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1.3 Experimental methods
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?
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
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?
Be a thinker p. 27
 Identify the Independent variable and dependent
variable in each of the following experimental
hypothesis.
Placebo
 People who receiving a treatment show a change in
behaviour because of their expectations, not because
the treatment itself had any specific benefit
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?
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
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
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
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
Confounding variables
 Participant variability
 When characteristics of the sample affect the
dependent varible
 To counteract: use random sampling
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
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.
+
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?
?