I. Psychology is the science of human behavior because we want to:

A.To get facts rather than opinions.

B. To get good information so we help--rather than hurt--people. Wouldn't it be nice if everyone in the health field used the same approach? Unfortunately, they don't.

II. There are two types of information that we want. That is, there are two types of questions we want answered:

A. Description/Prediction: "What?"

B. Explanation/Understanding/Control: "Why?"

Practice on this key distinction:

III. Nonexperimental methods describe behavior, but do not let us identify the causes or reasons for the behavior.

Why you may be biased against nonexperimental methods

Common misuse of the term "experiment" to mean any scientific study. Remember, "nonexperimental" does not mean nonscientific!

Our society's emphasis on causal explanations: We like experimental methods because we want to know why things happen and how to change things. However, nonexperimental methods also tell us important things--what people do and think.

Why you should like nonexperimental methods

They are useful when you

1. Can't do an experiment because you can't manipulate the predictor variable. Ex: You can't manipulate participants' gender or age.

2. Can't ethically do an experiment because you can't ethically manipulate the predictor variable (e.g., illness, poverty)

3. Want to describe or predict behavior

They are similar to what you do every day.

They are the backbone of many fields

IV. Common types of correlational methods

A. Observation:

Useful for describing behavior and for suggesting causal hypotheses that could be tested in experiments.

1. Scientific observation should be

a. Objective: "just the facts"

take special care to avoid anthropomorphism (also known as the anthropomorphic fallacy): giving animals human characteristics (warmth, genius, etc.) without objective evidence.
b. Systematically record data to avoid memory biases and errors, such as those caused by the availability heuristic)

c. Use good, fair sample if you are going to generalize your results.

2. Difficulties with observation--It can sometimes be:

a. Impossible: Can we observe thoughts?

b. Impractical: Do you want to wait to observe a robbery?

c. Unethical: How does observation differ from spying?

3. Uses

B. Surveys and Tests

* What people say, not what they do for four reasons

1. Social desirability bias: People may try to make themselves look better than they are. In other words, they lie.

 
2. Obeying demand characteristics: People may say what they think we want to hear.

 
3. Memory errors: Do people accurately remember what they do?

 
4. As revealed by Nisbett and Wilson's findings, people don't always know why they do things.

* Only as good as questions.

Two types of bad questions:

1. Leading questions, which "lead" respondents to the "correct" response. These questions make it quite clear to respondents what answer the researcher wants them to give.

Ex: "You like my website, right?"

 Note that some leading questions may lead to increasing the social desirability bias.

2. Confusing questions: Questions that are too long, use too many " and's," "or's," "but's,""no's," "not's," or big words.

* Only as good as sample.

Two factors help you have a good sample.

1. Large sample. Having a large sample is a good start, but, by itself, is not enough to guarantee a good sample. A big sample can be a bad sample.

 
2. Random sampling: a system where the sample should be similar to the larger group because every member of the larger group has an equal chance of being chosen to be in the study.

1. Learn from unique or extreme

a. Unique

b. Extreme

2. Problems in drawing conclusions from case studies

• Using a small, nonrandom sample means you can't generalize the results to other individuals

 
• Lack of control group means you can't make cause effect statements because things might have turned out the way they did even without the treatment.

V. Facts about all correlational methods

A. They do tell you whether 2 variables are related. But they do not tell you which variable influences which. They may hint or suggest that one variable influences another, but they are never proof of causality. That is, they are never proof that changes in variable A cause changes in variable B.  A humorous example.

That is, if variable A and variable B are correlated, you can't know which variable influences which. Why not?

Because the variables could be statistically related for any one of the following 3 reasons--and you have no way to know which of these reasons is the correct one:

1. A causes (influences, affects) B

2. B causes (influences, affects,changes) A

[thus, if you concluded that A-->B, you might

 be confusing effects for causes]

3. C causes both A and B.

That is, some other factor influences both A and B, but there is no direct relationship between A and B. In other words, A doesn't influence B, B doesn't influence A, but some other factor ("C") influences both of them. [Thus, if you concluded that A-->B or that B-->A, you might be ignoring the fact that you are really looking at two effects of some other cause].

B. The language of correlations. Correlation coefficients

can range from -1.00 to +1.00. The correlation coefficient contains two pieces of information:

1. One piece is the sign (positive or negative),
2. the other piece is the number itself.

1. The sign of the correlation indicates the kind or type of relationship (but not the strength of the relationship)

a. Positive correlations

the more ___ (fill in the blank with a variable, e.g. height), the more ____ (fill in the blank with another variable, e.g., weight)

the less ____(fill in the blank with a variable, e.g. height), the less ____(fill in the blank with a different variable, e.g., weight)

b. Zero correlations: no relationship

c. Negative correlations: reverse

the more ____ (fill in the blank with a variable, e.g., stress), the less _____ (fill in the blank with another variable, e.g., happiness)

 the less _____(fill in the blank with a variable, e.g., stress), the more _____(fill in the blank with another variable, e.g., happiness)

2. The further away from zero, the stronger the relationship.

• Counter-intuitive implications for comparing positive and negative correlations
• -.9 is a stronger correlation than +.7
• -.2 is a stronger correlation than 0

Practice estimating correlations.

If you are at a Macintosh computer and have some time, you can download a computer program that will help you understand what correlation coefficients mean.

1. Explain why psychologists want to use the scientific approach.

 
2. Give an example of a descriptive/predictive question and an example of a causal question. Then, explain the difference between the two types of questions.

 
3. Explain how anthropomorphism illustrates the difficulty of being objective in one's observations.

 
4. List three differences between informal observation and scientific observation.

 
5. List four reasons why what people say in response to a survey may not be the truth.

 
6. Explain why the case study is not as scientifically valid as a survey.

 
7. Describe the three main problems with surveys and explain what the worst ofthese problems is.

 
8. Explain why "correlation does not mean causation."

 
9. Explain the difference between a positive correlation and a negative correlation.
10. Explain why a -.90 correlation indicates a much stronger relationship than a +.60 correlation.
11. Do well on this quiz that explores your understanding of the relationship between correlation and causality.
12. Do well on a quiz that tests your general understanding of correlation coefficients and correlational methods.

THE EXPERIMENT: A Tool for Allowing Us to Determine Whether "A" Causes Changes in "B."

Definition of the simple experiment: a research tool that allows scientists to find out whether a treatment influences (causes) a given behavior or mental characteristic by randomly assigning some participants to get the treatment and other participants to not receive the treatment.

Brief overview of the Simple Experiment
 

No treatment group	Average Score
Treatment group	Average Score

Why do we do experiments?

To make causal statements.

1. Experimental hypothesis (H₁): prediction that the treatment causes an effect. It can be proven wrong.

2. Null hypothesis (H_o): prediction that the treatment does not cause an effect. It also can be proven wrong. It cannot, however, be accepted.

Joke illustrating problem of accepting the null hypothesis.

 (Hint: If the statement "I did not eat the candy bar" is false, what did I do?)

Why do we have two groups?

(Hint: In the Skinner experiment with the rats running the mazes, what could we have concluded if we had only used a caffeine group. That is, what could we have concluded about the effects of caffeine if all we knew was that the rats getting caffeine ran the maze in 9 seconds?)

How can we avoid comparing apples with oranges?

(How do we know that our treatment group and control group were similar before we introduced the treatment?)

Random assignment to treatment involves using a system where everyone who participates in the study has
an equal chance of being put into the treatment group.

What's the problem with random assignment?

Hint: Suppose we get these results:

Experimental Group = 75%

Control Group = 74%
 

Could these results be due to random assignment creating groups that were slightly different before we introduced the treatment?

How can this problem be solved?

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.

2. They look at the number of participants.

Two possible verdicts from statistical tests

1. statistically significant:

you are sure beyond a reasonable doubt (your doubt is less than 5%) that the difference between your groups is too big to be due to chance alone.  If there is no real difference on the parameter, you will be wrong 5% of the time if you're using p=.05 as your level of significance.  You will be committing a type I error.

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 accept the null hypothesis of no difference.   This could be due to having too small a sample or the (real) effect is small and hard to detect.  You could be making an incorrect decision and thereby commit a type II error.
 
 

Some important considerations:

1.  Whenever we measure anything we are only obtain an estimate of the true measure (called a parameter).  Our measure is called a statistic (e.g.,  mean GPA); e.g.,

• True for individuals.  The SAT score you received isn't your true SAT score, as demonstrated by a retest.

• True for groups.  The GPA scores of 2.91 for males and 3.09 for females that we obtained last year were for samples of 191 and 270 males and females, respectively (t=3.48, p<.001).  How different would they be with different samples?

3.  Moral of the story:  we can never be sure of the "truth."  We only use (somewhat) unreliable estimates.  The problem comes in having to make a decision about effectiveness of an IV; e.g., is this new drug effective (or more effective than the usual treatment)?  You might be wrong whatever your conclusion -- you must qualify your answer in probabilities.

4.  And behavioral science never proves anything!

Conclusions about the Experiment

The goal of the simple experiment: is to find the causes of behavior. That is, the goal is to find rules that will allow us to understand and control behavior.

Why the simple experiment can accomplish that goal: it uses random assignment. Random assignment accomplishes two things:

• It makes the groups similar before the start of the experiment

 
• It allows us to use statistics to determine how unlikely it is that the differences between the groups at the end of the experiment are due to chance alone. If it is extremely unlikely that the results are due to chance alone, we conclude that the treatment was at least partly responsible for the differences between the two groups.

Implications of the fact that the experiment depends on random assignment:

• Much research, even that done in labs, is not experimental research because it does not involve randomly assigning people to groups.

 
• Some field research is experimental because it involves randomly assigning people to groups.

 
• We have to do statistical tests to see if our treatment had an effect. We can't just say the treatment had an effect because the treatment group scored higher than the control group. We have to do statistics to see whether this difference was (probably) too big to be just a coincidence.

1. Explain why there are certain research questions that cannot be answered by using an experiment.

 
2. Explain why the case study illustrates the value of having a control group.

 
3. Explain why the null hypothesis can't be proven right.

 
4. Explain why researchers use double-blind techniques.

 
5. Explain why only experiments can answer causal questions.

 
6. Explain why a good hypothesis is one that could be disproven.

 
7. Explain the difference between an independent variable and a dependent variable.

 
8. State a rule for knowing whether a study that uses two groups is an experiment. State two implications of that rule.

 
9. Explain why the following statement is false: "To find out if the treatment had an effect, simply see if the treatment group scored differently than the no-treatment group."

 
10. Explain what it means if the results are statistically significant.

 
11. Explain what you can conclude if the results are not statistically significant.

 
12. Explain why the simple experiment is better than the before-after study.

 
13. Explain the difference between random assignment and random sampling.
14. Explain why you can't scientifically study the effects of race, gender, or personality.