1. Research in psychology
1.3 Experimental methods

2. Variables
A variable is any characteristics or factor that can vary, e.g: gender, age, grade points, stress, motivation, etc.
Independent variable - the IV produce a change in another variable - deliberately manipulated by the researcher - all other variables are kept constant - Example: new antidepressant medication
Dependent variable - measured after alteration of the IV - is it influenced by the IV? - Example: Did the medication affect the depression?

3. Operationalized
Operational definition translates an abstract term (variable) into something observable and measureable.
The operational definition gives the variable meaning within a particular study.’
In precise terms, what is being
measured? - Example: aggression vs.
how many times the participant
will kick the doll during one hour

4. Hypothesis
Experimental hypothesis is a prediction of how the IV will affect the DV
Example: the new therapy will decrease the participants anxiety more than the old one
A null hypothesis is prediction that there will be no change
Example: The new therapy will have no effect on the
participants anxiety compared to the old one
Most often two conditions:
Experimental (treatment) condition - Situation where a variable is being manipulated
Control condition - Situation where a variable is not being manipulated
Is there a significant difference between the two?

5. Be a thinker p. 27
Identify the Independent variable and dependent variable in each of the following experimental hypothesis.

6. Placebo
People who receiving a treatment show a change in behaviour because of their expectations, not because the treatment itself had any specific benefit

7. Case Study on the effect of the new antidepressant drug
One group receives the new antidepressant drug and told they receive it – experimental condition (treatment group)
One group receives a placebo pill but told they receive the new antidepressant drug – control condition (group)
Does the antidepressant work better than the placebo?

8. Experiments
Laboratory experiments + easy to replicate + easy to hold variables constant - artificial environment - low ecological validity
Field experiments + Ecological validity - hard to hold variables constant
Natural experiment + Unique situations - No control over variables

9. Experiments
Laboratory experiments Example:Study on the effect of the new antidepressant drug
Field experiments Example: Piliavin and Rodin (1969) in the New York subway – investigated helping behaviour regarding sober or drunk person
Natural experiment Example: aggression before and after TV came,
stroke victims

10. Confounding variables (undesirable variables that influence the IV and DV)
Demand Characteristics (aka Hawthorne effect, taken from the Hawthorne Works plant of Western Electric in the US) - Participants act differently because they are in a study and trying to guess what the researcher is after - To counteract: Use single blind control: participants are not told the aim

11. Confounding variables
Researcher bias (observer bias) - When expectations of the researcher affects the findings, often in subtle and unintentional ways - To counteract: Use double blind control in which both participant and experimenter are unaware if the participant is in the control group or the experimental group

12. Confounding variables
Participant variability
When characteristics of the sample affect the dependent varible
To counteract: use random sampling

13. Correlation studies – an experiment cannot be carried out but data are collected which show a relationship
Data is gathered that relates to the IV and the DV
If one variable change the other change as well
Positive correlation: - Same change in both variables - both in increase or both decrease - Example: Life expectancy and hours of exercise + 1 = perfect positive correlation
Negative correlation: - When one variable increase the other decrease - Education and time in jail - minus 1 = perfect negative correlation

14. Correlation studies
Example: 1. Researcher measures one variable (wealth) 2. Researcher measures a second variable (happiness) 3. The researcher statistically determines whether wealth and happiness are related. +

15. Bidirectional ambiguity
Cause-and-effect?
Example: Better social relationships and greater happiness are correlated
But, which causes which? Better social relationships = greater happiness or Greater happiness = better social relationships or
is there that another variable responsible for the behaviour?
Correlation between eating ice cream and drowning? = ?