Experimental Methods- RESEARCH METHODS Flashcards

RESEARCH METHODS

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1
Q

What must experiments must have

A

Independent Variable (I.V)
Dependent Variable (D.V)

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2
Q

Independent Variable

A

Variable that changes

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3
Q

Operationalised Independent Variable

A

Make measurable

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4
Q

Dependent Variable

A

Variable you measure

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5
Q

Extraneous Variable

A

Variables we need to control

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6
Q

Confounding variables

A

Factors other than the independent variable that may cause a result.

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7
Q

Examples of Participant Variables (3)

Any characteristic or aspect of a participant’s background

A
  • Age
  • Eyesight
  • Tolerance to caffeine
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8
Q

Examples of Situational Variables (3)

Environmental variables that alter participants’ behaviours

A
  • Speed of throw
  • Amount of coke
  • Rain
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9
Q

Standardised Procedure

A

Keeping everything the same. Control with sensible design

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10
Q

Counterbalancing

A
  • Combine results
  • Compare Condition A with Condition B
  • Order effects should cancel out
  • Any difference that remains must be due to the IV
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11
Q

Repeated measures design

A

Using the same participants in each condition. Two sets of materials matched for difficulty.

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12
Q

Strength of Repeated measures design (2)

A
  • Controls for individual differences
  • Need fewer participants
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13
Q

Weakness of Repeated measures design (3)

A
  • Each participant has to do at least 2 tasks
  • Order of doing tasks is significant- there are order effects.
  • More likely to work out the aim
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14
Q

Independent measures design

A

Using different participants in each condition.

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15
Q

Strength of Independent measures design (3)

A
  • Order effects not a problem
  • Less demand characteristics
  • Less likely to work out the aim
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16
Q

Weakness of Independent measures design (2)

A
  • Twice as many participants would be needed to produce equivalent data
  • Increases time and money spent on recruiting participants
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17
Q

Matched pairs design

A

Using different but similar participants in each condition

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18
Q

Strength of Matched pairs design (2)

A
  • Participants only take part in a single condition so order effects and demand characteristics are less of a problem
  • Less likely to work out the aim
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19
Q

Weakness of Matched pairs design (3)

A
  • Matching may be time- consuming and expensive, particularly if a pre- test is required
  • Less economical
  • Difficult to identify appropriate variables
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20
Q

Lab experiment

A
  1. Experimenter manipulates the IV
  2. Controlled conditions- situational variables are controlled
21
Q

Strength of Lab experiment

A
  • Internal validity is increased (can see cause and effect)- scientific
22
Q

Weakness of Lab experiment

A
  • Low ecological validity- (less we can apply our results to the real world)
23
Q

Field experiment

A
  1. Experimenter manipulates the IV
  2. Conducted in the real world
24
Q

Strength of Field experiment

A
  • Better ecological validity
25
Q

Weakness of Field experiment

A
  • Lack of control (can no longer see cause and effect)
26
Q

Natural experiment

A
  1. When the researcher measures the effect of an IV on a DV
27
Q

Strength of a Natural experiment

A
  • Have high external validity (as they involve the study of real- world issues/ problems)
28
Q

Weakness of a Natural experiment

A
  • May only happen very rarely, reducing opportunities for research
  • Participants may not be randomly allocated to experimental conditions (ONLY independent group design)
29
Q

Quasi experiment

A
  1. Have an IV based on an existing difference between people (e.g age/ gender) that no one has manipulated this variable, it simply exists.
30
Q

Strength of a Quasi experiment

A
  • Often carried out under controlled conditions e.g replication
31
Q

Weakness of a Quasi experiment

A
  • Cannot randomly allocate participants to conditions and therefore there may be confounding variables
32
Q

Difference between an aim and hypothesis

A
  • Aims are developed from theories and develop from reading about other similar research and is what you are investigating in a study
  • A hypothesis is a precise statement which clearly states the relationship between the variables being investigated, e.g directional/ non- directional
33
Q

Directional hypothesis

A

When you state what direction the results will be (e.g one group will perform better than the other group.) Previous research should suggest the direction of the results

34
Q

Non- directional hypothesis

A

When you think there will be a difference- but not sure which one will do better. No previous research exists

35
Q

Null hypothesis

A

There will be no difference- the IV will have no effect

36
Q

Difference between population and sample

A
  • Population is the entire group that you want to draw conclusions about.
  • Sample is the specific group that you will collect data from.
37
Q

Opportunity sample

A

Researchers select anyone who happens to be willing and available

38
Q

Volunteer sample

A

When participants select themselves to be part of the sample

39
Q

Random sample

A

All members of the target population have an equal chance of being selected:
1. Complete list of all members of target population.
2. All names/ members on the list are assigned a number

40
Q

Systematic sample

A

Is when every nth member/ participant of the target population is selected

41
Q

Stratified sample

A

Composition of the sample reflects the proportions of people in certain subgroups within the Target population OR Wider population

42
Q

Descriptive statistics

A

Describing data (patterns, range)

43
Q

What is central tendency?

A

Averages

44
Q

Examples of measures of central tendency

A

Mean
Median
Mode

45
Q

Mean

A

Strength- Most sophisticated measure because it uses all the data
Weakness- Sensitive to outliers/ extreme data

46
Q

Median

A

Strength- Not affected by outliers/ extreme scores
Weakness- Not as sensitive as it doesn’t use all the data

47
Q

Mode

A

Frequencies, most simple measure

48
Q

What is measures of dispersion?

A

How spread out the data is

49
Q

Examples of dispersion

A

Range spread
Standard deviation- how close is the data to the mean- variability of the data