- carried out in a lab setting
- Highest level of control - Loss of validity (esp.
- Repeatable, therefore ecological validity)
reliable if similar results - Demand characteristics.
- Can use sophisticated
measuring equipment in a
- More control over
variables (could lead to
knowing the cause/effect)
- carried out in their natural environment
• Improved ecological • Less control over IV and
validity measuring DV. With
• Reduction of demand addition of EV’s
characteristics (though (extraneous variables)
there may still be some) • Results cannot be
generalised to other
• Often more costly (as
things have to be
arranged outside – could
inc. technical equipment)
- IV is naturally occurring
• Reduction of demand • Loss of control – The
characteristics investigator doesn’t
• The investigator doesn’t control the IV.
intervene (however, the • A cause/effect
presence of an relationship is difficult
investigator could affect to establish
- a term that refers to the extent to which values co-vary
• Measures the strength • No cause/effect can be
of relationships measured
+ 1 = perfect positive correlation
-1 = perfect negative correlation
- No deliberate manipulation of the variables
Observed in a natural environment, e.g.
school or workplace.
Labs can be ‘dressed up’ to look more natural,
like a playroom, where children can be
observed using a one-way mirror.
Observation – evaluation:
• Good research can be • Control – cause/effect
collected relationship cannot be
• Ecological validity can established
be good • Replication may be
difficult due to variables
• Ecological validity may
• Costs can be high
Self report: Questionnaires:
• Closed questions – • Simple
Tickboxes • Cheap & quite quick
• Open ended questions • Researcher doesn’t
– ‘What are your views intrude
on…?’ • Ambiguous questions
• Leading questions – You could be misconstrued
love this PowerPoint, • Leading Q’s
• Social desirability bias
Self report: Interviews:
• Structured interviews – • Flexible (In
set set of Q’s semi&unstructured)
• Unstructured interviews – • Able to tackle personal
Q’s aren’t decided in topics
advance • Data can be
• Semi-structured misinterpreted
interviews – Some Q’s are • Time consuming
pre-prepared, however • Interviewees may not be
the investigator is free to able to convey their
add more during the thoughts
• Demand characteristics /
social desirability bias
• In depth studies • Not generalisable -
• Rich/interesting data they’re unique to the
individual (or small
• Findings may be
• Lots of data to chose
Quantitative & Qualitative:
Tends to be numerical. Comes from things like
tick boxes. (easily processed)
Data received from longer answer questions,
often from interviews. (gives more detail)
• Directional hypothesis:
Predicts the direction in which results will occur. E.g.
‘More words are recalled from a list when using
rehearsal as a mnemonic technique than when no
technique is being used.’
• Non-directional hypothesis:
Does not predict the direction of the outcome: ‘There is a
difference in the number of words recalled from word
lists presented with or without background music’
• Null hypothesis:
Would predict that the IV would have no effect. E.g.
‘Using mnemonic techniques will not improve
Using different participants for
each condition of the experiment.
Matching each ppt with someone
who is similar to them, and placing
them in different conditions.
Exposing each ppt to each
condition, so the ppts are
(technically) their own controls.
DV and IV
• Dependent variable:
The variable that is assumed to be effected by
the IV. Changes in the DV are presumed to have
been caused by the IV.
• Independent variable:
The variable which is manipulated by the
experimenter that is presumed to effect the DV.
Operationalising the variable:
General statement: ‘Mnemonics improve
It means ‘narrowing down the research focus’
So, figuring out the most simple IV and DV from
• Small scale trial run of the actual experiment
• Allows the investigator to identify flaws of the
• Tests for problems with
- design of the experiment
- Clarity of instructions for the ppts
- Measuring instruments
• Also allows a time scale of the actual
experiment to be estimated
• Should (try to) be controlled so as not to affect
the IV or the DV
Reliability & Validity:
Test whether something is reliable or not by doing
repeats & seeing if similar results are gathered.
Ecological validity – the extent to which the findings
can be generalised to outside the research setting
Population validity – the extent to which the
findings can be generalised to other groups of
Subjective & objective:
‘Based on or influenced by personal feelings,
tastes, or opinions.’
Data which is based on scientific information.
Eg. Using blood samples would be considered
• Informed consent (though, sometimes
presumptive consent is used on the basis that the
investigator would think that they’d get consent)
• Right to withdraw
• Protection from harm
Is the cost of the experiment worth the amount
of data we would get?
Types of sampling:
1. Random sampling
- Everyone in the population has an equal chance of
participating. E.g. using a random number generator
to find numbers
2. Opportunity sampling
- Unlikely to generate a representative sample, so
investigation could be offered to everyone at a school,
but then results couldn’t be generalised externally.
3. Volunteer sampling
- People sign up to the experiment (e.g. in Milgram
1960) Unlikely to be generalisable, as people who sign
up are most likely to have a certain personality type
• Predicting what the experiment is going to
measure and acting accordingly – which could
• Acting out-of-character due to surrounding
• Displaying social desirability bias
Investigator effects – When the investigator can get
too involved in the experiment, causing a change
Measures of central tendency and
• Central tendency:
- Mean (add up all no. Divide by amount of data)
- Median (middle number when arranged numerically)
- Mode (most frequently occurring no.)
• Dispersion – ‘shows the spread of data’:
- Range (highest score – lowest score)
- Standard deviation
Points on the spec. Not covered in this
• How to reduce investigator effects
• Be able to present quantitative data in
• Define types of reliability
• Analyse and interpret correlation data
• Define and know how to do content analysis
• Present qualitative data in a ticklist/table
• Pro’s and con’s of the matched pairs,
independent measures, and repeat designs
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