Intro Flashcards

(44 cards)

1
Q

Population

A

the whole group of people of interest to the researchers

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2
Q

Sample

A

The participants being studied in the research pulled from the population

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3
Q

what makes a good sample

A

a sample that is representative of the populations which makes the results able to generalise. For the sample size to be big enough it must be 10% of pop

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4
Q

How big must the sample size be

A

10% of the pop

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5
Q

Characteristics of an experimental design

A

Random allocation, manipulation of IV, control of extraneous variables.

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6
Q

What does an experimental design give you

A

Cause and effect

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7
Q

Where can experiments be conducted

A

in a field or lab setting. Lab gives more control, field is more realistic

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8
Q

Three types of extraneous variables

A

Participant, situation and experimenter

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9
Q

Participant variable

A

pre-existing conditions (age, gender, sex)

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10
Q

Situation variable

A

Temp, wind, time of day, noise, brightness

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11
Q

Experimenter variable

A

clarity of instructions, behaviour towards participants, biases.

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12
Q

What is an extraneous variable

A

Any variable other than the IV and DV that may influence the results of the experiment

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13
Q

What is the difference between the experimental group and the control group

A

The only difference between the two should be the treatment

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14
Q

When is a quantitative observational design used

A

when it is not possible or ethical to use an experimental design

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15
Q

What does a quantitative observational do differently to an experimental

A

It does not show cause and effect as it does not manipulate the IV.

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16
Q

What does a quantitative observational show instead of a cause and effect

A

A correlation

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17
Q

Is there random allocation in quantitative observational?

A

No, there are pre-existing groups (married people, 17 years olds, people with blue eyes, etc).

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18
Q

Why doesn’t quantitative observational show cause and effect

A

Because we don’t manipulate the IV, therefore can not say that the change in DV was due to that and not other factors.

19
Q

Qualitative design

A

Self-reporting data that does not seek to prove a specific hypothesis and could not prove it even if it did.

20
Q

Focus groups

A

6-12 people
Group discussion
Lead by a researcher and discusses a topic of interest
A benefit of this is the snowballing effect

21
Q

Delphi technique

A

A questionnaire is sent out to experts and cross-referenced to come to a consensus on the issue. Criticised for forcing an agreement.

22
Q

How to we analyse qualitative data

A

Through content analysis

23
Q

How do we analyse content

A

we short out answers into categories from which we can find the frequency of that answer

24
Q

Why do we analyse content

A

to convert qualitative data into quantitative data.

25
Advantages of experimental design
- Cause and effect - Maximum control - Replicable
26
Disadvantages of experimental design
- Often artificial and not applicable | - Sometimes unethical
27
Advantages of quantitative design
Can study subjects that it would be unethical to experiment on
28
Disadvantages of quantitative design
does not allow for cause and effect
29
Advantages of qualitative design
In-depth responses Avoid ethical problems Can be used with illiterate people Snowballing effect
30
Disadvantages of qualitative design
Does not show cause and effect not useful for testing hypothesis results can't be generalised social desirability
31
Quantitative data
Numerical measurements | Heart rate, reaction time, cm, mm, litres, behaviour counts, rating scale, time
32
Qualitative data
Non-numerical qualities, characteristics, images, descriptions
33
Objective data
can be directly observed and verified
34
Subjective data
Depends on perception, opinion or judgement. Cannot be directly observed or confirmed. Any type of self-report
35
Quantitative objective
heart rate, IQ, behavioural counts
36
Quantitve subjective
rating-scale personality test
37
Qualitative objective
does not exist
38
Qualitative subjective
Content analysis Focus groups Statements
39
Validity
whether the measurement tool (surveys, apparatuses for measurement, questionnaires, etc) actually measure what it is meant to.
40
Face validity
Whether a measure APPEARS as though it would measure what it is designed to measure
41
External validity
Can the findings be applied to the population? Are they correct for other situations other than a lab.
42
Reliability
How consistent the result of a measurement are. This is why we replicate.
43
Can a measure be reliable but not valid
Yes.
44
Social desirability
Is a participant variable The tendency of some respondents to report an answer in a way they deem to be more socially acceptable than their true answer would be.