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

Unlike experiments, they don't let us explain why the behavior occurs.

Tutorial to help you understand the correlational data does not allow you to make cause-effect conclusions.

Although correlational methods do not allow cause-effect statements, they allow you to accurately describe behavior--when correctly executed and interpreted.

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.

Tutorial to help you understand the correlational data does not allow you to make cause-effect conclusions.

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.

You watch people and ask people questions.

They are the backbone of many fields

(such as astronomy, ecology, medical science, metereology, political science, fields that emphasize describing and predicting behavior)

Take time out for an ungraded quiz

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.

C. Case 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.

Click here to take this ungraded quiz

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.

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

See this possibility for the relationship between the moods of two people (Person A and Person B).

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

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

be confusing effects for causes]

See this possibility for the relationship between the moods of two people (Person A and Person B).

This poor woman apparently made the mistake of confusing cause for effects -- and now people are making fun of her (admittedly, this is pretty funny)

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].

See this possibility for the relationship between the moods of two people.

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

Review of Correlational Methods

Tool for interpreting correlation coefficients: Just enter the correlation coefficient you want to interpret.

Nice tool for reviewing key research terms from "The Psych Files."

By now, you should be able to:

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

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

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

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

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

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

  7. Explain the difference between a positive correlation and a negative correlation.

  8. Explain why a -.90 correlation indicates a much stronger relationship than a +.60 correlation.

  9. Do well on this quiz which explores your understanding of the relationship between correlation and causality.

  10. Do well on this quiz which tests your general understanding of correlation coefficients and correlational methods.

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