Research Methods

Sharing the Results

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Research Methods Lecture on Chapter 11: Ecology of the Experiment

Ecology is the relationships of all the parts of an environment to one another. We typically

associate ecology with the natural environment, our planet, but it can also refer to smaller parts

of our planet, like the little environment of the experiment. This environment is made up of the

researcher, the researcher assistants, the subjects or participates, the apparatus and/or materials

used to conduct the study and gather the data, the

room and surrounding areas during the study, and

even the society, culture, and historical times in

which the study is taking place. Each of these

influence the outcome of the study making it

important to be aware of ecology and to be aware of

the potential bias that may result. If the bias is too

great we may have a problem with Ecological

Validity, the extent to which our study was

influenced by the environmental factors inherent in

all research.

Experimenter Factors

The most consistent principle in the universe is

accident and error. We are full of them. Just because

you decide to do some research does not mean you

now are above accident and error. You are the same error-prone, accident victim, you’ve always

been. Look at your GPA. It’s a measure of your mistakes! A part of your personal equation!

Have you seen the T.V. show about being a millionaire? If you get stumped on a question, you

can call a friend or ask the audience. The audience is right over 90% of the time and the friend is

right about 50% of the time. One way to reduce accident and error is to ask the audience. Go for

the group. The larger the group, the better. As experimenter it’s a good idea to find some other

experimenters to help us out. If you are watching video tapes of children’s play behavior and

counting instances of pro-social behavior, get some help. The more assistants involved in the

scoring the more accurate your findings. Experimenter effects consider how the results of a

study may have been influenced by the attitude or behavior of the experimenter. Two are

especially important.

Biased Data. An experimenter is not objective. An experimenter nearly always has a preferred

outcome. I want theory X to be supported. This experimenter is designed to show that Theory Y

is baloney. Researchers who study researchers have shown us that the errors that we make

(measurement errors, extraneous variables) most often

add support to our preferred outcomes. These errors are

not random, but are biased. This is often called the

personal equation. If a different researcher had collected

the data, a different outcome may have resulted. Of

course we take great effort in designing experiments to

reduce the effects of the personal equation as much as

possible. More than one research assistant is a good

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idea. With at least two researchers, an interrater reliability index can be calculated (Cohen’s

kappa is the usual statistic for this), which tells us the extent to which the two researchers agree

with one another.

Biased Interpretation. Just as unconscious bias adds favorably to our measurement error, our

preconceived view of the world adds to the way we interpret the findings. In my statistics class

there is a question requiring a z-score analysis. A local group home claims it is better than state

run programs and suggests that locally run programs should replace state programs. The data

indicate that indeed there is a statistically significant difference between the local and state

programs. But the state has the better program! This contradicts the stated claim. Nonetheless,

most students miss this and because they found a statistical difference they conclude the local

program is better. After all, that was the wording of the problem and the hypothesis. If a person

can be so easily duped, so easily led to not see the obvious, how can we honestly accept the

interpretations of data? We cannot. It is all hoax and fraud. The theories are temporary

metaphors, cute stories of phenomena. The data is the real thing. You can’t argue with data. But

you can claim it means things it doesn’t.

Biasing the Subjects. The experimenter can easily

bias the subjects responses/behavior and can do so

without being aware of it! One’s attitude, demeanor,

facial expressions, etc., can provide clues to the

subjects about what behavior the experimenter

expects. Subjects will then behave to fulfill the

expectation, or will behave counter to the expectation.

Additionally, such things as attractiveness, dress, and

gender can influence subjects’ behavior. The best way

out of this problem is what is called a Double-Blind

study. The subjects may know what the experiment is

about generally, but should not know which group

they are in (experimental or control); the subjects are

“blind” to the which level of the IV they are getting. The experimenter can also be “blind” about

which group the subjects are in! Thus it becomes a double-blind study and the bias of the

experimenter is now unrelated to the particular group of the subjects and is thus random error,

not systematic error.

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Reducing Experimenter Bias

There are ways we design studies to help reduce bias. One has been mentioned: use several

research assistants to help collect, analyze, and interpret the data. A second one is to use the

blind or double-blind method. A third way is to simply be open and honest about your bias and

make conscious effort to view your data from a theoretical perspective you don’t agree with! I

remember one of my favorite things in debate club in college was arguing for things I personally

disagree with. Stepping into the shoes of a perspective I thought was wrong, yet doing my best

to argue in favor of it taught me more about that position than any attempt to discredit it.

Subject Factors

Almost always, people know when they are subjects of a psychological study. This knowledge

almost always changes the way people behave. The classic example of this is the Hawthorne

Effect, named after the Western Electric company’s Hawthorne plant. The study was designed

to find ways to improve factory productivity by changing the factory environment: add music,

change the color of the walls, change jobs on the

assembly line, etc. What was eventually discovered

was that any change in the environment increased

productivity. Your textbook says that the subjects felt

they were “special” since they were selected to

participate in the studies. More likely, I say, is that

they knew the bosses were now looking over their

shoulders, so they better be productive! A more

general truth may be here too. Perhaps after years of

working in the same place, doing the same job, any

change in the workplace is seen as an improvement

and perceived improvements in the workplace produce

improvements in productivity.

A similar finding is discussed in education classes. It’s called the Rosenthal Effect. Rosenthal

was an educational researcher and discovered that teacher expectations were the most important

factor in educating the young (even more important than teaching method, supplies, salaries,

classroom setting, etc.). He faked some files on some students so teachers thought the good

students were bad and bad students were good. By tracking the progress of these students and

others he discovered that good students grades declined (teachers thought they were bad and sure

enough they got worse) and bad students improved. The teachers expectation biased their

teaching in such a way to support their expectation, or the students behaved in such a way to

fulfill the teachers expectations (or both). Together, the Hawthorne effect and the Rosenthal

effect bode ill for science. On the one hand, any treatment you impose on the experimental group

is likely to result in improvement and on the other hand, your subjects will pick up on your

expectations and behave accordingly. It’s a wonder science is useful at all. You can use the

double blind procedure (see above) to help reduce problems due to expectations, but subjects

almost always know when they are receiving some treatment. Often researchers will employ a

placebo to help reduce the Rosenthal effect. A placebo is a fake version of the real treatment; a

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sugar pill. You have two groups, the real experimental group and a “placebo control group.”

Both groups believe they are getting the treatment, so the Hawthorne effect is in full force for

both groups, while the Rosenthal effect is controlled by the double-blind procedure (since the

experimenter does not know which group is getting the real thing and which is getting the

placebo, the experimenter cannot form expectations that would affect the groups differently).

Thus, any differences between the two groups can only be attributed to the real treatment. If

there is no treatment effect, then both groups may show the same level of “improvement”.

Clearly if a sugar pill is as good as the real drug, then the drug is not responsible for

improvement, but it is the belief in the drug that is responsible.

Demand Characteristics

There are many factors to consider in designing and implementing a study. The various factors

that could influence the subject are collectively known as demand characteristics. Some

textbooks refer to this as reactivity because it is the features of the research setting that cause the

subjects to react differently than they normally would. Just being observed may cause subjects to

behave abnormally (like seeing a large mirror in the laboratory room – a one-way observation

window?). In survey research some people habitually answer “no,” while others habitually

answer “yes.” High School graduates who are going to college typically report that they do not

use and have not tried marijuana, but those not going to

college say they do use marijuana (a 2:1 difference).

Objective measures of marijuana use show us that both

groups are equal in marijuana use. Volunteer subjects

differ from non-volunteers. They are more intelligent,

more educated, more cooperative, better adjusted, and

desire social approval, compared to non-volunteers. It

requires considerable effort to measure the non-

volunteer, those who refused to participate in your study,

or left during the study, but if you don’t then your results may not be valid. Magazine surveys,

radio shows, television news programs, web-based surveys, etc., that rely on volunteers

answering questions are not valid.

Subjects often do things just because an “authority” told them too. They even do dangerous

things they would never ordinarily do. One study found that 50% of the subjects would reach in a

cage, grab, and remove a venomous snake, simply because the

experimenter told them to! In the same study, 83% of the subjects

reached in a bucket of acid to remove a coin and another 83%

threw the bucket of acid on the experimenter when told to!

There are various social roles subjects adopt during a study. The

good-subject role tries to comply with everything the

experimenter wants. The faithful-subject role tries to be honest

no matter what. The negativistic-subject role attempts to

sabotage the study (it’s been called the “screw you effect”). The

apprehensive-subject role is nervous about being evaluated and

tries to perform in a socially desirable way. Attempting to control

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these demand characteristics can be challenging, but in general the double-blind procedure is the

way to go. Some researchers go so far as to run a second experiment – a “simulated experiment”

in which you simply tell subjects to behave as if they were in the actual experiment. By

comparing the results of the real study to the simulated one, the experimenter can get a handle on

the extent to which demand characteristic may be confounding the results.

Culture and Society

Our culture dictates what is acceptable and what is not in

Psychological research. For example, one time it was

acceptable to use prisoners in research. This population is

mostly male and mostly black. External validity becomes

an obvious problem. A great deal of research is conducted

using soldiers in the military. Again, mostly male, and

now mostly poor and uneducated. Here at MTSU, we

typically use the subject pool. MTSU is predominantly

white and female! External validity becomes a major

issue.

Obviously, our culturally shared ideas about the world also influence

how we respond to questions in survey research and respond in

experiments to various manipulations. The nearly universal belief that

drugs are bad leads people to deny drug use making it virtually

impossible to assess the true level of drug use in high schools, colleges, on the job, etc. Our

belief that Psychologists are involved in deception research leads us to search for the deception

in the experiment, to try to figure out what is “really” going on.

The history of science is the history of

combating peoples beliefs. The world is flat,

the earth is the center of the universe, the

stars are holes through which heaven shines,

a divine creator is required to “make” life

happen, there is no such thing as global

warming, etc. Our notions about what is real

and what is not influences how we see thing,

how we react to things. My least favorite one

is the emphasis on genetics in today’s culture.

It is difficult to explore methods of behavior

change when the subjects believe that the

behavior is genetically determined and

therefore cannot be changed. (Of course you

are not responsible for your behavior then

either! Ain’t genetics wonderful!)

Research Methods Lecture on Chapter 8: Control, The Keystone of the Experimental

Method

Control has several different meanings in scientific research. A scientist has control over what

he or she studies; the individual decides what hypothesis to create and how best to test it. A

scientist controls how subjects are selected to participate in the research. A scientist controls

how the study will be set up, its basic design, how many groups, what will be done to the groups

in what order, etc. And finally there is a meaning of control that relates to logic…that is a logical

progression from cause to effect and how that is “captured” in the research design and analysis is

another meaning of control. Your textbook discusses each of these meanings of control and I’ll

cover them here as well.

Control in Subject Selection and Assignment

Random Sampling is the single best way to select subjects to participate in your study. However,

a true random sample is not possible. you are limited in resources and time and distance. You

randomly sample from a select group and hope for the best. It’s not good, but it’s not too bad.

True random sample means everyone had an equal chance of being in

our experiment. Everyone? That’s not really possible. And that’s why

we can’t every really randomly sample. So we use the next best thing,

the subject pool. This is at least a pool of people from a variety of

majors all taking a required general studies course. So in a sense they

are a random selection of college students in a given geographic

region. Once you randomly sample from the pool, you next randomly

assign them to groups. The laws of chance tell us that by randomly

assigning to groups, the groups will be equal on things like

intelligence, driving ability, personality, interests, even height and

weight! They will be the same as each other before you begin your

study. In your basic statistics class you learned about the Sampling

Distribution and the Normal Curve. Those are also the “laws of

chance” I’m talking about here that mean your sample of subjects, drawn at random, will be an

accurate representation of the population. A small sample of five people is sufficient to reflect

accurately the characteristics of a huge population! And that’s what you want.

But what if you want to study a particular group like drug addicts, autistic kids, etc.? There are

ways to do that involving special sampling techniques within the special population. There are

ways to properly design and implement that kind of Quasi-Experiment or Program Evaluation,

and we will talk about that in this class. It’s a whole other series of lectures!

In the previous paragraph I mentioned

random selection of subjects and

random assignment. Selection refers to

acquiring a single group of people from

a population. Assignment refers to

putting them into groups. So you

randomly select 20 people from the

subject pool (population), and then you

randomly assign them to two groups of

10. Typically, one group “gets the

treatment” and is called the

experimental group. The other group

does not get the treatment and is called

the control group. Random selection

relates to external validity. Only by

random selection can you be confident that your sample accurately represents the population and

if the sample is not representative of the population your results are “externally invalid” before

you even conduct the study! Random assignment relates to internal validity, the extent to

which the independent variable, the treatment, is the cause of the dependent variable, the

outcome or measurement. If you don’t randomly assign, there’s a good chance that your two

groups will be different before the

experiment and thus would of course be

different after the experiment. So the pre-

experiment difference is the true cause of the

post-experiment difference, instead of your

independent variable.

The most common way for us to randomly

assign subjects to groups is to use a random

number table. Assign each subject a

number, 1-20, then using the random

number table you assign them to groups. For

example, looking at this table I see 61424 in

the first column and can start there (although

you can start anywhere in the table you want and move any direction you want). Also remember

that with 20 subjects we are using two-digit numbers, so we must group the numbers in the

random number table in sets of two digits. So my first number is 61. I don’t have 61 subjects so

I just ignore that go to the next set, which is 42. Again I ignore that and move on. And I see 42

again. Moving on I next see 04. Ah Ha! Now I do have a subject 4, so that person joins group

1. Next I see 19, and that means subject number 19 goes into group 2. Next comes 86, 54, 60,

and then 05. So subject 5 goes into group 1. And I keep going on like this until all 20 subjects

have been randomly assigned to the two groups.

What if I wanted an equal number of men and women in my two groups? This is called an

equating procedure. We could assign numbers 1-10 to the men and 11-20 to the women and

proceed to use the random number table. If we notice that one group is getting too many women

or men, we can simply reassign the extra man or woman to the other group.

Another potential issue that often comes up in research relates to assigning subjects to groups

based on arrival time. If you assign the first 10 people to group 1 and the second 10 to group 2,

you now have one group of early arrivals and one group of late arrivals and they are no longer

equal to each other. You may alternate them, so that first arrival goes into group 1, second

arrival goes into group 2, etc. This is better, but still has a bit too much regularity: 1,2,1,2,1,2,

etc. It’s always 1, then 2. This we know can produce some small bias into our assignment and

we’d like to avoid bias, so instead we assign like this: 1,2,2,1,1,2,2,1,1, etc. Now you see that it’s

1, 2…then 2,1….1,2…then….etc. This is called counterbalancing and is the preferred method.

By using random selection and random assignment we have created two groups that are the same

on all things before we begin our study! That means that the one difference we make between

the groups, the experimental treatment, the independent variable, would be the cause of any

measured differences (dependent variable) that we may find.

A study found that those who eat breakfast are healthier than those who don’t. The researchers

claimed they used random selection so there should not be a problem. True? Well, this is an

observational study and cannot make cause-effect conclusions, like eating breakfast makes one

healthier than not eating breakfast. The researchers did not use random assignment! Remember,

for a true experiment, we must use both random selection and random assignment. So if they had

randomly assigned people to an “eat breakfast group” and to a “no breakfast group,” and imposed

these conditions for 6 months or so, then measured the health of the subjects, we could then

conclude that eating breakfast makes one healthier.

Control in Experimental Design

A research design is the plan for collecting data. This includes the number of groups you will be

using, the levels of the independent variable (IV) to be used, and the strategy for measuring

behavior. A good plan, or good design, eliminates all threats to validity (see chapter two).

Eliminates? Yeah, right. Maybe in that Ideal World that doesn’t exist! We can never eliminate

accident and error, but we can reduce. Random Sampling and Random Assignment help.

Another strategy most commonly used is “Holding Conditions Constant.” By holding conditions

constant the effects of things like history and maturation are equally present in two groups, the

control group and the experimental group (and in the other groups if any). As long as we treat the

two groups exactly the same (meaning we hold all conditions constant) except for the IV, then

we can detect the effects of the IV despite history and maturation because those two are

happening equally for both groups. History is the passage of time and events, while maturation is

the natural growth and development of people. History can as simple as a researcher talking to

one group more than another group, now the two groups have different histories. Maturation is

both long-term and short-term. For example, it’s well-known that drug addicts typically quit by

the time the reach the age of 50 or so (the ones that make to that age). So you can imagine a

drug rehab program that runs over a period of years. Since older drug addicts are more likely to

quit, it may not be the program that causes them to quit, but just plain old maturation.

The easiest and therefore most common experiment is the

posttest only design. Two groups, a control group and an

experimental group, are measured on some behavior. The

control group receives none of the IV, while the

experimental group does get the IV. In discussing designs we often use Code Letters. For

example: R – Grp Exp – T – M; indicates that subjects were Randomly assigned (R) to the

Experimental Group (Grp Exp) and were given some

treatment (T), then measured on some behavior (M).

The control group would then be: R – Grp Con – X –

M, where the X means no treatment given.

This design, the posttest only design controls for maturation and history because those two

factors are happening to both the control group and the experimental group equally.

There are better designs. Consider the pretest-posttest control group design. The name sounds

bad, but the design is very good. There are two groups, a control group and an experimental

group. They are both pretested (a Before measure).

The treatment is imposed on the experimental group,

then after the treatment both groups are post-tested

(the After measure). This design controls for all the

threats to internal validity. By pretesting and post-

testing a control group you can monitor the changes

taking place that are due to history, maturation,

instrumentation, mortality, etc. You do not eliminate

them, you control them by observing them, if they

occur, in the changes in the control group.

Another, better, design is the Solomon Four Group

design. This design is similar to the pretest posttest control group design, but with two additional

groups: another control and experimental group, but they don’t get the pretest. This design lets

you observe the effects of pretesting, if any, and controls for carry-over effects and practice

effects in addition to all the other threats to internal validity. In the diagram you see the R’s.

They mean Random Assignment. The O’s indicate an Observation, which is a Measurement or

M, in this case it is a Pretest, but only for two

groups. The X here means the treatment or T. I

know it’s a pain, but no one seems to agree on the

best way to abbreviate these designs, so you will

see them diagramed all kinds of ways. The final

O’s here are the post-test observations or

measurements.

Four Characteristics of True Experiments

To be considered a true experiment such that a cause-effect relationship between the IV and the

DV can be found (if it exists), a study needs four things. First, subjects must be randomly

selected and randomly assigned to groups. Second, there must be at least two levels of the IV,

the treatment. At the very least you need to have one group “get” the treatment and one group

“not-get” the treatment. This is often referred to as Presence vs. Absence of the IV. You could

have more than some vs. none. For example you may want to give an amount, say one glass of

juice for one group, two glasses for another group, and no juice for a third group. Third, true

experiments control for threats to internal validity. These are discussed in detail in chapter

seven, but I’ve mentioned a couple of these here (maturation and history). We’ll look at them in

detail later. Fourth, true experiments tend to compare alternative versions of a theory, or two

different theories or at the very least determine if an hypothesis is supported or not.

Control and the Logic of Experiments

How does the basic experiment give us truthful answers to questions? It’s a process of

converting an idea or question, into some means of manipulation of a believed cause while

carefully watching for any changes in the believed effect., taking the resulting numbers (the

measurements/observations) and converting them into the answer to the question. Simple. Not.

This all gets very statistical very quickly. The basic inferential statistics like the t-ratio and the

F-ratio tell us if the difference we see between or among groups is due to chance or due to a real

effect of the treatment. The “ratio” part is the heart of it all. The top number in the ratio, the

numerator, is the differences you measured. Let’s say it’s 90. That means your groups differed

by 90 units of whatever you measured. The bottom number, the denominator, is the amount of

chance, called error, in your measurements. The ratio is thus “differences/error,” or 90/error.

Let’s say the chance factors (that produce error) is equal to 90 also. That means we have as much

difference as chance or error: 90/90. And in ratio this means 1.00. We don’t like it when error

and difference are the same. It means the difference in our groups due to the treatment is the

same as the difference due to error. Our treatment is really error, it made no difference. So any

test statistic that equals 1.00, means the treatment did not affect the experimental group. F-ratios

(and t-ratios) that are greater than 1.00 tell us that the treatment did have an effect.

When we calculate the difference Between Groups we call it between-group variance. Variance

means differences. When we calculate the error we call it within-group variance. So anytime

you see “MS within-groups” think Error! When you see “MS between-groups” think treatment

effect. MS is another way of saying variance (mean square).

We also sometimes call it “Mean Square Error Term.”

Within-group variance (error) gets larger with larger

individual differences in our subjects. So if we don’t treat

them all the same, we see more error, larger “mean-squares.”

Of course everything is error-prone. We make mistakes.

Confounds, or confounding variables, are one kind of

mistake we can make in conducting a study. The idea of a confound is that it may be the true

cause of any differences we see, instead of the treatment we gave (the IV). These in turn mean

we can make two kinds of errors in converting our numbers (statistics) into the answer to our

original question, conveniently called Type I and Type II. The Type I error (also called alpha) is

the chance that you decide the treatment had an

effect, but it didn’t really. The Type II error (also

called beta) is the chance that you decide the

treatment had no effect, but it did really. The null

hypothesis says that the groups did not differ. So

the Type I error says reject the null hypothesis,

the groups did differ…but this is a mistaken

conclusion. The Type II error says accept the null

hypothesis, the groups do not differ…but this is a

mistake. The Type II error also relates to external

validity. Remember, external validity is whether

or not your results are true in the real world.

Conclusion

A true experiment includes random sampling and random assignment. It will involve at least two

groups or two conditions, one serving as a control and the other as the treatment. A true

experiment is designed to control for the threats to internal validity without which the cause-

effect relationship between the IV and DV cannot be determined.

Control: The Keystone of the Experimental Method

Research Methods in Psychology

Identifying Independent and Dependent Variables – Titles

Original provided by Dr. Gilroy, MTSU.
Modified somewhat by Dr. Kendrick, MTSU

Below are the titles of a number of studies. You can often identify the independent (IV) and
dependent (DV) variables from these titles. For example, the terms, “the effect of…” or, “the influence
of..” typically point to the IV whereas the terms following “on”, point to the DV. So, a general
statement would be something like, “The effects of the IV on the DV”, but it’s not always so clear cut.
Also, IV’s require more than one level, so identify two or more possible levels of each IV. DV’s require
a measurement or score, so for each DV identify a possible method of measurement.

Identify the independent and dependent variables in each of the studies.

1. Aggression toward minorities as a function of racial attitudes.

2. The effect of reinforcement on learning.

3. Stimulus attractiveness and memory in high and low sensation seekers.

4. The effect of independence training on creativity.

5. The effect of hunger on learning

6. Frustration leads to aggression

7. School performance as a function of intelligence

8. The relationship between self-esteem and school success among elementary school children

9. The effect of severity of initiation on liking for a group

10. The perceived size of coins in normal and hypnotically induced economic states

11. The effect of complexity and novelty in visual figures on orienting responses

12. Pupil response of hetero- and homosexual males to pictures of men and women

13. The influence of associative value upon the difficulty of nonsense syllable lists

14. Delay of feedback on the acquisition and retention of verbal materials in the classroom

15. Effects of motivating instructions and reports of failure on verbal learning

16. Effects of intensity of audio stimulation on directional eye movements in the human neonate

17. The relation of childhood training in independence to achievement motivation

Answers:

1.
IV attitude (survey/questionairre that creates three categories: negative, positive, neutral)
DV aggression (facial expression when shown pictures of different races)

2.
IV reinforcement (one group receives positive reinforcement during coaching and a second group
does not)
DV learning (number of golf balls hit into cup, more = better learning)

3.
IV High and low sensation seekers (as determined by scores on a survey)
DV Attractiveness of stimuli and memory for stimuli (as determined by self-report ratings and a
recognition memory test)

4.
IV Independence Training (one group gets the training and a second group does not)
DV Creativity (measured by a standard creativity test)

5.
IV Hunger (one group does not eat for 12 hours, a second group eats whenever they want)
DV Learning (number of attempts to correctly negogiate a maze)

6.
IV Frustration (One group is told to go to room 103 on the second floor but it does not exist, other
group told to go to room 303 on third floor which does exist – both groups to find forms and bring
them back)
DV Aggression (number of “bad words” uttered when asked to hand over forms)

7.
IV Intelligence (students asked to sit on left if very smart, on right if not so smart, and in the middle if
average – thus creating three groups based on self-reported level of intelligent)
DV School performance (self-reported GPA)

8.
IV Self-esteem (Score on survey of self-esteem)
DV School success (GPA)

9.
IV Severity of initiation (three groups, one getting congratulatory initiation, one having to jump in cold
lake water, and one having to jump in cold lake water and staying for five minutes)
DV Liking the group (self-report survey with questions regarding the importance of membership in
the group)

10.
IV Normal or hypnotic state (self-explanatory…two groups, one hypnotized and one not)
DV Perception of coin size (self reported estimates in inches and tenths of inches)

11.
Note there are two IV’s in this one.
IV Complexity (three stimuli differing in complexity – simple figure, complex figure, very complex
figure)
IV Novelty (three stimuli differing in novelty – common figure, unusual figure, very unusual figure)
DV Number of orienting responses (count the number of times people look at stimuli)

12.
Two IV’s in this one too.
IV Sexual Orientation of person (homosexual and heterosexual males)
IV Sex of picture (male and female pictures)
DV size of pupil (as measured by pupilometer)

13.
IV associative value (three levels: high, medium, low)
DV difficulty of memorizing (number of times repeated to reach 100% correct memorization of list)

14.
This one has two DV’s
IV Delay of Feedback (three groups: no delay, short delay, long delay)
DV acquisition of verbal materials (number of times repeated to reach 80% or better)
DV retention of verbal materials (memory test after 30 days)

15.
Two IV’s here
IV Type of instructions (motivating and nonmotivating)
IV Reports of failure (one group is told they failed on previous tests of verbal learning, a second
group told nothing, a third group told they did really well)
DV Verbal learning (scores on a verbal learning test)

16.
IV Intensity of audio stimulation (intense, less intense, and mild)
DV directional eye movements (measured by eye movement detector)

17.
IV Child training (one group trained in independence, another group not trained)
DV Acheivement motivation (as measured by self-report questionairre)