

about mandatory drug testing of all suspects on arrest.

One argument was that knowledge of this testing would

have biasing effects on bail judgments in legal

proceedings, whereas another argument was that drug use

information would have no prejudicial effects. Drawing

on correspondent inference theory, my hypothesis was that

harsher bail judgments are more likely when judges are

informed that the defendant has tested positive for drug

usage than when no testing information is made available.

The participants were college students, who were randomly

given one of two scenarios about a defendant who had been

arrested as a suspected burglar. The experimental scenario

told that the defendant’s blood test while he was in

custody revealed that he had very recently used drugs. In

the control scenario, the drug test information was

omitted. The students were asked to imagine that they

were the bail judge and to set a dollar amount from $0

to $50,000. The data were analyzed by an independent-

sample t and afterward, because of heterogeneity of

variance, by Satterthwaite’s adjusted t. Both results

were in the direction hypothesized and statistically

significant, and reffect size was .34. Methodological

limitations and ideas for follow-up research are

discussed.

Abstract is not indented.

Double spac- ing leaves one line between each line of type.

Although the left margin is even, the right margin is ragged.

The abstract tells why the research was important and worth doing, what was hypothesized, what the study involved, what the re- sults were, and what else appears in the discussion.

The abstract begins on a new page.

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APPENDIX A Communicating Your Research Findings 373

Biasing Effects 3

Biasing Effects of Knowledge of Drug-Testing

Results on Bail Judgments

McGuire (1997) listed various ways in which ideas for

research and theory arise, two of which are serendipity

and the resolution of conflicting views. Both played a

hand in the inspiration for this research. While watching

TV one evening, I was channel surfing when I happened to

see a discussion between lawyers who were arguing over

whether drug testing should be performed on all persons

arrested. One lawyer stated that mandatory drug testing

would be a valuable weapon for law enforcement officials

in fighting the drug war. Another lawyer countered that

mandatory drug testing could pose a threat to individual

rights because positive results would unfairly bias

judges’ decisions on how much bail to impose, even in

cases in which there is no connection between the drug

usage and the crime committed. The first lawyer then

insisted that such information would have no effect on a

bail judge’s decision. This difference in views (i.e.,

whether or not there might be a biasing effect on bail

judgments) whetted my curiosity. Attribution theory,

particularly an aspect known as correspondent inference

theory, provided a basis for a testable hypothesis.

The main task of the bail judge is to set bail at a

level that will make it likely the defendant will appear

for trial. In making this decision, the judge will perhaps

consider factors suggestive of the defendant’s traits,

with the idea that some traits should predict whether the

defendant will skip bail or show up for the trial.

Attribution theory is specifically concerned with factors

that influence how observers infer the traits of

The first line of every para- graph in the text is in- dented five to seven spaces.

The text be- gins on a new page.The text

opens with a repetition of the title.

Abbreviation for id est (“that is”).

The opening paragraph sets the stage in an inviting way.

➝

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374 APPENDIX A Communicating Your Research Findings

Biasing Effects 4

particular actors (cf. Jones & Davis, 1965; Kelley, 1972)

and how this trait information is used to make decisions or

judgments regarding actors (Baron & Byrne, 1987). In this

vein, Jones and Davis’s correspondent inference theory

provided a conceptual framework for the description of how

observers go about inferring traits of actors. According to

this theory, observers focus primarily on certain types of

observed behavior to infer traits on the assumption that

only certain actions are indicative of some traits. Three

questions that observers ask themselves, according to Jones

and Davis, are (a) Was the behavior freely chosen? (b) Did

the behavior produce uncommon effects? and (c) Was the

behavior low in social desirability?

The third question seemed especially relevant to the

controversial issue of interest in this research. On the

assumption that drug usage is generally held to be low in

social desirability (i.e., in our society), it seemed to

follow from correspondent inference theory that observers

are likely to concentrate on this form of socially

undesirable behavior in judging actors’ traits. Once

observers have inferred traits, they tend to use this

knowledge to predict the actors’ future behavior as well

as to assess and guide their own actions, decisions, and

judgments regarding those actors (Baron & Byrne, 1987).

The hypothesis in this study was that having information

about positive results from a drug test is likely to

result in harsher bail judgments.

Method

Participants and Study Design

A sample of 31 male and female undergraduate students

participated in this study. With the permission of the

Connecting points are let- tered for clar- ity.

The introduc- tion gives a concise his- tory and background of the topic, and it leads to the question of interest.

First-level headings are centered.

The introduc- tion con- cludes with your hypothe- ses, or theo- retical expectations, prior to your seeing the findings.

An ampersand appears in parentheses, where and is used other- wise.

Second-level headings are flush left and italicized.

➝

Citations but- tress the in- troduction.

Abbreviation for confer (“compare”).

➝

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APPENDIX A Communicating Your Research Findings 375

Biasing Effects 5

instructor and the consent of all the students who

participated, an experiment was conducted during a

scheduled class meeting. Students were randomly assigned to

either an experimental (n = 15) or a control (n = 16)

condition. Each student received a one-page questionnaire,

the questionnaires having been mixed together and then

administered to all the students simultaneously. At the

conclusion of the study, the students were fully debriefed.

The Questionnaire

The questionnaire asked for the respondent’s age,

sex, year in college, grade point average (GPA), and

major. Next came the instruction to “please read the

following paragraph carefully, and then answer the

question that follows it.” In the experimental condition,

the paragraph stated:

A man was arrested as a suspected burglar. He fit

the description of a man seen running from the

burglarized house. While in custody the man

submitted to a blood test, and it was determined

that he had very recently used drugs.

In the control condition, the last sentence in the

scenario above was deleted, and the following sentence was

substituted:

The man spent enough time in custody to receive two

meals and make three phone calls.

Immediately after either scenario was the following item:

“If you were the bail judge, what bail would you set?

Choose a dollar amount from $0 to $50,000.”

Results

The overall findings are given in Table 1, which

shows that the mean judgment of the students exposed to

Sample sizes of subgroups are denoted by italicized lower-case n.

Second-level heading is flush left in italics.

➝

Block quota- tions are in- dented five to seven spaces from the left margin.

Brief quota- tions incorpo- rated into the text are en- closed in double quota- tion marks.

Centered first-level heading.

➝

The results are a major section of the text and thus follow with- out a page break.

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376 APPENDIX A Communicating Your Research Findings

Biasing Effects 6

the drug information was higher than the mean judgment of

those exposed to the neutral scenario. The independent-

sample t test on these data yielded t = 2.08, df = 29,

p = .023 one-tailed, reffect size = .36, and a 95% confidence

interval around the effect size ranging from .01 to .63.

One assumption when the t test is used to compare two

means is that population variances of the samples are

equal, but the variances shown in Table 1 are noticeably

unequal. In a standard statistical procedure recommended by

the instructor, the result of dividing the larger of the two

variances (S2) by the smaller of the two variances yielded

F(14, 15) = 9.0,p = 6.3-5, indicating heterogeneity of

variance.

On the instructor’s suggestion, two ways of dealing

with heterogeneity of variance were considered: One

involved transformation of the raw scores, and the other

was a procedure known as Satterthwaite’s method. The

latter procedure was chosen (illustrated in Rosenthal,

Rosnow, & Rubin, 2000), and the calculations are shown in

the appendix at the end of this report. By this method,

the t test is calculated in a slightly different way, and

the degrees of freedom are adjusted. Both the t test result

and the effect size r were similar to the results above,

with t = 2.03, df = 16, p = .03 one-tailed, reffect size = .34.

The 95% confidence interval of the obtained effect size r

ranged from r = -.02 to .62, also similar to the previous

result above. The reason this confidence interval

crosses slightly into the negative side is that a 95%

confidence interval has .025 as the one-tailed p, and the

obtained p in this case did not quite make the .025 (i.e.,

it was .03).

Letters used as statistical symbols are italicized: t, F, n, df, S2, p, and so forth.

Degrees of freedom are 14 for the nu- merator and 15 for the de- nominator of the F ratio.

➝

F with numer- ator df � 1 is an omnibus test; therefore, no effect size is reported.

Statistical test, degrees of freedom, sig- nificance, ef- fect size, and confidence interval.

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Biasing Effects 7

Discussion

One fundamental purpose of our criminal justice system

is to be just and unbiased in all of its aspects. The

results of my experiment were in the hypothesized direction,

indicating that harsher bail judgments were more likely when

the “judges” were informed that the defendant had tested

positive for drug usage. This finding implies that the goal

of being just and unbiased may be jeopardized if drug

testing and the reporting of its results are mandated by

law. This biasing effect can be avoided if judges are not

given access to the results of the drug testing.

However, because I was unable to use real judges and

had to use college students, the results may not be

applicable to actual bail judges. Future research can be

designed to address this problem of external validity

and also to assess the participants’ inferences of

corresponding traits from socially undesirable behavior. It

is interesting that, although the participants seemingly

judged the suspect more harshly when the drug information

was included, there was no logical connection between the

drug usage and the burglary. Perhaps the participants were

drawing on a stereotype to assume that the association was

likely,because the media often report property crimes that

are motivated by the need to get money to purchase drugs.

Future research could use other crime scenarios that are

not stereotypically associated with drugs to determine

whether biasing effects occur and are general in nature.

Finally, this paper does not address the legal issue that

this kind of testing of someone “innocent until proved

guilty” is possibly unconstitutional, that is, on the

grounds that it is a violation of civil rights.

The discus- sion begins by reminding us of the study’s pur- pose and the main findings.

The discussion, as it is another major section of the text, does not require a break.

Mary gives the limitations of her study, and thus shows that she has a good under- standing of the limited generalizabil- ity of her findings.

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378 APPENDIX A Communicating Your Research Findings

Biasing Effects 8

References

Baron, R. A., & Byrne, D. (1987). Social psychology:

Understanding human interaction. Boston: Allyn &

Bacon.

Jones, E. E., & Davis, K. E. (1965). From acts to

dispositions: The attribution process in person

perception. In L. Berkowitz (Ed.), Advances in

experimental social psychology (Vol. 2, pp. 219-266).

New York: Academic Press.

Kelley, H. H. (1972). Attribution in social interaction. In

E. E. Jones, D. E. Kanouse, R. E. Nisbett, S. Valins,

& B. Weiner (Eds.), Attribution: Perceiving the

causes of behavior (pp. 1-26). Morristown, NJ:

General Learning Press.

McGuire, W. J. (1997). Creative hypothesis generating in

psychology: Some useful heuristics. Annual Review of

Psychology, 48, 1-30.

Rosenthal, R., Rosnow, R. L., & Rubin, D. B. (2000).

Contrasts and effect sizes in behavioral research: A

correlational approach. Cambridge, UK: Cambridge

University Press.

Book with two authors.

Chapter in an edited book in a series of volumes.

“Ed.” for one editor.

The references begin on a new page.

“Eds.” for more than one editor.

Book with three authors.

Ampersand before the last author’s name.

➝

Page numbers of chapter in edited book.

Journal titles and book titles are italicized, but not titles of articles or chapters.

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Biasing Effects 9

Table 1

Mean, Variability, and Number of Participants in Each Group

Measure Experimental group Control group

Mean $16,146.67 $6,990.63

S 16,645.07 5,549.60

S2 277,058,355.31 30,798,060.16

n 15 16

Table number and title are flush left.

Where means are reported, an associated measure of variability is also reported.

Tables appear after the references in the APA style, each table on a separate page.

Tables are used to present information efficiently.

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380 APPENDIX A Communicating Your Research Findings

Biasing Effects 10

Appendix

The following table shows the raw scores (i.e., the ind-

ividual bail judgments) of 31 college students who were

randomly assigned to an experimental or a control group:

Shown below are basic equations and my calculations,

starting with the independent-sample t test:

and the effect size r computed from t:

reffect size � B t2

t2 � df � B

(2.08)2

(2.08)2 � 29 � .36

t � M1 � M2

Ba 1n1 � 1n2b S 2 �

16,146.67 � 6,990.63

Ba 115 � 116b 149,682,340.575 � 2.08

Experimental

group

Control

group

$10,000 $10,000

12,500 4,000

2,000 5,000

50,000 350

20,000 5,000

200 15,000

500 500

30,000 5,000

2,000 500

10,000 1,500

5,000 10,000

10,000 10,000

50,000 20,000

30,000 5,000

10,000 10,000

— 10,000

Although these data are typed, check with your in- structor about whether handwritten data and equations are permissible in the appendix of your re- port.

The appendix of the student’s report begins on a new page.

Showing the raw data and the computa- tions of the study helps the instructor to grade the paper fairly.

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APPENDIX A Communicating Your Research Findings 381

Biasing Effects 11

For the Satterthwaite-adjusted t, I used the following

equation found in Rosenthal, Rosnow, & Rubin (2000):

and

As indicated in Rosenthal et al. (2000), I truncated the

16.90 df to the next lower integer, 16. The t of 2.03, with

df = 16, has a p of .030 one-tailed. To obtain the effect

size r, I used the t value of 1.96 associated with the

adjusted p value noted above (p = .030 one-tailed) and

used the original degrees of freedom (29), as indicated in

Rosenthal et al.:

reffect size � B t 2

t 2 � df � B

(1.96)2

(1.96)2 � 29 � .34

dfSatterthwaite �

a S 21 n1

� S 22 n2 b2

Da S 2 1

n1 b2

n1 � 1 T� ≥ aS

2 2

n2 b2

n2 � 1 ¥

�

a277,058,355.305 15

� 30,798,060.16

16 b2

Da277,058,355.30515 b2 15 � 1

T � Da30,798,060.1616 b 2

16 � 1 T

� 16.90

tSatterthwaite � M1 � M2

B S 21 n1

� S 22 n2

� 16,146.67 � 6,990.63

B 277,058,355.305

15 �

30,798,060.16

� 2.03

The student has included an expla- nation of a particular procedure, including the citation of an advanced text that she consulted.

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hypotheses or research questions. Basically, it describes the point of the research and also provides a framework for your later description of the method used. The idea of writing a strong introduction is to lead the reader to the thought, “Yes, of course, that’s what this researcher had to do to test this hypothesis.” Mary begins by describing a debate she happened to see on television, which puts her research in a practical light that is both compelling and socially significant. She develops her hypothesis in such a way that the method section (which follows) will seem a natural consequence of the introduction.

Here are some questions to help you plan the introduction:

• What got me thinking about this study? • How did I come up with my working hypothesis, and what did I expect to find? • What terms do I need to define for the reader who may be unfamiliar with this

area? • Do I need to define any terms for special reasons, because they are used differ-

ently in different contexts or because I use them in a new way? • How does the study build on, or derive from, other studies? • Is each of my hypotheses clearly explained and justified in terms of its logical

basis?

Outlining is a good way to organize your thoughts before you begin writing. How- ever, if you did not outline the introduction (or any other section) before you drafted it, a useful trick is to outline the introduction (or the whole paper) after it is written. Just list, in sentence fragments or phrases, the main ideas and what further ideas detail or substantiate those main ideas, also in sentence fragments or phrases. This process will show you whether you have proceeded logically or if there are any lapses in logic that need to be corrected.

Incidentally, notice that Mary uses the personal pronouns I (“I was channel surf- ing . . .”) and my (“whetted my curiosity”) in her opening paragraph. Not all instruc- tors find this usage acceptable, however, preferring instead that students use the third person rather than the first person in attributing an action to themselves, for example, referring to yourself as “the experimenter” (third person) rather than as “I” (first per- son). The APA manual cautions (pp. 37–38), however, that using the third person “may give the impression that you did not take part in your own study.”

Method

In the method section, you describe the procedures used and give a detailed account of the pertinent characteristics of the research participants. Although we use subjects and participants interchangeably, the APA manual suggests that you call the subjects of the study participants, individuals, college students, children, or respondents, because the term subjects strikes many people as “too impersonal.” The APA manual’s rule of thumb is to describe the participants at whatever is the appropriate level of specificity, but to be sensitive to the labels you choose.

A particular problem is avoiding sexist language in describing the participants. It would be a mistake, for example, to use the word man as a general term for both men and women, as the word creates a mental picture that is simply inaccurate (Dumond, 1990). On the other hand, if the subjects were only men, it would be misleading not to describe them by sex (and by other relevant distinguishing characteristics, such as age

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and level of education). When this issue first gained prominence some years ago, writ- ers began to coin contrived words such as “s/he” and “he/she” to avoid sexist language when referring to both sexes. You can avoid awkward terms like these by using plural pronouns when you are referring to both genders. The basic rule, however, is not to mislead people by creating the wrong mental picture.

Notice in Mary’s paper that the method section is divided into two parts, each with a side heading. She begins by describing the participants and giving an overall picture of the design of the study. She also tells how the two forms of her questionnaire were distributed so that both she and the participants were blind to which treatment any per- son received. She ends up noting that she debriefed the participants. She then gives a detailed description of the questionnaire she created. In other reports, the method sec- tion may need more than two parts, depending on how complicated the study is.

Notice also that Mary’s paper uses two formats of headings: center and flush left. The center heading is used to separate the paper into major sections, is written in uppercase and lowercase letters, and is not italicized. To subdivide the parts of the method section, Mary uses subheadings placed at the left margin, italicized, and typed in uppercase and lowercase. If she had wanted to use a further level of subheadings, they would have been indented, underlined, and followed by a period, with the body of the text then immediately following the heading.

Results

You describe your data in the results section, beginning with the main findings, those most relevant to your hypotheses. Try to strike a balance between being discursive and being overly precise. You might, as Mary does, present the results in a table or a graphic (as described in Chapter 10). Notice that Mary’s table appears on a separate page after her reference section, that it is numbered and labeled, and that this labeling includes specific row and column headings. Except for some reports of single-case studies (see the discussion of single-case studies in Chapter 8), you are usually not ex- pected to show individual scores in this section.

Mary’s results section tells how she analyzed the data to test her hypothesis. She reports the significance test, including that her p value is one-tailed, and mentions the effect size r and 95% confidence interval. She then mentions that she consulted with the instructor about an appropriate statistical procedure for dealing with heterogeneity of variance, and she gives these results as well. She also mentions that her calculations are shown in the appendix at the end of her report.

A trick to help you pull the results together before you start writing is to set down a list of your statistical findings. Divide the list into coherent sets of results, and then decide the sequence according to their order of importance or relevance to your hy- potheses, questions, and objectives. Experienced authors try to anticipate the questions that readers may have, particularly those about ambiguous results that call for clarifica- tion or further analysis. Here are some questions to help you structure this section:

• What were the different results, and what is their order of importance or rele- vance?

• How can I describe what I found in a careful, detailed way that will make com- plete sense to someone who is not informed on this topic?

• Have I omitted any necessary details or included superfluous information? • In reporting my statistical results, am I being sufficiently precise?

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Discussion

In the discussion section, you synthesize and interpret the various parts of your report to form a cohesive unit from the facts you have gathered. Without being overly repeti- tive, Mary begins by reminding us of the background that she developed in the intro- duction. She recapitulates her original hypothesis, underscoring the logical continuity of her presentation. Had she gotten any unexpected results, this would be the place to note how serendipity again entered into her study. She writes “defensively” in that she plays her own devil’s advocate by pointing out the limitations of her study. She also raises some potential implications and future directions of her research, thus communi- cating that she has thought about this area.

As you begin to structure this section, here are some questions to consider:

• What was the major purpose of this study, and were there any secondary objec- tives?

• How do my results relate to that purpose and those objectives? • Were there any unexpected findings of interest, and how can I describe them to

show their relevance to this project and to possible follow-up research? • How valid and generalizable are my findings, and what are their limitations? • What can I say about the wider implications of the results?

References

The title page and abstract are on separate pages, and the first page of the introduc- tion (page 3 of Mary’s paper) begins on a separate page, but the method, results, and discussion sections follow one another without any page breaks. The reference sec- tion also begins on a separate page, and you can now see why complete and accu- rate notes are crucial. This section is an alphabetized listing of all the sources of information on which you drew. Your notes (e.g., on an index card for each refer- ence) or a running list of sorted references in a file on your computer will now pro- vide the final list.

The APA manual requires you list only those references that you have actually dis- cussed or cited. Mary’s paper gives us examples of the style recommended by the APA in referencing books, journal articles, and chapters in edited books, and you will find many other examples in the APA manual as well as in Rosnow and Rosnow’s (2006) manual. Here is a condensed list of APA rules about referencing:

• List authors’ names in the exact order in which they appear on the title page of the publication and by last name, then first initial and middle initial.

• Authors’ names are separated by commas; use an ampersand (&) before the last author.

• Give the year the work was copyrighted (the year and month for magazine arti- cles and the year, month, and day for newspaper articles).

• For titles of books, chapters in books, and journal articles, generally capitalize only the first word of the title and of the subtitle (if any) as well as any proper names.

• Italicize (or underline) the title of a book or a journal and the volume number of a journal article.

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• Give the city and state of a book’s publisher, using postal abbreviations, but you do not need to list state abbreviations for Baltimore, Boston, Chicago, Los Ange- les, New York, Philadelphia, and San Francisco.

• If you are listing a foreign city other than Amsterdam, Jerusalem, London, Milan, Moscow, Paris, Rome, Stockholm, Tokyo, or Vienna, list the country as well.

End Material

The APA manual stipulates that tables and figures be placed in the manuscript after the reference section. The traditional purpose of this placement was to make it easier for the copy editor and the printer to work with the typewritten copy. It is now a com- mon practice, after a paper has been officially accepted for publication, to ask the au- thor to submit a disk containing the manuscript along with a hard copy or to send the manuscript as an e-mail attachment. The copy editor then has the option of working with the disk, the hard copy, or the e-mail version. Many instructors no longer insist that tables and figures be placed after the reference section and are quite amenable to having them inserted inside the results section so that they appear along with the nar- rative that refers to them. Before you decide on placement, however, ask your instruc- tor or grader if it is acceptable to put your tables or graphics inside the running narrative section.

As illustrated by Mary’s paper, many instructors also recommend that the final sec- tion of the student’s report be an appendix that displays raw materials not described fully in the method section. For example, if you have stimulus materials that can be photocopied, this is the place to include them. Mary’s appendix shows not her ques- tionnaire (which was described in the method section) but her raw data and gives some of the logic of the statistical procedures she used. The instructor can see that Mary’s analyses were done properly and that she has a clear understanding of the pro- cedures. Had there been an error, the purpose of including this appendix would be to allow the instructor to trace how far back a mistake in the data analysis goes. The stu- dent will not be penalized for making what might seem a mistake in interpretation or understanding when it is a less serious (but still to be avoided) typographical error or a recording mistake.

Writing and Revising

Now that you know what is expected, it is time to begin writing a first draft. A good way to start is to compose a self-motivator statement that you can refer to as a way of focusing your thoughts. Such a statement can be posted over your computer as a guidepost to keep you from wandering off on a tangent. Mary’s motivator might be “what I know about whether disclosing the results of drug tests influences bail judg- ments in criminal proceedings.”

If you are someone who has trouble getting started, one useful trick is to begin not at the beginning but with the section you feel will be easiest to write. Once the ideas begin to flow, you can tackle the introductory section. This approach will also bolster flagging spirits, because you can reread the sections that you have already written when you begin to feel a loss of energy or determination. Try not to fall into the trap of escaping by napping or watching television. If you recognize these counterproductive moves for what they are, you should be able to avoid them.

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Here are some helpful hints to make the writing go more smoothly:

• Find a quiet, well-lighted place in which to write, and do your writing in 2-hour stretches.

• Double-space your first draft so you can get an idea of how long the final (double- spaced) paper will be.

• Double spacing will also give you room for legible revisions if you like to revise your work in the printed version (which we each like to do) as well as on a com- puter screen.

• Number your printed pages using a header. Number your pages even if you are writing on a note pad.

• Pace your work so that you can complete the first draft and let it rest for at least 24 hours before you revise and polish what will be your final draft.

Layout and Printing

After you have revised your paper and are satisfied with the final version, it is time to prepare it for submission. The final report must not contain any typographical errors or spelling mistakes. To help you catch misspellings, use a spell check. Be sure that the spell check has not missed any misspelled technical terms, however. It may also not catch typos such as a capital I when you meant to type in. Using the grammar check would probably catch this kind of mistake, but a grammar check can drive writers to distraction by querying every phrase and line they write. Put the printed paper aside for a day or two, and then look at it again with a fresh eye. This process is called proofreading or proofing, and it is a final step before you submit your paper.

It is a good idea to proof the paper more than once, because gremlins in a pro- gram sometimes introduce weird changes. Also, you will be surprised how elusive some typos are; you can stare at them and still not see them right away. Ask yourself:

• Are there omissions? • Are there misspellings? • Are the numbers correct? • Are the hyphenations correct? • Are all the references cited in the body of the paper listed in the reference sec-

tion, and vice versa?

Make sure the print is dark enough to be easily read, as you do not want to frus- trate the grader by submitting a paper with typescript so light or blurry that it taxes the eyes. Use 81⁄2 × 11-inch white paper. The APA manual requires that there be at least 1-inch margins on all four sides of the page, that no typed line exceed 61⁄2 inches, and that there be no more than 27 lines of text on the page. In a student paper, however, these criteria are flexible. Double-space the printout, and print on one side of the paper only, using a page header to number the pages consecutively (described before and illustrated in Mary’s paper).

Be sure to back up your work routinely. You never know when somebody may play- fully touch a couple of keys and erase all your hard work. It is also a good idea to print a hard copy of your work periodically, so you have a double guarantee that you will not lose it. When the clean, corrected final draft is completed, make an extra copy—just in

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case. The original is for the instructor, and the duplicate copy ensures that a spare will be readily available if a problem arises.

Notice that Mary’s paper leaves the right margin ragged (i.e., uneven), which is also a requirement of the APA manual. If this were an article for submission to a jour- nal, although your manuscript would have a ragged right margin, the printed version would appear justified (i.e., even on the left and right, as in this book). Now is the time to give your paper a final look, checking to see that all the pages are there and in order, and then to turn it in on schedule. Having adhered to these guidelines, you should feel the satisfaction of a job well done.

Creating a Poster

If you plan to present a poster at a professional meeting, check to see whether the association has particular requirements, such as the number of pages permitted. The poster board surface also varies from one sponsoring organization to another but is usually about 4 feet high and either 6 or 8 feet wide. To give you a general idea, Exhibit A.2 shows a template for a poster consisting of six pages. This is usually the bare minimum, so if you have room for additional pages you can plan on including fur- ther information. Because you are obviously limited in how much you can say in a six- page poster, it is important to bring along copies of a more complete report to give to anyone who asks for one. On the title page, note your e-mail address and mailing address so that people can get in touch with you if they want to.

Your choice of font size should be determined by the distance from which people will be viewing the posters. The font height should not be less than 3/8 inches, though you might use a bigger font for the title and authors (no less than 1 inch or, depending on distance, as much as 2–3 inches high). As one instructor cautioned his students: Be prepared for a cramped area with relatively poor lighting, and a lot of distracting sen- sory activity (Rosnow & Rosnow, 2006, p. 115). Here are further tips from Rosnow and Rosnow’s manual:

• Use a typeface that is easy to read, such as Arial or Times New Roman, not a fancy one that has squiggles or loops.

• Stand back about 5 feet and see if you can read the poster; the font size should not be less than 24 points.

• Because viewers don’t usually want to stand around and study a poster assidu- ously, use as few words as possible.

• Don’t overcomplicate tables or figures, and don’t use jargon or exotic terms that may be unfamiliar to viewers and turn them away.

• Make your graphics simple so that they are eye-catching, such as a good photo or a good picture.

• Use color for important highlights, but use it sparingly because you are reporting a scientific study, not creating a work of art.

• Try looking at your poster through the eyes of those you want to attract, and also try it out on your instructor and other students for feedback.

Exhibit A.3 shows a poster based on Mary’s study and Exhibit A.2’s six-page tem- plate. You can see that it captures the highlights of her study, but it still would be neces- sary to read the report to understand the study thoroughly. Thus, it is important to bring

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along copies of a more complete report rather than simply hand out copies of the poster. However, your handout report should not be the paper you wrote for your instructor, which would be much too long and filled with irrelevant details for poster viewers. The idea is to try to boil down your research to a one-page (single-sided or double-sided) handout with the information single-spaced. At the very least, your handout should report group means, sample sizes, and measurement error. Based on what you have learned in this course, you should also have a sense of what other basic information a reader will need to reanalyze your results.

In preparing these materials, the idea is to try to anticipate people’s questions and to tell them enough so they can come to their own conclusions. For your poster, re- member that you are trying to draw attention to your study and not put people off by a cluttered presentation. You also want to chat with people who are interested in learn- ing more about your study.

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APPENDIX A Communicating Your Research Findings 389

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390 APPENDIX A Communicating Your Research Findings

1. The Problem

Suppose someone arrested for suspicion of burglary is up for bail judgment and the judge knows that the suspect, when arrested, tested positive for drug use. Is the judge likely to order a harsher bail because of this information?

The crime control side has argued that mandatory drug testing of all suspects on arrest will have no biasing effects in legal proceedings, whereas the due process side has argued that drug information is likely to have prejudicial effects.

The purpose of this simulation experiment was to determine whether informing “judges” that the defendant has tested positive for drug usage will result in higher bail judgments.

2. Hypothesis

According to correspondent inference theory, observers focus mainly on certain types of observed behavior of an actor to infer traits because they believe that only certain behaviors are indicative of the actor’s traits.

Once observers have inferred these traits, they use this information to predict the actor’s future behavior and, in turn, to assess and to guide their own actions, decisions, and judgments regarding the actor.

On the basis of these assumptions, I hypothesized that providing “judges” with the defendant’s positive results from a drug test would result in harsher bail judgments than when this information was unavailable.

3. Research Procedure

The “judges” in this experiment were 31 undergraduate students, who were randomly assigned to two conditions and asked to read one of two forms of a crime scenario. Both forms stated that “a man was arrested as a suspected burglar” and that “he fit the description of a man seen running from a burglarized house.”

In the experimental condition, the story was that “while in custody the man submitted to a blood test, and it was determined that he had very recently used drugs.” In the control con- dition, this information was omitted, and the story concluded with the statement that “the man spent enough time in custody to receive two meals and make three phone calls.”

The students were asked to pretend to be the bail judge and to choose a dollar figure from $0 to $50,000 as the amount of bail.

Exhibit A.3 Mary Jones’s Poster Presentation Using the Template in Exhibit A.3

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4. Summary of Results

The results, shown in the table in Frame 5, indicated that the mean judgment of the experimental participants was higher than the mean judgment of the control partici- pants, with t(29) � 2.08, one-tailed p � .023, reffect size �.36.

Underlying the use of the t test is the assumption that the population variances are equal, but dividing the larger of the two associated variances (S 2) by the smaller of the two variances yielded F (14, 15) � 9.0, p � 6.3�5, indicating heterogeneity of the population variances.

With the use of Satterthwaite’s procedure, including adjustment of the degrees of free- dom to make the t more accurate, the results were not much different: Satterthwaite t(16) � 2.03, one-tailed p � .03, reffect size �.34. With 95% confidence, the population value of reffect size can be estimated as between r � �.02 and .62.

5. Mean, Variability, and Number of Participants

6. Conclusions

The results were consistent with the hypothesis that bail judgments would be biased in a harsher direction in the experimental than in the control condition. One possible im- plication of this finding is that our justice system’s goal of being evenhanded and unbi- ased could be jeopardized if the results of mandatory drug testing were made available to judges before their bail judgments.

An important limitation of this study, however, is that the participants were college students, and thus, it might be argued that the results are not generalizable to actual bail judges. Another concern is whether the students’ inferences of corresponding traits from socially undesirable behavior may have used stereotypes in assuming that there was a likely association between drug usage and the information in the burglary scenario.

Measure Experimental

group Control group

Mean $16,146.67 $6,990.63 S 16,645.07 5,549.60 n 15 16

Exhibit A.3 (continued)

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APPENDIX B Statistical Tables

B1. z Values and Their Associated One-Tailed p Values

B2. t Values and Their Associated One-Tailed and Two-Tailed p Values

B3. F Values and Their Associated p Values

B4. χ2 Values and Their Associated p Values B5. r Values and Their Associated p Values

B6. Transformations of r to Fisher zr B7. Transformations of Fisher zr to r ]

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Table B.1 z Values and Their Associated One-Tailed p Values Second digit of z

z .00 .01 .02 .03 .04 .05 .06 .07 .08 .09

.0 .5000 .4960 .4920 .4880 .4840 .4801 .4761 .4721 .4681 .4641

.1 .4602 .4562 .4522 .4483 .4443 .4404 .4364 .4325 .4286 .4247

.2 .4207 .4168 .4129 .4090 .4052 .4013 .3974 .3936 .3897 .3859

.3 .3821 .3783 .3745 .3707 .3669 .3632 .3594 .3557 .3520 .3483

.4 .3446 .3409 .3372 .3336 .3300 .3264 .3228 .3192 .3156 .3121

.5 .3085 .3050 .3015 .2981 .2946 .2912 .2877 .2843 .2810 .2776

.6 .2743 .2709 .2676 .2643 .2611 .2578 .2546 .2514 .2483 .2451

.7 .2420 .2389 .2358 .2327 .2296 .2266 .2236 .2206 .2177 .2148

.8 .2119 .2090 .2061 .2033 .2005 .1977 .1949 .1922 .1894 .1867

.9 .1841 .1814 .1788 .1762 .1736 .1711 .1685 .1660 .1635 .1611 1.0 .1587 .1562 .1539 .1515 .1492 .1469 .1446 .1423 .1401 .1379 1.1 .1357 .1335 .1314 .1292 .1271 .1251 .1230 .1210 .1190 .1170 1.2 .1151 .1131 .1112 .1093 .1075 .1056 .1038 .1020 .1003 .0985 1.3 .0968 .0951 .0934 .0918 .0901 .0885 .0869 .0853 .0838 .0823 1.4 .0808 .0793 .0778 .0764 .0749 .0735 .0721 .0708 .0694 .0681 1.5 .0668 .0655 .0643 .0630 .0618 .0606 .0594 .0582 .0571 .0559 1.6 .0548 .0537 .0526 .0516 .0505 .0495 .0485 .0475 .0465 .0455 1.7 .0446 .0436 .0427 .0418 .0409 .0401 .0392 .0384 .0375 .0367 1.8 .0359 .0351 .0344 .0336 .0329 .0322 .0314 .0307 .0301 .0294 1.9 .0287 .0281 .0274 .0268 .0262 .0256 .0250 .0244 .0239 .0233 2.0 .0228 .0222 .0217 .0212 .0207 .0202 .0197 .0192 .0188 .0183 2.1 .0179 .0174 .0170 .0166 .0162 .0158 .0154 .0150 .0146 .0143 2.2 .0139 .0136 .0132 .0129 .0125 .0122 .0119 .0116 .0113 .0110 2.3 .0107 .0104 .0102 .0099 .0096 .0094 .0091 .0089 .0087 .0084 2.4 .0082 .0080 .0078 .0075 .0073 .0071 .0069 .0068 .0066 .0064 2.5 .0062 .0060 .0059 .0057 .0055 .0054 .0052 .0051 .0049 .0048 2.6 .0047 .0045 .0044 .0043 .0041 .0040 .0039 .0038 .0037 .0036 2.7 .0035 .0034 .0033 .0032 .0031 .0030 .0029 .0028 .0027 .0026 2.8 .0026 .0025 .0024 .0023 .0023 .0022 .0021 .0021 .0020 .0019 2.9 .0019 .0018 .0018 .0017 .0016 .0016 .0015 .0015 .0014 .0014 3.0 .0013 .0013 .0013 .0012 .0012 .0011 .0011 .0011 .0010 .0010 3.1 .0010 .0009 .0009 .0009 .0008 .0008 .0008 .0008 .0007 .0007 3.2 .0007 3.3 .0005 3.4 .0003 3.5 .00023 3.6 .00016 3.7 .00011 3.8 .00007 3.9 .00005 4.0 .00003

Source: From Nonparametric Statistics (p. 247), by S. Siegel, 1956, New York: McGraw-Hill. Reprinted by permission of McGraw-Hill, Inc.

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Table B.2 t Values and Their Associated One-Tailed and Two-Tailed p Values

p .50 .20 .10 .05 .02 .01 .005 .002 two-tailed

df .25 .10 .05 .025 .01 .005 .0025 .001 one-tailed

1 1.000 3.078 6.314 12.706 31.821 63.657 127.321 318.309 2 .816 1.886 2.920 4.303 6.965 9.925 14.089 22.327 3 .765 1.638 2.353 3.182 4.541 5.841 7.453 10.214 4 .741 1.533 2.132 2.776 3.747 4.604 5.598 7.173 5 .727 1.476 2.015 2.571 3.365 4.032 4.773 5.893

6 .718 1.440 1.943 2.447 3.143 3.707 4.317 5.208 7 .711 1.415 1.895 2.365 2.998 3.499 4.029 4.785 8 .706 1.397 1.860 2.306 2.896 3.355 3.833 4.501 9 .703 1.383 1.833 2.262 2.821 3.250 3.690 4.297

10 .700 1.372 1.812 2.228 2.764 3.169 3.581 4.144

11 .697 1.363 1.796 2.201 2.718 3.106 3.497 4.025 12 .695 1.356 1.782 2.179 2.681 3.055 3.428 3.930 13 .694 1.350 1.771 2.160 2.650 3.012 3.372 3.852 14 .692 1.345 1.761 2.145 2.624 2.977 3.326 3.787 15 .691 1.341 1.753 2.131 2.602 2.947 3.286 3.733

16 .690 1.337 1.746 2.120 2.583 2.921 3.252 3.686 17 .689 1.333 1.740 2.110 2.567 2.898 3.223 3.646 18 .688 1.330 1.734 2.101 2.552 2.878 3.197 3.610 19 .688 1.328 1.729 2.093 2.539 2.861 3.174 3.579 20 .687 1.325 1.725 2.086 2.528 2.845 3.153 3.552

21 .686 1.323 1.721 2.080 2.518 2.831 3.135 3.527 22 .686 1.321 1.717 2.074 2.508 2.819 3.119 3.505 23 .685 1.319 1.714 2.069 2.500 2.807 3.104 3.485 24 .685 1.318 1.711 2.064 2.492 2.797 3.090 3.467 25 .684 1.316 1.708 2.060 2.485 2.787 3.078 3.450

26 .684 1.315 1.706 2.056 2.479 2.779 3.067 3.435 27 .684 1.314 1.703 2.052 2.473 2.771 3.057 3.421 28 .683 1.313 1.701 2.048 2.467 2.763 3.047 3.408 29 .683 1.311 1.699 2.045 2.462 2.756 3.038 3.396 30 .683 1.310 1.697 2.042 2.457 2.750 3.030 3.385

35 .682 1.306 1.690 2.030 2.438 2.724 2.996 3.340 40 .681 1.303 1.684 2.021 2.423 2.704 2.971 3.307 45 .680 1.301 1.679 2.014 2.412 2.690 2.952 3.281 50 .679 1.299 1.676 2.009 2.403 2.678 2.937 3.261 55 .679 1.297 1.673 2.004 2.396 2.668 2.925 3.245

60 .679 1.296 1.671 2.000 2.390 2.660 2.915 3.232 70 .678 1.294 1.667 1.994 2.381 2.648 2.899 3.211 80 .678 1.292 1.664 1.990 2.374 2.639 2.887 3.195 90 .677 1.291 1.662 1.987 2.368 2.632 2.878 3.183

100 .677 1.290 1.660 1.984 2.364 2.626 2.871 3.174

200 .676 1.286 1.652 1.972 2.345 2.601 2.838 3.131 500 .675 1.283 1.648 1.965 2.334 2.586 2.820 3.107

1,000 .675 1.282 1.646 1.962 2.330 2.581 2.813 3.098 2,000 .675 1.282 1.645 1.961 2.328 2.578 2.810 3.094

10,000 .675 1.282 1.645 1.960 2.327 2.576 2.808 3.091

q .674 1.282 1.645 1.960 2.326 2.576 2.807 3.090 (continued)

394 APPENDIX B Statistical Tables

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Table B.2 t Values and Their Associated One-Tailed and Two-Tailed p Values

p .001 .0005 .0002 .0001 .00005 .00002 two-tailed

df .0005 .00025 .0001 .00005 .000025 .00001 one-tailed

1 636.619 1,273.239 3,183.099 6,366.198 12,732.395 31,830.989 2 31.598 44.705 70.700 99.992 141.416 223.603 3 12.924 16.326 22.204 28.000 35.298 47.928 4 8.610 10.306 13.034 15.544 18.522 23.332 5 6.869 7.976 9.678 11.178 12.893 15.547

6 5.959 6.788 8.025 9.082 10.261 12.032 7 5.408 6.082 7.063 7.885 8.782 10.103 8 5.041 5.618 6.442 7.120 7.851 8.907 9 4.781 5.291 6.010 6.594 7.215 8.102

10 4.587 5.049 5.694 6.211 6.757 7.527

11 4.437 4.863 5.453 5.921 6.412 7.098 12 4.318 4.716 5.263 5.694 6.143 6.756 13 4.221 4.597 5.111 5.513 5.928 6.501 14 4.140 4.499 4.985 5.363 5.753 6.287 15 4.073 4.417 4.880 5.239 5.607 6.109

16 4.015 4.346 4.791 5.134 5.484 5.960 17 3.965 4.286 4.714 5.044 5.379 5.832 18 3.922 4.233 4.648 4.966 5.288 5.722 19 3.883 4.187 4.590 4.897 5.209 5.627 20 3.850 4.146 4.539 4.837 5.139 5.543

21 3.819 4.110 4.493 4.784 5.077 5.469 22 3.792 4.077 4.452 4.736 5.022 5.402 23 3.768 4.048 4.415 4.693 4.972 5.343 24 3.745 4.021 4.382 4.654 4.927 5.290 25 3.725 3.997 4.352 4.619 4.887 5.241

26 3.707 3.974 4.324 4.587 4.850 5.197 27 3.690 3.954 4.299 4.558 4.816 5.157 28 3.674 3.935 4.275 4.530 4.784 5.120 29 3.659 3.918 4.254 4.506 4.756 5.086 30 3.646 3.902 4.234 4.482 4.729 5.054

35 3.591 3.836 4.153 4.389 4.622 4.927 40 3.551 3.788 4.094 4.321 4.544 4.835 45 3.520 3.752 4.049 4.269 4.485 4.766 50 3.496 3.723 4.014 4.228 4.438 4.711 55 3.476 3.700 3.986 4.196 4.401 4.667

60 3.460 3.681 3.926 4.169 4.370 4.631 70 3.435 3.651 3.962 4.127 4.323 4.576 80 3.416 3.629 3.899 4.096 4.288 4.535 90 3.402 3.612 3.878 4.072 4.261 4.503

100 3.390 3.598 3.862 4.053 4.240 4.478

200 3.340 3.539 3.789 3.970 4.146 4.369 500 3.310 3.504 3.747 3.922 4.091 4.306

1,000 3.300 3.492 3.733 3.906 4.073 4.285 2,000 3.295 3.486 3.726 3.898 4.064 4.275

10,000 3.292 3.482 3.720 3.892 4.058 4.267

q 3.291 3.481 3.719 3.891 4.056 4.265

A p

p en

d ix

Source: From “Extended Tables of the Percentage Points of Student’s t-Distribution,” by E. T. Federighi, 1959, Journal of the American Statistical Association, 54, pp. 683–688. Reprinted by permission of the American Statistical Association.

395

IS B

N : 0

-5 36

-5 64

36 -1

A p

p en

d ix

Table B.3 F Values and Their Associated p Values

df2 df1 p 1 2 3 4 5 6 8 12 24 q

1 .001 405284 500000 540379 562500 576405 585937 598144 610667 623497 636619 .005 16211 20000 21615 22500 23056 23437 23925 24426 24940 25465 .01 4052 4999 5403 5625 5764 5859 5981 6106 6234 6366 .025 647.79 799.50 864.16 899.58 921.85 937.11 956.66 976.71 997.25 1018.30 .05 161.45 199.50 215.71 224.58 230.16 233.99 238.88 243.91 249.05 254.32 .10 39.86 49.50 53.59 55.83 57.24 58.20 59.44 60.70 62.00 63.33 .20 9.47 12.00 13.06 13.73 14.01 14.26 14.59 14.90 15.24 15.58

2 .001 998.5 999.0 999.2 999.2 999.3 999.3 999.4 999.4 999.5 999.5 .005 198.50 199.00 199.17 199.25 199.30 199.33 199.37 199.42 199.46 199.51 .01 98.49 99.00 99.17 99.25 99.30 99.33 99.36 99.42 99.46 99.50 .025 38.51 39.00 39.17 39.25 39.30 39.33 39.37 39.42 39.46 39.50 .05 18.51 19.00 19.16 19.25 19.30 19.33 19.37 19.41 19.45 19.50 .10 8.53 9.00 9.16 9.24 9.29 9.33 9.37 9.41 9.45 9.49 .20 3.56 4.00 4.16 4.24 4.28 4.32 4.36 4.40 4.44 4.48

3 .001 167.5 148.5 141.1 137.1 134.6 132.8 130.6 128.3 125.9 123.5 .005 55.55 49.80 47.47 46.20 45.39 44.84 44.13 43.39 42.62 41.83 .01 34.12 30.81 29.46 28.71 28.24 27.91 27.49 27.05 26.60 26.12 .025 17.44 16.04 15.44 15.10 14.89 14.74 14.54 14.34 14.12 13.90 .05 10.13 9.55 9.28 9.12 9.01 8.94 8.84 8.74 8.64 8.53 .10 5.54 5.46 5.39 5.34 5.31 5.28 5.25 5.22 5.18 5.13 .20 2.68 2.89 2.94 2.96 2.97 2.97 2.98 2.98 2.98 2.98

4 .001 74.14 61.25 56.18 53.44 51.71 50.53 49.00 47.41 45.77 44.05 .005 31.33 26.28 24.26 23.16 22.46 21.98 21.35 20.71 20.03 19.33 .01 21.20 18.00 16.69 15.98 15.52 15.21 14.80 14.37 13.93 13.46 .025 12.22 10.65 9.98 9.60 9.36 9.20 8.98 8.75 8.51 8.26 .05 7.71 6.94 6.59 6.39 6.26 6.16 6.04 5.91 5.77 5.63 .10 4.54 4.32 4.19 4.11 4.05 4.01 3.95 3.90 3.83 3.76 .20 2.35 2.47 2.48 2.48 2.48 2.47 2.47 2.46 2.44 2.43

5 .001 47.04 36.61 33.20 31.09 29.75 28.84 27.64 26.42 25.14 23.78 .005 22.79 18.31 16.53 15.56 14.94 14.51 13.96 13.38 12.78 12.14 .01 16.26 13.27 12.06 11.39 10.97 10.67 10.29 9.89 9.47 9.02 .025 10.01 8.43 7.76 7.39 7.15 6.98 6.76 6.52 6.28 6.02 .05 6.61 5.79 5.41 5.19 5.05 4.95 4.82 4.68 4.53 4.36 .10 4.06 3.78 3.62 3.52 3.45 3.40 3.34 3.27 3.19 3.10 .20 2.18 2.26 2.25 2.24 2.23 2.22 2.20 2.18 2.16 2.13

6 .001 35.51 27.00 23.70 21.90 20.81 20.03 19.03 17.99 16.89 15.75 .005 18.64 14.54 12.92 12.03 11.46 11.07 10.57 10.03 9.47 8.88 .01 13.74 10.92 9.78 9.15 8.75 8.47 8.10 7.72 7.31 6.88 .025 8.81 7.26 6.60 6.23 5.99 5.82 5.60 5.37 5.12 4.85 .05 5.99 5.14 4.76 4.53 4.39 4.28 4.15 4.00 3.84 3.67 .10 3.78 3.46 3.29 3.18 3.11 3.05 2.98 2.90 2.82 2.72 .20 2.07 2.13 2.11 2.09 2.08 2.06 2.04 2.02 1.99 1.95

7 .001 29.22 21.69 18.77 17.19 16.21 15.52 14.63 13.71 12.73 11.69 .005 16.24 12.40 10.88 10.05 9.52 9.16 8.68 8.18 7.65 7.08 .01 12.25 9.55 8.45 7.85 7.46 7.19 6.84 6.47 6.07 5.65 .025 8.07 6.54 5.89 5.52 5.29 5.12 4.90 4.67 4.42 4.14 .05 5.59 4.74 4.35 4.12 3.97 3.87 3.73 3.57 3.41 3.23 .10 3.59 3.26 3.07 2.96 2.88 2.83 2.75 2.67 2.58 2.47 .20 2.00 2.04 2.02 1.99 1.97 1.96 1.93 1.91 1.87 1.83

(continued)

396 APPENDIX B Statistical Tables

IS B

N : 0-536-56436-1

A p

p en

d ix

Table B.3 F Values and Their Associated p Values

df2 df1 p 1 2 3 4 5 6 8 12 24 q

8 .001 25.42 18.49 15.83 14.39 13.49 12.86 12.04 11.19 10.30 9.34 .005 14.69 11.04 9.60 8.81 8.30 7.95 7.50 7.01 6.50 5.95 .01 11.26 8.65 7.59 7.01 6.63 6.37 6.03 5.67 5.28 4.86 .025 7.57 6.06 5.42 5.05 4.82 4.65 4.43 4.20 3.95 3.67 .05 5.32 4.46 4.07 3.84 3.69 3.58 3.44 3.28 3.12 2.93 .10 3.46 3.11 2.92 2.81 2.73 2.67 2.59 2.50 2.40 2.29 .20 1.95 1.98 1.95 1.92 1.90 1.88 1.86 1.83 1.79 1.74

9 .001 22.86 16.39 13.90 12.56 11.71 11.13 10.37 9.57 8.72 7.81 .005 13.61 10.11 8.72 7.96 7.47 7.13 6.69 6.23 5.73 5.19 .01 10.56 8.02 6.99 6.42 6.06 5.80 5.47 5.11 4.73 4.31 .025 7.21 5.71 5.08 4.72 4.48 4.32 4.10 3.87 3.61 3.33 .05 5.12 4.26 3.86 3.63 3.48 3.37 3.23 3.07 2.90 2.71 .10 3.36 3.01 2.81 2.69 2.61 2.55 2.47 2.38 2.28 2.16 .20 1.91 1.94 1.90 1.87 1.85 1.83 1.80 1.76 1.73 1.67

10 .001 21.04 14.91 12.55 11.28 10.48 9.92 9.20 8.45 7.64 6.76 .005 12.83 9.43 8.08 7.34 6.87 6.54 6.12 5.66 5.17 4.64 .01 10.04 7.56 6.55 5.99 5.64 5.39 5.06 4.71 4.33 3.91 .025 6.94 5.46 4.83 4.47 4.24 4.07 3.85 3.62 3.37 3.08 .05 4.96 4.10 3.71 3.48 3.33 3.22 3.07 2.91 2.74 2.54 .10 3.28 2.92 2.73 2.61 2.52 2.46 2.38 2.28 2.18 2.06 .20 1.88 1.90 1.86 1.83 1.80 1.78 1.75 1.72 1.67 1.62

11 .001 19.69 13.81 11.56 10.35 9.58 9.05 8.35 7.63 6.85 6.00 .005 12.23 8.91 7.60 6.88 6.42 6.10 5.68 5.24 4.76 4.23 .01 9.65 7.20 6.22 5.67 5.32 5.07 4.74 4.40 4.02 3.60 .025 6.72 5.26 4.63 4.28 4.04 3.88 3.66 3.43 3.17 2.88 .05 4.84 3.98 3.59 3.36 3.20 3.09 2.95 2.79 2.61 2.40 .10 3.23 2.86 2.66 2.54 2.45 2.39 2.30 2.21 2.10 1.97 .20 1.86 1.87 1.83 1.80 1.77 1.75 1.72 1.68 1.63 1.57

12 .001 18.64 12.97 10.80 9.63 8.89 8.38 7.71 7.00 6.25 5.42 .005 11.75 8.51 7.23 6.52 6.07 5.76 5.35 4.91 4.43 3.90 .01 9.33 6.93 5.95 5.41 5.06 4.82 4.50 4.16 3.78 3.36 .025 6.55 5.10 4.47 4.12 3.89 3.73 3.51 3.28 3.02 2.72 .05 4.75 3.88 3.49 3.26 3.11 3.00 2.85 2.69 2.50 2.30 .10 3.18 2.81 2.61 2.48 2.39 2.33 2.24 2.15 2.04 1.90 .20 1.84 1.85 1.80 1.77 1.74 1.72 1.69 1.65 1.60 1.54

13 .001 17.81 12.31 10.21 9.07 8.35 7.86 7.21 6.52 5.78 4.97 .005 11.37 8.19 6.93 6.23 5.79 5.48 5.08 4.64 4.17 3.65 .01 9.07 6.70 5.74 5.20 4.86 4.62 4.30 3.96 3.59 3.16 .025 6.41 4.97 4.35 4.00 3.77 3.60 3.39 3.15 2.89 2.60 .05 4.67 3.80 3.41 3.18 3.02 2.92 2.77 2.60 2.42 2.21 .10 3.14 2.76 2.56 2.43 2.35 2.28 2.20 2.10 1.98 1.85 .20 1.82 1.83 1.78 1.75 1.72 1.69 1.66 1.62 1.57 1.51

14 .001 17.14 11.78 9.73 8.62 7.92 7.43 6.80 6.13 5.41 4.60 .005 11.06 7.92 6.68 6.00 5.56 5.26 4.86 4.43 3.96 3.44 .01 8.86 6.51 5.56 5.03 4.69 4.46 4.14 3.80 3.43 3.00 .025 6.30 4.86 4.24 3.89 3.66 3.50 3.29 3.05 2.79 2.49 .05 4.60 3.74 3.34 3.11 2.96 2.85 2.70 2.53 2.35 2.13 .10 3.10 2.73 2.52 2.39 2.31 2.24 2.15 2.05 1.94 1.80 .20 1.81 1.81 1.76 1.73 1.70 1.67 1.64 1.60 1.55 1.48

(continued)

APPENDIX B Statistical Tables 397

IS B

N : 0

-5 36

-5 64

36 -1

A p

p en

d ix

Table B.3 F Values and Their Associated p Values

df2 df1 p 1 2 3 4 5 6 8 12 24 q

15 .001 16.59 11.34 9.34 8.25 7.57 7.09 6.47 5.81 5.10 4.31 .005 10.80 7.70 6.48 5.80 5.37 5.07 4.67 4.25 3.79 3.26 .01 8.68 6.36 5.42 4.89 4.56 4.32 4.00 3.67 3.29 2.87 .025 6.20 4.77 4.15 3.80 3.58 3.41 3.20 2.96 2.70 2.40 .05 4.54 3.68 3.29 3.06 2.90 2.79 2.64 2.48 2.29 2.07 .10 3.07 2.70 2.49 2.36 2.27 2.21 2.12 2.02 1.90 1.76 .20 1.80 1.79 1.75 1.71 1.68 1.66 1.62 1.58 1.53 1.46

16 .001 16.12 10.97 9.00 7.94 7.27 6.81 6.19 5.55 4.85 4.06 .005 10.58 7.51 6.30 5.64 5.21 4.91 4.52 4.10 3.64 3.11 .01 8.53 6.23 5.29 4.77 4.44 4.20 3.89 3.55 3.18 2.75 .025 6.12 4.69 4.08 3.73 3.50 3.34 3.12 2.89 2.63 2.32 .05 4.49 3.63 3.24 3.01 2.85 2.74 2.59 2.42 2.24 2.01 .10 3.05 2.67 2.46 2.33 2.24 2.18 2.09 1.99 1.87 1.72 .20 1.79 1.78 1.74 1.70 1.67 1.64 1.61 1.56 1.51 1.43

17 .001 15.72 10.66 8.73 7.68 7.02 6.56 5.96 5.32 4.63 3.85 .005 10.38 7.35 6.16 5.50 5.07 4.78 4.39 3.97 3.51 2.98 .01 8.40 6.11 5.18 4.67 4.34 4.10 3.79 3.45 3.08 2.65 .025 6.04 4.62 4.01 3.66 3.44 3.28 3.06 2.82 2.56 2.25 .05 4.45 3.59 3.20 2.96 2.81 2.70 2.55 2.38 2.19 1.96 .10 3.03 2.64 2.44 2.31 2.22 2.15 2.06 1.96 1.84 1.69 .20 1.78 1.77 1.72 1.68 1.65 1.63 1.59 1.55 1.49 1.42

18 .001 15.38 10.39 8.49 7.46 6.81 6.35 5.76 5.13 4.45 3.67 .005 10.22 7.21 6.03 5.37 4.96 4.66 4.28 3.86 3.40 2.87 .01 8.28 6.01 5.09 4.58 4.25 4.01 3.71 3.37 3.00 2.57 .025 5.98 4.56 3.95 3.61 3.38 3.22 3.01 2.77 2.50 2.19 .05 4.41 3.55 3.16 2.93 2.77 2.66 2.51 2.34 2.15 1.92 .10 3.01 2.62 2.42 2.29 2.20 2.13 2.04 1.93 1.81 1.66 .20 1.77 1.76 1.71 1.67 1.64 1.62 1.58 1.53 1.48 1.40

19 .001 15.08 10.16 8.28 7.26 6.61 6.18 5.59 4.97 4.29 3.52 .005 10.07 7.09 5.92 5.27 4.85 4.56 4.18 3.76 3.31 2.78 .01 8.18 5.93 5.01 4.50 4.17 3.94 3.63 3.30 2.92 2.49 .025 5.92 4.51 3.90 3.56 3.33 3.17 2.96 2.72 2.45 2.13 .05 4.38 3.52 3.13 2.90 2.74 2.63 2.48 2.31 2.11 1.88 .10 2.99 2.61 2.40 2.27 2.18 2.11 2.02 1.91 1.79 1.63 .20 1.76 1.75 1.70 1.66 1.63 1.61 1.57 1.52 1.46 1.39

20 .001 14.82 9.95 8.10 7.10 6.46 6.02 5.44 4.82 4.15 3.38 .005 9.94 6.99 5.82 5.17 4.76 4.47 4.09 3.68 3.22 2.69 .01 8.10 5.85 4.94 4.43 4.10 3.87 3.56 3.23 2.86 2.42 .025 5.87 4.46 3.86 3.51 3.29 3.13 2.91 2.68 2.41 2.09 .05 4.35 3.49 3.10 2.87 2.71 2.60 2.45 2.28 2.08 1.84 .10 2.97 2.59 2.38 2.25 2.16 2.09 2.00 1.89 1.77 1.61 .20 1.76 1.75 1.70 1.65 1.62 1.60 1.56 1.51 1.45 1.37

21 .001 14.59 9.77 7.94 6.95 6.32 5.88 5.31 4.70 4.03 3.26 .005 9.83 6.89 5.73 5.09 4.68 4.39 4.01 3.60 3.15 2.61 .01 8.02 5.78 4.87 4.37 4.04 3.81 3.51 3.17 2.80 2.36 .025 5.83 4.42 3.82 3.48 3.25 3.09 2.87 2.64 2.37 2.04 .05 4.32 3.47 3.07 2.84 2.68 2.57 2.42 2.25 2.05 1.81 .10 2.96 2.57 2.36 2.23 2.14 2.08 1.98 1.88 1.75 1.59 .20 1.75 1.74 1.69 1.65 1.61 1.59 1.55 1.50 1.44 1.36

(continued)

398 APPENDIX B Statistical Tables

IS B

N : 0-536-56436-1

A p

p en

d ix

Table B.3 F Values and Their Associated p Values

df2 df1 p 1 2 3 4 5 6 8 12 24 q

22 .001 14.38 9.61 7.80 6.81 6.19 5.76 5.19 4.58 3.92 3.15 .005 9.73 6.81 5.65 5.02 4.61 4.32 3.94 3.54 3.08 2.55 .01 7.94 5.72 4.82 4.31 3.99 3.76 3.45 3.12 2.75 2.31 .025 5.79 4.38 3.78 3.44 3.22 3.05 2.84 2.60 2.33 2.00 .05 4.30 3.44 3.05 2.82 2.66 2.55 2.40 2.23 2.03 1.78 .10 2.95 2.56 2.35 2.22 2.13 2.06 1.97 1.86 1.73 1.57 .20 1.75 1.73 1.68 1.64 1.61 1.58 1.54 1.49 1.43 1.35

23 .001 14.19 9.47 7.67 6.69 6.08 5.65 5.09 4.48 3.82 3.05 .005 9.63 6.73 5.58 4.95 4.54 4.26 3.88 3.47 3.02 2.48 .01 7.88 5.66 4.76 4.26 3.94 3.71 3.41 3.07 2.70 2.26 .025 5.75 4.35 3.75 3.41 3.18 3.02 2.81 2.57 2.30 1.97 .05 4.28 3.42 3.03 2.80 2.64 2.53 2.38 2.20 2.00 1.76 .10 2.94 2.55 2.34 2.21 2.11 2.05 1.95 1.84 1.72 1.55 .20 1.74 1.73 1.68 1.63 1.60 1.57 1.53 1.49 1.42 1.34

24 .001 14.03 9.34 7.55 6.59 5.98 5.55 4.99 4.39 3.74 2.97 .005 9.55 6.66 5.52 4.89 4.49 4.20 3.83 3.42 2.97 2.43 .01 7.82 5.61 4.72 4.22 3.90 3.67 3.36 3.03 2.66 2.21 .025 5.72 4.32 3.72 3.38 3.15 2.99 2.78 2.54 2.27 1.94 .05 4.26 3.40 3.01 2.78 2.62 2.51 2.36 2.18 1.98 1.73 .10 2.93 2.54 2.33 2.19 2.10 2.04 1.94 1.83 1.70 1.53 .20 1.74 1.72 1.67 1.63 1.59 1.57 1.53 1.48 1.42 1.33

25 .001 13.88 9.22 7.45 6.49 5.88 5.46 4.91 4.31 3.66 2.89 .005 9.48 6.60 5.46 4.84 4.43 4.15 3.78 3.37 2.92 2.38 .01 7.77 5.57 4.68 4.18 3.86 3.63 3.32 2.99 2.62 2.17 .025 5.69 4.29 3.69 3.35 3.13 2.97 2.75 2.51 2.24 1.91 .05 4.24 3.38 2.99 2.76 2.60 2.49 2.34 2.16 1.96 1.71 .10 2.92 2.53 2.32 2.18 2.09 2.02 1.93 1.82 1.69 1.52 .20 1.73 1.72 1.66 1.62 1.59 1.56 1.52 1.47 1.41 1.32

26 .001 13.74 9.12 7.36 6.41 5.80 5.38 4.83 4.24 3.59 2.82 .005 9.41 6.54 5.41 4.79 4.38 4.10 3.73 3.33 2.87 2.33 .01 7.72 5.53 4.64 4.14 3.82 3.59 3.29 2.96 2.58 2.13 .025 5.66 4.27 3.67 3.33 3.10 2.94 2.73 2.49 2.22 1.88 .05 4.22 3.37 2.98 2.74 2.59 2.47 2.32 2.15 1.95 1.69 .10 2.91 2.52 2.31 2.17 2.08 2.01 1.92 1.81 1.68 1.50 .20 1.73 1.71 1.66 1.62 1.58 1.56 1.52 1.47 1.40 1.31

27 .001 13.61 9.02 7.27 6.33 5.73 5.31 4.76 4.17 3.52 2.75 .005 9.34 6.49 5.36 4.74 4.34 4.06 3.69 3.28 2.83 2.29 .01 7.68 5.49 4.60 4.11 3.78 3.56 3.26 2.93 2.55 2.10 .025 5.63 4.24 3.65 3.31 3.08 2.92 2.71 2.47 2.19 1.85 .05 4.21 3.35 2.96 2.73 2.57 2.46 2.30 2.13 1.93 1.67 .10 2.90 2.51 2.30 2.17 2.07 2.00 1.91 1.80 1.67 1.49 .20 1.73 1.71 1.66 1.61 1.58 1.55 1.51 1.46 1.40 1.30

28 .001 13.50 8.93 7.19 6.25 5.66 5.24 4.69 4.11 3.46 2.70 .005 9.28 6.44 5.32 4.70 4.30 4.02 3.65 3.25 2.79 2.25 .01 7.64 5.45 4.57 4.07 3.75 3.53 3.23 2.90 2.52 2.06 .025 5.61 4.22 3.63 3.29 3.06 2.90 2.69 2.45 2.17 1.83 .05 4.20 3.34 2.95 2.71 2.56 2.44 2.29 2.12 1.91 1.65 .10 2.89 2.50 2.29 2.16 2.06 2.00 1.90 1.79 1.66 1.48 .20 1.72 1.71 1.65 1.61 1.57 1.55 1.51 1.46 1.39 1.30

(continued)

APPENDIX B Statistical Tables 399

IS B

N : 0

-5 36

-5 64

36 -1

A p

p en

d ix

Table B.3 F Values and Their Associated p Values

df2 df1 p 1 2 3 4 5 6 8 12 24 q

29 .001 13.39 8.85 7.12 6.19 5.59 5.18 4.64 4.05 3.41 2.64 .005 9.23 6.40 5.28 4.66 4.26 3.98 3.61 3.21 2.76 2.21 .01 7.60 5.42 4.54 4.04 3.73 3.50 3.20 2.87 2.49 2.03 .025 5.59 4.20 3.61 3.27 3.04 2.88 2.67 2.43 2.15 1.81 .05 4.18 3.33 2.93 2.70 2.54 2.43 2.28 2.10 1.90 1.64 .10 2.89 2.50 2.28 2.15 2.06 1.99 1.89 1.78 1.65 1.47 .20 1.72 1.70 1.65 1.60 1.57 1.54 1.50 1.45 1.39 1.29

30 .001 13.29 8.77 7.05 6.12 5.53 5.12 4.58 4.00 3.36 2.59 .005 9.18 6.35 5.24 4.62 4.23 3.95 3.58 3.18 2.73 2.18 .01 7.56 5.39 4.51 4.02 3.70 3.47 3.17 2.84 2.47 2.01 .025 5.57 4.18 3.59 3.25 3.03 2.87 2.65 2.41 2.14 1.79 .05 4.17 3.32 2.92 2.69 2.53 2.42 2.27 2.09 1.89 1.62 .10 2.88 2.49 2.28 2.14 2.05 1.98 1.88 1.77 1.64 1.46 .20 1.72 1.70 1.64 1.60 1.57 1.54 1.50 1.45 1.38 1.28

40 .001 12.61 8.25 6.60 5.70 5.13 4.73 4.21 3.64 3.01 2.23 .005 8.83 6.07 4.98 4.37 3.99 3.71 3.35 2.95 2.50 1.93 .01 7.31 5.18 4.31 3.83 3.51 3.29 2.99 2.66 2.29 1.80 .025 5.42 4.05 3.46 3.13 2.90 2.74 2.53 2.29 2.01 1.64 .05 4.08 3.23 2.84 2.61 2.45 2.34 2.18 2.00 1.79 1.51 .10 2.84 2.44 2.23 2.09 2.00 1.93 1.83 1.71 1.57 1.38 .20 1.70 1.68 1.62 1.57 1.54 1.51 1.47 1.41 1.34 1.24

60 .001 11.97 7.76 6.17 5.31 4.76 4.37 3.87 3.31 2.69 1.90 .005 8.49 5.80 4.73 4.14 3.76 3.49 3.13 2.74 2.29 1.69 .01 7.08 4.98 4.13 3.65 3.34 3.12 2.82 2.50 2.12 1.60 .025 5.29 3.93 3.34 3.01 2.79 2.63 2.41 2.17 1.88 1.48 .05 4.00 3.15 2.76 2.52 2.37 2.25 2.10 1.92 1.70 1.39 .10 2.79 2.39 2.18 2.04 1.95 1.87 1.77 1.66 1.51 1.29 .20 1.68 1.65 1.59 1.55 1.51 1.48 1.44 1.38 1.31 1.18

120 .001 11.38 7.31 5.79 4.95 4.42 4.04 3.55 3.02 2.40 1.56 .005 8.18 5.54 4.50 3.92 3.55 3.28 2.93 2.54 2.09 1.43 .01 6.85 4.79 3.95 3.48 3.17 2.96 2.66 2.34 1.95 1.38 .025 5.15 3.80 3.23 2.89 2.67 2.52 2.30 2.05 1.76 1.31 .05 3.92 3.07 2.68 2.45 2.29 2.17 2.02 1.83 1.61 1.25 .10 2.75 2.35 2.13 1.99 1.90 1.82 1.72 1.60 1.45 1.19 .20 1.66 1.63 1.57 1.52 1.48 1.45 1.41 1.35 1.27 1.12

q .001 10.83 6.91 5.42 4.62 4.10 3.74 3.27 2.74 2.13 1.00 .005 7.88 5.30 4.28 3.72 3.35 3.09 2.74 2.36 1.90 1.00 .01 6.64 4.60 3.78 3.32 3.02 2.80 2.51 2.18 1.79 1.00 .025 5.02 3.69 3.12 2.79 2.57 2.41 2.19 1.94 1.64 1.00 .05 3.84 2.99 2.60 2.37 2.21 2.09 1.94 1.75 1.52 1.00 .10 2.71 2.30 2.08 1.94 1.85 1.77 1.67 1.55 1.38 1.00 .20 1.64 1.61 1.55 1.50 1.46 1.43 1.38 1.32 1.23 1.00

Source: Reproduced from Table V of R. A. Fisher and F. Yates, Statistical Tables for Biological, Agricultural and Medical Research (6th ed.), 1974, published by Longman Group UK Ltd., London (previously published by Oliver and Boyd Ltd., Edinburgh) and by permission of the authors and publishers. The 0.5% and 2.5% points are reproduced from “Tables of Percentage Points of the Inverted Beta (F) Distribution,” Biometrika, Vol. 33 (April 1943), pp. 73–88, by permission of the Biometrika Trustees, Imperial College of Science, Technology, and Medicine, London, England.

400 APPENDIX B Statistical Tables

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A p

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401

T ab

le B

.4 χ2

V al

ue s a

nd T

he ir

A ss

oc ia

te d

p V

al ue

P ro

b ab

ili ty

d f

.9 9

.9 8

.9 5

.9 0

.8 0

.7 0

.5 0

.3 0

.2 0

.1 0

.0 5

.0 2

.0 1

.0 01

1 .0

3 1 57

.0 3 6

28 .0

03 93

.0 15

8 .0

64 2

.1 48

.4 55

1. 07

4 1.

64 2

2. 70

6 3.

84 1

5. 41

2 6.

63 5

10 .8

27 2

.0 20

1 .0

40 4

.1 03

.2 11

.4 46

.7 13

1. 38

6 2.

40 8

3. 21

9 4.

60 5

5. 99

1 7.

82 4

9. 21

0 13

.8 15

3 .1

15 .1

85 .3

52 .5

84 1.

00 5

1. 42

4 2.

36 6

3. 66

5 4.

64 2

6. 25

1 7.

81 5

9. 83

7 11

.3 45

16 .2

68 4

.2 97

.4 29

.7 11

1. 06

4 1.

64 9

2. 19

5 3.

35 7

4. 87

8 5.

98 9

7. 77

9 9.

48 8

11 .6

68 13

.2 77

18 .4

65 5

.5 54

.7 52

1. 14

5 1.

61 0

2. 34

3 3.

00 0

4. 35

1 6.

06 4

7. 28

9 9.

23 6

11 .0

70 13

.3 88

15 .0

86 20

.5 17

6 .8

72 1.

13 4

1. 63

5 2.

20 4

3. 07

0 3.

82 8

5. 34

8 7.

23 1

8. 55

8 10

.6 45

12 .5

92 15

.0 33

16 .8

12 22

.4 57

7 1.

23 9

1. 56

4 2.

16 7

2. 83

3 3.

82 2

4. 67

1 6.

34 6

8. 38

3 9.

80 3

12 .0

17 14

.0 67

16 .6

22 18

.4 75

24 .3

22 8

1. 64

6 2.

03 2

2. 73

3 3.

49 0

4. 59

4 5.

52 7

7. 34

4 9.

52 4

11 .0

30 13

.3 62

15 .5

07 18

.1 68

20 .0

90 26

.1 25

9 2.

08 8

2. 53

2 3.

32 5

4. 16

8 5.

38 0

6. 39

3 8.

34 3

10 .6

56 12

.2 42

14 .6

84 16

.9 19

19 .6

79 21

.6 66

27 .8

77 10

2. 55

8 3.

05 9

3. 94

0 4.

86 5

6. 17

9 7.

26 7

9. 34

2 11

.7 81

13 .4

42 15

.9 87

18 .3

07 21

.1 61

23 .2

09 29

.5 88

11 3.

05 3

3. 60

9 4.

57 5

5. 57

8 6.

98 9

8. 14

8 10

.3 41

12 .8

99 14

.6 31

17 .2

75 19

.6 75

22 .6

18 24

.7 25

31 .2

64 12

3. 57

1 4.

17 8

5. 22

6 6.

30 4

7. 80

7 9.

03 4

11 .3

40 14

.0 11

15 .8

12 18

.5 49

21 .0

26 24

.0 54

26 .2

17 32

.9 09

13 4.

10 7

4. 76

5 5.

89 2

7. 04

2 8.

63 4

9. 92

6 12

.3 40

15 .1

19 16

.9 85

19 .8

12 22

.3 62

25 .4

72 27

.6 88

34 .5

28 14

4. 66

0 5.

36 8

6. 57

1 7.

79 0

9. 46

7 10

.8 21

13 .3

39 16

.2 22

18 .1

51 21

.0 64

23 .6

85 26

.8 73

29 .1

41 36

.1 23

15 5.

22 9

5. 98

5 7.

26 1

8. 54

7 10

.3 07

11 .7

21 14

.3 39

17 .3

22 19

.3 11

22 .3

07 24

.9 96

28 .5

29 30

.5 78

37 .6

16 5.

81 2

6. 61

4 7.

96 2

9. 31

2 11

.1 52

12 .6

24 15

.3 38

18 .4

18 20

.4 65

23 .5

42 26

.2 96

29 .6

33 32

.0 00

39 .2

52 17

6. 40

8 7.

25 5

8. 67

2 10

.0 85

12 .0

02 13

.5 31

16 .3

38 19

.5 11

21 .6

15 24

.7 69

27 .5

87 30

.9 95

33 .4

09 40

.7 90

18 7.

01 5

7. 90

6 9.

39 0

10 .8

65 12

.8 57

14 .4

40 17

.3 38

20 .6

01 22

.7 60

25 .9

89 28

.8 69

32 .3

46 34

.8 05

42 .3

12 19

7. 63

3 8.

56 7

10 .1

17 11

.6 51

13 .7

16 15

.3 52

18 .3

38 21

.6 89

23 .9

00 27

.2 04

30 .1

44 33

.6 87

36 .1

91 43

.8 20

20 8.

26 0

9. 23

7 10

.8 51

12 .4

43 14

.5 78

16 .2

66 19

.3 37

22 .7

75 25

.0 38

28 .4

12 31

.4 10

35 .0

20 37

.5 66

45 .3

21 8.

89 7

9. 91

5 11

.5 91

13 .2

40 15

.4 45

17 .1

82 20

.3 37

23 .8

58 26

.1 71

29 .6

15 32

.6 71

36 .3

43 38

.9 32

46 .7

97 22

9. 54

2 10

.6 00

12 .3

38 13

.0 41

16 .3

14 18

.1 01

21 .3

37 24

.9 39

27 .3

01 30

.8 13

33 .9

24 37

.6 59

40 .2

89 48

.2 68

23 10

.1 96

11 .2

93 13

.0 91

14 .8

48 17

.1 87

19 .0

21 22

.3 37

26 .0

18 28

.4 29

32 .0

07 35

.1 72

38 .9

68 41

.6 38

49 .7

28 24

10 .8

56 11

.9 92

13 .8

48 15

.6 59

18 .0

62 19

.9 43

23 .3

37 27

.0 96

29 .5

53 33

.1 96

36 .4

15 40

.2 70

42 .9

80 51

.1 79

25 11

.5 24

12 .6

97 14

.6 11

16 .4

73 18

.9 40

20 .8

67 24

.3 37

28 .1

72 30

.6 75

34 .3

82 37

.6 52

41 .5

66 44

.3 14

52 .6

26 12

.1 98

13 .4

09 15

.3 79

17 .2

92 19

.8 20

21 .7

92 25

.3 36

29 .2

46 31

.7 95

35 .5

63 38

.8 85

42 .8

56 45

.6 42

54 .0

52 27

12 .8

79 14

.1 25

16 .1

51 18

.1 14

20 .7

03 22

.7 19

26 .3

36 30

.3 19

32 .9

12 36

.7 41

40 .1

13 44

.1 40

46 .9

63 55

.4 76

28 13

.5 65

14 .8

47 16

.9 28

18 .9

39 21

.5 88

23 .6

47 27

.3 36

31 .3

91 34

.0 27

37 .9

16 41

.3 37

45 .4

19 48

.2 78

56 .8

93 29

14 .2

56 15

.5 74

17 .7

08 19

.7 68

22 .4

75 24

.5 77

28 .3

36 32

.4 61

35 .1

39 39

.0 87

42 .5

57 46

.6 93

49 .5

88 58

.3 02

30 14

.9 53

16 .3

06 18

.4 93

20 .5

99 23

.3 64

25 .5

08 29

.3 36

33 .5

30 36

.2 50

40 .2

56 43

.7 73

47 .9

62 50

.8 92

59 .7

So u

rc e:

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A .

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IS B

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A p

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402

Table B.5 r Values and Their Associated p Values

Probability level

(N - 2) .10 .05 .02 .01 .001 1 .988 .997 .9995 .9999 1.000 2 .900 .950 .980 .990 .999 3 .805 .878 .934 .959 .991 4 .729 .811 .882 .917 .974 5 .669 .754 .833 .874 .951

6 .622 .707 .789 .834 .925 7 .582 .666 .750 .798 .898 8 .549 .632 .716 .765 .872 9 .522 .602 .685 .735 .847

10 .497 .576 .658 .708 .823

11 .476 .553 .634 .684 .801 12 .458 .532 .612 .661 .780 13 .441 .514 .592 .641 .760 14 .426 .497 .574 .623 .742 15 .412 .482 .558 .606 .725

16 .400 .468 .542 .590 .708 17 .389 .456 .528 .575 .693 18 .378 .444 .516 .561 .679 19 .369 .433 .503 .549 .665 20 .360 .423 .492 .537 .652

22 .344 .404 .472 .515 .629 24 .330 .388 .453 .496 .607 25 .323 .381 .445 .487 .597 30 .296 .349 .409 .449 .554 35 .275 .325 .381 .418 .519

40 .257 .304 .358 .393 .490 45 .243 .288 .338 .372 .465 50 .231 .273 .322 .354 .443 55 .220 .261 .307 .338 .424 60 .211 .250 .295 .325 .408

65 .203 .240 .284 .312 .393 70 .195 .232 .274 .302 .380 75 .189 .224 .264 .292 .368 80 .183 .217 .256 .283 .357 85 .178 .211 .249 .275 .347

90 .173 .205 .242 .267 .338 95 .168 .200 .236 .260 .329

100 .164 .195 .230 .254 .321 125 .147 .174 .206 .228 .288 150 .134 .159 .189 .208 .264

175 .124 .148 .174 .194 .248 200 .116 .138 .164 .181 .235 300 .095 .113 .134 .148 .188 500 .074 .088 .104 .115 .148

1,000 .052 .062 .073 .081 .104 2,000 .037 .044 .052 .058 .074

Note: All p values are two-tailed in this table. Source: From Some Extensions of Student’s t and Pearson’s r Central Distributions, by A. L. Sockloff and J. N. Edney, May 1972, Temple University Technical Report 72–5, Measurement and Research Center. Reprinted with the permis- sion of Alan Sockloff.

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A p

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Table B.6 Transformations of r to Fisher zr

Second digit of r

r .00 .01 .02 .03 .04 .05 .06 .07 .08 .09

.0 .000 .010 .020 .030 .040 .050 .060 .070 .080 .090

.1 .100 .110 .121 .131 .141 .151 .161 .172 .182 .192

.2 .203 .213 .224 .234 .245 .255 .266 .277 .288 .299

.3 .310 .321 .332 .343 .354 .365 .377 .388 .400 .412

.4 .424 .436 .448 .460 .472 .485 .497 .510 .523 .536

.5 .549 .563 .576 .590 .604 .618 .633 .648 .662 .678

.6 .693 .709 .725 .741 .758 .775 .793 .811 .829 .848

.7 .867 .887 .908 .929 .950 .973 .996 1.020 1.045 1.071

.8 1.099 1.127 1.157 1.188 1.221 1.256 1.293 1.333 1.376 1.422

Third digit of r

r .000 .001 .002 .003 .004 .005 .006 .007 .008 .009

.90 1.472 1.478 1.483 1.488 1.494 1.499 1.505 1.510 1.516 1.522

.91 1.528 1.533 1.539 1.545 1.551 1.557 1.564 1.570 1.576 1.583

.92 1.589 1.596 1.602 1.609 1.616 1.623 1.630 1.637 1.644 1.651

.93 1.658 1.666 1.673 1.681 1.689 1.697 1.705 1.713 1.721 1.730

.94 1.738 1.747 1.756 1.764 1.774 1.783 1.792 1.802 1.812 1.822

.95 1.832 1.842 1.853 1.863 1.874 1.886 1.897 1.909 1.921 1.933

.96 1.946 1.959 1.972 1.986 2.000 2.014 2.029 2.044 2.060 2.076

.97 2.092 2.109 2.127 2.146 2.165 2.185 2.205 2.227 2.249 2.273

.98 2.298 2.323 2.351 2.380 2.410 2.443 2.477 2.515 2.555 2.599

.99 2.646 2.700 2.759 2.826 2.903 2.994 3.106 3.250 3.453 3.800

APPENDIX B Statistical Tables 403

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A p

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Table B.7 Transformations of Fisher zr to r

zr .00 .01 .02 .03 .04 .05 .06 .07 .08 .09

.0 .000 .010 .020 .030 .040 .050 .060 .070 .080 .090

.1 .100 .110 .119 .129 .139 .149 .159 .168 .178 .187

.2 .197 .207 .216 .226 .236 .245 .254 .264 .273 .282

.3 .291 .300 .310 .319 .327 .336 .345 .354 .363 .371

.4 .380 .389 .397 .405 .414 .422 .430 .438 .446 .454

.5 .462 .470 .478 .485 .493 .500 .508 .515 .523 .530

.6 .537 .544 .551 .558 .565 .572 .578 .585 .592 .598

.7 .604 .611 .617 .623 .629 .635 .641 .647 .653 .658

.8 .664 .670 .675 .680 .686 .691 .696 .701 .706 .711

.9 .716 .721 .726 .731 .735 .740 .744 .749 .753 .757

1.0 .762 .766 .770 .774 .778 .782 .786 .790 .793 .797 1.1 .800 .804 .808 .811 .814 .818 .821 .824 .828 .831 1.2 .834 .837 .840 .843 .846 .848 .851 .854 .856 .859 1.3 .862 .864 .867 .869 .872 .874 .876 .879 .881 .883 1.4 .885 .888 .890 .892 .894 .896 .898 .900 .902 .903

1.5 .905 .907 .909 .910 .912 .914 .915 .917 .919 .920 1.6 .922 .923 .925 .926 .928 .929 .930 .932 .933 .934 1.7 .935 .937 .938 .939 .940 .941 .942 .944 .945 .946 1.8 .947 .948 .949 .950 .951 .952 .953 .954 .954 .955 1.9 .956 .957 .958 .959 .960 .960 .961 .962 .963 .963

2.0 .964 .965 .965 .966 .967 .967 .968 .969 .969 .970 2.1 .970 .971 .972 .972 .973 .973 .974 .974 .975 .975 2.2 .976 .976 .977 .977 .978 .978 .978 .979 .979 .980 2.3 .980 .980 .981 .981 .982 .982 .982 .983 .983 .983 2.4 .984 .984 .984 .985 .985 .985 .986 .986 .986 .986

2.5 .987 .987 .987 .987 .988 .988 .988 .988 .989 .989 2.6 .989 .989 .989 .990 .990 .990 .990 .990 .991 .991 2.7 .991 .991 .991 .992 .992 .992 .992 .992 .992 .992 2.8 .993 .993 .993 .993 .993 .993 .993 .994 .994 .994 2.9 .994 .994 .994 .994 .994 .995 .995 .995 .995 .995

404 APPENDIX B Statistical Tables

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405

APPENDIX C Introduction to Meta-Analysis

The Purpose of Meta-Analysis

It seems that almost every time we watch a television news broadcast or open a news- paper we learn about some new pharmaceutical study. We may be told that the study reported an effect in one direction but that other studies have reported an effect in the opposite direction or an effect that is close to zero. How do researchers resolve this conflicting evidence? Meta-analysis is the method of choice for providing a summary of all the research findings and identifying variables (called moderator variables) that may be responsible for increases or decreases in the degree of association between two events. The name meta-analysis (coined by Gene Glass, 1976) actually refers not to a single procedure for combining research findings and overcoming the equivocation of conflicting results, but to a collection of quantitative and graphic procedures. In using the term research findings in this context, meta-analysts typically are referring to effect size indices, which are the primary coin of the realm in meta-analysis.

In this appendix, we will give you a sense of some of the quantitative procedures that are used to compare and combine effect sizes and to estimate overall p values. We emphasize r-type indices because they can be used in situations in which other effect size indices would not make sense, but it is also common to see standardized differ- ence indices (such as Cohen’s d, described in Chapter 13) used in the meta-analysis of two group designs. If you would like to learn more about meta-analysis, you will find an engaging introduction in Hunt’s How Science Takes Stock (1997) and in Cooper and Hedges’s comprehensive Handbook of Research Synthesis (1994). There is also an overview of meta-analysis in a review article by R. Rosenthal and DiMatteo (2001) and further discussion in our advanced text (R. Rosenthal & Rosnow, 2008, Ch. 21). Although we focus only on the comparison and combination of two independent studies in this appendix, you will find procedures for comparing and combining any number of studies in texts and handbooks, including Chalmers and Altman (1995); Cook et al. (1994); H. Cooper (1998); Glass, McGaw, and Smith (1981); Hedges and Olkin (1985); Hunter and Schmidt (2004); Light and Pillemer (1984); Lipsey and Wilson (2001); R. Rosenthal (1991); and Wachter and Straf (1990).

Behavioral and social researchers not only have pioneered innovative procedures for use in meta-analysis but have embraced this approach to reveal moderator vari- ables in a wide variety of areas. For example, social psychologist Alice H. Eagly (1978) used the methodology of meta-analysis to discover a fascinating moderator variable. Textbooks had long asserted that women were more conforming and more easily

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influenced than men, presumably because socialization processes had taught men to be independent thinkers, a cultural value that was seldom as suitable for women. Hypothesizing that the historical period in which the results were collected might be a moderator of the association between gender and influenceability, Eagly’s meta- analysis revealed a pronounced difference in the association between gender and in- fluenceability in the research published before 1970 from that found in research published during the era of the women’s movement in the 1970s. In contrast to the older research, which found greater influenceability among females than among males, the later studies uncovered few gender differences in influenceability.

The first step in a meta-analysis is to define the independent and dependent vari- ables of interest. The next step is to collect the relevant studies systematically and to collate them by defining specific categories of information. The ensuing steps use both quantitative and graphical procedures (e.g., stem-and-leaf charts) to examine the vari- ability among the obtained effect sizes and use simple statistical formulas to make esti- mates of the average effect sizes and to determine the significance levels of the combined effects. Meta-analysts seek to develop a meaningful mosaic that reveals pat- terns in the data and, with the help of moderator variables, can explain seemingly con- tradictory results. Thus, meta-analysis helps researchers to develop a cumulative picture of the research results in a particular area rather than to rely on a single study or on a traditional narrative, nonquantitative review in attempting to understand a phenome- non. Meta-analysis also encourages a deeper understanding of each study, because each study has to be read carefully for the essential information needed.

Comparing Two Effect Sizes

Suppose we believe that two studies are conceptually similar, but we also want to com- pare their effect sizes as further insurance that they constitute a homogeneous set. First, we give the values of reffect size the same sign if both studies show effects in the same direction, but we give them different signs if the results are in the opposite direction. Second, we find for each reffect size value the Fisher zr, which (as you learned in Chapter 12) is the log transformation of r found in Table B.6 (p. 403). Third, we substitute in the following formula to get the standard normal deviate (z) corresponding to the differ- ence between the Fisher zr scores:

where zr1 and zr2 are the log transformations found in Step 2 for the reffect size values of Studies 1 and 2, respectively, and N1 and N2 are the total sample sizes of Studies 1 and 2, respectively. The final step is to look up the result in Table B.1 (p. 393), which gives us the associated one-tailed p of the z of difference. Let us try some examples.

Example 1. Suppose you have used 100 subjects to try to replicate an experiment that, when you computed the effect size on the reported t (using the procedure de- scribed in Chapter 13), yielded reffect size = .50 based on only 10 subjects. In your study, you found reffect size = .31, but your result was in the opposite direction of the one pre- viously reported. You code your effect as negative to reflect the fact that it is in the oppo- site direction, and then you consult Table B.6 (p. 403) to find the Fisher zr corresponding

z of difference � zr1 � zr2

B 1

N1 � 3 �

N2 � 3

406 APPENDIX C Introduction to Meta-Analysis

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to each reffect size. For r = .50, you find zr = .549 in Table B.6 at the intersection of the .5 row and the .00 column. For r = -.31, you find zr = .321 at the .3 row and the .01 col- umn intersection, and you code the result as -.321 because it is based on your empiri- cal finding, which was in the opposite direction of the earlier result.

Next, from the previous formula, you compute

as the z of the difference between the two effect sizes. Looking up the p value asso- ciated with z = 2.22 in Table B.1 (p. 393), you find p = .0132 one-tailed, which you can round to .01 one-tailed or .03 two-tailed (i.e., .0132 * 2 = .03 rounded). The p value is small enough to convince you that your result differs from the original one, and as the difference between the r values is obviously large as well as significant statistically, you decide that they cannot simply be combined without careful thought and comment. In describing the results of both studies considered together, you would report the differences between them and give a tentative explanation for the differences.

Example 2. Alternatively, suppose your result is in the same direction as the origi- nal one and of a similar magnitude, and you have used the same number of subjects. This time, imagine that the original reffect size = .45 (N = 120) and your reffect size = .40 (N = 120). Following the same procedure as in Example 1, you find in Table B.6 the zr values corresponding to the effect size r values to be .485 and .424, respectively. From the preceding formula you compute

as your obtained z of the difference. In Table B.1 you find the p associated with z = .47 to be .3192 one-tailed. Here, then, is an example of two studies that do not disagree sig- nificantly in their estimates of the size of the relationship between X and Y and are quite similar in magnitude. They can now be routinely combined by means of a simple meta- analytic technique, as shown next.

Combining Two Effect Sizes

Given two effect size r values that can be combined on conceptual and statistical grounds, we find the typical (or average) effect size by using the following formula:

We afterward transform the resulting Fisher zr into the metric of an effect size corre- lation. In this formula the denominator is the number of zr scores in the numerator. The resulting value is an average Fisher zr (symbolized here as , where the bar over the zr signifies that it is a mean value). Example 3 shows how this number crunching proceeds.

zr � zr1 � zr2

z of difference � .485 � .424

B 1

117 �

117

� .061

.131 � .47

z of difference � (.549) � (�.321)

B 1

7 �

� .870

.391 � 2.22

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Example 3. In Example 2, one reffect size = .45 and the other reffect size = .40 (both coded as positive to show that both results were in the predicted direction). You found the Fisher zr scores corresponding to the effect size r values to be .485 and .424, re- spectively. From the formula above you compute

as the average Fisher zr. Finally, looking in Table B.7 (p. 404), you find that a Fisher zr of .45 is associated with an r of .422, which is the reffect size estimate of the two studies combined.

Obtaining an Overall Significance Level

Although meta-analysts are generally more interested in effect sizes than in p val- ues, they might be interested in the overall statistical significance of a set of com- parable studies. It is an easy matter to combine the p values and get an overall estimate of the probability that the p values might have been obtained if the null hypothesis of no relationship between X and Y were true. We first obtain an accu- rate p value for each study (accurate, say, to two digits, not counting zeros before the first nonzero value, such as p = .43 or .024 or .0012). That is, if t (with 30 df ) = 3.03, we code p as .0025, not as p .05. Extended tables of the t distribution may be helpful here (such as Table B.2 on pp. 394–395), but more helpful still is a computer program or a good calculator that gives accurate p values at the touch of a couple of buttons. For each p, the meta-analyst finds z (not the Fisher zr, but the standard normal deviate z in Table B.1 of Appendix B). Both p values should also be one-tailed, and we give the corresponding z values the same sign if both stud- ies show effects in the same direction, but different signs if the results are in the opposite direction.

In our continuing example of working with two studies, the formula used to com- bine the two z values is as follows:

That is, the sum of the two z values when divided by the square root of the number of z values combined yields a new z. This new z corresponds to the p value of the two studies combined if the null hypothesis of no relationship between X and Y were true.

Example 4. As an illustration, suppose we assume that Studies A and B are a combinable set with results in the same direction, but neither is statistically significant. One p is .121, and the other is .084. Their z values are 1.17 and 1.38, respectively. From the preceding formula we have

as our combined z. The p associated with this combined z is .035 one-tailed (or .07 two-tailed).

Combined z � 1.17 � 1.38

22 � 2.55

1.41 � 1.81

Combined z � z1 � z2

zr � .485 � .424

2 � .45

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Detective-Like Probing of Reported Data

For our illustrations of meta-analytic comparisons and combinations of effect size r and p values, we have concentrated on the case of only two results. Meta-analysts often work with many more results to be coded, compared, and combined, however. The procedures used are quite similar in spirit to the procedures described above, and you will find descriptions in the texts cited earlier—and also some tips on how to estimate effect sizes when published reports provide only limited details. Even from very limited information, it is often possible to re-create the original summary ANOVA table and to compute our own contrasts and effect sizes from the reconstituted summary (e.g., Rosnow & Rosenthal, 2007). There are also examples in the chapters in this text, such as com- puting an effect size index from a reported t test (Chapter 13), or computing a contrast from a reported omnibus F test (Box 14.5 in Chapter 14) and then computing your own r-type effect size indices (Chapter 14), or computing effect sizes from news reports of biomedical trials (Box 15.3 in Chapter 15).

Sometimes all that is reported are statistical test results for which there is no formal index of effect size available, but an exact probability is reported. This is often true for what are called nonparametric statistics (Higgins, 2004; Siegel & Castellan, 1988). Sup- pose a study in which four children were taught by a new educational method (the treatment) and five children were taught by an old (control) method. All four of the treated children were ranked higher than any of the five control children, and the non- parametric test (the Mann-Whitney U ) had an exact p of .008 one-tailed. We simply find the value of t corresponding to the exact p and df and then substitute in the following familiar formula:

where the result is indicated as requivalent because it is analogous to reffect size between the treatment indicator (e.g., with the treatment dummy-coded 1 and the control coded 0) and the continuous outcome measure (i.e., a point-biserial r) with N/2 units in each group (Rosenthal & Rubin, 2003). In this example, where p = .008 and N = 9, the t value for 7 degrees of freedom is 3.16, and substituting in the formula above gives us requivalent = .77.

The File Drawer Problem

Because many journal editors are reluctant to accept “nonsignificant” results, re- searchers’ file drawers may contain unpublished studies that failed to yield significant results (Bakan, 1967; Sterling, 1959). If there were a substantial number of such studies in the file drawers, the meta-analyst’s evaluation of the overall significance level might be unduly optimistic. One solution to this file drawer problem is to calculate the number of studies averaging null results that would be required to nudge the signifi- cance level for all studies (retrieved and unretrieved combined) to the less coveted side of p = .05 (R. Rosenthal, 1979, 1983, 1991). If the overall significance level computed on the basis of the retrieved studies can be brought down to p .05 by the addition of just a few more null results, then the original estimate of p is clearly not robust (i.e., not resistant to the file drawer threat).

r equivalent � B t2

t2 � df

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Table C.1 Tolerances for Future Null Results

Retrieved studies Original average significance level

.05 .01 .001

1 1 2 4 2 4 8 15 3 9 18 32 4 16 32 57 5 25 50 89

6 36 72 128 7 49 98 173 8 64 128 226 9 81 162 286

10 100 200 353

15 225 450 795 20 400 800 1,412 25 625 1,250 2,206 30 900 1,800 3,177 40 1,600 3,200 5,648 50 2,500 5,000 8,824

Table C.1 illustrates the results of such calculations. It shows a table of tolerance values in which the rows represent the number of retrieved (i.e., meta-analyzed) stud- ies and the columns represent three different levels of the average statistical signifi- cance of the retrieved studies. The intersection of any row and column shows the sum of old and new studies required to bring the p for all studies (retrieved and unre- trieved) down to the level of being barely “nonsignificant” at p .05.

Suppose we have meta-analyzed 8 studies and found the average (not the com- bined, but the mean) p value to be .05. The 64 in Table C.1 tells us that it will take an additional 56 unretrieved studies averaging null results to bring the original average p = .05 based on 8 studies (64 – 8 = 56) down to p .05. As a general rule of thumb, it has been suggested that we regard as robust any combined results for which the toler- ance level reaches 5(k) + 10, where k is the number of studies retrieved (R. Rosenthal, 1991). In our example of 8 studies retrieved, this means that we will be satisfied that the original estimate of p .05 is robust if we think that there are fewer than an addi- tional 5(8) + 10 = 50 studies with null results squirreled away in file drawers. Because this table shows a tolerance for an additional 56 studies, we conclude that the original estimate is robust.

The Counternull Statistic

The procedures we have discussed in this appendix should also be useful to you if you have done a study and also its replication, or if you are replicating an older study. One final point, however, is that if the results of your study include both an estimate of the

�

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Note: Entries in this table are the total number of old and new studies required to bring an original average p of .05, .01, or .001 down to an overall p .05 (i.e., just barely to “nonsignificance”).7

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effect size and (when possible) an interval estimate, you will have better protected yourself against Type I and Type II error. In Chapter 12, we described how to create a confidence interval around reffect size, and in Chapter 13, we showed how to do the same thing for Cohen’s d on two independent groups. Another way of protecting your- self against Type II error (the error of failing to reject the null hypothesis when it is false) is to consider the counternull value of the effect size whenever you have a p value that is nonsignificant and are about to conclude that “nothing happened” statisti- cally. This value, called the counternull statistic (R. Rosenthal & Rubin, 1994) is a kind of “confidence interval” except that it involves the null hypothesis and the obtained p value. In the case of reffect size, given a null value of zero, the counternull value can usually be estimated as

where rcounternull is the point-biserial r (R. Rosenthal & Rubin, 1994; Rosnow & Rosenthal, 1996).

For example, suppose you do a t test and find p = .115 one-tailed, although the ef- fect size r is far from zero, say, r = .30 (which is quite respectable). Before concluding there was “no effect,” you will want to do a power analysis to see how much “effective power” your t test actually had (discussed in Chapter 12). In addition, substituting in the formula above, you find

which means that, although it is true that your reffect size of .30 did not differ significantly from .00, it is no more true than that your reffect size of .30 does not differ significantly from .53. Thus, were you to conclude that there was “no effect” based on r = .30 and p = .115, your conclusion would be seriously in error because the counternull value (.53) implies a large effect size as the upper limit of the “null-counternull interval,” which in this example is a 77% confidence interval.

rcountemull � B 4r2

1 � 3r2 � B

(4)(.302)

1 � (3)(.302) � .53

rcounternull � B 4r2

1 � 3r2

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Note: Indicated in parentheses is the primary chapter(s) or appendix where most of the follow- ing appear either as Key Terms (listed at the end of the chapter and indicated in boldface within the chapter) or are defined as important concepts or common terms.

A-B design Simplest single-case design, in which the dependent variable is measured throughout the pretreatment or baseline period (the A phase) and the treatment period (the B phase). (8) A-B-A design Single-case design in which there are repeated measures before the treatment (the A phase), during the treatment (the B phase), and then with the treatment withdrawn (the final A phase). (8) A-B-A-B design Single-case design in which there are two types of occasions (B to A and A to B) for demonstrating the effects of the treatment variable. (8) A-B-A-B-A design Single-case design in which there are repeated measures before, during, and after treatment (the B phase). (8) A-B-BC-B design Single-case design in which there are repeated measures before the introduc- tion of the treatments (the A phase), then during Treatment B, during the combination of Treat- ments B and C, and, finally, during Treatment B alone; the purpose of the design is to tease out the effect of B both in combination with C and apart from C. (8) A-B-C design General term for single-case de- sign in which B and C are two different treat- ments. (8) abscissa The horizontal axis of a distribu- tion. (10)

abstract Brief, comprehensive summary of the content of a report or paper. (Appendix A) accidental plagiarism Unwittingly falling into plagiarism. (3) account for conflicting results One of several possible hypothesis-generating heuristics. (2) acquiescent response set The tendency of an individual to go along with any request or attitu- dinal statement. (5) active deception (deception by commission) Actively misleading the research participants, such as giving them false information about the pur- pose of the research, or having them unwittingly interact with confederates. (3) additive model Statistical model in which the components sum to the group means in ANOVA. (14) ad hoc hypothesis A conjecture or speculation developed on the spot to explain a result. (1) aesthetic aspect of science The beauty or ele- gance of scientific theories, experiments, or other facets. (1) after-only design Research design in which the subjects are measured after the treatment but not before; also called a posttest-only design. (7) alerting r (ralerting) The correlation between group means (M) and contrast (λ) weights; also designated as rMλ. (14) alpha (�) Probability of a Type I error. (12) alpha coefficient See Cronbach’s alpha. alternate-form reliability The correlation be- tween two forms of a test with different items that are measuring the same attribute. (6) alternative hypothesis (H1) The working hy- pothesis or the experimental hypothesis (that is, as opposed to the null hypothesis in null hypoth- esis significance testing). (12)

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analogical thinking Reasoning by analogy. (2) analogies and metaphors Devices used to explain or describe one thing in terms of another. (1, 2) analysis of variance (ANOVA) Subdivision of the total variance of a set of scores into its com- ponents. (7, 14) ancestry search Tracking “ancestral” citations of published work. (2) ANOVA See analysis of variance. “anything goes” philosophy of science Feyerabend’s view that doing research involves a “let’s-try-it-and-see” attitude, where anything that works is permissible. (4) APA American Psychological Association. APA manual Publication Manual of the Ameri- can Psychological Association. (1, Appendix A) a priori method The use of individual powers of pure reason and logic as a basis of explanation (Charles Peirce). (1) APS Association for Psychological Science. archival material A relatively permanent repos- itory of data or material. (4) area probability sampling A type of survey sampling in which the subclasses are geographic areas. (9) arithmetic mean (M) The simple average of a set of values. (10) artifact A specific threat to validity, or a con- founded aspect of the scientist’s observations. (7) asymmetrical distribution A distribution of scores in which there is not an exact correspon- dence in arrangement on the opposite sides of the middle line. (10) autonomy The person’s “independence,” in the context of research ethics; also refers to a prospective participant’s right as well as ability “to choose” whether to participate in the study or to continue in the study. (3) back-to-back stem-and-leaf chart The back-to- back plots of distributions in which the original data are preserved with any desired precision. (10) back translation See translation and back translation. bar graphs Distributions where the bars repre- sent the number (frequency) of scores. (10) before-after design A research design in which the subjects are measured before and after treat- ment; also called a pre-post design. (7)

behavior What someone does or how someone acts. (1) behavioral baseline A comparison base, oper- ationally defined as the continuous, and continu- ing, performance of a single unit in single-case research. (8) behavioral diary Data collection technique in which the research participant keeps a record of events at the time they occur. (5) behavioral science A general term that encom- passes scientific disciplines in which empirical in- quiry is used to study motivation, cognition, and behavior. (1) Belmont Report The name given to a study developed by a national commission in 1974 to protect the rights and welfare of participants in biomedical and behavioral research. (3) beneficence The “doing of good,” which is one of the guidelines of the discussed ethical princi- ples. (3) BESD See binomial effect-size display. beta (�) Probability of a Type II error. (12) between-subjects design Statistical design in which the sampling units are exposed to one treatment each. (7) bias Net systematic error. (6, 7, 9) Big Five factors (OCEAN) The collective name for five broad domains of individual personality: (a) openness to experience, (b) conscientious- ness, (c) extraversion, (d) agreeableness, and (e) neuroticism. (5) bimodal A distribution showing two modes. (10) binomial effect-size display (BESD) Procedure for the display of the practical importance of an effect size correlation (reffect size) of any magni- tude. (12) bipolar rating scales Rating scales in which the ends of the scales are extreme opposites. (5) blind experimenters Experimenters who are unaware of which participants are in the experi- mental and control conditions. (7) byline The author’s name as it appears on the title page of an article. (Appendix A) causal inference The act or process of infer- ring that X causes Y. (7) causation The relation of cause to effect. (7) ceiling effect Situation in which the amount of change that can be produced is limited by the upper boundary of the measure. (5)

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central tendency Location of the bulk of a dis- tribution; measured by means, medians, modes, and trimmed means. (10) central tendency bias A type of response set in which the respondent is reluctant to give ex- treme ratings and instead rates in the direction of the mean of the total group. (5) certificate of confidentiality A formal agree- ment between the investigator and the govern- ment agency sponsoring the research that requires the investigator to keep the data confidential. (3) chi-square (�2) A statistic used to test the degree of agreement between the frequency data actually obtained and those expected under a particular hypothesis (e.g., the null hypothesis). (15) closed (structured, fixed-choice, or precoded) measures See structured items. clusters See strata. coefficient of determination (r2) Proportion of variance shared by two variables. (12) cognitive heuristics Information-processing rules of thumb. (1) Cohen’s d Index of effect size in standard devi- ation units. (13) coherence The extent to which things (e.g., components of a theory or hypothesis) “stick together” logically. (2) cohort A collection of individuals who were born in the same period, or a generation. (8) column effect Column mean minus grand mean. (14) composite reliability The aggregate reliability of two or more items or judges’ ratings. (6) concealed measurement The use of hidden measurements, such as a hidden recording device that eavesdrops on conversations. (4) conceptual definitions See theoretical defini- tions. concurrent validity The extent to which test results are correlated with some criterion in the present. (6) confidence interval The upper and lower bounds of a statistic, where confidence is defined as 1 - �. (9, 10, 12, 13) confidentiality Protection of research partici- pants’ or survey respondents’ disclosures against unwarranted access. (3) confirmatory data analysis Analysis of data for the purpose of testing hypotheses. (10)

confounded Mixed or confused. confounded hypotheses (in panel designs) The inability to separate the effect attributed to one hypothesis from the effect attributed to an- other hypothesis in cross-lagged panel designs. (8) construct Abstract variable, formulated from ideas or images, that serves as an explanatory concept. (2) construct validity A type of test or research va- lidity that addresses the psychological qualities con- tributing to the relationship between X and Y. (6) content analysis A method of decomposing written messages and pictorial documents. (4) content validity A type of test validity that ad- dresses whether the test adequately samples the relevant material. (6) contingency table A table of frequencies (counts) coded by row and column variables. (11) continuous variable A variable for which we can imagine another value falling between any two adjacent scores. (11) contrast r (rcontrast) The pure correlation be- tween scores on the dependent variable (Y ) and the lambda (λ) coefficients after the removal of any other patterns in the data; also designated as rYλ·NC. (14) contrasts Statistical procedures that address specific questions or predictions in the data, such as testing for a particular trend in the results. (14) contrast weights See lambda weights. contrived observation Unobtrusive observa- tion of the effects of some variable that was intro- duced into a situation. (4) control group A condition with which the ef- fects of the experimental or test condition are compared. (7) convergent validity Validity supported by a substantial correlation of conceptually similar measures. (6) corrected range See extended range. correlated replicators Nonindependent repli- cators. (6) correlated-sample t See paired t. correlational research Another common name for relational research, that is, research in which two or more variables or conditions are measured and related to one another. (1) correlation coefficient An index of the degree of association between two variables, typically Pear- son r or related product-moment correlation. (11) IS

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correspondence with reality The extent to which a hypothesis agrees with accepted truths based on reliable empirical findings. (2) counterbalancing A procedure in which some subjects receive Treatment A before Treatment B, and the others receive B before A (e.g., in Latin square designs). (7, 13) counternull statistic A measure of the non- null magnitude of the effect size that is supported by the same amount of evidence as the null value of the effect size. (Appendix C) counts Frequencies. (11, 15) covariation The principle that, in order to demonstrate causality, what is labeled as the “cause” should be shown to be positively corre- lated with what is labeled as the “effect.” (7) criterion validity The extent to which a meas- ure correlates with one or more criterion vari- ables. (6) critical incident technique Open-ended method that instructs the respondent to describe an ob- servable action the purpose of which is fairly clear to the respondent and the consequences of which are sufficiently definite to leave little doubt about its effects. (5) Cronbach’s alpha A measure of internal con- sistency reliability, proposed by L. J. Cronbach. (6) crossed design Another name for the basic within-subjects design, because the subjects can be said to be “crossed” by treatment conditions. (7) cross-lagged correlations Correlations of the degree of association between two sets of vari- ables, of which one is treated as a lagged (time- delayed) value. (6, 8) cross-lagged panel design Relational research design using cross-lagged correlations, cross- sectional correlations repeated over time, and test-retest correlations. (8) cross-sectional design Research that compares subjects on one or more variables at one point in time. (8) crude range Highest score minus lowest score. (10) cue words Guiding labels that define particular points or categories of response. (5) debriefing Disclosing to participants the nature of the research in which they have participated. (3) deception by commission See active deception. deception by omission See passive deception.

decision-plane model A two-dimensional schema of the risks and benefits of doing re- search. (3) degrees of freedom (df ) The number of obser- vations minus the number of restrictions limiting the observations’ freedom to vary. (13) demand characteristics M. T. Orne’s term for the mixture of hints and cues that govern the par- ticipant’s perception of (a) his or her role as research subject and (b) the experimenter’s hypo- thesis. (3, 7) dependent variable A variable the changes in which are viewed as dependent on changes in one or more other variables. (2) descriptive measures Measures such as σ and σ2 that are used to calculate population val- ues. (10) descriptive research An empirical investiga- tion in which the objective is to map out a situa- tion or set of events. (1) df See degrees of freedom. dichotomous variable A variable that is di- vided into two classes. (11) discovery phase H. Reichenbach’s general term for the origin, creation, or invention of ideas for investigation. (2) discrete variable A variable taking on two or more distinct values. (11) discriminant validity Validity supported by a lack of correlation between conceptually unre- lated measures. (6) dispersion Spread or variability. (10) double-blind procedures Procedures in which neither the experimenter nor the participants know who has been assigned to the experimental and control groups. (7) double deception A deception embedded in what the research participant thinks is the official debriefing. (3) drunkard’s search The mistake of looking for something in a convenient but not particularly relevant place. (1) dummy coding Giving arbitrary numerical val- ues (often 0 and 1) to the two levels of a dichoto- mous variable. (11) effective power The actual power (i.e., 1 - �) of the statistical test used. (12) effective sample size The net equivalent sam- ple size that the researcher ends up with. (9)

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effect size A concept originally developed by J. Cohen, which typically refers to the magnitude of an experimental outcome or to the strength of re- lationship between two variables (e.g., an inde- pendent and a dependent variable). (12) effect size r (reffect size) The magnitude of an experimental effect (i.e., the size of the relation between X and Y, also designated as rYλ in con- trast analysis. (7, 12, 13, 14, Appendix C) efficient causality The idea that a propelling or instigating event or condition sets some other event in motion or alters another condition to some degree. (7) empirical Controlled observation and measure- ment. (1) empirical reasoning A use of logic and evi- dence. (1) equivalence, coefficient of The correlation be- tween alternate measures of the same condition. (6) error Fluctuation in measurements; also devia- tion of a score from the mean of the group or condition. (6, 14) error of estimate Closeness of estimate to ac- tual value. (9) errors of measurement Random errors in clas- sical test theory. (6) ethical guidelines Principles intended to help researchers decide what aspects of a study might pose an ethical problem and, in general terms, how to avoid it. (3) ethics The system of moral values by which be- havior is judged. (3) evaluation apprehension M. J. Rosenberg’s term for the subject’s emotional discomfort about possibly being negatively evaluated or not posi- tively evaluated. (5, 7) evaluation, potency, and activity Three pri- mary dimensions of subjective meaning, which are typically measured by a semantic differential. (5) expectancy control design An experimental design in which the expectancy variable oper- ates separately from the independent variable of interest. (7) expected frequency ( fe) Counts expected under specified row and column conditions if certain hypotheses (e.g., the null hypothesis) are true. (15) expedited review The evaluation of proposed research without undue delay. (3)

experimental group A group or condition in which the subjects undergo a manipulation or some other experimental intervention. (7) experimental hypothesis The experimenter’s working hypothesis; also an alternative to the null hypothesis. (2) experimental research An empirical investiga- tion in which the objective is a causal explana- tion. (1) experimenter expectancy bias Another name for the experimenter expectancy effect. (2, 7) experimenter expectancy effect An experi- menter-related artifact that results when the hypothesis held by the experimenter leads unin- tentionally to behavior toward the subjects that, in turn, increases the likelihood that the hypothesis will be confirmed. (2, 7) exploratory data analysis Searching in the data for relationships, patterns, clues, leads, or insights. (10) exploratory research An empirical investiga- tion guided more by general questions than by specific hypotheses. (2) extended range (corrected range) Crude range plus one unit. (10) external validity The degree of generaliz- ability. (6) extraneous effect The result of an unaccounted- for variable. (7) Fcontrast The symbol used in this book to de- note an F test (with numerator df = 1) that is used to address a focused prediction involving more than two groups or conditions. (14) Fnoncontrast The result of dividing the mean square noncontrast by the mean square within, which is then used to compute an effect size r for Feffect size. (14) face-to-face interview An interview in which the interviewer and the respondent directly inter- act with one another face to face. (5) face validity The extent to which a test seems on its surface to be measuring what it purports to measure. (6) factor A general name for a variable, the inde- pendent variable. (7) factorial design A research design with more than one factor and two or more levels of each factor. (7) fair-mindedness Impartiality. (3) IS

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falsifiability (refutability) The principle (ad- vanced by K. Popper) that a theoretical assertion is scientific only if it is stated in such a way that it can, if incorrect, be refuted by some empirical means. (2) field experiments Experimental research that is done in a naturalistic setting. (4) file drawer problem The concern that a sub- stantial number of studies with nonsignificant re- sults are tucked away in file drawers. (Appendix C) final causality An emphasis on the end goal or objective (Aristotle). (7) finite Term applied when all the units or events can, at least in theory, be completely counted. (10) Fisher zr The log transformation of r, as shown in Table B.6. (12, Appendix C) fixed-choice measures. See structured items. floor effect A condition in which the amount of change that can be produced is limited by the lower boundary of the measure. (5) focused chi-square χ2 with 1 df. (15) focused statistical procedure Any t test, 1-df χ2, or F with numerator df � 1. (14) forced-choice scales Measures that use an item format requiring the respondent to select a single item (or a specified number of items) from a pre- sented set of choices, even when the respondent finds no choice or more than one of the choices acceptable. (5) formal causality Emphasis on the implicit form or meaning of something (Aristotle). (7) found experiments D. P. Phillips’s term for naturally occurring experiments. (8) frames Sampling lists. (9) F ratio Ratio of mean squares that are distrib- uted as F when the null hypothesis is true, where F is a test of significance used to judge the tenability of the null hypothesis of no rela- tionship between two or more variables (or of no difference between two or more variabili- ties). (14) free-text searching Using terms that seem in- tuitively relevant in information retrieval. (2) frequency distribution A chart that shows the number of times each score or other unit of ob- servation occurs in a set of scores. (10) F test See F ratio. fugitive literature Hard-to-find literature. (2) full-text database Information databank that contains the entire work, not just an abstract. (2)

good subject Participant who is overly sensitive to and compliant with demand characteristics. (7) grand mean (MG) The mean of means, or the mean of all observations. (14) graphic scales Rating scales in the form of a straight line with cue words attached. (5) halo effect A response set in which the bias re- sults from the judge’s overextending a favorable impression of someone, based on some central trait, to the person’s other characteristics. (5) harmonic mean sample size (nh) The recip- rocal of the arithmetic mean of sample sizes that have been transformed to their reciprocals. (13) heterogeneous Dissimilarity among the ele- ments of a set. (9) heuristic Something general (e.g., a rule of thumb) that provokes interest and further thought. (2) history A plausible threat to internal validity when an event or incident that takes place be- tween the premeasurement and the postmeasure- ment contaminates the results of research not using randomization. (7) homogeneity of variance Equality of the pop- ulation variance of the groups to be compared. (13) homogeneous Similarity among the elements of a set. (9) hyperclaim An exaggerated assertion or con- clusion. (3) hypothesis Research idea that serves as a premise or supposition that organizes facts and guides observations. (2) hypothesis-generating heuristics W. J. Mc- Guire’s term for strategic rules of thumb for com- ing up with research ideas and predictions. (2) improve on older ideas One of several possi- ble hypothesis-generating heuristics. (2) independent-sample t test The t statistic, cre- ated by W. S. Gosset, used to compare samples that are independent. (13) independent variable A variable on which the dependent variable depends; in experiments, a variable that the experimenter manipulates to determine whether there are effects on another variable. (2) inferential measure A measure such as S and S2 that is used to estimate population values based on a sample of values. (10) infinite Boundless, or without limits. (10)

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informants Term sometimes used (particu- larly in sociology) to describe research respon- dents. (4) informed consent The procedure in which prospective subjects, who have been told what they will be getting into, give their formal consent to participate in the research. (3) institutional review board (IRB) A group set up to make risk-benefit analyses of proposed studies. (3) instrumentation A plausible threat to internal validity that occurs when changes in the measur- ing instrument (e.g., deterioration of the instru- ment) bias the results of research not using randomization. (7) intensive case study In-depth examination of a particular incident, individual, phenomenon, etc. (2) interaction effects (residuals) In factorial de- signs, condition means minus grand mean, row effects, and column effects. (14) interaction of independent variables The mutually moderating effects of two or more inde- pendent variables. (2) interactions See interaction of independent variables. intercoder reliability The extent to which raters or judges who do coding of data are in agreement. (4) interitem correlation (rii) The relationship of responses to one item with the responses to an- other item. (6) internal-consistency reliability Reliability based on the intercorrelation among components of a test, such as subtests or all the individual test items. (6) internal validity The degree of validity of statements made about whether X causes Y. (6, 7) interquartile range The difference between the 75th and 25th percentiles. (10) interrupted time-series design Time-series design punctuated by an intervention. (8) interval estimates The extent to which point estimates are likely to be in error. (9) intervention An experimental treatment. interview schedule A script that contains the questions the interviewer will ask. (5) intrinsically repeated measures Measurements that must be repeated to address the question of interest. (14)

introspection The subject’s reflection on his or her sensations and perceptions. (5) IRB See institutional review board. item analysis A procedure used for selecting items (e.g., for a Likert attitude scale). (5) item-to-item reliability (rii) The relationship of responses to one item with those to another item. (6) iterations Repetitions, as in standardizing the margins of a large table of counts. (15) judges Coders, raters, decoders, or others who assist in describing and categorizing ongoing events or existing records of events. (4) judge-to-judge reliability (rjj) The relationship of one judge’s responses to those of another judge. (6) judgment study The use of observers (judges or raters) to scale, sort, or rate certain variables (e.g., observable behavior). (4) justification phase Reichenbach’s term for the defense or confirmation of hypotheses, theories, or other proposed explanations. (2) K-R 20 A measure of internal-consistency relia- bility, developed by Kuder and Richardson. (6) lambda (�) weights Values that sum to zero and are used to state a prediction. (7, 14) Latin square design A specific repeated-meas- ures design with built-in counterbalancing. (7, 14) lazy writing The writing in papers saturated with quoted material that, with a little more effort, could be paraphrased. (3) leading questions Questions that can constrain responses and produce biased answers. (5) leftover effects See residual effects. leniency bias A type of rating error in which the ratings are consistently more positive than they should be. (5) Lie (L) Scale A set of items in the MMPI that were designed to identify respondents who are deliberately trying to appear “better” than they believe they are. (5) Likert scales Attitude scales constructed by the method of summated ratings, developed by R. Likert. (5) linearity Relationship between two variables that resembles a straight line. (11) line graphs Visual displays of changes in the frequency or proportion of scores over time. (10) literature search Retrieval of background in- formation. (2) IS

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logical error in rating A type of response set in which the judge gives similar ratings for vari- ables or traits that are only intuitively related. (5) longitudinal study Research in which the same subjects are studied over a period of time. (8) main effect The effect of an independent vari- able apart from its interaction with other inde- pendent variables. (14) margin of error Interval within which an antic- ipated value is expected to occur. (9) margins Row and column (marginal) total values. (12) Marlowe-Crowne Social Desirability Scale (MCSD scale) A standardized test that measures social desirability responding and need for social approval. (6) matched-pair t See paired t. matching The pairing of sampling units on cer- tain relevant variables. (7) material causality Emphasis on the composi- tion or substance of which something is made (Aristotle). (7) maturation A plausible threat to internal valid- ity that occurs when results not using randomiza- tion are contaminated by the participants’ having, for instance, grown older, wiser, stronger, or more experienced between the pretest and the posttest. (7) MCSD See Marlowe-Crowne social desirability scale. Mdn See median. mean (M) The arithmetic average of a set of scores. (10) mean square (S2), or MS Variance. (10, 14) mean square for error Variance used as the denominator of F ratios. (14) median (Mdn) The midmost score of a distri- bution. (10) meta-analysis The use of quantitative and graphic methods to summarize a group of similar studies. (12, Appendix C) metaphor A word or phrase applied to a concept or phenomenon it does not literally denote. (1, 2) metaphorical themes Thematic analogies that allow a particular view of the world. (2) method of agreement If X, then Y—which im- plies that X is a sufficient condition of Y ( J. S. Mill). (7)

method of authority The acceptance of an idea as valid because it is stated by someone in a position of power or authority (C. Peirce). (1) method of difference If not-X, then not-Y— which implies that X is a necessary condition of Y (J. S. Mill). (7) method of equal-appearing intervals An attitude-scaling technique, developed by L. L. Thurstone, in which values are obtained for items on the assumption that the underlying intervals are equidistant; also called a Thurstone scale. (5) method of self-report The procedure of hav- ing the research participants describe their own behavior or state of mind (e.g., used in inter- views, questionnaires, and behavioral diaries). (5) method of tenacity Clinging stubbornly to an idea because it seems obvious or is “common sense” (C. Peirce). (1) methodological pluralism The use of multiple methods of controlled observation in science. (1) methodological triangulation The approach of “zeroing in” on a pattern by using multiple but imperfect perspectives. (4) microworld simulations The use of computer- generated environments to simulate real-world settings. (4) Milgram experiments A set of experiments performed by Stanley Milgram in which he inves- tigated the willingness of participants to give “electric shocks” to another subject, who was ac- tually a confederate. (3) Mill’s methods Logical methods (or proposi- tions) popularized by the 19th-century English philosopher J. S. Mill, exemplified by the method of agreement and the method of difference. (7) minimal risk Studies in which the likelihood and extent of harm to subjects are no greater than those typically experienced in everyday life; such studies are generally eligible for an expedited review. (3) Minnesota Multiphasic Personality Inventory (MMPI) A structured personality test containing hundreds of statements that reflect general health, sexual attitudes, religious attitudes, emotional state, and so on. (5) mixed factorial design A design with two or more factors, with at least one between and one within subjects. (7) MMPI See Minnesota Multiphasic Personality Inventory.

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mode The score occurring with the greatest fre- quency. (10) moderator variables Conditions that alter the relationship between X and Y. (2, Appendix C) MS See mean square. MScontrast The contrast mean square, which is equivalent to the contrast sum of squares. (14) mutually exclusive Describing the condition: If A is true, then not-A is false. (12) N The total number of scores in a study; the number of scores in one condition or subgroup is denoted as n. naturalistic observation Research that looks at behavior in its usual natural environment. (4) necessary condition A requisite or essential condition. (7) need for social approval The desire to be pos- itively evaluated, or approved of. (6) negatively skewed distribution An asymmet- rical distribution in which the pointed end is to- ward the left (i.e., toward the negative tail). (10) nested design Another name for the basic between-subjects design, because the subjects are “nested” within their own treatment conditions. (7) network analysis See social network analysis. NHST See null hypothesis significance testing. N-of-1 experimental research Another name for single-case experimental research. (8) noise Random error, or the variability within the samples. (6, 13, 14) nonequivalent-groups designs Nonrandomized research in which the responses of a treatment group and a control group are compared on measures collected at the beginning and end of the research. (8) nonintrinsically repeated measures Repeated- measures research in which it is not actually es- sential to use repeated measures, but their use in- creases the efficiency, precision, and power of the study. (14) nonlinearity Relationship between two vari- ables that does not resemble a straight line. (11) nonmaleficence Not doing harm, which is one of the guidelines of the discussed ethical princi- ples. (3) nonreactive observation Any observation that does not affect what is being observed. (4) nonresponse bias Systematic error that is due to nonresponse or nonparticipation. (9)

nonskewed distribution A symmetrical distri- bution. (10) normal distribution A bell-shaped curve that is completely described by its mean and standard deviation. (10) norm-referenced Indicating that a standard- ized test has norms (i.e., typical values), so that a person’s score can be compared with the scores of a reference group. (5) norms Tables of values representing the typical performance of a given group. (9) no-shows People who fail to show up for their scheduled research appointments. (10) null-counternull interval Range from the null value to the counternull value of the effect size. (Appendix C) null hypothesis (H0) The hypothesis to be nullified; usually states that there is no relation- ship between two or more variables. (12) null hypothesis significance testing (NHST) The use of statistics and probabilities to evaluate the null hypothesis. (12) numerical scales Rating scales in which the re- spondent works with a sequence of defined num- bers. (5) observed frequency ( fo) Counts obtained in specific rows and columns. (15) observed scores Raw scores. observer bias The systematic overestimation or underestimation of observable events. (4) Occam’s razor The principle that explanations should be as parsimonious as possible (William of Occam, or Ockham). (2) omnibus chi-square χ2 with df � 1. (15) omnibus statistical procedures F with numer- ator df � 1, or χ2 with df > 1. (14) one-group pre-post design (O-X-O) A preex- perimental design in which the reactions of only one group of subjects are measured before and after exposure to the treatment. (7) one-sample t test See paired t. one-shot case study (X-O) A preexperimental design in which the reactions of only one group of subjects are measured after the event or treat- ment has occurred. (7) one-tailed p value The p value associated with a result supporting a prediction of a specific di- rection of a research result, e.g., MA � MB or the sign of r is positive. (10, 12) IS

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one-way design A statistical design in which two or more groups comprise a single dimension. (7, 14) open-ended items Questions or statements that offer the respondent an opportunity to ex- press feelings, motives, or behaviors sponta- neously. (5) operational definitions The meaning of a variable in terms of the operations used to meas- ure it or the experimental methods involved in its determination. (2) operations Empirical conditions. (2) opportunity samples The selection of subjects because they are available and convenient to re- cruit. (9) ordinate The vertical axis of a distribution. (10) outliers Scores lying far outside the normal range. (10) prep P. R. Killeen’s statistic for estimating the replicability of an obtained effect. (12) page header Two or three words from the title that are typed in the upper-right corner of the manuscript. (Appendix A) paired t (correlated-sample t, or matched- pair t, or one-sample t) The t test computed on nonindependent samples. (13) panel study Another name for a longitudinal study. (8) paradoxical incident An occurrence character- ized by seemingly self-contradictory aspects. (2) parsimony The degree to which the proposi- tions of a theory are “sparing” or “frugal”; see also Occam’s razor. (2) partial concealment Observation in which the researcher conceals only who or what is being observed. (4) participant observation Studying a group or a community from within by recording behavior as its occurs. (4) partitioning of tables Subdividing larger chi- square tables into smaller tables (e.g., into 2 × 2 tables). (15) passive deception (deception by omission) The withholding of certain information from the subjects, such as not informing them of the meaning of their responses when they are given a projective test or not telling them the full details of the study. (3) payoff potential Subjective assessment of the likelihood that the idea will be corroborated. (2)

Pearson r Standard index of linear relation- ship. (11) peer-reviewed journals Journals in which arti- cles submitted for publication undergo reviews by experts in the field. (1) percentile A point in a distribution of scores below and above which a specified percentage of scores falls. (10) phi coefficient (φ) Pearson r where both vari- ables are dichotomous. (11, 15) physical traces Material evidence of behavior. (4) pilot testing The evaluation of some aspect of the research before the study is implemented. placebo A substance without any pharmacolog- ical benefit given as a pseudomedicine to a con- trol group. (3, 7) placebo control group A control group that receives a placebo. (7) placebo effects The “healing” effects of inert substances or nonspecific treatments. (7) plagiarism Representing someone else’s work as one’s own. (3) plausible rival hypotheses Propositions, or sets of propositions, that provide a reasonable al- ternative to the working hypothesis. (6) point-biserial correlation (rpb) Pearson r where one of the variables is continuous and the other is dichotomous. (11) point estimates Estimates of particular (usually average) characteristics of the population (e.g, the number of times an event occurs). (9) population The universe of elements from which sample elements are drawn, or the universe of elements to which we want to gen- eralize. (9) positively skewed distribution An asymmetri- cal distribution in which the pointed end is to- ward the right (i.e., the positive tail). (10) posttest-only design See after-only design. power (1 - �) In significance testing, the prob- ability of not making a Type II error. (12) power analysis Estimation of the effective power of a statistical test, or of the sample size needed to detect an obtained effect given a speci- fied level of power. (12) power of a test The probability, when using a particular test statistic (e.g., t, F, χ2), of not mak- ing a Type II error. (12) precoded items Fixed-choice items. (5)

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predictive validity The extent to which a test can predict future outcomes. (6) preexperimental designs D. T. Campbell and J. C. Stanley’s term for research designs in which there is such a total absence of control that they are of minimal value in establishing causality. (7) pre-post design See before-after design. preratings The ratings made before an experi- mental treatment. (2) pretest The measurement made before an ex- perimental manipulation or intervention. (5) pretest sensitization The confounding of pretest- ing and X, the independent variable of interest. (7) probability The mathematical chance of an event’s occurring. (9, 12) probability sampling The random selection of sampling units so that the laws of mathematical probability apply. (9) product-moment correlation Standard index of linear relationship, or Pearson r. (11) projective test A psychological measure that operates on the principle that the subject will project some unconscious aspect of his or her life experience and emotions onto ambiguous stimuli in the spontaneous responses that come to mind (e.g, the Rorschach test and the Thematic Apper- ception Test). (5) prospective data Information collected by fol- lowing the subject’s behavior or reaction forward in time. (8) propensity score A single composite variable that summarizes differences between “treated” and “untreated” subjects on a number of different variables. (8) proportion of variation explained See coefficient of determination. proposal See research proposal. prospective data The data are collected for- ward in time. (8) pseudoscience Bogus claims masquerading as scientific facts. (1) PsycARTICLES The American Psychological As- sociation’s full-text database of APA journal arti- cles. (2) psychophysics The study of the relationship between physical stimuli and our experience of them. (1) PsycINFO The American Psychological Associa- tion’s main informational database. (2)

push polls An insidious form of negative politi- cal campaigning disguised as opinion polling but designed to push opinions in a particular direc- tion. (9) p value Probability value or level obtained in a test of significance. (12) qualitative research Studies in which the raw data exist in a nonnumerical form. (4) quantitative research Studies in which the raw data exist in a numerical form. (4) quasi-control subjects Research participants who are asked to reflect on the context in which an experiment is conducted and to speculate on the ways in which the context may influence their own and other subjects’ behaviors (M. T. Orne). (7) quasi-experimental research D. T. Campbell and J. C. Stanley’s term for study designs that re- semble an experimental design (in that there are treatments, outcome measures, and experimental units), but in which there is no random assign- ment to create the comparisons from which treatment-caused changes can be inferred in ran- domized designs. (8) quota sampling A procedure that assigns a quota of people to be interviewed and lets the questioner build up a sample that is roughly representative of the population. (9) r2 See coefficient of determination. ralerting See alerting r. rcontrast See contrast r. rcounternull See counternull statistic. reffect size See effect size r. random assignment, rule of The plan ac- cording to which random allocation is imple- mented. (1, 7) random digit dialing Procedure in which the researcher selects the first three digits of tele- phone numbers and uses a computer program to select the last digits at random. (9) random error The effects of uncontrolled vari- ables that cannot be specifically identified; such effects are, theoretically speaking, self-canceling in that the average of the errors will equal zero in the long run. (6) randomization (random assignment) Random allocation of sampling units to treatment condi- tions. (7) randomized experiments Experimental designs that use randomization. (1, 7) IS

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randomized trials Another name for random- ized biomedical experiments. (1, 7) random sampling Selecting a sample by chance procedures and with known probabilities of selection. (1, 9) random selection Another name for random sampling. (1) range Distance between the highest and lowest score. (10) rater biases Another name for systematic rating errors or response biases. (5) rating errors Errors in responses on rating scales. (5) rating scales The common name for a variety of measuring instruments on which the observer or judge gives a numerical value (either explicitly or implicitly) to certain judgments or assessments. (5) raw scores Observed (obtained) scores. reactive observation An observation that af- fects what is being observed or measured. (4) refutability A synonym for falsifiability. (2) relational research An empirical investigation in which the objective is to identify relationships among variables. (1) reliability The extent to which observations or measures are consistent or stable. (6) reliability of components Another name for internal-consistency reliability. (6) repeated-measures design Statistical design in which the sampling units generate two or more measurements. (7, 14) replicate To repeat or duplicate. (1) replication The duplication of a scientific ob- servation, usually an experimental result. (6) representative Typical, such as when the seg- ment is representative (or typical) of the larger pool. (9) research proposal What you propose to study and how you plan to go about it. (2) residuals Leftover effects when appropriate com- ponents are subtracted from scores or means. (14) residuals See interaction effects, row effects, and column effects. response biases Systematic errors in respond- ing. (5) retest reliability Another name for test-retest reliability. (6) retrospective data Information collected by going back in time. (8)

rhetoric The language of a given field, which in science encompasses the proper use of techni- cal terms. (1) rhetoric of justification Persuasive or ex- planatory terminology. (1) risk-benefit analysis An evaluation of the ethical risks and benefits of proposed studies. (3) rival hypotheses Competing hypotheses. (4) rival interpretations Plausible explanations that provide reasonable alternatives to working hypotheses. (4) root mean square See standard deviation. Rorschach test A projective test that consists of a set of inkblots on pieces of cardboard. (5) row effect Row mean minus grand mean. (14) r-type indices Correlational effect size measures. Rushton study A field experiment, conducted in a mining company, that raised the issue of fair- mindedness. (3) sample A subset of the population. (9) sampling frames Lists of sampling units, also called sampling lists. (9) sampling plan A design, scheme of action, or procedure that specifies how the participants are to be selected in a survey study. (9) sampling units The elements that make up the sample. (9) sampling without replacement A type of ran- dom sampling in which a previously selected name cannot be chosen again and must be disre- garded in any later draw. (9) sampling with replacement A type of random sampling in which the selected names are placed in the selection pool again and may be reselected in subsequent draws. (9) Satterthwaite’s method A procedure used to make t tests more accurate when suitable trans- formations are unavailable or ineffective. (13, Ap- pendix A) scatter diagram See scatter plot. scatter plot (scatter diagram) A visual display of the correlation between two variables that looks like a cloud of scattered dots. (11) SCI Science Citation Index—a reference data- base on the Web of Science. (2) scientific method General expression for the methodology of science, or a systematic research

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approach or outlook emphasizing the use of em- pirical reasoning. (1) scientific notation A compact way of reporting numbers with many decimal places. secondary observation Information that is twice removed from the source. (4) segmented graphic scale A rating scale in the form of a line that is broken into segments. (5) selection A plausible threat to the internal va- lidity of research not using randomization when the kinds of research subjects selected for one treatment group are different from those selected for another group. (7) self-fulfilling prophecy R. Merton’s term for a prediction that is fulfilled because those aware of the prediction then act accordingly. (1, 7) self-report measures See method of self-report. semantic differential method C. E. Osgood et al.’s rating procedure, in which subjective meaning is judged in terms of several dimensions, usually evaluation, potency, and activity. (5) seminal theories Conceptualizations that shape or stimulate further work. (2) sensitization effect The effect of a pretest on subjects’ reactions to the experimental treatment (R. L. Solomon). (7) serendipity Making a desirable discovery by accident. (2) signal Information. (13, 14) signal-to-noise ratio A ratio of information to lack of information, for example, the ratio of the variability between samples (the signal) to the variability within the samples (the noise). (13, 14) significance level The probability of a Type I error. (12) Significance test � Size of effect Size of study The basic conceptual form of all signifi- cance tests. (13, 15) simple effects Differences between group or condition means. (14) simple observation Unobtrusive observation of events without any attempt to affect them. (4) simple random sampling A sampling plan in which the participants are selected individually on the basis of a randomized procedure. (9) single-case experimental research Studies using repeated-measures designs in which N � 1 subject or 1 group. (2, 8)

size of the study The number of sampling units or some index of that number. (13) small-N experimental research Studies using repeated-measures designs in which the treatment effect is evaluated within the same subject or a small number of subjects. (8) socially desirable responding The tendency to respond in ways that seem to elicit a favorable evaluation. (5, 6) social network analysis (SNA) The use of visual and quantitative techniques to map networks of in- terpersonal communication or social interactions. (4) social psychology of the experiment The study of the ways in which subject-related and experimenter-related artifacts operate. (7) Social Science Citation Index (SSCI) A refer- ence database on the Web of Science. (2) Solomon design A four-group experimental design, developed by R. L. Solomon, as a means of isolating pretest sensitization effects without contamination by pretesting. (7) Spearman-Brown prophecy formula A tradi- tional equation that measures the overall internal- consistency reliability of a test from a knowledge of the reliability of its components. (6) Spearman rho (rs) Pearson r computed on scores in ranked form. (11) spread Dispersion or variability. (10) stability The extent to which a set of measure- ments does not vary. (9) standard deviation (root mean square) An index of the variability of a set of data around the mean value in a distribution. (10) standardized measures Measurements (e.g., of ability, personality, judgment, and attitude) re- quiring that certain rules be followed in the devel- opment, administration, and scoring of the measuring instrument. (5) standardizing the margins F. Mosteller’s pro- cedure for setting all row totals equal to each other and all column totals equal to each other in large tables of counts. (15) standard normal curve Normal curve with mean � 0 and σ � 1. (10) standard score (z score) Score converted to a standard deviation unit. (10) statistical conclusion validity The relative ac- curacy of drawing statistical conclusions. (6) statistical power See power. IS

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stem-and-leaf chart The plot of a distribution in which the original data are preserved with any desired precision ( J. W. Tukey). (10) strata (clusters) Subpopulations (or layers) in survey sampling. (9) stratified random sampling Probability sam- pling plan in which a separate sample is ran- domly selected within each homogeneous stratum (or layer) of the population. (9) structured items Response items with fixed options. (5) Student’s t The pen name used by the inventor of the t test, W. S. Gosset, was “Student.” (13) subclassification on propensity scores Using propensity scores to form matched subgroups of “treated” and “untreated” subjects (D. B. Rubin). (8) sufficient condition A condition that is ade- quate to bring about some effect or result. (7) summated ratings method A method of atti- tude scaling, developed by R. Likert, that uses item analysis to select the best items. (5) sum of squares (SS) The sum of the squared deviations from the mean in a set of scores. (14). symmetrical distribution A distribution of scores in which there is an exact correspondence in arrangement on the opposite sides of the mid- dle line. (10) synchronous correlations In panel designs, correlations of the degree of relationship of vari- ables at a point in time. (8) syndrome A set of symptoms. systematic error The effect of uncontrolled variables that often can be specifically identified; such effects are, theoretically speaking, not self- canceling (in contrast to the self-canceling nature of random errors). (6, 7) systematic observational research Obser- vational research that is guided or influenced by preexisting questions or hypotheses. (4) tcontrast The symbol used in this book to denote a t test that is used to address a focused question or hypothesis in a comparison of more than two groups or conditions. (14) tally sheets Recording materials for counting frequencies. target population The population to which we want to generalize findings and conclusions. (1) TAT See Thematic Apperception Test.

t distribution Family of curves, each resem- bling the standard normal distribution, for every possible value of the df of the t test. (13) teleological causality The cause when the ac- tion is goal-directed. telephone interview A survey interview con- ducted by phone rather than face to face. (5) temporal precedence The principle that the “cause” must be shown to have occurred before the “effect.” (7) test-retest correlations Correlations that repre- sent the stability of a variable over time. (6, 8) test-retest reliability The degree of consistency of a test or measurement, or the characteristic it is designed to measure, from one administration to another. (6) tests of simple effects Significance tests of the difference between two groups or two condition means in a multigroup design. (14) Thematic Apperception Test (TAT) A projec- tive test, developed by H. A. Murray, consisting of a set of pictures, usually of people in different life contexts. (5) theoretical (conceptual) definition The meaning of a variable in abstract or conceptual terms. (2) theoretical ecumenism The use of more than one relevant theoretical perspective, in order to foster a relatively holistic picture. (1) theory A set of proposed explanatory state- ments connected by logical arguments and by ex- plicit and implicit assumptions. (2) third-variable problem A condition in which a variable correlated with X and Y is the cause of both. (8, 11) three Rs principle (of humane animal experimentation) The argument that scien- tists should (a) reduce the number of animals used in research, (b) refine their animal experi- ments so that the animals suffer less, and (c) replace animals with other procedures when- ever possible. (3) Thurstone scale See method of equal- appearing intervals. time-series designs Studies in which the ef- fects of an intervention are inferred from a com- parison of the outcome measures obtained at different time intervals before and after the inter- vention. (8)

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transformation Conversion of data to another mathematical form. (10) translation and back translation Process in which questionnaire items and instructions are translated from the source to the target language and then independently translated back into the source language. The researcher compares the original with the twice-translated version to see whether anything important was lost in the trans- lations. (4) treatments The procedures or conditions of an experiment. (7) trials Biomedical term for randomized experi- ments. (7) trimmed mean The mean of a distribution from which a specified highest and lowest per- centage of scores has been dropped. (10) t test A test of significance used to judge the tenability of the null hypothesis of no relationship between two variables. (13) Tuskegee study Notorious study, from 1932 to 1973, of the course of syphilis in more than 400 low-income African American men in Tuskegee, Al- abama; they were told only that they had “bad blood” and were not given penicillin when, in 1947, it was found to be an effective treatment for syphilis. two-by-two factorial design ANOVA design with two rows and two columns. (7) two-tailed p value The p value associated with a result supporting a prediction of a nonspecific direction of a research result; e.g., either MA � MB or MB � MA or the sign of r is either positive or negative. (12) two-way design (two-way factorial) ANOVA design in which each entry in the table is associ- ated with a row variable and a column variable. (14) two-way factorial See two-way design. Type I error The error of rejecting the null hy- pothesis when it is true. (12) Type II error The error of failing to reject the null hypothesis when it is false. (12) unbiased Describes the outcome when the av- erage of the sample values coincides with the cor- responding “true” population value. (9) unbiased estimator of the population value of �2 A specific statistic usually written as S 2. (10,13)

unbiased sampling plan Survey design in which the range of the sample values coincides with the corresponding “true” population value.(9) unipolar rating scales Rating scales in which one end represents a great deal of a quality and the other end represents a complete absence of that quality. (5) unobtrusive observation Measurements or ob- servations used to study behavior when the subjects are unaware of being measured or ob- served. (4) unstructured measures See open-ended mea- sures. validity The degree to which what was ob- served or measured is the same as what was pur- ported to be observed or measured. (6) variability See spread. variables Attributes of sampling units, events, or conditions that can take on two or more val- ues, or observed or measured events or condi- tions that vary or are likely to vary. (2) variance (mean square) The mean of the squared deviations of scores from their means in a population, or its unbiased estimate. (10) varied replication Repeating (replicating) a previous study but with some new twist. (2) visualization Seeing things in the “mind’s eye”; also called perceptibility in this book. (1) volunteer bias Systematic error resulting when participants who volunteer respond differently from the way individuals in the general popula- tion would respond. (9) WAIS See Wechsler Adult Intelligence Scale. wait-list control group A control group in which the subjects wait to be given the experi- mental treatment until after it has been adminis- tered to the experimental group. (8) Wechsler Adult Intelligence Scale (WAIS) The most widely used of the individual intelli- gence tests; divided into verbal and performance scores. (6) wild scores Extreme scores that result from computational or recording mistakes. (10) within-subjects design Statistical design in which the sampling units (e.g., the research participants) generate two or more measure- ments. (7)

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working hypothesis An empirically testable supposition. (2) x axis (abscissa) The horizontal axis of a distri- bution. (10) y axis (ordinate) The vertical axis of a distribu- tion. (10)

yea-sayers Respondents who answer questions consistently in the affirmative. (5) z _

r The average Fisher zr. (Appendix C) zero control group A group that receives no treatment of any kind. (7) z score See standard score.

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449

Name Index Adair, J. G., 169, 428 Adair, R. K., 158, 428 Adhikari, A., 127, 208, 433 Aditya, R. N., 169, 177, 233, 428, 444 Agard, E., 58, 97, 432 Aguinis, H., 86, 440 Aiken, L. R., Jr., 103, 428 Ainsworth, M. D. S., 134, 428 Albers, J., 226, 428 Allaman, J. D., 143, 428 Allport, G. W., 31, 428 Altman, D. G., 59, 405, 430, 439 American Association for the Ad-

vancement of Science, 19, 428 American Psychiatric Association, 38 American Psychological Association,

19, 50, 64, 102, 233, 317, 368, 428 Anastasi, A., 139–141, 428 Anderson, A. B., 116, 444 Anderson, C. A., 86, 428 Anderson, D. C., 189, 428 Andrew, F. M., 192, 440 Anhalt, R. L., 105, 439 Archer, D., 84, 442 Arellano-Galdames, F. J., 63, 428 Aristotle, 7–8 Aronson, E., 17, 65, 428, 445 Asch, S. E., 9–10, 14, 20, 60–61,

143, 428 Ashe, D. K., 119, 438 Ashmore, R. D., 282, 432 Association of Psychological Sci-

ence, 97 Athenaeus, 7 Atkinson, J. W., 101, 438 Atkinson, L., 133, 428 Atwell, J. E., 50, 429 Axinn, S., 276, 429

Babad, E., 17, 30, 429 Bachrach, C. A., 96, 117, 446 Bacon, F., 38 Baenninger, M., 67, 429 Baenninger, R., 67, 429, 434 Bailey, P., 43, 429 Bakan, D., 409, 429 Baldwin, W., 95, 429 Bales, R. F., 79–81, 91, 429 Baltes, P. B., 39, 445 Baltimore, D., 3, 429 Barker, P., 11, 429 Barlow, D. H., 189, 435 Barnett, F. T., 187, 437 Barrass, R., 19, 429 Bartoshuk, L., 98, 429 Bauer, M. I., 11, 429

Baumrind, D., 62, 429 Beall, A. C., 86, 438 Beatty, J., 152, 273, 293, 434 Beck, A., 101, 429 Beck, A. T., 39, 429 Beck, S. J., 101, 429 Becker, H. S., 26, 441 Bellack, A. S., 50, 64–65, 220, 429 Bem, D. J., 29, 45, 429 Bennett, E. S., 97, 439 Ben-Shachar, T., 98, 439 Bergum, B. O., 29, 429 Bernard, H. B., 117, 429 Bernstein, I. H., 129, 439 Berry, D. T. R., 133, 435 Bersoff, D. M., 97, 429 Bersoff, D. N., 97, 429 Bielawski, D., 82–83, 91, 431 Billow, R. M., 11, 429 Binkley, S., 67, 429 Biocca, F., 86, 429 Blaine, B., 17, 89, 447 Blanck, P. D., 50, 58, 64–65, 169,

199, 220, 429–430, 444 Blascovich, J., 86, 438 Blau, G., 107, 439 Blehar, M. C., 134, 428 Blumberg, M., 57, 430 Bogdan, R., 77, 447 Boisen, M., 151, 281, 433 Bonanno, G. A., 199–200, 430 Bond, C. F., Jr., 282, 441 Boorstein, D. J., 4, 430 Bordia, P., 17, 31, 52, 86, 114, 430,

432, 441 Borgatti, S. P., 78, 430 Bornstein, R. F., 133, 435 Boyer, J. L., 52, 435 Boyer, L. B., 101, 432 Bradburn, N. M., 113, 116, 430 Brady, J. V., 163, 447 Braun, H. I., 132, 430 Brehm, S. S., 29, 430 Brehmer, B., 86, 430 Bremer, F., 43, 429 Bridgstock, M., 19, 430 Brittingham, A., 97, 448 Brody, J. E., 3, 430 Brooks-Gunn, J., 56, 430 Broome, J., 57, 204, 430 Brown, R., 17, 430 Brown, W., 131, 430 Brownlee, K. A., 151,281, 430 Bruck, M., 8, 14, 430 Brunell-Neuleib, S., 133, 435 Bruner, J. S., 87, 441

Bryman, A., 39, 438 Bucciarelli, A., 199–200, 430 Burch, R. L., 67, 444 Burnham, J. R., 172, 430 Busch, J. C., 96, 434 Bushman, B. J., 86, 428 Buunk, B. P., 41, 430 Byrne, R. M. J., 11, 177, 432, 436

Cain, V. S., 96, 117, 446 Campbell, D. T., xiv, 74, 88, 90,

136–138, 141, 144–145, 161, 163–164, 179–180, 186, 192, 430–431, 436, 444, 447

Cannell, C. F., 116–117, 439 Cantril, H., 208, 436 Carlsmith, J. M., 65, 428 Carr, K., 86, 430 Carroll, M. B., 227, 271, 446 Carstensen, M. S., 184–185, 440 Castellan, N. J., Jr., 163, 409, 445 Ceci, S. J., 8, 14, 17, 20, 50, 56, 133,

177, 430, 441, 444, 448 Chalmers, I., 405, 430 Chambers, J. M., 228, 430 Chandrasekhar, S., 10, 431 Chassin, L., 4, 445 Ching, P., 97, 448 Cho, A., 28, 431 Choi, Y. S., 98, 439 Christie, R., 169, 431 Clark, R. A., 101, 438 Clark, R. W., 11, 431 Cleveland, W. S., 228, 430 Cobben, N. P., 193, 434 Cochran, W. G., 183, 212, 214, 293,

403–404, 431, 445 Coffman, W. E., 11, 441 Cohen, J., 278, 283–284, 301,

303–304, 431 Cohen, S. P., 79, 429 Conant, J. B., 7, 431 Conrath, D. W., 117, 119, 431 Converse, J. M., 114, 431 Cook, T. D., 13, 137–138, 144–145,

161, 164, 179, 405, 431, 435, 444 Cooper, H., 405, 431 Copernicus, 4 Cordray, D. S., 405, 431 Corsini, R. J., 39, 431 Costa, P. T., Jr., 100, 438 Crabb, P. B., 82–83, 91, 431 Crancer, J., 87, 431 Crandall, V. C., 143, 428 Cronbach, L. J., 39, 130, 139, 141,

144, 431

IS B

N : 0

-5 36

-5 64

36 -1

Crowell, C. R., 189, 428 Crowne, D. P., 107, 141–144, 431 Cryer, J. D., 184, 431 Csikszentmihalyi, M., 117–118, 431

Danziger, K., 169, 431 Darley, J. M., 27, 45, 144, 431, 437 Darwin, C., 3 Daston, L., 152, 273, 293, 434 Davis, J. D., 42–43, 431 Davison, G. C., 26, 431 Day, D. D., 111–112, 133, 431 deCastle, O., 102 deCastle, V., 102 Deegan, J. G., 77, 433 Delaney, H. D., 152, 157, 438 Delay, J., 87, 431 deMestral, G., 30 Department of Health and Human

Services, 53 DePaulo, B. M., 117, 432, 436 Detterman, D. K., 133, 446 Devine, E. C., 282, 432 DeVos, G. A., 101, 432 Dewhirst, J. R., 97, 447 deWolff, M. S., 134, 432 Diener, E., 96, 432 DiFonzo, N., 31, 86, 114, 432 Diggle, P. J., 194, 432 Dille, J., 87, 431 DiMatteo, M. R., 84, 405, 442 Dirac, P., 10 Doherty, L., xv Donovan, T., 41, 435 Dorn, L. D., 52, 432, 447 Dörner, D., 86, 430 Downs, C. W., 115–116, 432 Dumond, V., 382, 432 Dunning, D., 97, 432

Eagly, A. H., 282, 405–406, 432 Ebbinghaus, H., 26, 31, 45, 432 Edney, J. N., 402, 445 Egan, T., 5, 432 Egelson, P., 96, 434 Einstein, A., 10–11, 20 Elkin, I., 52, 435 Emerson, J. D., 228, 230, 432 Emery, D., 39, 429 Engelman, L., 234, 448 England, R., 86, 430 Entwisle, D. R., 167, 432 Epstein, J. A., 117, 432 Ericsson, K. A., 96, 432 Esposito, J. L., 17, 58, 97,

432, 443 Etzersdorfer, E., 185, 446 Evans, J. St. B. T., 177, 432 Everett, M. G., 78, 430 Exline, J. J., 8, 432 Exner, J. E., 101, 432

Gilovich, T., 6, 434 Gitter, A. G., 17, 443 Glanz, L. M., 52, 435 Glass, G. V, 405, 434 Glynn, L. M., 184, 440 Gniech, G., 169, 434 Goldberg, L. R., 100, 434 Goldman, B. A., 96, 132, 434 Golinkoff, R. M., 171, 435 Gombrich, E. H., 10, 434 Goodstadt, B. E., 17, 443 Gossett, W. S., 293 Gottman, J. M., 184, 434 Gould, M. S., 184, 434 Govern, J., 67, 434 Grady, D., 358, 434 Greco, M., 67, 434 Greene, D., 4, 13, 444 Gross, A. E., 60, 434 Gross, A. G., 11, 434 Grove, J. B., 90, 447 Guilford, J. P., 104, 129, 434 Gulliksen, H., 129, 434

Hagenaars, J. A., 193, 434 Hall, J. A., 18–20, 84, 97, 146, 434,

442, 447 Hall, R. V., 187, 434 Hanson, R. K., 132–133, 440 Hantula, D. A., 187, 189, 428, 434 Harré, R., 39, 435 Harris, B., 64–65, 435 Harris, L., 200 Härtel, C. E. J., 84, 435 Harter, S., 89, 439 Hartmann, G. W., 89–91, 435 Hartmann, H., 405, 431 Haybin, M., 87, 431 Haywood, H. C., 56, 435 Heath, C., 97, 432 Hedges, L. V., 405, 431, 435 Heise, G. A., 17, 435 Hellweg, S. A., 17, 435 Hemphill, F., 151, 281, 433 Hersen, M., 189, 435 Higbee, K. L., 17, 435 Higgins, C. A., 119, 431 Higgins, J. J., 163, 409, 435 Hiller, J. B., 133, 435 Hineline, P. H., 10, 186, 435 Hippler, H.-J., 97, 445 Hirsh-Pasek, K., 171, 435 Hoaglin, D. C., 228, 230, 432 Hodges, B. H., 10, 435 Hodgins, H. S., 333, 448 Hogan, J., 96, 435 Hogan, R., 96, 435 Holyoak, K. J., 11, 434–435 Hoover, K., 41, 435 House, P., 4, 13, 444 House, R. J., 177, 428

Faber, J. E., 89, 446 Fairbanks, L. A., 193, 432 Faust, K., 78, 447 Federighi, E. T., 395, 432 Ferster, C. B., 29, 432 Festinger, L., 40, 45, 69, 76, 91, 432–433 Feyerabend, P., 44, 75, 91, 433 Fiedler, K., 158, 433 Fienberg, S. E., 220, 433 Fine, G. A., 31, 77, 97, 433, 443, 447 Finkner, A. L., 214, 433 Fischler, C., 43, 444 Fisher, R. A., 42, 293, 316, 326, 352,

400–401, 433 Fisher, R. J., 107, 433 Fiske, D. W., 74, 141, 169, 430, 433 Fitzsimons, G. J., 88, 437 Flanagan, J. C., 114, 433 Fleming, I., 60, 434 Fletcher, J. C., 52, 432, 447 Fode, K., 102, 170, 433, 442 Folkman, S., 50, 56, 444 Forrest, D. W., 8, 433 Fossey, D., 67, 433 Foster, E. K., 78–79, 433 Foucault, J.-B.-L., 8, 10 Fowler, F. J., Jr., 207, 215–216, 433 Fowler, R. G., 89, 438 Francis, T., Jr., 151, 281, 433 Freedman, D., 127, 208, 433 Freeman, E., 43, 439 Freeman, L. C., 78, 430 Frey, J. H., 97, 208, 433 Friedman, A. F., 102, 433 Friedman, C. J., 102, 433 Friedman, H., 39, 433 Fuerbringer, J., 186, 433 Funke, J., 86, 433

Gale, L., 43, 439 Galea, S., 199–200, 430 Galileo, 7, 10 Gallagher, R. L., 42–43, 431 Gallup, G., 208–209, 433 Galton, F., 8, 20, 250, 433 Gantt, W. H., 169, 433 Garb, H. N., 133, 433 Garbin, G. M., 39, 429 Gardner, D., 56, 448 Gardner, H., 133, 177, 433–434 Gardner, M., 5, 434 Garfield, E., 10, 434 Gazzaniga, M. S., 67, 434 Gentner, D., 11, 434 Georgoudi, M., xiv, 443 Gerard, H. B., 65, 436 Geyer, A. L., 10, 435 Gibbons, F. X., 41, 430 Gibney, L., 17, 443 Gibson, E. J., 44, 434 Gigerenzer, G., 11, 152, 273, 293, 434

450 Name Index

IS B

N : 0-536-56436-1

Houts, A. C., 13, 435 Hovell, M. F., 187, 437 Howard, K. I., 97, 439 Howes, D., 87, 167, 445 Hoyt, W. T., 105, 435 Hult, C. A., 1, 435 Hume, D., 158–160, 435 Hunsley, J., 132–133, 440 Hunt, M., 405, 435 Hunter, J. E., 405, 435 Huprich, S. K., 101, 435

Imada, S., 43, 444 Imber, S. D., 52, 435 Iversen, I. H., 187, 435 Iversen, L. L., 87, 435

Jackson, D., 187, 434 Jacobson, L., 17, 30, 442 Jacobson, L. I., 143, 439 Jaeger, M. E., 13, 177, 435, 444 Jako, R. A., 105, 439 James, W., 8 Jammer, M., 11, 435 Janis, I. L., 84–85, 435, 438 Jobe, J. B., 96, 117, 446 Johnson, C. A., 102, 433 Johnson, G., 10, 436 Johnson, L. B., 42 Johnson, M., 11, 437 Johnson-Laird, P. N., 11, 177, 429, 436 Johnston, J. M., 187, 436 Jones, E. E., 65, 436 Jones, J. H., 51, 436 Joyce, C. S., 143, 428 Judd, C. M., 184, 436 Jung, C. G., 5, 436 Jung, J., 17

Kagay, M. R., 202, 436 Kahane, H., 178–179, 436 Kahneman, D., 13, 436, 447 Kaiz, M., 97, 439 Kanaya, T., 17, 441 Kanner, L., 25, 30, 436 Kaplan, A., 276, 436 Kaplan, B., 28, 448 Karatekin, C., xv Kashy, D. A., 117, 432, 436 Kassin, S. M., 29, 430 Katz, D., 208, 436 Kazdin, A. E., 26, 39, 187, 189, 436 Kelley, H. H., 4, 436 Kelman, H. C., 60, 436 Kendon, A., 184, 436 Kenny, D. A., 184, 192, 430, 436 Keppel, G., 157, 283, 436 Kerner, O., 42, 436 Kidder, L. H., 191, 436 Kidder, R. L., 191, 436 Kilborn, P. T., 199, 436

Liang, K. Y., 194, 432 Liao, T. F., 39, 438 Light, R. J., 405, 431, 438 Likert, R. A., 108, 438 Lilienfeld, S. O., 133, 433 Link, R. F., 227–228, 447 Linsky, A. S., 215, 438 Lipsey, M. W., 405, 438 Liss, M. B., 58, 438 Longo, L. C., 282, 432 Loomis, J. M., 86, 438 Louis, T. A., 405, 431 Lowell, E. L., 101, 438 Lund, D., 187, 434 Luria, Z., 102, 440

Mahler, I., 109–110, 133, 438 Main, M., 134, 438 Makhijani, M. G., 282, 432 Malloy, T., xv Mann, L., 84–85, 89, 435, 438–439 Mann, T., 53, 438 Markman, A. B., 11, 434 Marks, G., 4, 438 Marlowe, D., 141–144, 431 Marshall, J., xv Martin, D., 5, 438 Martin, E., 115–116, 432 Martin, R., 41, 446, 448 Maurer, T. J., 119, 438 Maxwell, S. E., 152, 157, 438 McClean, R. J., 119, 431 McClelland, D. C., 101, 438 McCloskey, M., 37, 447 McCrae, R. R., 100, 438 McGaw, B., 405, 434 McGinnies, E., 87, 441 McGuire, W. J., 11, 25, 27–28, 45, 438 McNemar, Q., 17, 438 Medawar, P. B., 8, 438 Meehl, P. E., 39, 144, 431 Meier, P., 151, 281, 438 Melton, G. B., 50, 446 Melzack, R., 96, 447 Mendel, G., 352 Menges, R. J., 60, 438 Merriam, S. B., 26, 438 Merritt, C. B., 89, 438 Merton, R. K., 14, 438 Michener, W., 43, 439 Milgram, S., 6, 60–65, 89, 439 Mill, J. S., 161, 439 Miller, A. I., 11, 439 Miller, E. R., 97, 445 Miller, F. D., 67, 97, 440, 447 Miller, G. A., 17, 435, 439 Miller, N., 4, 438 Miller, P. V., 116–117, 439 Miller, R. L., 41, 446 Millham, J., 143, 439 Mitchell, D. F., 96, 132, 434

Killeen, P. R., xiv, 286–287, 436 Killworth, P. D., 117, 429 Kimble, G. A., 12, 436 Kimmel, A. J., 31, 50–51, 56, 64, 66,

436–437 Kirk, R. E., 152, 157, 283, 437 Kirkendol, S. E., 117, 432 Kish, L., 207, 437 Kleiner, B., 228, 430 Kleinmuntz, B., 101, 437 Kokinov, B. N., 11, 434 Kolata, G. B., 204, 437 Kolodner, J. L., 11, 437 Komaki, J., 187, 437 Koocher, G. P., 50, 444 Kornhaber, M. L., 133, 434 Korns, R. F., 151, 281, 433 Koshland, D. E., Jr., 19, 437 Kossinets, G., 78, 437 Kraemer, H. C., 283, 437 Kragh, H., 10, 437 Kratochwill, T. R., 189, 437 Krüger, L.,152, 273, 293, 434 Kruskal, W. H., 227–228, 447 Kuder, G. F., 130, 437 Kuhn, T. S., 3, 437 Kurtzman, H. S., 96, 117, 446

Labaw, P., 114, 437 Ladove, R., 42–43, 431 LaGreca, A. M., 97, 437 Lakoff, G., 11, 437 Lamb, R., 39, 435 Lana, R. E., 167, 437 Lando, H. A., 77, 437 Lane, F. W., 77, 437 Larson, R., 117–118, 431 Latané, B., 27, 45, 144, 431, 437 Lattal, K. A., 186–187, 435 Lavelle, J. M., 187, 437 Lavrakas, P. J., 99, 116, 437 Lawson, R., 171, 442 Lazarsfeld, P. F., 190, 437 Leary, D. E., 11, 437 Leber, W. R., 52, 435 LeDoux, J. E., 67, 434 Lee, L., 97, 448 Lee, R. M., 220, 437 Lehmann, E. L., 227–228, 447 Lehr, D. J., 29, 429 Leichtman, M. D., 8, 448 Lessac, M. S., 167, 446 Lesyna, K., 184, 440 Levav, J., 88, 437 Levin, J. R., 189, 437 Levitt, E., 101, 429 Levy, M. R., 86, 429 Lewak, R., 102, 433 Lewin, T., 57, 438 Lewis-Beck, M., 39, 438 Li, H., 131–132, 438

Name Index 451

IS B

N : 0

-5 36

-5 64

36 -1

Mitchell, J., 4, 439 Moher, D., 59, 439 Molish, H., 101, 429 Mook, D. G., 85, 439 Moreno, J. L., 78, 439 Mosteller, F., xiii, 227–228, 360, 405,

431, 439, 447 Murphy, K. R., 105, 283, 439 Myers, D. G., 8, 439 Myors, B., 283, 439

Nagel-Kuess, S., 185, 446 Napier, J., 151, 281, 433 National Commission for the Protection

of Human Subjects of Biomedical and Behavioral Research, 51, 439

National Heart Institute, 178, 439 Neuringer, A., 203, 439 Newman, E. B., 17, 439 Newstead, S. E., 177, 432 Newton, I., 7, 10 Nezworski, M. T., 133, 433 Nichols, D. S., 102, 433 Nisbet, R., 10, 439 Nisbett, R. E., 97 , 439 Norwick, R., 98, 439 Nouri, H., 107, 439 Nunnally, J. C., 129, 439 Nyberg, S. E., 117, 448

Ockham (William of Occam), 41 Offer, D., 97, 439 Olkin, I., 405, 435 Omodei, M. M., 86, 439 Ones, D. S., 107, 439 Oppenheimer, R., 11, 440 Orne, M. T., 169–170, 440 Osborne, W. L., 96, 434 Osgood, C. E., 102, 107–108, 440, 445 Otten, M. W., 102, 440 Ouwehand, A., 284 Overman, E. S., xiv, 440

Paight, D. J., 184, 440 Palmer, J. K., 119, 438 Pargament, K. I., 8, 440 Parker, K. C. H., 132–133, 440 Parloff, D. N., 50, 440 Paul, E. F., 67, 440 Paul, J., 67, 440 Paulhus, D. L., 143–144, 440 Paulos, J. A., 180, 252, 440 Pavlov, I., 29–30, 160 Pearl, J., 150–151, 157–160, 440 Pearson, K., 249–250, 351 Peirce, C. S., 4–6, 20, 440 Pelz, D. C., 192, 440 Pennypacker, H. S., 187, 436 Pera, M., 11, 440 Perloff, E., 96 Perloff, L., 96

217–220, 232–233, 251, 272, 279, 281–283, 302, 306, 308–310, 333, 336, 338, 342–343, 358, 360, 405, 409–411, 430, 435, 438, 441–443, 448

Rosnow, M., xvi, xvii, 59, 68, 368, 384, 443

Rosnow, R. L., xiii-xiv, xvii-xviii, 6, 13, 17, 31, 42, 45, 50, 52, 54, 56, 58–59, 64–65, 68, 78–79, 86, 97, 114, 130, 156–157, 160, 164–165, 167, 169, 177, 189–190, 194, 201, 208, 214, 217–220, 232–233, 251, 272, 282, 302, 306, 308–310, 336, 338, 342–343, 358, 360, 368, 384, 405, 411, 428–429, 430, 432–433, 435, 437, 442–444, 446–447

Ross, L., 4, 13, 444 Rossi, P. H., 116, 444 Rothenberg, R., 214, 444 Rotheram-Borus, M. J., 50, 56,

64–65, 220, 429–430, 444 Rozelle, R. M., 192, 444 Rozin, P., 43, 439, 444 Rubin, D. B., 30, 132, 152–153, 163,

183,189, 198, 272, 279, 281, 336, 338, 342, 409, 411, 438, 441–444

Rubin, Z., 60, 444 Rubinstein, J., 66, 445 Rush, A. J., 39, 429 Russell, B., xvi Russell, M. S., 67, 444 Ryan, C. S., xvi

Saks, M. J., 17, 199, 444–445 Sales, B. D., 50, 56, 444 Salsburg, D., 316, 444 Sarubin, A., 43, 444 Saunders, J. L., 96, 434 Saxe, L., 60, 444 Schachter, S., 43, 76, 433, 444 Schacter, D. L., 98, 117, 444 Schaeffer, N. C., 97, 444 Schartz, H. A., 58, 430 Schmidt, F. L., 405, 435 Schooler, N. R., 50, 64–65, 220, 429 Schuler, H., 50, 444 Schultz, D. P., 17, 444 Schulz, K. R., 59, 439 Schuman, H., 103, 116, 220, 444 Schwartz, G. 163, 448 Schwartz, R. F., 90, 447 Schwarz, N., 97, 445 Scott, W. A., 99–100, 444 Scott-Jones, D., 52, 444 Sears, D. O., 17, 444 Sechrest, L., 90, 447 Seely, M. R., xvi Shadish, W., Jr., 13, 435 Shadish, W. R., 137–138, 144–145,

161, 164, 179, 444

Perloff, R., 11, 440 Pessin, J., 29, 440 Peters, D., 56, 430 Phillips, D. P., 184–185, 440 Pierce, C. A., 86, 440 Pieters, R. S., 227–228, 447 Pillemer, D. B., 405, 438 Pisani, R., 127, 208, 433 Plotkin, J., 56, 430 Popper, K. R., 3, 40–41, 45, 440–441 Porter, T., 152, 273, 293, 434 Postman, L., 31, 87, 428, 441 Powell, F. A., 4, 441 Presser, S., 103, 114, 116, 220, 431, 444 Presson, C., 4, 445 Principe, G. F., 17, 441 Pringle, C. D., 57, 430 Ptolemy, 4 Purves, R., 127, 208, 433

Quackenbush, O. F., 111–112, 133, 431 Quirk, T. J., 139, 141, 431

Ragin, C. C., 26, 441 Ramachandran, V. S., 39, 441 Rand Corporation, 204–205, 441 Randhawa, B. S., 11, 441 Raudenbush, S. W., 17, 30, 441 Reed, S. K., 117, 441 Regis, E., 19, 441 Reichenbach, H., 24, 45, 441 Reifschneider, E., 282, 432 Reiss, A. D., 107, 439 Richard, F. D., 282, 441 Richardson, M. W., 130, 437 Riecken, H., 76, 433 Rind, B., xv, 25, 52, 177, 441, 444 Rising, G. R., 227–228, 447 Ritchie, H., 58, 430 Ritchie, M., xv Ritzler, B. A., 133, 444 Roberts, B. W., 96, 435 Roberts, C. W., 82, 441 Roberts, H. V., 203, 447 Roberts, R. M., 30, 441 Robin, H., 28, 441 Robinson, J. P., 144, 441 Rogers, P. L., 84, 442 Rogosa, D., 192, 441 Rokeach, M., 4, 441 Rorschach, H., 101 Rosenbaum, P. R., 152, 183, 441 Rosenberg, M. J., 97, 170, 441 Rosenhan, D. L., 76–77, 91, 441 Rosenthal, M. C., xv, xvi, 36–37,

81, 441 Rosenthal, R., xiii, xvii-xviii, 17, 19,

30–31, 45, 50, 54, 58–59, 84, 130, 132–133, 137–138, 140–141, 156–157, 160, 163–165, 169–171, 189–190, 194, 201, 208, 214,

452 Name Index

IS B

N : 0-536-56436-1

Shaffer, D., 184, 434 Shahid, A., 107, 439 Sharkey, K. J., 133, 444 Shaver, P. R., 144, 441 Shaw, B. F., 39, 429 Shaw, M. E., 111–112, 445 Shea, W. R., 11, 440 Sherman, S. J., 4, 445 Shermer, M., 5, 445 Sidman, M., 138, 445 Sieber, J. E., 17, 50, 65, 445–446 Siegel, S., 163, 393, 409, 445 Sigall, H., 17, 445 Silverman, I., 60, 169, 445 Simon, H. A., 96, 432 Simonton, D. K., 81, 445 Singer, E., 97, 445 Singh, M., 17, 441 Siroky, L. M., 189, 428 Skinner, B. F., 29–30, 45, 186, 432, 445 Skleder, A. A., 177, 444 Slife, B., 66, 445 Slovic, P., 13, 256–257, 436, 445 Smart, R. G., 17, 445 Smelser, N. J., 39, 445 Smeyak, G. P., 115–116, 432 Smith, C., 116, 445 Smith, C. P., 82, 445 Smith, M. B., 50, 445 Smith, M. L., 405, 434 Smith, T. W., 96, 445 Snedecor, G. W., 212, 293,

403–404, 445 Snider, J. G., 107–108, 445 Snyderman, P., 191, 436 Sockloff, A. L., 402, 445 Solomon, J., 134, 438 Solomon, R. L., 87, 166–167, 445–446 Sonneck, G., 185, 446 Sotsky, S. M., 52, 435 Spearman, C., 131, 446 Sperry, R. W., 67, 446 Stanley, B., 50, 446 Stanley, J. C., 88, 128, 136, 161, 163,

180, 186, 192, 430, 446 Steer, R. A., 39, 429 Steering Committee of the Physi-

cians’ Health Study Research Group, 279–280, 446

Steinberg, J., 203, 446 Sterling, T. D., 409, 446 Stern, S. E., 89, 446 Sternberg, R. J., 38, 133, 446 Steuer, J., 86, 446 Stigler, S. M., 250, 446 Stillman, F. A., 187, 434 Stokes-Zoota, J. J., 282, 441 Stone, A. A., 96, 117, 446 Stone, P., 82, 446 Straf, M. L., 405, 447 Street, E., 227, 271, 446

Walker, R., 6, 447 Wall, S., 134, 428 Wallace, J., 87, 431 Wallace, R. L., 5 Wallis, W. A., 203, 447 Waranch, H. R., 187, 434 Wasserman, S., 78, 447 Waters, E., 134, 428 Watts, D. J., 78, 437 Wearing, A. J., 86, 439 Weaver, C., 115, 447 Webb, E. J., 90, 447 Webb, J. T., 102, 433 Webber, R. A., 117, 448 Wechler, J., 10, 448 Wechsler, D., 132 Weick, K. E., 77, 88, 448 Weinberger, D. A., 143, 448 Weiner, B., 11, 448 Weiner, I. B., 101, 448 Weisberg, R. W., 230, 448 Wells, M. G., 17, 435 Werner, H. C., 28, 448 West, M. P., 187, 437 Westen, D., 141, 448 Wheeler, L., 41, 446, 448 White, D. M., 78, 448 White, L., 163, 448 White, T. L., 9, 448 Wickesberg, A. K., 117, 448 Wiggins, J. S., 100, 448 Wilcox, B., 56, 448 Wilcox, R. R., 302, 448 Wilkinson, L., xiv, 234, 448 Willis, G., 97, 448 Wilson, D. B., 405, 438 Wilson, T. D., 97, 439 Wolman, B. B., 39, 448 Wood, J., 41, 448 Wood, J. M., 133, 433 Woodrum, E., 83, 448 Wright, J. D., 116, 444 Wright, J. M., 111–112, 445 Wrightsman, L. S., 144, 441 Wrzesniewski, A., 43, 444 Wundt, W., 8 Wyer, M. M., 117, 432

Yates, F., 400, 433 Yin, R. K., 26, 448 Yonge, C. D., 7, 448

Zajonc, R. F., 28–29, 45, 448 Zechmeister, E. G., 117, 448 Zeger, S. L., 194, 432 Zipf, G. K., 17, 448 Zuckerman, A., 333, 448 Zuckerman, M., 333, 448

Strickland, B. R., 143, 446 Strohmetz, D., 169, 444, 446 Stryker, J., 51, 446 Student, 293, 446 Stuntz, P., xvi Suci, G. L., 107, 440 Suls, J. M., 17, 41, 97, 167, 169, 432,

443–444, 446, 448 Susman, E. J., 52, 432, 447 Swijtink, Z., 152, 273, 293, 434 Symonds, P. M., 105, 447

Tannenbaum, P. H., 107, 440 Tanur, J. M., 220, 227–228, 433, 447 Task Force on Statistical Inference,

xiv, 448 Taylor, M., 97, 447 Taylor, S. J., 77, 447 Thagard, P., 11, 435 Thibaut, J. W., 4, 436 Thiemann, S., 283, 437 Thissen, D., 132, 447 Thomas, C. B., Jr., 97, 447 Thomas, N., 152–153, 444 Thorndike, R. L., 29–30 Thurstone, L. L., 111, 447 Tolchinsky, E., 151, 281, 433 Tolman, E. C., 18, 447 Tourangeau, R., 97, 115, 117, 447–448 Treadway, M., 37, 447 Troisi, J. R., II, xvi Tryfos, P., 215–216, 447 Tufte, E. R., 226, 447 Tukey, J. W., xv, 228, 232, 447 Tukey, P. A., 228, 430 Turk, D. C., 96, 447 Turkkan, J. S., 96, 117, 163, 446–447 Turner, P. A., 31, 433 Tursky, B., 163, 448 Tversky, A., 13, 436, 447 Twain, M., 37

Urbina, S., 139–141, 428

Van de Castle, R. L., 102, 440 Van Hoose, T., 17, 445 Van Ijzendoorn, M. H.,134, 432 VandenBos, G. R., 39, 447 Vhahov, D., 199–200, 430 Vickers, B., 38, 447 Viswesvaran, C., 107, 439 Voight, R. B., 151, 281, 433 Von Thurn, D. R., 97, 445 Voss, C., 203, 439

Wachter, K. W., 405, 447 Wahlgren, D. R., 187, 437 Wainer, H., 226, 131–132, 272, 430,

438, 447 Wake, W. K., 133, 434 Walk, R. D., 44, 434 Walker, C. J., 17, 89, 447

Name Index 453

IS B

N : 0

-5 36

-5 64

36 -1

454

Subject Index (Additional terms are listed in the

Glossary of this book.)

a priori method, 6, 20, 413 A-B-A (and variant) designs,

188–189, 195, 412 Abscissa (x axis), 227, 245, 251,

255, 412 Accidental plagiarism, 68, 412 Accounting for conflicting results,

25, 28–29, 45, 412 Acquiescent response set, 106–107,

120, 412 Active deception (deception by com-

mission), 63–64, 70–71, 412 Activity (as dimension of subjective

meaning), 107, 416 ad hoc hypothesis, 15, 20, 412 Additive model, 328, 346, 412 Aesthetic aspect of science, 10,

20, 412 After-only design, 164–165, 412 Alerting r (ralerting), 333–337,

346, 412 alpha (�), 278, 288, 286, 412 Alternate-form reliability, 126, 129,

146–147, 412 Alternative hypothesis (H1), 274,

277, 288, 412 Analogical thinking, 27–28, 45, 413 Analogies, 10–11, 26 Analysis of variance (ANOVA), 146,

345–346, 413 and effects, 328–329 and F and t, 315–318 one-way, 318–321 summary tables, 321–324,

329–334 two-way, 326–327, 329–333

Ancestry search, 36, 413 Animal research, 66–68 ANOVA. See Analysis of variance Anything goes view of science, 75,

91, 413 APA manual (Publication Manual of

the American Psychological Asso- ciation), 11, 368, 382–385, 370, 413

Archival material, 81–82, 91–92, 413

Area probability sampling, 206–207, 220–221, 413

Arithmetic mean (M), 233–235, 413

Artifact(s), 413

experimenter-related, 170–173 subject-related, 169–170

Aspirin study (as example), 279–281 Asymmetrical distributions, 234–235,

245, 413 Authority, obeying (in

example), 5–6 Autonomy (in research ethics),

50, 413

Back-to-back stem-and-leaf chart, 230, 245, 413

Bar graphs, 227–229, 244–245, 413 Bar workers study (as

example), 189 Beatles rumor (as example), 31 Before-after design, 165, 413 Behavior, 12, 20, 413 Behavioral baseline, 187–188,

195, 413 Behavioral diaries,

117–120, 413 Behavioral science, 12–13,

20, 413 Belmont Report (as example), 51,

71, 413 Beneficence (in research ethics), 51,

70–71, 413 BESD (binomial effect-size

display), 251, 272, 279–283, 288, 301, 308–309, 413

beta (�), 278, 288, 413 Between-conditions, 319–321 Between-subjects (nested) designs,

154–156, 165, 173–174, 293, 316–317, 413

Bias, 413 central tendency, 106, 120 experimenter, 31 nonresponse, 213–216, 221, 420 observer, 77, 420 response (rater), 105, 423 in survey research, 200–203,

220–221 volunteer, 218–221, 426

Big Five factors (OCEAN) (in personality assessment), 100, 119, 413

Bigfoot (as example), 5 Bimodel (scores), 233, 413 Binomial effect-size display (BESD),

251, 272, 279–283, 288, 301, 308–309, 413

Bipolar rating scale items, 107–108, 413

Blind experimenters (as expectancy control), 171, 176, 276, 413

Blocking (in replication), 137 Blood clot risk (in example), 358

Causal inference, 145–146, 413 Causal reasoning, 177–179 Causality, 157–161, 173, 413 Ceiling effect, 106, 120, 413 Central tendency, 231, 233, 413 Central tendency bias, 106,

120, 413 Child Abuse Prevention and

Treatment Act, 58 Children’s book study

(as example), 82 Children’s nutrition study

(as example), 155 Children’s sociability scores

(in example), 307–309 Chi-square (�2), 351–353, 363, 414

and CI (95%), 356–357 and df, 355–357 and null hypothesis, 356 and partitioning of tables,

360, 363 and phi coefficient (φ),

355–356, 363 and reffect size, 356–357 and tables of counts, 352–355,

358–360 values table, 401

Chi-square contingency table, 264 Citron (in example), 7 Classical test theory, 127 Coefficient of determination (r2),

282–283, 414 Cognitive dissonance

(in example), 76 Cognitive heuristics, 13, 414 Cohen’s d, 272, 301–306,

310–311, 414 Coherence (in hypothesis),

45–46, 414 Cohorts (in longitudinal research),

192–194, 414 Coin-flipping (in examples),

272–276 Column effects, 328–333, 346, 414 Combining two effect sizes, 407–408 Comparing two effect sizes, 406–407 Computer statistical programs, 226 Concealed measurement, 88,

90–92, 414 IS B

N : 0-536-56436-1

Concurrent validity, 125, 140, 147, 414

Conditioning experiments (as examples), 29–30

Confidence interval (CI), 211, 221, 239–241, 284–286, 414 95%, 300–301, 356–357

Confidentiality (in research ethics), 58, 70–71, 414

Confirmatory data analysis, 232, 245, 414

Conflicting results, 25, 28–29, 45 Conformity research (as

examples), 232, 245 Confounding (or confounded), 145,

152, 191, 414 Construct validity (and

validation), 39, 124–125, 138, 140–145, 147, 414

Constructs, 39, 45–46, 414 Content analysis, 81–84,

91–92, 414 Content validity, 124–125, 138–139,

147, 414 Contingency table, 224, 414 Continuous variable, 249–250,

265, 414 Contrast (L) score, 339–342 Contrast r (rcontrast), 338, 346, 414 Contrast (λ) weights, 334–335,

340–341, 346 Contrasts, 251, 334–338, 340–341,

346, 414 Contrived observation,

90–92, 414 Control (condition) group, 153–154,

162, 414 Convergent validity, 125, 141,

147, 414 Corrected range, 236, 243 Correlated sample t,

306–309, 311 Correlation coefficient, 249,

265, 414 Correlational research, 15, 20,

190–192, 414 Counterbalancing, 157, 415 Counternull statistic,

410–411, 414 Counts, 264, 352–355,

358–362, 415 Covariation, 45, 159–160,

173–174, 415 Crime reports (in example), 191–192 Criterion validity, 124–125, 138, 140,

147, 415 Critical incident technique, 114,

120, 415 Cronbach’s alpha, 130,

146–147, 415

repeated measures, 155–156, 173–174, 338–345, 423

single-case (small N, N-of-1), 26, 186–189, 195, 424

Solomon, 165–168, 424 time-series, 184–185, 195, 425 2 × 2 factorial, 156, 326, 426 within-subjects (repeated meas-

ures, or crossed), 155–156, 173–174, 426

df (degrees of freedom), 240–241, 297–299, 310–311 321–324, 332–333, 415

Diagonal of indecision, 54 Diaries, behavioral, 117–120 Dichotomous variable, 249–251,

261, 265, 415 Difference (D) score, 165,

256–257, 259 Diffusion of responsibility studies

(as examples), 27 Discovery phase, 24–26,

45–46, 415 Discrete variable, 249–250,

265, 415 Discriminant validity, 125, 141,

147, 415 Distributions, 233–235, 242 Dog bite (in example), 177–178 Double deception, 66, 71, 415 Double-blind procedures, 171,

173, 415 Drives (in example), 43–44 Driving tests studies (in

examples), 87–88 Drug studies (in examples), 151,

162, 180–181 Drunkard’s search, principle of,

18, 415 Dummy coding, 260–265, 415

Eating behavior (in examples), 43–44

Effect size, 144, 147, 416 Effect size estimates, 282, 316 Effect size r (reffect size), 151,

272, 278, 282, 284–286, 288, 299–301, 321, 336, 338, 345–346, 416 in meta-analysis,

406–408, 411 Effective power, 272 Effective sample size, 214,

221, 415 Effects, 30–31, 170–173,

327–333 Efficient causality, 158,

173–174, 416 Electric shocks (in examples),

61–62

Crossed design, 155–156, 173–174, 415

Cross-lagged correlation, 130, 190, 195, 415

Cross-lagged panel design, 190–192, 195, 415

Cross-sectional design, 192–195, 415

Crude range, 236, 245, 415 Cue words, 103, 120, 107–108, 415 Curve ball (in example), 158

Databases, reference, 36–37 Debriefing, 58, 64–66, 71, 415 Deception (and research ethics),

60–64, 415 Decision-plane model, 54, 56, 415 Degrees of freedom (df ), 240–241,

297–299, 310–311, 321–324, 332–333, 415

Demand characteristics, 53–54, 168–170, 173, 415

Dependent variable (Y), 42–46, 144–147, 159–161, 249–259, 262, 415

Descriptive measure, 238–239, 245, 415

Descriptive research, 14, 17, 20 Designs, experimental and quasi-

experimental, A-B-A (and variant), 188–189,

195, 412 after-only, 164–165, 412 before-after, 165, 413 between subjects (nested),

154–156, 165, 173–174, 293, 316–317, 413

cross-lagged panel, 190–192, 195, 415

cross-sectional, 192–195, 415 expectancy control, 161–163,

172–174, 416 factorial, 155–157, 173–174, 416 interrupted time-series, 184–185,

195, 418 Latin square, 157, 173–174, 418 longitudinal, 190, 192–195, 419 mixed factorial, 156, 419 nonequivalent groups, 166,

182–184, 195, 420 nonrandomized, 182–184 one-group pre-post (O-X-O),

164–165, 173–174, 420 one-shot case (X-O), 163–165,

173–174, 420 one-way, 155, 421 preexperimental, 163–164, 422 posttest-only, 164, 421 randomized, 16, 150–153,

173–174, 423

Subject Index 455

IS B

N : 0

-5 36

-5 64

36 -1

Empirical (reasoning), 6–10, 20, 416

Equal-appearing intervals method, 97, 111–113, 120

Equivalence, measure of, 130, 146, 416

Error, random, 126–128, 422 Error, systematic, 126–128, 425 Error of estimate, 212–213,

221, 416 Errors of measurement, 126, 416 Estimated values table, 135–136 Ethics (in research), 50,

70–71, 416 in animal research, 66–68 and confidentiality, 58, 70–71 and debriefing, 58, 64–66, 71 and deception, 60–64, 70–71 guidelines (principles), 49–51,

70–71 and informed consent, 52–54,

70–71 and IRB (Institutional Review

Board), 54–56, 62–63, 71 and justice, 51, 56–57, 70 review questions, 55 and risk-benefit analysis, 54,

70–71 and scientific integrity, 58–59 and trust, 57–58 in writing and reporting, 68–71

Evaluation apprehension, 97, 170, 416

Evaluation (as dimension of subjec- tive meaning), 107, 416

Expectancy, 168–173 Expectancy control design, 161–163,

172–174, 416 Expected frequency ( fe), 352–355,

363, 416 Expedited review (by IRB), 54,

70–71, 416 Experimental analysis of

behavior, 172–173 Experimental (condition) group,

153–154, 162, 416 Experimental designs. See Designs Experimental hypotheses, 40,

46, 416 Experimental research, 15–18,

20, 416 Experimenter expectancy bias, un-

conscious, 31, 416 Experimenter expectancy effect,

30–31, 170–173, 416 Exploratory data analysis, 232,

245, 416 Exploratory research, 24–25, 416 Extended range, 236, 245, 416

Full-text databases, 36, 412, 417

Gallup surveys (in examples), 202–203

Generative (theories), 41 Ghost story (in example), 90 Good researchers

(characteristics of), 18–19 “Good subject,” 169–170, 417 “Good” theories, 40–41 Gossip (in examples), 277–278 GPA (grade point average) (in

example), 140 Grand mean (MG), 328–329, 417 Graphic scales, 104–105, 120, 417 Graphing results, 225–227 Graphs for qualitative data, 78–79 Green Earthies (in examples),

353–356, 358–362 Group mean, 328–329, 337

H0 (null hypothesis), 274–277, 288

H1 (alternative hypothesis), 274, 288

Hair length (in example), 90 Halo effect, 104, 120, 417 Hazardous activities study (as

example), 256–259 Head Start program (in

example), 144 Health and Psychosocial Instruments

(HaPI), 96 Heart study (as example), 178 History (as threat to internal

validity), 167–168, 173–174, 417 Homogeneity of variance,

309–311, 417 Homogeneous/heterogeneous

(population), 202, 212–213 Hyperclaiming, 58–59, 417 Hyperactive children study (as

example), 182 Hypnosis (in examples), 169 Hypotheses-generating heuristics,

26–31, 417 Hypothesis, 40–41, 46, 417

rival, 86–88, 91–92

Improving on older ideas, 29–30, 45, 417

Independent sample t test, 294–295, 299–303, 310–311, 417

Independent variable (X ), 42–46, 144–147, 159–161, 249–250, 262, 417

Infantile autism (as example), 25 Infants (in example), 44

External validity, 125, 136–138, 144, 146–147, 416

F test (ratio), 318, 322–324, 345–346, 417 focused, 333–336 omnibus, 317, 325–326 and p, 324, 337

F values table, 323, 396–400 Face validity, 125, 139,

147, 416 Face-to-face interview, 114–116,

120, 416 Factorial design, 155–157,

173–174, 416 False consensus, 4 Falsifiability (refutability), 41,

45–46, 417 Fcontrast, 336, 416 File drawer problem,

409–410, 417 Final (teleological) causality, 158,

173–174, 417 Finite (sample or population),

239, 417 Fisher zr (transformation),

407–408, 417 Fisher zr (transformation) table,

403–404 5% significance level, 274–275,

278–279 Fixation of beliefs, 4, 20 Fixed-choice items (measures),

99–100, 119, 417 Floor effect, 106, 120, 417 Flying saucers (in example), 5, 76 Fnoncontrast, 336, 416 Focused statistical procedures, 317,

321, 333–336, 346, 417 Food (in examples), 43–44,

353–356, 358–362 Food poisoning study (in examples),

178–179, 262–264 Food tasters (in example), 227–228 Forced-choice scales, 104,

120, 417 Formal causality, 158,

173–174, 417 Foucault’s pendulum

(in example), 8, 10 Framingham Heart Study (as

example), 178 Frequency distribution, 227–228,

244–245, 417 Froehlich’s syndrome (in

example), 43 Frustration-aggression study (as

example), 85–86 Fugitive literature, 36, 417

456 Subject Index

IS B

N : 0-536-56436-1

Subject Index 457

Inferential measure, 238–239, 245, 417

Informed consent, 52–54, 70–71, 418

Innocent or guilty example, 276 Institutional Review Board (IRB),

54–56, 62–63, 71, 418 Instrumental conditioning (in

example), 30 Instrumentation (as threat to internal

validity), 168, 173–174, 418 Intensive case study, 25–26,

45, 418 Interaction effects, 327–329,

345–346, 418 Internal validity, 125, 144–145, 147,

161, 164–168, 173–174, 418 Internal-consistency reliability,

125–126, 130–133, 146–147, 418

Interquartile range, 232–233, 245, 418

Interrupted time-series design, 184–185, 195, 418

Interval estimates, 210–211, 221, 418

Interview items, 113–114 Interview schedule, 115, 120 Interviews, 114–117 Intrinsically repeated measures,

338–342, 418 IQ (in example), 126 Item analysis procedures, 97, 418 Item-to-item (inter-item) reliability

(rii), 125, 130–131, 146–147, 418

Judge (rater) selection, 83–84, 91 Judge-to-judge reliability (r jj),

79, 82, 125–126, 133–136, 146–147, 418

Judges (raters), 82–84, 418 Judgment studies, 79–84,

92, 418 Judgments (as examples), 13 Junk Food Junkies (in examples),

353–356, 358–362 Justice (in research ethics), 51,

56–57, 70 Justification phase, 24–26,

45–46, 418

Kerner Commission, 42 K-R 20, 130, 146–147, 418

L (contrast) scores, 339–342, 344–346

Lambda (�) weights, 155, 174, 418 Latin square design, 157, 173–174,

342–345, 418

Metaphors, 10–11, 25, 419 Method of agreement, 161–162,

173–174, 419 Method of authority, 5–6, 20, 419 Method of difference, 161–162,

173–174, 419 Method of equal-appearing intervals,

97, 111–113, 120, 419 Method of tenacity, 4–5, 20, 419 Methodological pluralism, 13,

20, 419 Methodological research

(examples), 17 Methodological triangulation, 74,

91–92, 419 Microworld simulations, 86, 419 Milk/vitamin study (in examples),

325–333 Mill’s methods, 161–162,

173–174, 419 Minimal risk (in risk-benefit

analysis), 54, 70–71, 419 Mining company (in example), 57 Minnesota Multiphasic Personality

Inventory (MMPI) (in examples), 101–102, 119–120, 132–133, 139, 419

Mixed factorial design, 156, 419 Mode, 233, 245, 420 Moderator variables, 26, 46, 179,

405–406, 420 MScontrast, 335–336, 420 Murder (in example), 27 Music (in example), 108–109 Mutually exclusive (hypotheses),

274, 420

N-of-1 experimental designs, 26, 186–189, 195, 424

National Bureau of Standards (in example), 127

National Football League (NFL) (in example), 96

National Science Foundation, 12 Necessary condition, 162, 174,

179, 420 Negatively skewed distribution, 235,

245, 420 Nested (between-subjects) designs,

154–156, 165, 173–174, 420 Netherlands, religion in

(as example), 193–194 Newton’s prism experiment

(as example), 7–8, 10 “No-show” volunteers,

232–233, 420 Noise (random error), 292–295,

310, 420 Noise spread, 36

Lazy writing, 70–71, 418 Leading questions, 113, 418 Leaning Tower of Pisa experiment

(as example), 7, 10 Leniency bias, 105–106, 120, 418 Lie (L) Scale, 107, 418 Likert Scale, 108–111, 120,

133, 418 Line graphs, 228–229, 244–245, 418 Line length experiment (as

example), 9–10 Linearity, 249, 255, 265, 418 Literary Digest case (in

example), 208–210 Literature search, 32, 36–39,

45, 418 Logical error in rating, 106,

120, 419 Longitudinal study, 190, 195,

192–194, 419 Lost letter technique (in

examples), 89 Lottery (in ethics example), 57 Lottery for draft (as

example), 204

M (mean), 233–235, 245, 419 Main effect, 327–329, 345–346, 419 Managerial styles (in

examples), 283 Mann-Whitney test, 409 Margin of error, 209–210, 419 Marlowe Crowne Social Desirability

Scale (MCSD) (in examples), 142–144, 419

Material causality, 158, 173–174, 419

Mathematics Club (in example), 359–362

Maturation (as threat to internal validity), 168, 173–174, 419

Mean (M), 233–235, 245, 419 Mean square (S2 or MS), 236–237,

245, 318, 346, 419 Measures

fixed-choice, 99–100, 119, 417 open-ended, 99, 119–120, 421 self-report, 95–98, 119–120, 424 standardized, 95–96, 120, 424

Median (Mdn), 231–233, 235, 419 Median split, 251 Memory (in example), 98 Meta-analysis, 26, 282,

405–406, 419 and comparing two effect sizes,

406–407 and combining two effect sizes,

407–408 and significance level, 408

IS B

N : 0

-5 36

-5 64

36 -1

Nonequivalent-groups designs, 166, 182–184, 195, 420

Nonintrinsically repeated measures, 342–345, 420

Nonlinearity, 255, 265, 420 Nonmaleficence (in research ethics),

51, 70–71, 420 Nonrandomized designs,

182–184 Nonreactive observation, 88–89,

91–92, 420 Nonresponse bias, 213–216,

221, 420 Nonsense syllables (in

examples), 26 Nonverbal research (as

example), 84 Nonvolunteers, 216–217 Normal distribution, 241–242,

245, 420 Norm-referenced values, 98,

120, 420 Null hypothesis (H0), 274–277,

288, 420 Null hypothesis significance testing

(NHST), 271–277, 288, 420 Numerical scales, 103, 120, 420 Nutrition study (as example), 155

Obedience studies (as examples), 60–66

Observer bias, 77, 91–92, 420 Observation

participant, 76, 91–92, 421 reactive and nonreactive, 88–89,

91–92, 420 secondary, 82, 91–92, 424 systematic, 74–76, 90–92, 425 unobtrusive, 89–92, 426

Observed frequency ( fo), 352–355, 363, 420

Occam’s razor, 41, 45–46, 420 OCEAN (Big Five factors in person-

ality assessment), 100, 119 Omnibus F, 317, 337 Omnibus statistical procedures, 321,

345–346, 420 One sample t test, 306–309,

311, 420 One-group pre-post design

(O-X-O), 164, 173–174, 420 One-shot case study (X-O),

163–164, 173–174, 420 One-tailed p value, 243, 277–278,

288, 394–395, 420 One-way design, 155, 421 Open-ended items (measures), 99,

119–120, 421 Operant conditioning (in

example), 30

Positively skewed distribution, 235, 245, 421

Posters (as research presentation), 387–388

Posttest-only (after-only) design, 164, 421

Potency (as dimension of subjective meaning), 107, 416

Power analysis, 272, 283–284, 288, 421

Power (1-�), 283–284, 288, 421 Predictive validity, 125, 140,

147, 422 Preexperimental designs,

163–164, 422 prep, 286–288, 421 Presidential elections polling

(in examples), 208–210 Pretest, 164–167, 422 Prism experiment (as example),

7–8, 10 Probabilities, 273–274, 287, 298, 422 Probability sampling, 199,

220–221, 422 Product-moment correlation,

249–251, 253–255, 337, 422 Projective tests (as examples),

100–102, 120, 422 Propensity scores, 183, 195, 422 Proportion of variation explained,

282–283, 422 Prospective data, 178,

194–195, 422 Pseudo patients (in example), 76–77 PsycARTICLES, 37, 422 Psycholinguistics (in example), 17 Psychology Club (in example),

358–362 PsycINFO, 36, 422 Publication Manual of the Ameri-

can Psychological Association, 11, 368, 382–385, 370, 413

Publishing experimental results, 11–12

Push polls, 210, 221, 422

Qualitative research, 75–76, 92, 422 graphing, 78–79

Quantitative research, 75–76, 92, 422

Quasi-control subjects, 170, 174, 422

Quasi-experimental research, 180, 194–195, 422

Queens, NY murder (as example), 27

Questions ethics review, 55 fixed-choice, 99–100, 120

Operational definitions, 38, 45–46, 421

Opportunity samples, 198–199, 220–221, 421

Ordinate ( y axis), 227, 245, 251, 255, 421

Outliers, 235, 245, 421

p values, 59, 147, 271–272, 274, 287–288, 297–299, 324, 422

Paired t test, 306–309, 311, 421 Paradoxical incident, 25–27,

45, 421 Parsimony (in hypothesis), 41,

45–46, 421 Partial concealment, 88,

91–92, 421 Partial correlation, 251 Participant observation, 76,

91–92, 421 Partitioning of tables, 360,

363, 421 Passive deception (deception by

omission), 63–64, 421 Payoff potential, 41, 421 Peach growers (in example),

214–215 Pearson r correlation coefficient,

128–130, 249–255, 421 Pendulum experiment (as example),

8, 10 Percentiles, 231–233, 245, 421 Perceptibility, 10–11, 20 Personality tests (as examples),

100–102 phi coefficient ( ), 250, 262–265,

355–356, 363, 421 Physical traces, 90–92, 421 Picture books study (as

example), 82 Pigeon study (as example), 186 Pilot test(ing), 84, 421 Placebo, 52, 56, 71, 151, 421 Placebo control group,

162, 421 Placebo effects, 163, 421 Plagiarism, 68–71, 421 Plausible rival hypotheses, 125, 145,

147, 421 Point estimates, 210–211, 221,

215, 421 Point-biserial correlation (rpb), 250,

260–261, 265, 421 Polio vaccine (in example),

151–152 Political leaflets (in example), 89 Polling and pollsters (in

examples), 202–203, 208–210 Population (in survey sampling),

199, 212–215, 220–221,421

458 Subject Index

IS B

N : 0-536-56436-1

Subject Index 459

leading, 113 open-ended, 99, 120 preparing, 113–114

Quota sampling, 209, 221

r (Pearson correlation coefficient), 128–130, 253–255, 421

r values table, 402 r2 (coefficient of determination),

282–283, 414 Radio buttons (in example), 90 ralerting, 336–337, 346 Random assignment (randomiza-

tion), 16, 20, 151, 153–154, 173–174, 200, 422

Random digit dialing, 200, 204, 422 Random digits table, 204–206 Random error, 126–128,

146–147, 422 Random sampling procedure, 16,

20, 423 Random selection, 200, 423 Randomized experiments (trials), 16,

150–153, 173–174, 423 Randomness, 203 Range, 236, 245, 423 Rank ordering, 259–260 Rapper (in example), 229–230 Rat studies (in example),

171–173 Rating errors, 105–107, 120, 423 Rating scales, 103–105, 120, 423 Raw (observed) scores, 126–127, 146,

259–260, 262, 423 rcontrast, 338, 346 rcounternull, 410–411 Reactive observation, 88 reffect size (effect size r), 151, 272,

278, 282, 284–286, 288, 299–301, 321, 336, 338, 345–346, 416 in meta-analysis, 406–408, 411

Reference materials, 38–39 Relational (correlational) research,

15–18, 20, 423 Reliability, 125–126, 146–147, 423

alternate-form, 125, 129, 146–147, 412

of components, 125, 130–132, 146–147, 423

internal consistency, 125–126, 130–133, 146–147, 418

item-to-item (rii), 125, 130–131, 146–147, 418

judge-to-judge (rjj), 125–126, 133–136, 146–147, 418

test-retest, 125, 128–129, 132–133, 146–147, 425

Religion in Netherlands study (as example), 193–194

Sampling with (or without) replace- ment, 206, 220–221, 423

SAT (Scholastic Assessment Test) (in examples), 132, 242–244, 250

Satterthwaite’s method, 309, 311, 423

Scatter plot (diagram), 251–253, 265, 423

Schumann, Robert, study (in examples), 230–231

Scientific integrity (in research ethics), 58–59

Scientific method, 1–6, 20, 423 Secondary observation, 82, 424 Selection (as threat to internal

validity), 168, 173–174, 424 Self-fulfilling prophecy, 14, 16–17,

30, 424 Self-report measures, 95–98,

119–120, 424 Semantic differential method, 96,

107–109, 120, 424 Seminal theories, 41, 424 September 11 (in examples),

200, 210 Serendipity, 25, 30–31, 45–46, 424 Signal spread, 316 Signal-to-noise ratio, 292–295,

310, 424 Significance level, 274, 278–279,

288, 408, 424 Significance test = Size of effect

Size of study, 305–306, 310–311, 357, 424

Simple effects, 327, 345–346, 424 Simple observation, 90–92, 424 Simple random sampling,

203–206, 424 Single-case (small-N, N-of-1)

experimental research, 26, 186–189, 195, 424

60 Minutes (in example), 60 Size of study, 305–306,

310–311, 424 Small groups research (as example),

79–81 Small-N experimental design,

186–189, 195, 424 Smallpox vaccination (in

example), 27–28 Smokers study (as example),

183–184 Smoking attitudes (as

example), 84–85 Social Network Analysis (SNA),

78–79, 91–92, 424 Sociability study (as example),

307–309 Social psychology of the

experiment, 169–173, 424

Repeated-measures design, 155–156, 173–174, 338–345, 423

Replication, 25–26, 137–138, 147, 423

Reports. See Research reports Representative (sample), 199,

220–221, 423 requivalent, 409 Research methods, study of,

1–3, 19 Research proposal, 32–35, 45, 423 Research reports, 368–370,

382–387 and posters, 387–391 sample, 25, 371–381 sections described, 370,

382–385 Researchers (good), 18–19 Residual (leftover) effects, 327,

346, 423 Response (rater) bias, 105, 423 Retrospective data, 178–179,

194–195, 423 Rhetoric of justification, 11–12,

20, 423 rii (item-to-item reliability), 125,

130–131, 146–147, 418 Risk-benefit analysis, 54,

70–71, 423 Rival interpretations (hypotheses),

86–88, 91–92, 423 rjj (judge-to-judge reliability),

125–126, 133–136, 146–147, 418 “Robbie” study (as example),

187–189 Root mean square, 237–239, 245 Rorschach test (in examples), 100,

119–120, 132–133, 423 Row effects, 328–333, 346, 423 rpb (point-biserial correlation), 250,

260–261, 265, 421 R SB (Spearman-Brown formula),

130–132, 134–136, 146–147, 424 Royal Air Force (in example), 65 Royal families (in example), 8 Rumor (in examples), 17, 31 Rushton study (as example),

57, 423

S2 (mean square), 236–237, 245 Salk vaccine (in examples),

151–152, 281–283 Sam Stone experiment (as

example), 9, 14 Sample, representative, 199,

220–221 Sample research report, 371–381 Sampling plan, 199,

220–221, 423 Sampling units, 152, 423

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Socialized medicine scale (as example), 109–111

Socially desirable responding, 142–144, 424

Solomon design, 165–168, 424 Spearman-Brown formula (RSB),

130–132, 134–136, 146–147, 424

Spearman rho (rs), 250, 255–260, 265, 424

Spelling study (as example), 164–167

Spiral (as analogy), 28 Stability, 424

measure of, 130, 146 in sampling plan, 200–202,

220–221 Standard deviation (σ), 237–238,

301–302, 245, 424 Standard normal curve, 242,

245, 424 Standard score (z score), 242–245,

262–263, 424 Standardized measures, 95–96,

120, 424 Standardizing the margins,

360–363, 424 Statistical conclusion validity, 125,

144–145, 147, 424 Stem-and-leaf charts, 228–230,

233, 425 Stratified random sampling, 206–207,

211–213, 220–221, 425 Student learning (in example),

15–17 Student’s t, 293, 425 Subclassification on propensity

scores, 183, 425 Sufficient condition, 161, 174,

179, 425 Suicide studies (as examples),

184–185, 228 � (sum), 234 Sum of squares (SS), 319–321,

346, 425 Summated ratings method, 108–111,

120, 425 Summary tables (ANOVA), 321–324,

329–334 “Survival of the fittest” (in

example), 3 Symmetrical distributions, 234–235,

245, 425 Synchronous correlations, 190,

195, 425 Syphilis study (as example), 51 Systematic error, 126–128,

147–147, 425 Systematic observational

research, 74–76, 90–92, 425

Trimmed mean, 235, 245, 426 Trust (in research ethics), 57–58 Tuskegee Study (as example),

51, 426 2 2 factorial design, 156,

326, 426 2 2 table of frequencies

(counts), 264 Two-tailed p value, 277–279, 288,

394–395, 426 Two-way ANOVA, 326–327 Type I error, 274–277, 288, 426 Type II error, 274–277, 288, 426

UFOs (in example), 5 Unbiased estimator of the popula-

tion value of σ2 (S2), 239, 245, 295–297, 426

Unbiased sampling plan, 200–203, 212, 221, 426

Unconscious experimenter bias, 31 Unobtrusive observation,

89–92, 426

Validity, 124–125, 146–147, 426 concurrent, 125, 140, 147, 414 construct, 124–125, 138, 140–145,

147, 414 content, 124–125, 138–139,

147, 414 convergent, 125, 141, 147, 414 criterion, 124–125, 138, 140,

147, 415 discriminant, 125, 141, 147, 415 external, 125, 136–138, 144,

146–147, 416 face, 125, 139, 147, 416 internal, 125, 144–145,

147, 418 predictive, 125, 140, 147, 422 statistical conclusion, 125,

144–145, 147, 424 threats to, 167–168, 173–174

Values studies (as examples), 86–88

Variable(s), 42–46, 249–250, 253–255 defining, 38–40

Variance (mean square), 236–237, 245, 426

Velcro (in example), 30 Visual cliff study (as example), 44 Visual displays criteria, 226, 244 Visualization, 10, 426 Vitamin studies (as examples), 301,

325–333 Volunteer bias, 218–221, 426 Volunteer subjects, characteristics of,

216–218, 232–233

t distributions, 297–299, 310–311, 425 t test, 426

and confidence intervals, 300–301

and df (degrees of freedom), 297–299, 310–311

focused, 333–336 and homogeneity of variance,

309–311 and independent sample,

299–303, 305–306, 310–311 one-sample, 306–309, 311, 420 and p values, 297–299 paired, 306–309 and r effect size, 299–301 and signal-to-noise ratio,

292–295 values, 298

t values table, 394–395 Table of counts, 264, 352–355,

358–362 Target population, 15, 425 tcontrast, 334–335, 425 Teachers’ expectations (in example),

16–17 Telephone interviews, 116–117,

120, 425 Temporal precedence, 159–161,

173–174, 425 Temporal stability

(dependability), 129 TESS (Time-Sharing Experiments for

the Social Sciences), 12–13 Test-retest correlations, 190,

195, 425 Test-retest reliability, 125, 128–129,

132–133, 146–147, 425 Tests of simple effects, 325–326,

346, 425 Thematic Apperception Test (TAT),

101, 119–120, 133, 425 Theoretical definitions, 38, 46, 425 Theoretical ecumenism, 13, 20, 425 Theories, 40–41, 45–46, 425 Third variable problem, 180–181,

194–195, 252, 425 Three Faces of Eve (in

example), 102 Three Rs principle in animal

research, 67, 71, 425 Thurstone Scales, 111–113, 120,

133, 425 Time-series designs, 184–185,

195, 425 Tolerance values, 410 Transformation (of scores), 242,

245, 426 Translation (and back-translation),

78–79, 426 Treatment condition, 152, 426

460 Subject Index

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Wait-list control group, 182, 195, 426 War attitude scale (as example),

111–113 Wechsler Adult Intelligence Scale

(WAIS) (in examples), 132–133, 426 Weights and measures (in

examples), 127–128 Widget workers (in examples),

211–213, 221 Wine taster (as example), 202 Within-conditions, 319–321 Within-subjects designs, 155–156,

173–174, 426

Y (as effect), 179–180, 252 y axis (ordinate), 227, 245,

250–251, 427 Y (dependent variable), 42–46,

144–147, 159–161, 249–259, 262, 415

Yea-sayers, 106, 427

z score (standard score), 242–245, 262–263, 427

z values table, 393 zr, Fisher (transformation),

403–404, 417

Word identification study (as example), 87

Working (experimental) hypotheses, 40–41, 45–46, 426

World end belief study (in example), 76

x axis (abscissa), 227, 245, 250–251, 427

X (as cause), 179–180, 252 X (independent variable), 42–46,

144–147, 159–161, 249–250, 262, 417

Subject Index 461

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C H A P T E R 1 4 (pp. 319–322) Sums of squares (SS) and degrees of freedom (df) in ANOVA:

C H A P T E R 1 4 (p. 322) Mean squares (MS) and F ratio in one-way ANOVA:

C H A P T E R 1 4 (pp. 330–332) Row, column, and interaction SS and df in two-way ANOVA:

C H A P T E R 1 4 (p. 333) Mean squares (MS) and F ratios in two-way ANOVA:

C H A P T E R 1 4 (pp. 334–335) Contrast t on more than two independent groups:

C H A P T E R 1 4 (p. 336) reffect size from contrast F on more than two independent groups:

reffect size � rY� � B Fcontrast

Fcontrast � Fnoncontrast (dfnoncontrast) � dfwithin

BMS within aa �2n b Finteraction �

MSinteraction MS within

MS interaction � Interaction SS

dfinteraction

Fcolumns � MScolumns MS within

MScolumns � Column SS

dfcolumns

Frows � MS rows MS within

MS rows � Row SS

dfrows

Interaction SS � Total SS � (Row SS � Column SS � Within SS) dfinteraction � (r � 1)(c � 1)

F � MS between MS within

MS within � Within SS

dfwithin MS between �

Between SS

df between

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C H A P T E R 1 4 (p. 338) rcontrast from contrast F on more than two independent groups:

C H A P T E R 1 4 (p. 341) Contrast t on more than two repeated measures:

C H A P T E R 1 5 (pp. 353–354) Chi-square (�2) and expected frequency (fe):

C H A P T E R 1 5 (pp. 354–355) 1-df �2 computed directly from 2 2 table of counts:

C H A P T E R 1 5 (p. 357) Effect size r (phi) for 1-df chi-square:

A P P E N D I X C (pp. 406–407) Comparing two independent effect size correlations:

A P P E N D I X C (pp. 406–407) Combining two independent effect size correlations:

A P P E N D I X C (p. 408) Combining two significance (p) levels:

A P P E N D I X C (p. 411) Counternull value of r:

rcounternull � B 4r 2

1 � 3r 2

Combined z � z1 � z2

zr � zr1 � zr2

z of difference � zr1 � zr2

B 1

N1 � 3 �

N2 � 3

reffect size � � B � (1)

�(1) 2 �

N(BC � AD)2

(A � B)(C � D)(A � C )(B � D)

� 2 � a ( fo � fe)

fe fe �

(Column total)(Row total)

Grand total

t(df ) � ML

Ba 1NbS L2 rcontrast � rY�.NC � B

Fcontrast Fcontrast � dfwithin

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