1. The Scientific Method
A Theory: “an explanation of why an event or outcome occurs; it identifies the underlying causes of an event.”
A Hypothesis: “A specific prediction about what should occur if a theory is valid. It provides the means by which a theory could be tested.”
If our theory is true, then our hypothesis should be true.
Test the hypothesis.
If the hypothesis turns out to be true, then our theory gains support.
However, that doesn’t mean the theory is necessarily true.
There could be some other reason why the hypothesis is true.
If the hypothesis turns out to be false, then (more or less) that means that our theory is false… or at least needs to be changed somewhat.

2. The Scientific Method.
Theory: It is cold out.
Hypothesis: People will wear long pants. (If the theory is true, then the hypothesis will be true).
People are wearing long pants.
That makes us think we are more likely to be right that it is cold.
But, is that the only reason people might be wearing long pants???
What to do?
No one is wearing long pants.
Conclusion: It is probably not cold out.
Theory is disproven.

3. That makes us more confident that self-presentation is right.
The Scientific Method.
Theory: People blame their failures on external factors because they don’t want other people to think they are losers. (Self-Presentation)
Hypothesis: People will only do this if they think others will find out. (If the theory is true, then the hypothesis will be true).
People don’t blame failure on external factors when they think no one will know.
That makes us more confident that self-presentation is right.
But, is that the only reason people might not blame external factors in private?
People DO blame external factors, even in private.
Conclusion: Self-presentation doesn’t explain why people blame external factors for their failures.
Theory is disproven.

4. Research Methods: An Outline
Qualitative Methods
Quantitative Methods
Surveys
Correlational Methods
Archival Research
Observational Studies
Between Subjects
Experimental Methods
Within Subjects

5. Quantitiative versus Qualitative Methods
Qualitative Methods – Using observation, interviews, etc to develop detailed, descriptive information about the topic being studied.
Quantitative Methods – Collecting data using operational definitions of concepts that provide numeric measures of the concepts.

6. Qualitative Methods Strengths
In-depth, detailed description of area of study.
Can look at how the research participants see the situation.
Can provide information that the researcher wasn’t looking for.
Weaknesses
Doesn’t allow for easy comparison between people or situations.
Doesn’t lend itself well to statistical analysis.
Greater potential for researcher biases to affect the interpretation of the data.

7. Quantitative Methods – Collecting data using operational definitions of concepts that provide numeric measures of the concepts.

8. Operational and Conceptual Definitions
CONCEPTUAL DEFINITION: Like a dictionary definition of a concept. What we think we are talking about.
OPERATIONAL DEFINITION: “A specific, observable response that is used to measure a concept.” This is something we are actually using in research.

9. Operational and Conceptual Definitions: Examples
Operational Definition
Love
A feeling of intense positive emotion toward another individual. A desire to be with and protect that person
Number of times someone glances toward their loved one.
Aggression
Behavior that is intended to injure someone physically or psychologically.
Intensity of shock someone (supposedly) gives to a partner.
Self-Esteem
The degree to which someone feels that they are worthwhile.
Answers to questions on a well-designed self-esteem questionnaire.

10. Provides “objective” measures of concepts.
Quantitative Methods
Strengths
Provides “objective” measures of concepts.
Allows for direct comparison – 4 is always less than 5.
Data can be analyzed statistically.
Is usually easier, quicker, cheaper.
Weaknesses
Only find out what you think to ask.
Can your concepts really be boiled down just to numbers.
“Forces” the researcher’s perspective on the participants.

11. Correlational Research versus Experimental Research
Correlational – Two or more variables are measured. We look at the relationship(s) between/among the variables. (e.g., Which variables are correlated with each other?).
Experimental – Manipulate one or more variables (the independent variable(s)), then measure one or more other variables (the dependent variable(s)). See if the DV is different at different levels of the IV.

12. Correlational Research
Any time we do a study where we measure all of the variables, that is correlational research. If the researcher does not control the level of at least one variable for participants, then it is correlational research.

13. Examples of Correlational Research
As a test of whether people make internal attributions for success, and external attributions for failure, ask people what they got on their most recent test, and ask them to rate how much their grade depended on something about themselves versus something external to themselves.

14. Examples of Correlational Research
To test a theory that says that intelligence comes from the amount of intellectual stimulation one gets as a child: Observe a child’s environment, measuring the amount of intellectual stimulation. Then, give an IQ test. See if they correlate.

15. Examples of Correlational Research
To test the evolutionary theory of parental investment, ask men and women how important physical attraction is in their choice of a mate. Also, ask them how important wealth, status, or earning potential is.

16. Correlation Coefficients.
Correlational studies often (but not always) involve the use of a correlation coefficient.
A correlation coefficient, usually symbolized by r, is a statistic that tells us how much two variables are linearly related.
In other words, the higher r is, the more a graph of the two variables looks like a straight line.

17. The Correlation Coefficient

18. The Correlation Coefficient

19. The Correlation Coefficient

20. The Correlation Coefficient

21. The Correlation Coefficient

22. The Correlation Coefficient
We should remember that a zero correlation does not mean that there is no relationship between our two variables. It means that there is no linear relationship. In other words, it means that the graph doesn’t look anything like a straight line.

23. The Correlation Coefficient

24. In social psychology, correlational data can be collected by:
Surveys
Archival Research
Observational Research

25. Strengths of Correlational Research
Usually fairly cheap and easy to do.
You can measure things realistically (especially with observational or archival research).
Some things can not be manipulated (controlled by the researcher), so correlation is your only choice.

26. Weaknesses of Correlational Research
The big weakness of correlational research is that you cannot tell how your variables are causally related by correlational research.
Correlation does not equal causation.

27. Correlation and Causation.
If two variables are correlated, then one of three possible causal relationships exists:
1. Variable A is the cause and Variable B is the effect.
2. Variable B is the cause and Variable A is the effect.
3. Some third, extraneous variable (C) is the cause of changes in both A and B, but A and B are not directly related.

28. Correlation and Causation
For example, suppose we do a study and find that the amount of time people have been dating is positively correlated with how much they want to get married.
What are the possible causal relationships?

29. Correlation and Causation
We know that one of the three patterns is true (A causes B; B causes A; or C causes A and B). But, we don’t know which one is true. Therefore, we cannot conclude anything about the causal relationship between the variables.

30. The Experiment
An experiment is the method most commonly used by social psychologists. Social psychologists like it because it lets us tell what are causes and what are effects.
With correlations, a big problem is that some extraneous variable could be causing the relationship between the two things that correlate. With an experiment, we control the extraneous variables so that we know that isn’t a problem.

31. The Independent Variable The Hypothesized Cause
In an experiment, we have three kinds of variables that we are concerned with:
Independent Variables (IV): These are the variables that we think are the cause of something. We manipulate the independent variable. In other words, this is the variable that we control what level everyone in the experiment has.

32. The Dependent Variable The Hypothesized Effect
Dependent Variables (DV): These are the variables that we think are the effect. We believe that differences in the independent variable cause changes in the dependent variable. We measure the dependent variable.

33. Extraneous Variables Potential Sources of Error
Extraneous Variables: These are the variables that might cause a relationship between our independent and dependent variables. We want to control these so we know that IF we find a relationship between the IV and the DV, we know its because the IV is causing a change in the DV – because all of the extraneous variables are controlled.

34. How to Control Extraneous Variables
The goal is to make all of the procedures the same across the different conditions of the study, except the independent variable. If the only thing different between two situations is the independent variable, then that HAS to be the cause of any difference in how people act.

35. An Example
Suppose I have the hypothesis that people will be more persuaded by an argument made by an expert, than an argument made by a non-expert.
To test this, I want people to read a newspaper article about term limits for politicians. Some people will read the article as written by an expert political scientist, and other people will read an article written by someone without any particular expertise in politics.

36. What is my independent variable? What should my dependent variable be?
Persuasion by Experts
What is my independent variable?
What should my dependent variable be?
What are some possible extraneous variables that I should be worried about?

37. Controlling Extraneous Variables
How can we make sure that everything is the same in all the conditions of the experiment except for the independent variable (level of expertise)?
Standardized Procedures: Make sure that you are following a fixed set of procedures for everyone in the study (except, of course, the IV).
Same Experimenter.
Say the same things.
Same stimulus materials.

38. Controlling the Extraneous Variables.
But what about things like the fact that people have different personalities, different initial positions, etc?
One Solution: The within-subjects experiment.
Problem: Practice effects.

39. Controlling the Extraneous Variables.
Another solution: Between subjects experiment
Use random assignment to balance out individual differences. By deciding who is in each condition at random (flip of a coin, for example) we are likely to find that the individual characteristics of people are, on average, equal in the two conditions.

40. Strengths and Weaknesses of Experiments
Strength: Causality!!!
Weakness: Artificiality.

41. Research Methods: An Outline
Qualitative Methods
Quantitative Methods
Surveys
Correlational Methods
Archival Research
Observational Studies
Between Subjects
Experimental Methods
Within Subjects

42. Some Other Issues in Research
Generalizability, Representativness and Random Sampling.

43. Some Other Issues in Research
Internal Validity: The extent to which an experiment can really tell about cause and effect. How well you have made everything the same except the independent variable.
External Validity: The extent to which research results really tell us about the real world, and not just about the particular situation we studied.

44. Some Other Issues in Research
One way to increase external validity is to increase mundane realism: “the extent to which the study’s setting looks and feels like the outside world.”
You also need to worry about experimental realism: “the extent to which the study’s setting feels realistic and involving to participants…”

45. Issues in Ethics for Social Psychological Research
The Risk/Benefit Tradeoff
Risks
Emotional
Physical
Benefits
For Society
For the Individual

46. Issues in Ethics for Social Psychological Research
Informed Consent.
Deception.