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Myers 1 research methods review Guide
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An Exercise in STATISTICS
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Steps in Conducting Research
Formulating a "testable" hypothesis
Designing the study
Collecting the data
Analyze the data and draw conclusions
But we don't stop there . . . Reporting the findings
Formulating a "testable" hypothesis
What is a TESTABLE hypothesis? A prediction that can be precisely measured.
To do this we must identify the variables we wish to study and clearly define how we are going to measure or
control them. This is what is called an OPERATIONAL DEFINITION.
Exercise1 : Can science assess this question?
Read each question below and decide whether you think science could answer this question. You need to
consider whether science can measure the important variables in each question.
1. Is the content of dreams the result of unconscious motives and desires?
2. Is there any relationship between birth order and personality?
3. Do college students consume more pizza than any other group?
4. Can humans be innately evil?
5. Do sales of pain relievers increase during periods of economic crisis?
6. Do animals dream?
Designing the study
There are several research methods to choose from. The choice is greatly influenced by the nature of the
research question. For example, if you are simply interested in whether certain groups of people endorse a
particular attitude a survey would be the most efficient method.
Once a method of study has been chosen, concerns such as how will it be conducted and who will be the
subjects (and how will you obtain the subjects) need to be worked out.
Collecting the data
There are several techniques used to collect data. With our survey we could decide whether we want to
"interview" our subjects or will they complete a pencil and paper "questionnaire." If we want to assess
whether caffeine intake makes people more jittery and anxious we might use a physiological measure such as
heart rate to assess anxiety. As you can see the nature of the topic and the design being used influences the
choice of data collection.
Analyzing the data and drawing conclusions
Researchers use statistics to help them to "organize, summarize, and interpret numerical data" (Weiten,
1998, p. 53). With the use of statistics researchers can assess whether their predictions (hypotheses) were
supported or were not supported by the data gathered.
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Reporting the findings
Researchers share their findings with other scientists and with the general public. This serves two purposes.
First, it informs others about what was found. Second, it allows others to comment on the research. Were its
methods sound? Did its conclusions go beyond the established facts? What new questions does it raise for
science? How might we use this information to better our lives?
Typically, researchers prepare a written report that is submitted to a journal in the appropriate area of
research. For example, if our survey examined the attitudes of mothers and fathers on child care issues, then
a journal in Developmental Psychology would be more appropriate than one in Clinical Psychology. Many
journals that publish scientific research in psychology (as in most other disciplines) are "refereed journals."
This means that before the report is published experts in the area review the study. They consider the
appropriateness of the article for the particular journal; the importance of the issue; whether there are flaws
in the study's design, or analysis.
Experimental Designs: Independent & Dependent Variables
Example 1 Dr. Imanut wants to examine whether a new drug increases the maze running performance of
older rats. Just like aging humans, older rats show signs of poorer memory for new things. Dr. Imanut
teaches two groups of older rats to find a piece of tasty rat chow in the maze. One group of rats is given the
new drug while they are learning the maze. The second group is not given the drug. One week after having
learned the maze he retests the rats and records how long it takes them to find the rat chow.
What is the independent variable? Hint: What did the researcher manipulate (allow to vary) in this study?
a) age of the rats.
b) type of maze.
c) length of time it took the rats to run the maze.
d) presence or absence of the new drug.
What is the dependent variable? Hint: What was the measure of the research subjects' responses?
a) age of the rats.
b) type of maze.
c) length of time it took the rats to run the maze.
d) presence or absence of the new drug.
Example 2 A researcher wanted to study the effects of sleep deprivation on physical coordination. The
researcher selected 25 year-old male college students and deprived some of the subjects to either 24, 36, or
45 hours of sleep.
In the present study the independent variable was:
a) the length of time the subjects were deprived of sleep.
b) the age of the subjects.
c) the gender of the subjects.
d) the physical coordination skills of the subjects.
In the present study the dependent variable was:
a) the length of time the subjects were deprived of sleep.
b) the age of the subjects.
c) the gender of the subjects.
d) the physical coordination skills of the subjects.
Example 3 A researcher wanted to know whether the number of people present would influence subjects'
judgments on a simple perceptual task. In each case the other members of the group gave an incorrect
answer. The researcher then noted whether the subject conformed to the group decision.
In the present study the independent variable was:
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a) the number of people in the group.
b) whether the group members gave the correct or incorrect answer.
c) whether the subjects conformed with the group.
d) the type of perceptual task.
In the present study the dependent variable was:
a) the number of people in the group.
b) whether the group members gave the correct or incorrect answer.
c) whether the subjects conformed with the group.
d) the type of perceptual task.
Example 4 An investigator had 60 subjects watch a videotaped re-enactment of a bank robbery. Half of the
subjects were asked by a police investigator to recall the event, while the remaining subjects were
interviewed by a police investigator while they were hypnotized.
In the present study the independent variable was:
a) whether a police investigator was used.
b) whether subjects were hypnotized.
c) how much subjects recalled.
d) what subjects watched.
In the present study the dependent variable was:
a) whether a police investigator was used.
b) whether subjects were hypnotized.
c) how much subjects recalled.
d) what subjects watched.
Control & Experimental Groups
In an experiment, researchers are typically concerned about the performance of subjects in the experimental
group. If a researcher wants to know if a new drug helps improve memory, the researcher is most interested
in the how people who are given the drug perform on the memory test. However, in order to conclude that
the drug "improves" memory, people who take it must perform better than those who do not take the drug.
The CONTROL GROUP serves as the BASELINE performance. The group given the drug serves as the
EXPERIMENTAL GROUP.
Confounding/Extraneous Variables
In order to isolate the effect of the independent variable on the dependent variable, researchers must rule out
alternative explanations. In other words, only the independent variable can be allowed to vary.
The term CONFOUNDING/EXTRANEOUS VARIABLE is used to refer to any other factor that might affect the
dependent variable.
Try the following exercise to see if you can spot potential problems in these hypothetical research studies.
EXAMPLE 1 A researcher wanted to assess whether mood influenced people's memory. The researcher
hypothesized that positive moods would lead to greater memory performance than would a negative mood
state. On Monday the researcher had 50 subjects learn a list of nonsense syllables and then watch a very
humorous comedy film. Their recall of the list of syllables was then assessed. On Tuesday the researcher had
a second group of 50 subjects learn the same list of nonsense syllables and then watch an upsetting
documentary on World War II. Their recall of the list was then assessed after having watched the film.
EXAMPLE 2 A researcher wanted to see whether a new way of teaching English was superior to a more
traditional approach. The researcher selected two Thursday night classes at a local community college. In
one class the instructor used a traditional method, the second instructor used the newer approach. The
researcher then assessed students language ability after they had completed the program.
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How Researchers Control Sources of Error
To control for potential extraneous variables and other sources of error researchers use:
 A standardized set of procedures
 Equivalent Control and Experimental Groups
Standardized procedures means that subjects are treated the same way in all regards except for the
independent variable(s). Researchers also need to ensure that the control and experimental groups are
similar on important variables at the outset. To do this researchers can use one of three methods.
 Use the same subjects in both the control and experimental groups. (This is called a repeated
measures design).
 Match subjects on important variables (e.g., for every 20 year old female in the control group there
is a 20 year old female in the experimental group).
 Random assignment. (Let chance decide who gets placed into which group. Thus, each subject
has an equal chance of being placed in either group).
Why three methods?
Sometimes we cannot use the same subjects in both the control and experimental groups. Sometimes after
having been in one of the conditions it alters the subjects' behavior. This change may carry over to the next
condition and thus serve as an extraneous variable.
For example, a researcher wants to study whether a new drug is better than an old drug to reduce anxiety
symptoms. If we gave the old drug to the subjects and assessed them and then gave the new drug, there
might be carry-over effects from the old drug still. Thus, we might want to use two different groups of people
who suffer from anxiety.
We could match our subjects on important variables such as age, gender, severity of symptoms. Thus, for
every 40 year old male with mild symptoms in the old drug (control) group there is a similar subject in the new
drug (experimental) group. However, in finding perfect matches for our subjects we might have to go
through many people. This is not very resource efficient. In order to find 50 people who are perfect matches
for another group of 50 we might have to go through a few hundred potenital subjects.
A more simple method is random assignment. We let chance determine who is in the control and
experimental groups. With a large enough sample of subjects it is highly unlikely that the majority of people
with severe symptoms would be in one group.
Advantages & Disadvantages of the Experiment
The advantage of the experimental approach is that it allows investigators enough control to examine cause
and effect relationships. Experiments allow us to answer "what causes something to occur?" This is the
second goal of science, understanding and prediction.
However, this degree of control can also be a potential weakness for experiments. By controlling features of
the environments of subjects the researcher may create too artificial an environment. This means that while
the researcher may have accurately understood the "cause" of the subjects' behavior, the findings only apply
under such rigid, non-real world conditions, to have limited use in explaining real-world behavior. (The desire
of psychology is to understand this real-world behavior too).
A second weakness for experiments is that some questions for ethical or technical reasons cannot be studied
using an experiment. An important question is whether people who have had less optimal rearing
experiences, such as poverty or abuse, continue to have difficulties in their adult years because of this poor
rearing. Yet, we cannot place children in abusive environments just to see if it "causes" damage that persists
into adulthood. Thus, we use other research methods, such as correlational studies. We might see whether
there is a relationship between childhood poverty or abuse and psychological and behavioral problems of
adults, by asking adults about their childhood experiences and their life as an adult.
Descriptive & Correlational Designs
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These designs allow us to fulfill the first goal of science, and to isolate possible causes for experiments to then
assess. Remember only experiments can assess cause and effect. No matter how convincing data from
descriptive and correlational studies may sound, because they have less control over the variables and the
environments that they study, non-experimental designs cannot rule out extraneous variables as the cause of
what is being observed.
There are many types of non-experimental methods. We will focus on three approaches:
Case Study
Naturalistic Observation
Survey
CASE STUDIES involve in-depth examination of a single person or a few people. This approach is frequently
employed in clinical psychology. Typically the individual or small group of individuals being examined
possesses some skill, or has some problem that is unusual.
STRENGTH: Such cases can expand our knowledge about the variations in human behavior. While most
researchers are interested in what is the "general" trend in behavior, those using a case study approach
highlight individuality. Considerable information is gathered. Thus, the conclusions drawn are based on a
more complete set of information about the subjects.
WEAKNESS: Despite their strengths, case studies have some very big drawbacks. First, like all nonexperimental approaches, they are merely describing what is occurring, but cannot tell us "why" it is
occurring. Second, there is considerable room in case studies for "researcher bias" to creep in. While no
approach, including the experiment, is immune from researcher bias when in the hands of an incompetent or
poorly trained researcher, some approaches are at greater risk for this problem even when conducted by
capable people.
Why is the case study more at risk?
The case study method involves considerably more interaction between the researcher and the subjects than
most other research methods. In addition, it is from the researcher's journals of his or her subjects that the
data comes from. While this might also be supplemented by test scores and more objective measures, it is
the researcher that brings all this together in the form of a descriptive "case study" of the individual(s) in
question.
A final problem with case studies is that the small number of cases examined make it unlikely that they
represent those who may have similar problems or abilities as those studied. This problem means we might
not be able to generalize (apply) the study's findings to other people with similar problems. Thus, a case
study of a single person with schizophrenia is unlikely to be representative of all people who suffer from this
disorder.
NATURALISTIC OBSERVATION studies as their name implies observe organisms in their natural settings. A
researcher who wants to examine aggressive behavior in male and female youngsters may watch children in
the school playground, and record the number of aggressive acts boys and girls display.
STRENGTH: The behavior of the subjects is likely to reflect their true behavior as it takes place in a natural
setting, where they do not realize that they are being observed.
WEAKNESS: The researcher has no control over the setting. For example, in our playground study, more
than a child's gender may be affecting the child's aggressive behavior. In addition, subjects may not have an
opportunity to display the behavior the researcher is trying to observe because of factors beyond the
researcher's control. For example, some of the children who are usually the most aggressive may not be at
school that day or in detention because of previous misconduct, thus they are not in the sample of children on
the playground. Finally, the topics of study are limited to only people's overt behavior. A researcher cannot
study topics like attitudes or thoughts using a naturalistic observation study.
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SURVEY studies ask large numbers of people questions about their behaviors, attitudes, and opinions. Some
surveys merely describe what people say they think and do. Other survey studies attempt to find
relationships between the characteristics of the respondents and their reported behaviors and opinions. For
example, is there a relationship between gender and people's attitudes about some social issue? When
surveys have this second purpose we refer to them as CORRELATIONAL STUDIES.
STRENGTH: Surveys allow us to gather information from large groups of people. Surveys also allow us to
assess a wider variety of behaviors than can be studied in a naturalistic observation study.
WEAKNESS: Surveys require that the subjects understand the language. Thus, some members of the
population may be excluded from survey research. Surveys also rely heavily on subjects' memory and
honesty.
CORRELATIONAL STUDIES
Correlational studies look for relationships between variables. Do people who experience divorce have more
psychological problems? Do children who come from economically advantaged families perform better
academically? In each case we are asking is there a relationship between variable X and variable Y?
Correlational studies only tell us that there is a relationship between the two variables. They do not tell us
which variable "caused" the other.
For example, a researcher measures people's marital status and their psychological adjustment and finds that
there is a correlation between the two variables. More people who are no longer married report experiencing
psychological problems. It might be tempting to conclude that the stress of experiencing a divorce causes
depression and anxiety. However, it is also likely that people who suffer from psychological problems are
harder for partners to live with, and thus more likely to have their marriage end in divorce. The researcher
would need to determine which variable came first, the marital breakup or the psychological problems.
Establishing Causality
In order to establish causality we need three things.
That there is a correlation between the two variables
Time order. That the presumed cause came before the presumed effect
Rule out alternative explanations
Correlational studies give us the first thing. Certain studies if they follow subjects over a period of time may
provide us with the second. But correlational studies have less control over the subjects' environment and
thus have difficulty ruling out alternative explanations.
Correlation
Some studies are interested in whether two variables are related to each other.
 Is there a relationship between birth order and IQ scores?
 Is there a relationship between socioeconomic status (SES) and health?
The CORRELATION COEFFICIENT is a statistic that shows the strength of the relationship between the two
variables. The correlation coefficient falls between -1.00 and +1.00. The statistic shows both the STRENGTH
of the relationship between the variables, and the DIRECTION of the relationship. The numerical value
indicates the strength of the relationship. The sign in front of the numerical value indicates the direction of
the relationship. Let us consider each of these in more detail.
THE NUMBERICAL VALUE: Correlation coefficient values that are close to zero (e.g., -.13, +.08) suggest that
there is no relationship between the two variables. The closer the correlation is to one (e.g., -.97, +.83) the
stronger the relationship between the two variables. Thus, we might expect that there would be no
relationship between the height of college students and their SAT scores, and we would be correct. The
correlation coefficient is very close to zero. However, we might expect a correlation between adult height
and weight to be stronger, and again we would be correct.
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THE SIGN: The sign of the correlation coefficient tells us whether these two variable are directly related or
inversely related.
Do the two variables increase and decrease in the same direction?
The more time a student spends studying the better their grade, the less time spent studying the lower the
grade. Notice how both study time and grade vary in the same direction. As studying increases grades
increase, and when studying decreases grades decline. Grade and study time would be POSITIVELY
correlated. The term POSITIVE does not necessarily mean it’s a good thing (when is getting a poor grade a
"good" thing!). It simply means that there is a direct relationship; the variables are varying (changing) in the
same direction.
Do the two variables vary in opposing directions?
As the number of children in a family increase the lower the IQ scores of the children. Thus, family size and
children's IQ scores vary in the opposite direction. As family size increases the IQ scores decline, as the family
size decreases IQ scores increase. IQ and family size are NEGATIVELY correlated (inversely related).
Try the following exercise to see if you understand the concept of correlation. INSTRUCTIONS
Read each of the descriptions below. Then determine whether the relationship described suggests a positive
or negative correlation (the section on "statistics: correlation" will review what is meant by Positive and
Negative correlation). Then consider why we might find this relationship. The more you think about the
correlation suggested the more possible explanations for this relationship you are likely to find. This
highlights why causality cannot be established through correlational research. (The section on correlational
studies reviews this idea).
A researcher finds that students who attend fewer classes get poorer grades.
 Is this a positive or negative correlation?
 Why might we find a relationship between attendance and grades?
Example 1: A researcher finds that students who have more absences get poorer grades.
Cities with more stores selling pornography have higher rates of violence.
 Is this a positive or negative correlation?
 Why might we find a relationship between attendance and grades?
 Example 2: Cities with more stores selling pornography have higher rates of violence.
The longer couples have been together the more similar they are in their attitudes and opinions.
Is this a positive or negative correlation?
 Why might we find a relationship between attendance and grades?
 Example 3: The longer couples have been together the more similar they are in their attitudes and
opinions.
Moral of the Lesson:
In each case above there was more than one explanation for why we might find the relationship between the
variables. Since we cannot rule out these alternative explanations, we cannot conclude that changes in one
variable "caused" changes in the other variable.
The snappy phrase to express this idea is: CORRELATION does not equal CAUSATION
Inferential Statistics
Inferential Statistics allow researchers to draw conclusions (inferences) from the data. There are several
types of inferential statistics. The choice of statistic depends on the nature of the study. Covering the
different procedures used is beyond the scope of this course. However, understanding why they are used is
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important.
A researcher asks two groups of children to complete a personality test. The researcher then wants to know
whether the males scored differently than the females on certain measures of personality. We will create a
fictitious personality trait "Q." Here are the scores for the girls and the boys:
Girls
Boys
23
37
40
56
37
18
41
41
41
42
33
38
28
50
25
22
24
33
13
47
28
25
44
46
Mean=31.42
Mean=37.92
SD=9.03
SD=11.14
The mean score for the "Q" trait in boys was
higher than the mean score for "Q" in the
girls. But notice how within the two groups
there was considerable fluctuation. By
"chance" alone we might have obtained these
different values. Thus, in order to conclude
that "Q" shows a gender difference, we need
to rule out that these differences were just a
fluke. This is where inferential statistics come
in to play.
An important concept in inferential statistics is STATISTICAL SIGNIFICANCE. When an inferential statistic
reveals a statistically significant result the differences between the groups were unlikely due to chance. Thus,
we can rule out chance with a certain degree of confidence. When the results of the inferential statistic are
not statistically significant, chance could still be a reason why we obtained the observations that we did.
In the example above we would use an inferential statistic called a T-TEST. The t-test is used when we are
comparing TWO groups. In this instance the t-test does not yield a statistically significant difference. In
other words, the differences between the scores for the boys and the scores for the girls are not large enough
for us to rule out chance as a possible explanation. We would have to conclude then that there is no gender
difference for our hypothetical "Q" trait.
Inferential statistics do not tell you whether your study is accurate or whether your findings are important.
Statistics cannot make up for an ill-conceived study or theory. They simply assess whether we can rule out
the first "extraneous" variable of all research, CHANCE.
Statistics
Statistics are used to organize, summarize, and interpret empirical data.
 Descriptive Statistics helps us to organize and summarize the data.
 Inferential Statistics help us to interpret the data gathered.
Organizing the Data
Data can be organized using frequency counts and graphs to visually structure the data set. For example, a
researcher tallies the following scores on a memory test gathered from 23 subjects.
5 13 11 12 12 11 11 12
8 12 8 11 10 13 7 12 7 5 7 12 9 11 14
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Arranged in this way the data set is very confusing. However, we could group the numbers in a frequency
count. The data above ranges from 5 to 14.
5
xx
2
6
0
7
xxx
3
8
xx
2
9
x
1
10
x
1
11
xxxxx
5
12
xxxxxx
6
13
xx
2
14
x
1
We can see that the data is grouped more toward the higher end than the lower end, with almost half of the
sample scoring 11 or 12.
Summarizing the Data
While a frequency table like the one above helps us to make some sense out of the numbers, it would be nice
if we could somehow summarize the scores of 23 subjects with a single score.
 Measures of Central Tendency
 Measures of Variability
 Measures of Central Tendency
There are three measures of central tendency: MEAN, MEDIAN, MODE
Each is a single score to represent a group of scores. As their collective name suggests they are looking for
the most "central" or typical response.
The MEAN is the arithmetic average. Sum the data points in the above example and divide by the number of
data points. 233 / 23 = 10.13
The MEDIAN is the exact midpoint of the data set. To calculate the median you place the numbers in order.
5 5 7 7 7 8 8 9 10 11 11 11 11 11 12 12 12 12 12 12 13 13 14
The midpoint is the observation that is in the middle of the set. As there were 23 people this would be the
12th data point.
5 5 7 7 7 8 8 9 10 11 11 11 11 11 12 12 12 12 12 12 13 13 14
What if there had been 24 people? The median is the mean of the middle two points. In our example above it
would still be 11.
The MODE is the most frequent score in the data set. In the example above this is 12. Six people scored 12
on the memory test.
Why Three Different Types of Measures?
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MODE: Survey researchers are often interested in what is the most common score and thus the MODE is the
measure of choice. If I asked students which they prefer COKE or PEPSI, the mean and median score is
meaningless. What I want to know is which soda is preferred. Thus, I want the modal response.
MEDIAN: Notice in our memory score example the MEAN, MEDIAN, and MODE were not identical. The
mean was a little over 10, the median 11, and the mode 12.
Why? In our example, the data was SKEWED. What is "Skewed"? It means that many scores were bunched
down one end with a few scores existing at the other end of the scale. In what is called a NORMAL
DISTRIBUTION the data points are symmetric and the mean, median, and mode are the same value. In our
example the data was not strongly skewed, because the three values were at least close together. But
consider the following data set.
0
xxxxxxx
1
xxxxx
2
xxxx
3
xx
4
x
5
6
7
Many scores bunch between 0 and 4, with a
few trailing off at 7-10.
The MEAN is 60 / 23=2.6. The MEDIAN is 1.
As you can see with the following example,
the MEAN is strongly influenced by the more
extreme (atypical) scores. When the data are
very skewed, the MEAN can be a poor
representative of the data set, while the
MEDIAN is unaffected by extreme values.
x
8
9
10
xxx
MEAN: The mean is the preferred choice, especially when the data are not highly skewed. The mean is used
in the calculation of most Inferential Statistics and is used to calculate variability.
Try the following exercise to see if you understand the concepts of mean, median, and mode.
Exercise - Measures of Central Tendency
1. The measure that is most commonly used by researchers because it is used to calculate inferential
statistics is: _____________________________
2. The measure that is least affected by extreme scores is: _________________________
3. The Mode of the following set of data (5 6 6 7 8 8 8 9) is: _______________________
4. The Mean of the data set is: __________________________
5. The Median is: __________________________________
The measures of central tendency summarize the data in terms of a single number, but not all scores in the
data set reflect that value. Measures of variability allow us to assess how much the scores in the data differ
from each other. The simplest measure of variability is the RANGE (highest and lowest score). However, the
range is also strongly influenced by more extreme scores.
0 1 1 1 2 2 2 3 5 12
The range is 0-12. But most scores are really 0-5, thus, the range can be misleading.
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Another method is to subtract each observation from the mean of the data set. This is the Standard
Deviation. Below is an example of how to calculate the standard deviation of the data set above. The mean
in the above data set is 29 / 10=2.9. We will subtract this value from each and every score in our data set.
0
1
1
1
2
2
2
3
5
12
-
-2.92
-1.92
-1.92
-1.92
-0.92
-0.92
-0.92
0.12
2.12
9.12
2.9
2.9
2.9
2.9
2.9
2.9
2.9
2.9
2.9
2.9
=
=
=
=
=
=
=
=
=
=
-2.9
-1.9
-1.9
-1.9
-0.9
-0.9
-0.9
0.1
2.1
9.1
=
8.41
3.61
= 3.61
= 3.61
= 0.81
= 0.81
= 0.81
= 0.01
= 4.41
= 82.81
If we were to sum up the values in the final column we would have a score of
zero. In addition, working with negative numbers is always annoying, so if
we square the values in the last column all numbers will end up as positive
values (a negative value squared ends up as a positive value).
The sum of the final column is 108.9. We need to calculate the Mean of this
score (108.9 / 10). This value is 10.89.
This is the variance of the data, but as it is based on squared values, it is also
a squared value. If the values above were people's reaction times, the value
of 10.89 would be in squared reaction times, which is hard to comprehend.
Thus, we square root this value. The standard deviation is 3.3.
=
The larger the standard deviation the greater the degree of variability in the data set. Thus, let us compare
the following two groups of responses.
2224 4 5 5 6
1 1 1 2367 9
Group 1 mean = 3.75
Group 2 mean = 3.75
Standard deviation (SD)= 1.48
Standard deviation (SD)= 2.95
Groups 1 and 2 have the same mean score. However, the scores in group 2 are more variable. The SD value
reflects this greater variation of the individual scores from the mean.
Correlation
Some studies are interested in whether two variables are related to each other.
 Is there a relationship between birth order and IQ scores?
 Is there a relationship between socioeconomic status (SES) and health?
The CORRELATION COEFFICIENT is a statistic that shows the strength of the relationship between the two
variables. The correlation coefficient falls between -1.00 and +1.00. The statistic shows both the STRENGTH
of the relationship between the variables, and the DIRECTION of the relationship. The numerical value
indicates the strength of the relationship. The sign in front of the numerical value indicates the direction of
the relationship. Let us consider each of these in more detail.
THE NUMBERICAL VALUE:
Correlation coefficient values that are close to zero (e.g., -.13, +.08) suggest that there is no relationship
between the two variables. The closer the correlation is to one (e.g., -.97, +.83) the stronger the relationship
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between the two variables. Thus, we might expect that there would be no relationship between the height
of college students and their SAT scores, and we would be correct. The correlation coefficient is very close to
zero. However, we might expect a correlation between adult height and weight to be stronger, and again we
would be correct.
THE SIGN:
The sign of the correlation coefficient tells us whether these two variable are directly related or inversely
related.
Do the two variables increase and decrease in the same direction?
The more time a student spends studying the better their grade, the less time spent studying the lower the
grade. Notice how both study time and grade vary in the same direction. As studying increases grades
increase, and when studying decreases grades decline. Grade and study time would be POSITIVELY
correlated. The term POSITIVE does not necessarily mean its a good thing (when is getting a poor grade a
"good" thing!). It simply means that there is a direct relationship, the variables are varying (changing) in the
same direction.
Do the two variables vary in opposing directions?
As the number of children in a family increase the lower the IQ scores of the children. Thus, family size and
children's IQ scores vary in the opposite direction. As family size increases the IQ scores decline, as the family
size decreases IQ scores increase. IQ and family size are NEGATIVELY correlated (inversely related).
Inferential Statistics
Inferential Statistics allow researchers to draw conclusions (inferences) from the data. There are several
types of inferential statistics. The choice of statistic depends on the nature of the study. Covering the
different procedures used is beyond the scope of this course. However, understanding why they are used is
important.
A researcher asks two groups of children to complete a personality test. The researcher then wants to know
whether the males scored differently than the females on certain measures of personality. We will create a
fictitious personality trait "QZ." Here are the scores for the girls and the boys:
Girls
Boys
23
37
40
56
37
18
41
41
41
42
33
38
28
50
25
22
24
33
13
47
28
25
44
46
The mean score for the "QZ" trait in boys was
higher than the mean score for "QZ" in the
girls. But notice how within the two groups
there was considerable fluctuation. By
"chance" alone we might have obtained these
different values. Thus, in order to conclude
that "QZ" shows a gender difference, we need
to rule out that these differences were just a
fluke. This is where inferential statistics come
in to play.
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Mean=31.42
Mean=37.92
SD=9.03
SD=11.14
An important concept in inferential statistics is STATISTICAL SIGNIFICANCE. When an inferential statistic
reveals a statistically significant result the differences between the groups were unlikely due to chance. Thus,
we can rule out chance with a certain degree of confidence. When the results of the inferential statistic are
not statistically significant, chance could still be a reason why we obtained the observations that we did.
In the example above we would use an inferential statistic called a T-TEST. The t-test is used when we are
comparing TWO groups. In this instance the t-test does not yield a statistically significant difference. In
other words, the differences between the scores for the boys and the scores for the girls are not large enough
for us to rule out chance as a possible explanation. We would have to conclude then that there is no gender
difference for our hypothetical "QZ" trait.
Inferential statistics do not tell you whether your study is accurate or whether your findings are important.
Statistics cannot make up for an ill-conceived study or theory. They simply assess whether we can rule out
the first "extraneous" variable of all research, CHANCE.
How do we know if our research has concluded anything of value?
Tests of statistical significance determine if the difference is to big to be due to chance alone
The tests look at two factors:
1. They look at the size of the difference.
The bigger the difference between the groups, the more likely the results are to be statistically significant.
For example, if the Experimental group averages 95% and the control group averages 45% on our test, that
difference would probably be statistically significant. (Intuitively, you do the same thing. If your team gets
beat by one point, you point out that the other team was lucky. You don't have to concede that the other
team is better. However, if they beat your team by 30 points, you may have to admit that the other team is
better).
2. They look at the number of participants.
The more participants that are used, the more likely the results are to be statistically significant. (Why?
Because if you only have a few participants, the groups might be very different at the beginning of the study.
However, if you have 100 participants in each group, the groups should be pretty similar before the start of
the study. If they are very similar at the start, then, if they are even slightly different at the end, that
difference could be due to the treatment. Similarly, in sports, if one team beats another in a seven game
series that's more convincing evidence of the team's superiority than winning a single game.)
Two possible verdicts from statistical tests
1. statistically significant: you are sure beyond a reasonable doubt (your doubt is less than 5% (<.05%)) that
the difference between your groups is too big to be due to chance alone.
So, if the difference between the treatment group and the no-treatment group is too big to be due to chance
alone, then some of that difference is probably due to treatment. In other words, the treatment probably had
an effect.
2. not statistically significant: you are not sure, beyond a reasonable doubt, that the difference between the
groups is due to anything more than just chance.
So, you can't conclude anything. The results are inconclusive.
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T-test - What is it?
The T-test is used to determine whether there’s a significant difference between two group
means. It helps to answer the underlying question: do the two groups come from the same
population, and only appear different because of chance errors, or is there some significant
difference between these two groups, such that we can say that they’re really from two
entirely different populations? For example, is the PROBABLITY of group 1 acting calmer, after
taking a new anxiety medication, because of the meds or is there “calm-ness” due to chance/was
it an accident?
Three basic factors help determine whether an apparent difference between two groups
is a true difference or just an error due to chance:
1. the larger the sample, the less likely that the difference is due to sampling errors or chance
2. the larger the difference between the two means, the less likely the difference is due to
sampling errors
3. The smaller variance among the participants, the less likely that the difference was created by
sampling errors
Reporting Data -When t is significant: basically, is your results is due to the meds, or, is your
results due to chance?
** The difference between the means must be statistically significant for you to be able to
claim that your experiment created change.
The Z-test, similar to the t-test, is a statistical test used in inference which determines if the difference
between a sample mean and the population mean is large enough to be statistically significant, that is, if it is
unlikely to have occurred by chance.
 The Z-test is used primarily with standardized testing to determine if the test scores of a particular sample
of test takers are within or outside of the standard performance of test takers.
Definition of a P value
Consider an experiment where you've measured values in two samples, and the means are different. How
sure are you that the population means are different as well? There are two possibilities:
 The populations have different means.
 The populations have the same mean, and the difference you observed is a coincidence of random
sampling.
The P value is a probability, with a value ranging from zero to one. It is the answer to this question: If the
populations really have the same mean overall, what is the probability that random sampling would lead to a
difference between sample means as large (or larger) than you observed?
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How are P values calculated? There are many methods, and you'll need to read a statistics text, and take
some Tylenol, to learn about them. The choice of statistical tests depends on how you express the results of
an experiment (measurement, survival time, proportion, etc.), on whether the treatment groups are paired,
and on whether you are willing to assume that measured values follow a Gaussian bell-shaped distribution.
We use the T and Z Test to determine PROBILITY levels in your experiment.
Common misinterpretation of a P value
Many people misunderstand what question a P value answers.
If the P value is 0.03, that means that there is a 3% chance of observing a difference as large as you observed
even if the two population means are identical. It is tempting to conclude, therefore, that there is a 97%
chance that the difference you observed reflects a real difference between populations and a 3% chance that
the difference is due to chance. Wrong. What you can say is that random sampling from identical populations
would lead to a difference smaller than you observed in 97% of experiments and larger than you observed in
3% of experiments.
You have to choose. Would you rather believe in a 3% coincidence? Or that the population means are really
different?
"Extremely significant" results
Intuitively, you probably think that P=0.0001 is more statistically significant than P=0.04. Using strict
definitions, this is not correct. Once you have set a threshold P/alpha value for statistical significance, every
result is either statistically significant or is not statistically significant. Some statisticians feel very strongly
about this.
Many scientists are not so rigid, and refer to results as being "very significant" or "extremely significant" when
the P value is tiny. Often, results are flagged with a single asterisk when the P value is less than 0.05, with two
asterisks when the P value is less than 0.01, and three asterisks when the P value is less than 0.001.
P<.05 – results are significant
P<.01 – results are very significant
P<.001 – results are extremely significant
Statistical hypothesis testing
The P value is a fraction. In many situations, the best thing to do is report that number to summarize the
results of a comparison.
1. Set a threshold P value (also called the alpha for significance) before you do the experiment.
Traditionally 0.05 is a minimum threshold for significance.
2. Define the null hypothesis. If you are comparing two means, the null hypothesis is that the two
populations have the same mean.
3. Do the appropriate statistical test to compute the P value.
4. Compare the P value to the preset threshold value. If the P value is less than the threshold, state that
you "reject the null hypothesis" and that the difference is "statistically significant". If the P value is
greater than the threshold, state that you "do not reject the null hypothesis" and that the difference is
"not statistically significant".
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Answers Only
Answers:
1. Is the content of dreams the result of unconscious motives and desires?
While this was the assumption that Freud made, one of the reasons his theory was challenged was because it
was unscientific. We have no objective measure of the "unconscious" to even establish that it exists. So
science cannot answer this question at present.
2. Is there any relationship between birth order and personality?
Birth order is fairly straight-forward to measure, personality would have to be more precisely defined.
However, science can assess whether birth order is related to certain personality characteristics. Asking
whether it "causes" such characteristics would be more difficult to assess as there are far too many
uncontrolled factors.
3. Do college students consume more pizza than any other group?
While we would have to be more precise in defining who are the college students and who are the other
groups, science could assess this more precisely defined question.
4. Can humans be innately evil?
No, science cannot answer if humans are innately evil. How do you measure "evil"? This is too much of a
value judgment, perhaps best left to philosophy and theology.
5. Do sales of pain relievers increase during periods of economic crisis?
While we would have to be more precise about which types of pain relievers and what would be defined as an
economic crisis, science could assess this.
6. Do animals dream?
While most mammals do experience REM sleep, we cannot ask Fido and Fluffy what they were
experiencing. We need more objective measures. Science has the same problem with answering whether
human infants and fetuses dream. Both groups experience REM sleep (in fact, 50% or more of their sleep
time is spent in REM), but we cannot ask either what they were experiencing. Thus, at present science cannot
answer this question.
IV and DV
Answers/Example 1 D) Independent variable was the presence or absence of the drug. This was the variable
being manipulated by the researcher.
C) Dependent variable was the length of time it took the rats to remember where the rat chow was after one
week. This was the measure of the subjects' response.
Example 2 Independent variable was the length of time the subjects were sleep deprived.
Dependent variable was the physical coordination skills of the subjects.
Example 3 Independent variable was the number of people in the group.
Dependent variable was whether the subjects conformed with the group.
Example 4 Independent variable was whether the subjects were hypnotized.
Dependent variable was how much subjects recalled.
Confounding Variables / Answers
EXAMPLE 1 ANSWER - In any study where different subjects are being used in the treatment groups it is
important that you establish that the groups are the same at the outset. Thus, any differences found at the
end were due to your manipulation and not to preexisting differences. There is no mention of a pre-test of
subjects' mood or that subjects' moods had even been altered by watching the films (post-test). The
researcher is assuming that watching a funny film would make someone happy, or witnessing an upsetting
film will produce a negative mood. This is not always a safe assumption, and should always be verified.
Second, the day of the week might be a possible confound. In this case, it might lead to fewer "happy"
subjects in the "positive mood group" as this group was assessed on a MONDAY!
EXAMPLE 2 ANSWER - There are a couple of problems with this study. Although the researcher did attempt
to control potential extraneous variables (the subjects were all night students at the same community
Page 16
college), the experimental and control groups were not only taught using two different methods of
instruction, but by two different instructors. One teacher may have been a much better instructor than the
other. If this was the teacher who used the newer method, students superior performance may have had as
much to do with the teacher as it did the new approach. A second problem, is the students. The researcher
did not establish the level of ability of the two classes at the outset. One class may have had students with a
higher level of language skills than the other. The control and experimental group should be very similar to
one another at the outset. Thus, any differences at the end cannot be attributed to any preexisting
differences.
CORRELATION SECTION
 EXAMPLE 1 - ANSWER NEGATIVE correlation: As the number of absences increase, the grade
declines. The variables are changing in the opposite direction.
We might find this relationship for many reasons. (1) Students who are more absent miss important pieces
of information that would increase their chances of performing better in the class. (2) Students who have
difficulty in a class may stop attending because they see no reason for going. Thus, does the greater class
absence "cause" the poor grades or do poor grades "cause" the greater absence. (3) A student who is not
highly motivated may be absent more often and may do poorly. Thus, these two variables are related to
other variables (such as motivation) which may be the real reason for the relationship between class absences
and grades.


Example 2 Correlation answer - POSITIVE correlation: As the number of stores selling pornography
increases violence increases. Both variables are varying in the same direction.
One possibility is that pornography (which often depicts violence as well as sexuality) "causes"
aggression. It is also possible that people who are aggressive are drawn to images that depict
aggression. But there is likely a third factor in this case, the POPULATION of a CITY. As cities
increase in population there is an increase in violence, there is also an increase in the number of stores
selling anything (pornography, cars, grocery stores).
Example 3 Correlation Answer - POSITIVE correlation: As the amount of time spent together
increases, similarity increases. Both variables are varying in the same direction.
This might happen because over time people influence each other. It might also occur because people
who are most similar to each other to start with stay together longer. Thus, does the amount of time
together "cause" the similarity, or does initial similarity make it more likely that people will stay
together.
Measuring Answers: Mean, Median, 8, 7.12, 7.5 Measures of Variation
The Scientific Method
Theory: An integrated set of principles that organizes and predicts behavior.
Hypothesis: A testable prediction often implied by a theory.
Operational Definitions: Statements (descriptions) of the procedures used to define research
variables.
Replication: Repeating the essence of a study, usually with different participants and in different
situations.
Hindsight Bias: The tendency to believe, after learning the outcome, that you knew that was how it
would turn out.
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Goal of research: To describe, predict, & explain behavior.
I. Research that Describes only
Case Study: A descriptive technique in which one person is studied in depth in the hope of revealing
universal principles.
Naturalistic Observation: Observing & recording behavior in naturally occurring situations without
manipulating or controlling the situation.
Survey: A techniques for obtaining self-reported attitudes or behaviors of people, usually by
questioning a representative, random sample of them.
Population: All of the people in a particular group from with a sample may be drawn.
Random Sample: A subset of people who fairly represent the population because each person
has an equal chance of being selected. Using a random sample increase the generalizability
(external validity) of a study.
Generalizability: The extent to which results of a study can be applied to the outside world.
Also called External Validity.
False Consensus Effect: The tendency to overestimate the extent to which others share our beliefs
and behaviors.
Social Desirability Bias: Tendency of subjects to present themselves in a socially desirable light.
II. Research the Describes and Predicts Behavior (Non-Experimental Designs)
Correlational Research: Research that seeks to measure the RELATIONSHIP between two
variables without trying to determine causality or manipulating either of the variables.
Scatterplot: A graphed cluster of dots, each which represents the values of two variables. The
slope of the dots represents the direction (+ or -) of the relationship while the amount of "scatter"
suggests the strength of the correlation.
Correlation Coefficient: A statistical measure of the extent to which two factors vary together,
and thus how well either factor predicts the other. The statistic is always between -1.00 and +1.00.
A Positive correlation coefficient means that as one variable increases, so does the other.
A Negative correlation coefficient means that as one variable increases, the other decreases (i.e., an
inverse relationship).
Regardless of the strength of the relationship, correlations cannot tell us that one variable CAUSES
changes in the other because:
1) Variable X could be affecting variable Y OR
variable Y could be affecting variable X.
2) Third variables could be affecting BOTH variables X and Y.
Illusory Correlation: The perception of a relationship between two variables where none truly
Page 18
exists.
Differential Research: Research that involves comparing two or more exiting groups on some
variable of interest. The groups are typically based on some pre-existing subject variable (e.g.,
gender, age, IQ, personality trait, etc.)
III. Research that Describes, Predicts, & Explains Behavior (i.e., cause and effect)
The True Experiment: A research method in which an investigator manipulates one or more factors
(independent variables) in order to observe the effect on some behavior or mental process
(dependent variable). By randomly assigning participants to groups, other relevant factors are
controlled.
Independent Variable: The factor that is being manipulated by the researchers. The theoretical
"cause" in the cause and effect relationship.
Dependent Variable: The factor (a behavior or mental process) that is being measured by the
researchers. The variable that is predicted to change in response to the manipulation of the IV.
Operational Definitions: Specific statements describing how the the IV is manipulated and how
the DV is measured.
Random Assignment: Assigning participants to control and experimental conditions on the basis
of chance, thus minimizing pre-existing differences between the groups (i.e., it controls preexisting
subject variables.
Experimental Condition (or Group): The condition of an experiment that exposes participants to
the treatment of interest, that is, to one level of the independent variable.
Control Condition (or Group): The condition of an experiment that contrasts with the
experimental condition and serves as a comparison for evaluating the effect of the treatment.
*At the conclusion of an experiment, the mean scores the experimental and control groups receive on
the DEPENDENT VARIABLE are COMPARED to determine if a statistically significant difference
exists.
Internal Validity: The extent to which one can be confident that the manipulation of the IV
caused the changes in the DV. Internal validity can be assured only if all potential confounding
variables have been controlled.
Control Techniques used to control confounding variables.
Random Assignment: Controls pre-existing subject variables.
Control Group: Controls history, maturation, and testing effects.
Placebo: An inert substance given to the control group in place of an actual medication. It
controls the Placebo Effect.
Placebo Effect: Experimental results caused by the subjects' expectations alone.
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Double-Blind Design: An experimental procedure in which both the research participants and the
research staff are ignorant (blind) about whether subjects are in the control or experimental groups
(commonly used in drug-evaluation studies). This type of design controls subject and experimenter
effects.
Subject Effects or Biases: Any response by subjects in a study that does not represent
how they would normally behave if not under study. Two powerful subject effects are the placebo
effect and the demand characteristics of the study.
Demand Characteristics: Aspects of the study that suggest to the subjects what type of
behavior is expected or desired by the researchers.
Experimenter Effects or Biases: Any behavior of a researcher that might affect the
behavior of the subjects or affect the measurement and recording of the dependent variable.
The Quasi-experimental Design: Designs similar to true experiments, but without all of the
control techniques built in (e.g., random assignment may not be used).
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Comparison of Common Research Designs
Non-experimental
Correlational
Experimental
Quasi-experimental
True Experiment
Manipulation
No manipulation
of variables.
Manipulation of the
independent variable
Manipulation of
the independent
variable
Subjects
Subjects are NOT
assigned to
groups. Usually,
there is only ONE
group of subjects.
However,
subjects are
Randomly
SELECTED for
participation.
Subjects are NOT
randomly assigned to
control and experimental
groups because it is
logistically difficult (e.g.,
comparing 3rd period and
5th period AP psych classes
after each class has be
"treated" differently.) But,
there are control &
experimental groups in this
type of design....just no
random assignment.
If possible, they should be
randomly selected for
participation.
Subjects are
randomly
assigned to
control and
experimental
groups.
(Ex: control group
gets regular
teaching and the
experimental
group gets new
teaching method)
If possible, they
should be
randomly
selected for
participation.
Variables
Two variables (X
and Y) are
measured and the
STRENGTH and
DIRECTION of
the
RELATIONSHIP
is determined.
(Ex: measuring
GPA and
depression level)
Subjects are in pre-formed
groups. But, unlike
correlational and differential
research, an independent
variable (IV) is manipulated
and the groups are
measured & compared on a
dependent variable (DV).
(Ex: Using one teaching
technique with 3rd period
and a new technique with
5th period. Then the two
classes would be compared
on final grades (the DV) to
see if a statistically
significant difference
existed)
The Independent
variable (IV) is
manipulated and
the dependent
variable (DV) is
measured. The
groups’ scores on
the dependent
variable are then
COMPARED to
determine if a
STATISTICALLY
SIGNIFICANT
DIFFERENCE
EXISTS.
Statistics
Pearson productmoment,
correlation
(Pearson’s r)
Chi-square, t-test, ANOVA
Chi-square, t-test,
ANOVA
Conclusions
Variable X covaries with
variable Y (i.e.,
there is a
relationship
between the two
variables.) Cause
and effect
cannot be
proven.
While we may be able to
draw some causal
conclusions, we can’t do it
with as much confidence as
if we had used a TRUE
experimental design. (This
is due to lack of random
assignment and other
controls).
Changes in the IV
CAUSED changes
in the DV. We can
be most confident
when we have
controlled for as
many threats to
internal validity as
possible.
III. External Validity
External Validity: The generalizability of the results of the study. The extent to which the results of a particular study can extend to other
subjects, times, and settings.
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The more NATURAL* the setting, the LARGER the sample, and the more REPRESENTATIVE the sample is of the larger population from
which it was drawn, the greater the external validity of the study.
REPLICATING a study using different subjects in different settings with slightly different procedures will help you demonstrate external
validity of a study.
*Keep in mind that while a "natural" setting increases external validity, it greatly decreases internal validity
B. Subject Effects: Any response by subjects in a study that does not represent the way they would normally behave if not in the study.
Subject Effects
Description
Control
Demand Characteristics
Elements in a study that may cue a subject as to the
purpose of the study.
Use deception (i.e., keep the subjects in the
dark as to the true purpose of the study).
Placebo Effect or Self-fulfilling
Prophecy
An observed improvement in subjects because subjects
believe a change will occur.
Provide a placebo for the control group and
ensure that all subjects are "blind" as to which
group they are in (a single-blind placebo
design).
Measures of Central Tendency
Mean: Arithmetical average calculated by dividing a sum of values by the total number of cases
Median: Point that divides a set of scores in half.
Mode: The most frequent score in a distribution of scores
*Of these three measures, the MEAN is most affected by outliers or extreme scores.
Measures of Variation
Range: Difference between the largest and smallest scores in a distribution.
Variance: A statistical average of the amount of dispersion around the mean in a distribution of the scores. It is
the Standard Deviation squared.
Standard Deviation: A statistical measure of the amount of dispersion in a set of scores. Specifically, it is the
square root of the average squared deviations from the mean of a set of scores. It is simply the square root of
the variance.
*Of the three measures, the STANDARD DEVIATION is most affected by outliers.
Distributions of Scores
Normal Curve: Hypothetical, bell-shaped distribution of scores that occurs when a normal distribution is
plotted as a frequency polygon.
In a normal distribution, the mean, median, and mode are all equal and divide the distribution in half
(the 50th percentile).
Percentile Rank: Reflects the percentage of subjects who score lower than the subject in question
Normal Distribution
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The
Positively Skewed Distribution: A distribution where most scores are
clustered at the lower end of the curve, with a few very high scores creating a
long "tail" to the right. In this case, the mean is greater than the median and the
median is greater than the mode.
Scatterplot: A graphed cluster of dots, each which represents the values of two
variables. The slope of the dots represents the direction (+ or -) of the
relationship while the amount of "scatter" suggests the strength of the correlation.
Correlation Coefficient (r): A statistical measure of the extent to which two factors vary together, and thus
how well either factor predicts the other. The statistic, r, is always between -1.00 and +1.00.
A Positive correlation coefficient means that as one variable increases,
so does the other.
A Negative correlation
coefficient means that as one
variable increases, the other
decreases (i.e., an inverse
relationship).
Regression to the Mean: The tendency for extreme or unusual scores to fall back (regress) toward their
average.
Statistical Significance: Probability that the results obtained were due to chance (represented by the value of
'p').
In psychology, it is standard that a p-value of .05 or less means that results were statistically significant (i.e., not
due to chance).
t-test: A statistical procedure designed to test the difference between the means of two groups
Test Construction
Reliability: Ability of a test to produce consistent and stable scores. Test-retest Reliability: give the same test to
the same group of subjects twice and correlate the results.
Validity: Ability of a test to actually measure what it has been designed to measure.
Face Validity: Do the questions "appear" to measure the construct of interest.
Content Validity: Does the test adequately sample the skills or knowledge that it is supposed to measure.
Predictive Validity: The success with which a test predicts the behavior it is designed to predict. This is
assessed by computing the correlation between the test scores (e.g., SAT scores) and the criterion (e.g., college
GPA).
Page 23
Criterion: The behavior that a test is designed to predict.
Restricted Range: A narrow range of scores (such as only very high GRE score for graduate school
admission) reduces the predictive validity of the test.
Standardization: Giving individual scores meaning by comparing them with the performance of a pretested
group (e.g., give the test to a large representative sample of subjects and determine the mean and standard
deviation. Now, you know if individual score are high, low, or average).
Normal Curve
OUTLINE OF AN EXPERIMENT
VI.
VI.
(or How to Kick Butt on the AP Psychology Exam Essay)
I. Identify you subjects
A. Provide a reasonable number (ex: 100-300 subjects AT
MOST)
B. Provide any subject characteristics that are important (ex:
100 subjects suffering from depression)
C. While it often is not possible, talk about selecting a random
sample (or representative sample) of subjects who AGREE
to participate in your study.
II.
RANDOMLY ASSIGN your subjects to a:
A. CONTROL GROUP and an
B. EXPERIMENTAL GROUP
C. Mention that random assignment will make the groups
equivalent with respect to PRE-EXISTING SUBJECT
VARIABLES.
II.
Identify the:
A. Independent Variable (the variable you will manipulate and
that you believe will cause a change in the dependent
variable
B. Dependent Variable (the variable you will measure and that
you believe will be AFFECTED by the I.V.)
IV. Operationalize (provide an operational definition for) the:
A. Independent Variable: Explain how you will
MANIPULATE it. Explain how the two groups will be
treated DIFFERENTLY with respect to the I.V.
B. Dependent Variable: Explain SPECIFICALLY how you
will MEASURE the D.V.
V. Discuss CONTROL techniques you will use and what they control.
ALWAYS include:
A. Random Assignment to the control and experimental groups.
This controls pre-existing subject variables
B. Use of a control group.
This controls for history, maturation, and testing effects
Other Control Techniques: (Discuss these when they are appropriate for the experiment)
A. Single or double-blind design to control subject and experimenter biases
B. Use of a placebo to control the placebo effect (which includes experimenter and subject biases)
C. Any other EXTRANEOUS variables you want to be the SAME in both groups. This allows you to rule out alternative
explanations for your results.
Describe how you would EVALUATE your results
A. Explain that you will COMPARE the control and experimental groups with respect to the DEPENDENT VARIABLE
B. Explain that you need to find a STATISTICALLY SIGNIFICANT DIFFERENCE (not a correlation) between the scores of
the two groups.
C. Explain that to be sure of your results, you would probably want to REPLICATE your experiment.
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___ 1.
After the horror of 9/11, many people said the CIA and FBI should obviously have foreseen the likelihood of
this form of terrorism. This perception most clearly illustrates:
A) the false consensus effect.
B) the hindsight bias.
C) random sampling.
D) the placebo effect.
___ 2. When Leanne heard about experimental evidence that orange juice consumption triggers
hyperactivity in children, she questioned whether the tested children had been randomly
assigned to experimental conditions. Leanne's reaction best illustrates:
A) illusory correlation.
B) an illusion of control.
C) the hindsight bias.
D) critical thinking.
E) overconfidence.
___ 3. Stacey suggests that because children are more impulsive than adults, they will have more
difficulty controlling their anger. Stacey's prediction regarding anger management
exemplifies:
A) a hypothesis.
B) the hindsight bias.
C) illusory correlation.
D) the false consensus effect.
___ 4. Which research technique is most directly useful for avoiding the thinking error known as the
false consensus effect?
A) operational definition
B) naturalistic observation
C) random sampling
D) experimental control
E) case study
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___ 5. Professor Carter observes and records the behavior of grocery shoppers as they select items to
purchase. Which type of research is Professor Carter employing?
A) survey research
B) case study
C) experimentation
D) naturalistic observation
___ 6. A negative correlation between people's physical health and their marital happiness would
indicate that:
A) poor physical health has a negative impact on marital happiness.
B) marital unhappiness promotes poor health.
C) higher levels of marital happiness are associated with lower levels of physical health.
D) marital happiness has no causal influence on physical health.
___ 7. Mr. Brown has gathered evidence that the self-esteem of students is negatively correlated
with their typical levels of anxiety. Before he uses this evidence to conclude that self-esteem
reduces anxiety, Mr. Brown should first be reminded that:
A) events often seem more probable in hindsight.
B) random sequences of events often don't look random.
C) sampling extreme cases leads to false generalizations.
D) we often exaggerate the extent to which others share our opinions.
E) correlation does not prove causation.
___ 8. Which method offers the most reliable way of assessing whether athletic performance is
boosted by caffeine consumption?
A) the survey
B) the case study
C) the experiment
D) naturalistic observation
___ 9. In drug-treatment studies, double-blind procedures minimize outcome differences between
experimental and control conditions that could be attributed to:
A) replication.
B) random assignment.
C) operational definitions.
D) random sampling.
E) placebo effects.
___ 10. In an experimental study of the extent to which mental alertness is inhibited by sleep
deprivation, alertness would be the:
A) control condition.
B) independent variable.
C) experimental condition.
D) dependent variable.
___ 11. In order to assess whether sense of humor is affected by sexual stimulation, researchers
exposed married couples to either sexually stimulating or to sexually nonstimulating movie
scenes prior to watching a comedy skit. In this research, the independent variable consisted
of:
A) reactions to the comedy skit.
B) level of sexual stimulation.
C) marital status.
D) sense of humor.
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___ 12. One person in a ten-person group is ten times older than any of the other members. With
respect to age, it is most likely that the majority of group members are younger than the
group's:
A) mode.
B) median.
C) mean.
D) standard deviation.
___ 13. The ________ is a measure of ________.
A) standard deviation; central tendency
B) mean; variation
C) correlation coefficient; central tendency
D) mode; variation
E) median; central tendency
___ 14. Janet has five brothers who are 4, 6, 6, 9, and 15 years of age. The mean age of Janet's
brothers is:
A) 5.
B) 6.
C) 7.
D) 8.
E) 9
___ 15. Random samples provide ________ estimates of population averages if the samples have
small ________.
A) good; means
B) good; standard deviations
C) poor; means
D) poor; standard deviations
___ 16. Jamie and Lynn were sure that they had answered most of the multiple-choice questions
correctly because “the questions required only common sense.” However, they each scored
less than 60% on the exam. This best illustrates:
A) illusory correlation.
B) random assignment.
C) the false consensus effect.
D) the hindsight bias.
E) overconfidence.
___ 17. Psychological theories:
A) organize scientific observations.
B) explain observed facts.
C) generate hypotheses.
D) do all of the above.
___ 18. Which research method runs the greatest risk of collecting evidence that may be
unrepresentative of what is generally true?
A) naturalistic observation
B) the case study
C) experimentation
D) the survey
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___ 19. Every twenty-fifth person who ordered a subscription to a weekly news magazine was
contacted by market researchers to complete a survey of opinions regarding the magazine's
contents. The researchers were most clearly employing a technique known as:
A) naturalistic observation.
B) the double-blind procedure.
C) random sampling.
D) the case study.
E) replication.
___ 20. Surveys are most likely to indicate that reckless behavior and self-control are:
A) independent variables.
B) positively correlated.
C) dependent variables.
D) negatively correlated.
___ 21. A correlation of +0.70 between children's physical height and their popularity among their
peers indicates that:
A) higher levels of popularity among one's peers is associated with greater physical height
in children.
B) there is no statistically significant relationship between children's height and their
popularity.
C) being unusually short or tall has a negative impact on children's popularity.
D) children's height has no causal impact on their popularity.
___ 22. A tendency to notice and remember instances in which our premonitions of disaster are
subsequently followed by harmful events is most likely to contribute to:
A) random assignment.
B) the hindsight bias.
C) illusory correlations.
D) the placebo effect.
___ 23. In order to test the potential effect of hunger on taste sensitivity, groups of research
participants are deprived of food for differing lengths of time before they engage in a tastesensitivity test. This research is an example of:
A) correlational research.
B) an experiment.
C) survey research.
D) a case study.
E) naturalistic observation.
___ 24. Researchers control factors that might influence a dependent variable by means of:
A) random assignment.
B) replication.
C) naturalistic observation.
D) operational definitions.
___ 25. In a study of factors that might affect memory, research participants were assigned to drink
either an alcoholic or a nonalcoholic beverage prior to completing a memory test. Those who
drank the nonalcoholic beverage participated in the ________ condition.
A) survey
B) control
C) experimental
D) correlational
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___ 26. In an experimental study of the effects of dieting on weight loss, dieting would be the:
A) control condition.
B) independent variable.
C) operational definition.
D) dependent variable.
E) placebo.
___ 27. The ________ can be a particularly misleading indication of what is average for a ________
distribution of scores.
A) mean; skewed
B) median; skewed
C) mean; normal
D) median; normal
___ 28. The ________ is a measure of ________.
A) median; variation
B) range; central tendency
C) standard deviation; variation
D) correlation coefficient; central tendency
___ 29. Ahmed has five sisters who are 3, 3, 5, 9, and 10 years of age. The number “5” represents the
________ of the sisters' ages.
A) mode
B) median
C) mean
D) range
___ 30. Differences between two samples are least likely to be statistically significant if:
A) the samples are small and the standard deviations of the samples are small.
B) the samples are large and the standard deviations of the samples are large.
C) the samples are small and the standard deviations of the samples are large.
D) the samples are large and the standard deviations of the samples are small.
___ 31. Which of the following best describes the hindsight bias?
A) Events seem more predictable before they have occurred.
B) Events seem more predictable after they have occurred.
C) A person's intuition is usually correct.
D) A person's intuition is usually not correct.
___ 32. Juwan eagerly opened an online trading account, believing that his market savvy would allow
him to pick stocks that would make him a rich day trader. This belief best illustrates:
A) the false consensus effect.
B) illusory correlation.
C) hindsight bias.
D) overconfidence.
___ 33. To say that “psychology is a science” means that:
A) psychologists study only observable behaviors.
B) psychologists study thoughts and actions with an attitude of skepticism and derive their
conclusions from direct observations.
C) psychological research should be free of value judgments.
D) all of the above are true.
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___ 34. The scientific attitude of humility is based on the idea that:
A) researchers must evaluate new ideas and theories objectively rather than accept them
blindly.
B) scientific theories must be testable.
C) simple explanations of behavior make better theories than do complex explanations.
D) researchers must be prepared to reject their own ideas in the face of conflicting evidence.
___ 35. The scientific attitude of skepticism is based on the belief that:
A) people are rarely candid in revealing their thoughts.
B) mental processes can't be studied objectively.
C) the scientist's intuition about behavior is usually correct.
D) ideas need to be tested against observable evidence.
___ 36. Theories are defined as:
A) testable propositions.
B) factors that may change in response to manipulation.
C) statistical indexes.
D) principles that help to organize, predict, and explain facts.
___ 37. You decide to test your belief that men drink more soft drinks than women by finding out
whether more soft drinks are consumed per day in the men's dorm than in the women's dorm.
Your belief is a(n) ________, and your research prediction is a(n) ________.
A) hypothesis; theory
B) theory; hypothesis
C) independent variable; dependent variable
D) dependent variable; independent variable
___ 38. Which of the following is true, according to the text?
A) Because laboratory experiments are artificial, any principles discovered cannot be
applied to everyday behaviors.
B) No psychological theory can be considered a good one until it produces testable
predictions.
C) Psychology's theories reflect common sense.
D) Psychology has few ties to other disciplines.
___ 39. Which of the following is not a basic research strategy used by psychologists?
A) description
B) replication
C) experimentation
D) correlation
___ 40. To ensure that other researchers can repeat their work, psychologists use:
A) control groups.
B) random assignment.
C) double-blind procedures.
D) operational definitions.
___ 41. After detailed study of a gunshot wound victim, a psychologist concludes that the brain
region destroyed is likely to be important for memory functions. Which research strategy did
the psychologist use to deduce this?
A) the case study
B) a survey
C) correlation
D) experimentation
Page 30
___ 42. Your roommate is conducting a survey to learn how many hours the typical college student
studies each day. She plans to pass out her questionnaire to the members of her sorority. You
point out that her findings will be flawed because:
A) she has not specified an independent variable.
B) she has not specified a dependent variable.
C) the sample will probably not be representative of the population of interest.
D) of all the above reasons.
___ 43. One reason researchers base their findings on representative samples is to avoid the false
consensus effect, which refers to our tendency to:
A) overestimate the extent to which others share our belief.
B) falsely perceive a relationship between two events when none exists.
C) underestimate errors in our judgment.
D) make all of the above reasoning errors.
___ 44. Well-done surveys measure attitudes in a representative subset, or ________, of an entire
group, or ________.
A) population; random sample
B) control group; experimental group
C) experimental group; control group
D) random sample; population
___ 45. A professor constructs a questionnaire to determine how students at the university feel about
nuclear disarmament. Which of the following techniques should be used in order to survey a
random sample of the student body?
A) Every student should be sent the questionnaire.
B) Only students majoring in psychology should be asked to complete the questionnaire.
C) Only students living on campus should be asked to complete the questionnaire.
D) From an alphabetical listing of all students, every tenth (or fifteenth, e.g.) student should
be asked to complete the questionnaire.
___ 46. A psychologist studies the play behavior of third-grade children by watching groups during
recess at school. Which type of research is being used?
A) correlation
B) case study
C) experimentation
D) naturalistic observation
___ 47. If height and body weight are positively correlated, which of the following is true?
A) There is a cause-effect relationship between height and weight.
B) As height increases, weight decreases.
C) Knowing a person's height, one can predict his or her weight.
D) All of the above are true.
___ 48. Which type of research would allow you to determine whether students' college grades
accurately predict later income?
A) case study
B) naturalistic observation
C) experimentation
D) correlation
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___ 49. A researcher was interested in determining whether her students' test performance could be
predicted from their proximity to the front of the classroom. So she matched her students'
scores on a math test with their seating position. This study is an example of:
A) experimentation.
B) correlational research.
C) a survey.
D) naturalistic observation.
___ 50. If eating saturated fat and the likelihood of contracting cancer are positively correlated, which
of the following is true?
A) Saturated fat causes cancer.
B) People who are prone to develop cancer prefer foods containing saturated fat.
C) A separate factor links the consumption of saturated fat to cancer.
D) None of the above is necessarily true.
___ 51. If shoe size and IQ are negatively correlated, which of the following is true?
A) People with large feet tend to have high IQs.
B) People with small feet tend to have high IQs.
C) People with small feet tend to have low IQs.
D) IQ is unpredictable based on a person's shoe size.
___ 52. Joe believes that his basketball game is always best when he wears his old gray athletic socks.
Joe is a victim of the phenomenon called:
A) statistical significance.
B) overconfidence.
C) illusory correlation.
D) hindsight bias.
___ 53. Illusory correlation refers to:
A) the perception that two negatively correlated variables are positively correlated.
B) the perception of a correlation where there is none.
C) an insignificant correlation.
D) a correlation that equals –1.0.
___ 54. The strength of the relationship between two vivid events will most likely be:
A) significant.
B) positive.
C) negative.
D) overestimated.
___ 55. Which of the following research methods does not belong with the others?
A) case study
B) survey
C) naturalistic observation
D) experiment
___ 56. Which of the following research strategies would be best for determining whether alcohol
impairs memory?
A) case study
B) naturalistic observation
C) survey
D) experiment
Page 32
___ 57. To prevent the possibility that a placebo effect or researchers' expectations will influence a
study's results, scientists employ:
A) control groups.
B) experimental groups.
C) random assignment.
D) the double-blind procedure.
___ 58. Which of the following procedures is an example of the use of a placebo?
A) In a test of the effects of a drug on memory, a participant is led to believe that a harmless
pill actually contains an active drug.
B) A participant in an experiment is led to believe that a pill, which actually contains an
active drug, is harmless.
C) Participants in an experiment are not told which treatment condition is in effect.
D) Neither the participants nor the experimenter knows which treatment condition is in
effect.
___ 59. In a test of the effects of air pollution, groups of students performed a reaction-time task in a
polluted or an unpolluted room. To what condition were students in the unpolluted room
exposed?
A) experimental
B) control
C) randomly assigned
D) dependent
___ 60. Rashad, who is participating in a psychology experiment on the effects of alcohol on
perception, is truthfully told by the experimenter that he has been assigned to the “high-dose
condition.” What is wrong with this experiment?
A) There is no control condition.
B) Rashad's expectations concerning the effects of “high doses” of alcohol on perception
may influence his performance.
C) Knowing that Rashad is in the “high-dose” condition may influence the experimenter's
interpretations of Rashad's results.
D) Both b. and c. are correct.
___ 61. Martina believes that high doses of caffeine slow a person's reaction time. In order to test this
belief, she has five friends each drink three 8-ounce cups of coffee and then measures their
reaction time on a learning task. What is wrong with Martina's research strategy?
A) No independent variable is specified.
B) No dependent variable is specified.
C) There is no control condition.
D) There is no provision for replication of the findings.
___ 62. In order to determine the effects of a new drug on memory, one group of people is given a
pill that contains the drug. A second group is given a sugar pill that does not contain the drug.
This second group constitutes the:
A) random sample.
B) experimental group.
C) control group.
D) test group.
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___ 63. In order to study the effects of lighting on mood, Dr. Cooper had students fill out
questionnaires in brightly lit or dimly lit rooms. In this study, the independent variable
consisted of:
A) the number of students assigned to each group.
B) the students' responses to the questionnaire.
C) the room lighting.
D) the subject matter of the questions asked.
___ 64. The concept of control is important in psychological research because:
A) without control over independent and dependent variables, researchers cannot describe,
predict, or explain behavior.
B) experimental control allows researchers to study the influence of one or two independent
variables on a dependent variable while holding other potential influences constant.
C) without experimental control, results cannot be generalized from a sample to a
population.
D) of all the above reasons.
___ 65. In an experiment to determine the effects of exercise on motivation, exercise is the:
A) control condition.
B) intervening variable.
C) independent variable.
D) dependent variable.
___ 66. In an experiment to determine the effects of attention on memory, memory is the:
A) control condition.
B) intervening variable.
C) independent variable.
D) dependent variable.
___ 67. The procedure designed to ensure that the experimental and control groups do not differ in
any way that might affect the experiment's results is called:
A) variable controlling.
B) random assignment.
C) representative sampling.
D) stratification.
___ 68. What is the mean of the following distribution of scores: 2, 3, 7, 6, 1, 4, 9, 5, 8, 2?
A) 5
B) 4
C) 4.7
D) 3.7
___ 69. What is the median of the following distribution of scores: 1, 3, 7, 7, 2, 8, 4?
A) 1
B) 2
C) 3
D) 4
___ 70. What is the mode of the following distribution: 8, 2, 1, 1, 3, 7, 6, 2, 0, 2?
A) 1
B) 2
C) 3
D) 7
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___ 71. Which of the following is the measure of central tendency that would be most affected by a
few extreme scores?
A) mean
B) range
C) median
D) mode
___ 72. What is the mode of the following distribution of scores: 2, 2, 4, 4, 4, 14?
A) 2
B) 4
C) 5
D) 6
___ 73. What is the mean of the following distribution of scores: 2, 5, 8, 10, 11, 4, 6, 9, 1, 4?
A) 2
B) 10
C) 6
D) 15
___ 74. Bob scored 43 out of 70 points on his psychology exam. He was worried until he discovered
that most of the class earned the same score. Bob's score was equal to the:
A) mean.
B) median.
C) mode.
D) range.
___ 75. The four families on your block all have annual household incomes of $25,000. If a new
family with an annual income of $75,000 moved in, which measure of central tendency
would be most affected?
A) mean
B) median
C) mode
D) standard deviation
___ 76. What is the median of the following distribution: 10, 7, 5, 11, 8, 6, 9?
A) 6
B) 7
C) 8
D) 9
___ 77. Which of the following is the measure of variation that is most affected by extreme scores?
A) mean
B) standard deviation
C) mode
D) range
___ 78. A lopsided set of scores that includes a number of extreme or unusual values is said to be:
A) symmetrical.
B) normal.
C) skewed.
D) dispersed.
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___ 79. Esteban refuses to be persuaded by an advertiser's claim that people using their brand of
gasoline average 50 miles per gallon. His decision probably is based on:
A) the possibility that the average is the mean, which could be artificially inflated by a few
extreme scores.
B) the absence of information about the size of the sample studied.
C) the absence of information about the variation in sample scores.
D) all of the above.
___ 80. In generalizing from a sample to the population, it is important that:
A) the sample be representative.
B) the sample be nonrandom.
C) the sample not be too large.
D) all of the above be true.
___ 81. The football team's punter wants to determine how consistent his punting distances have been
during the past season. He should compute the:
A) mean.
B) median.
C) mode.
D) standard deviation.
___ 82. In generalizing from a sample to the population, it is important that:
A) the sample is representative of the population.
B) the sample is large.
C) the scores in the sample have low variability.
D) all of the above are observed.
___ 83. The set of scores that would likely be most representative of the population from which it was
drawn would be a sample with a relatively:
A) large standard deviation.
B) small standard deviation.
C) large range.
D) small range.
___ 84. Dr. Salazar recently completed an experiment in which she compared reasoning ability in a
sample of females and a sample of males. The means of the female and male samples equaled
21 and 19, respectively, on a 25-point scale. A statistical test revealed that her results were
not statistically significant. What can Dr. Salazar conclude?
A) Females have superior reasoning ability.
B) The difference in the means of the two samples is probably due to chance variation.
C) The difference in the means of the two samples is reliable.
D) None of the above is true
___ 85. If a difference between two samples is not statistically significant, which of the following can
be concluded?
A) The difference is probably not a true one.
B) The difference is probably not reliable.
C) The difference could be due to sampling variation.
D) All of the above can be concluded.
___ 86. When a difference between two groups is “statistically significant,” this means that:
A) the difference is statistically real but of little practical significance.
B) the difference is probably the result of sampling variation.
C) the difference is not likely to be due to chance variation.
D) all of the above are true.
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___ 87. Your best friend criticizes psychological research for being artificial and having no relevance
to behavior in real life. In defense of psychology's use of laboratory experiments you point
out that:
A) psychologists make every attempt to avoid artificiality by setting up experiments that
closely simulate real-world environments.
B) psychologists who conduct basic research are not concerned with the applicability of
their findings to the real world.
C) most psychological research is not conducted in a laboratory environment.
D) psychologists intentionally study behavior in simplified environments in order to gain
greater control over variables and to test general principles that help to explain many
behaviors.
___ 88. A friend majoring in anthropology is critical of psychological research because it often
ignores the influence of culture on thoughts and actions. You point out that:
A) there is very little evidence that cultural diversity has a significant effect on specific
behaviors and attitudes.
B) most researchers assign subjects to experimental and control conditions in such a way as
to fairly represent the cultural diversity of the population under study.
C) it is impossible for psychologists to control for every possible variable that might
influence research participants.
D) even when specific thoughts and actions vary across cultures, as they often do, the
underlying processes are much the same.
___ 89. Which statement about the ethics of experimentation with people and animals is false?
A) Only a small percentage of animal experiments use shock.
B) Allegations that psychologists routinely subject animals to pain, starvation, and other
inhumane conditions have been proven untrue.
C) The American Psychological Association and the British Psychological Society have set
strict guidelines for the care and treatment of human and animal subjects.
D) Animals are used in psychological research more often than they are killed by humane
animal shelters.
___ 90. Psychologists' personal values:
A) have little influence on how their experiments are conducted.
B) do not influence the interpretation of experimental results because of the use of statistical
techniques that guard against subjective bias.
C) can bias both scientific observation and interpretation of data.
D) have little influence on investigative methods but a significant effect on interpretation.
Page 37
Research Methods SG 1 Answer Key
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
B
D
A
C
D
C
E
C
E
D
B
C
E
D
B
E
D
B
C
D
A
C
B
A
B
B
A
C
B
C
B
D
B
D
D
D
B
B
B
D
A
C
A
D
D
D
C
D
B
D
B
C
B
D
D
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56.
57.
58.
59.
60.
61.
62.
63.
64.
65.
66.
67.
68.
69.
70.
71.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
D
D
A
B
D
C
C
C
B
C
D
B
C
D
B
A
B
C
C
A
C
D
C
D
A
D
D
B
B
D
C
D
D
D
C
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