1. Research in Psychology
Psychology is defined as the scientific study of ……

2. Introducing Research
1. What does it mean to say that someone has a scientific attitude?
What’s the difference between a correlational study and an experiment?
3. Identify three types of correlational studies and provide some strengths and weaknesses of each.
4. What role do confounding variables play in an experimental study? Provide some examples.
5. What role do controls play in a research study? (Provide some examples.)

3. Why do we need research? The Dangers of Common Sense
Hindsight Bias or “I-knew-it-all-along”
tendency people have to view events as more predictable than they really were.
Intuition :
sensing without the use of rational process
False Consensus effect – tendency to overestimate others’ agreement with us
Water in glass example

4. Overconfidence
Confidence level is not a consistent predictor of accuracy

5. The Limits of “Common Sense”
Remember – Hindsight bias and overconfidence often lead us to overestimate our intuition.
We tend to remember our correct predictions and disregard our wrong ones.

6. Research Strategies Theory
an explanation that organizes and predicts observations
Hypothesis (If…, then…)
a testable prediction often implied by a theory
Operational Definitions
the specific procedures or steps used in the research study
Research Strategies
generate or refine
research and
observations
lead to
hypothesis
theories

7. Scientific Method State the Problem Gather Information
Form a Hypothesis
Test the Hypothesis
Record and Analyze Data
State the Conclusion
Repeat the Work

8. Types of Research Correlational Studies
Explores the strength of a relationship between two or more variables
Can make predictions, but cannot explain cause and effect
IOW: Does not explain behavior, only describes it

9. Types of Correlational Studies
Case Study
An in-depth study of one person.
Strength: Can provide valuable insight on an issue / condition
Weakness: findings can rarely be generalized to a population
Exceptions: Phineas Gage / brain injury, Piaget’s study of childhood development, and chimpanzees and language.

10. Correlational Studies….
Survey Method
Used to research the self-reported attitudes or behaviors of people
Questionnaire / Survey / interview...
Strength: can be generalized to a larger population (if sample is random)
Weakness: No cause and effect
Wording of survey can bias the research
Students create survey designed to ask same question but illicit different results.

11. Correlational Methods
Random Samples
Random Sample – when every person in the entire group has an equal chance of participating.
The larger the sample size the more likely to represent the whole
Ex. Computer generated list of every 5th person.
Why not send survey to entire school?

12. Correlational Methods
Naturalistic Observation
Observing subjects in their natural habitats / no interaction between researcher and subjects
Strength: valuable insight into natural behavior
Weakness: no cause and effect

13. Bias in Naturalistic Observation
Observer Effect: Behavior is changed because the subject knows its being watched (animals or humans)
Observer Bias:
When researchers notice what they want to see and ignore what doesn’t support their theory
Anthropomorphic Fallacy: Attributing human thoughts, feelings, or motives to animals during research (especially when explaining behavior)

14. Bias in research…
Experimenter Effects: Changes in behavior caused by the unintended influence of the experimenter
Self-Fulfilling Prophecy: Negative or positive beliefs / attitudes predict negative or positive outcomes (teacher hears a new student is a troublemaker…)

15. Correlational Research Analyzing Data
Correlation Coefficient: measures the strength of a relationship.
A Positive correlation means the presence of one thing predicts the presence of the other thing. (2 sets of scores rise or fall together: +1=perfect correlation)
A Negative correlation means the presence of one thing predicts the absence of the other thing. (one goes up, the other down: ---1= perfect negative
0 – no correlation

16. Correlations: Statistical Analysis
Scatterplot (aka scattergram, scatter diagram)
Graphed data that shows the strength of a correlation
The more the data forms a line, the stronger the correlation, IOW:
little scatter indicates high correlation
extensive scatter = little or no correlation
the slope of the line suggests a positive or negative correlation

17. Research Methods Perfect positive correlation (+1.00)
No relationship (0.00)
Perfect negative
correlation (-1.00)

18. Fig. 1.9 The relationship between years of college completed and personal income (hypothetical data).

19. Research Strategies Scatter plot of Height and Temperament 9590 85 80 75 70 65 60 55 50 45 40 35 30 25
Temperament
scores
Height in inches
Scatter plot of Height and Temperament

20. Remember… Correlations do not prove causation.
Correlations only make predictions about the relationship between two variables
Example: There is a positive correlation between a child watching violent TV and that child displaying violent behavior. Why can we not claim that violent TV causes violent behavior?

21. So…
Correlation indicates the possibility of a cause-effect relationship, but it cannot prove causation

22. Illusionary Correlation
Illusionary Correlation – If we believe there is a relationship between two things, we are likely to notice and recall instances that confirm our belief.
Examples:
More babies born during a full moon, infertile couples who conceive after they adopt, premonition of a phone call followed by the phone call
Self- fulfilling and/or Hindsight bias

23. Remember…
Correlational methods can be used in case studies, surveys, and field experiences.

24. Experimentation: Cause and Effect
The experiment: the clearest, cleanest way to isolate cause & effect
Experiments manipulate variables to test the results

25. Know the Difference…
Correlational studies uncover naturally occurring relationships.
experiments manipulates a setting to determine its effect.

26. Key Experimental Principles
Experimental group – Group that receives the treatment
Control group – Group that does not receive the treatment (placebo)
Each group must be from a random sample, and randomly assigned

27. Variables (Anything that can vary…)
Independent Variable (IV)
the variable that is manipulated (example, the drug)
Dependent Variable (DV)
the variable that is measured (example: behavior, or memory)

28. If you give a child sugar, then the child’s activity level increases.
….Variables
Determine the IV and DV in the following hypothesis:
If you give a child sugar, then the child’s activity level increases.

29. Confounding Variables
Inconsistencies between experimental and control groups that can skew the results (bias!)
Examples: Time, place, frequency, amount etc.

30. …Confounding Variables
If you give a child sugar, then the child’s activity level increases.
What confounding principles might affect this experiment?
Scheduled activities after the experiment
Location of observed behavior
Peer group during experiment
Size of peer group during experiment
Amount and type of sugar ingested
Time of the experiment
Conditions prior to the experiment

31. Controls (prevent confounding variables)
Placebos – “sugar pills” or any inert substance given to the control group for a measured comparative basis against the experimental group
Single blind procedure: Subjects don’t know which group they are in…
Double-blind procedure – neither the subjects nor the experimenter know if the per subjects are in the experimental group or the control group
Random sample and random assignment

32. Remember…
Experiments aim to manipulate an independent variable, measure a dependent variable, and control all other variables.

33. Good Research is…
Valid – when it measures what the researcher set out to measure and is accurate
Reliable – when replication , with same operational definitions, results in the same outcome.