Introduction to Behavioral Research Methods [with Research Navigator] Quotes

“make them progress more smoothly. As you write, be sure that the transitions between one idea and another are clear. If you move from one idea to another too abruptly, the reader may miss the connection between them and lose your train of thought. Pay particular attention to the transitions from one paragraph to another. Often, you’ll need to write transition sentences that explicitly lead the reader from one paragraph to the next. Clarity Perhaps the fundamental requirement of scientific writing is clarity. Unlike some forms of fiction in which vagueness enhances the reader’s experience, the goal of scientific writing is to communicate information. It is essential, then, that the information is conveyed in a clear, articulate, and unclouded manner. This is a very difficult task, however. You don’t have to read many articles published in scientific journals to know that not all scientific writers express themselves clearly. Often writers find it difficult to step outside themselves and imagine how readers will interpret their words. Even so, clarity must be a writer’s first and foremost goal. Two primary factors contribute to the clarity of one’s writing: sentence construction and word choice. SENTENCE CONSTRUCTION. The best way to enhance the clarity of your writing is to pay close attention to how you construct your sentences; awkwardly constructed sentences distract and confuse the reader. First, state your ideas in the most explicit and straightforward manner possible. One way to do this is to avoid the passive voice. For example, compare the following sentences: The participants were told by the experimenter to press the button when they were finished (passive voice). The experimenter told the participants to press the button when they finished (active voice). I think you can see that the second sentence, which is written in the active voice, is the better of the two. Second, avoid overly complicated sentences. Be economical in the phrases you use. For example, the sentence, “There were several different participants who had not previously been told what their IQ scores were,” is terribly convoluted. It can be streamlined to, “Several participants did not know their IQ scores.” (In a moment, I’ll share with you one method I use to identify wordy and awkwardly constructed sentences in my own writing.) WORD CHOICE. A second way to enhance the clarity of one’s writing is to choose one’s words carefully. Choose words that convey precisely the idea you wish to express. “Say what you mean and mean what you say” is the scientific writer’s dictum. In everyday language, we often use words in ways that are discrepant from their dictionary definition. For example, we tend to use theory and hypothesis interchangeably in everyday language, but they mean different things to researchers. Similarly, people talk informally about seeing a therapist or counselor, but psychologists draw a distinction between therapists and counselors. Can you identify the problem in this”
― Mark R. Leary, Introduction to Behavioral Research Methods

“quasi-experimental”
― Mark R. Leary, Introduction to Behavioral Research Methods

“When”
― Mark R. Leary, Introduction to Behavioral Research Methods

“Increasing the Reliability
of Observational Methods
To be useful, observational”
― Mark R. Leary, Introduction to Behavioral Research Methods [with Research Navigator]

“Question”
― Mark R. Leary, Introduction to Behavioral Research Methods

“Numbers”
― Mark R. Leary, Introduction to Behavioral Research Methods

“In Depth
Types of Effect Size Indicators
Researchers use several different statistics to indicate effect size depending on the nature of their data. Roughly
speaking, these effect size statistics fall into three broad categories. Some effect size indices, sometimes called dbased effect sizes, are based on the size of the difference between the means of two groups, such as the difference between the average scores of men and women on some measure or the differences in the average scores
that participants obtained in two experimental conditions. The larger the difference between the means, relative
to the total variability of the data, the stronger the effect and the larger the effect size statistic.
The r-based effect size indices are based on the size of the correlation between two variables. The larger the
correlation, the more strongly two variables are related and the more of the total variance in one variable is systematic variance related to the other variable.
A third category of effect sizes index involves the odds-ratio, which tells us the ratio of the odds of an
event occurring in one group to the odds of the event occurring in another group. If the event is equally likely in
both groups, the odds ratio is 1.0. An odds ratio greater than 1.0 shows that the odds of the event is greater in
one group than in another, and the larger the odds ratio, the stronger the effect. The odds ratio is used when the
variable being measured has only two levels. For example, imagine doing research in which first-year students in
college are either assigned to attend a special course on how to study or not assigned to attend the study skills
course, and we wish to know whether the course reduces the likelihood that students will drop out of college.
We could use the odds ratio to see how much of an effect the course had on the odds of students dropping out.
You do not need to understand the statistical differences among these effect size indices, but you will
find it useful in reading journal articles to know what some of the most commonly used effect sizes are called.
These are all ways of expressing how strongly variables are related to one another—that is, the effect size.
Symbol Name
d Cohen’s d
g Hedge’s g
h
2 eta squared
v
2
omega squared
r or r
2 correlation effect size
OR odds ratio
The strength of the relationships between
variables varies a great deal across studies. In some
studies, as little as 1% of the total variance may be
systematic variance, whereas in other contexts,
the proportion of the total variance that is systematic
variance may be quite large,”
― Mark R. Leary, Introduction to Behavioral Research Methods

“that is empirical, systematic, and publicly
verifiable. This does not necessarily imply that angels
do not exist or that the question is unimportant. It
simply means that this question is beyond the scope of
scientific investigation.
In Depth
Science and Pseudoscience
The results of scientific investigations are not always correct, but because researchers abide by the criteria of systematic empiricism, public verification, and solvable problems, scientific findings are the most trustworthy source
of knowledge that we have. Unfortunately, not all research findings that appear to be scientific actually are, but
people sometimes have trouble telling the difference. The term pseudoscience refers to claims of evidence that
masquerade as science but in fact violate the basic criteria of scientific investigation that we just discussed (Radner
& Radner, 1982).
NONSYSTEMATIC AND NONEMPIRICAL EVIDENCE
As we have seen, scientists rely on systematic observation. Pseudoscientific”
― Mark R. Leary, Introduction to Behavioral Research Methods

“fact that participants in different conditions receive
different levels of the independent variable, all participants in the various experimental conditions
should be treated in precisely the same way. The
only thing that may differ between the conditions is
the independent variable. Only when this is so can
we conclude that changes in the dependent variable
were caused by manipulation of the independent
variable.”
― Mark R. Leary, Introduction to Behavioral Research Methods

“Scientific Approach 7
Systematic Empiricism 7
Public Verification 7
Solvable Problems 8
The Scientist’s Two Jobs:”
― Mark R. Leary, Introduction to Behavioral Research Methods