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Experimental Methods- RESEARCH METHODS Flashcards

RESEARCH METHODS (66 cards)

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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

Random allocation

A

The process of assigning participants to different groups in a study using a random method to reduce bias.

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12
Q

Randomisation

A

A technique used in experiments to ensure every participant has an equal chance of being assigned to any group, enhancing the validity of the results.

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13
Q

Standardisation

A

The use of consistent procedures and conditions in a study to ensure that differences in outcomes are due to the variables being tested, not external factors.

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14
Q

Repeated measures design

A

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

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15
Q

Strength of Repeated measures design (2)

A
  • Controls for individual differences
  • Need fewer participants
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16
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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17
Q

Independent measures design

A

Using different participants in each condition.

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18
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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19
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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20
Q

Matched pairs design

A

Using different but similar participants in each condition

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21
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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22
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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23
Q

Lab experiment

A
  1. Experimenter manipulates the IV
  2. Controlled conditions- situational variables are controlled
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24
Q

Strength of Lab experiment

A
  • Internal validity is increased (can see cause and effect)- scientific
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25
Weakness of Lab experiment
- Low ecological validity- (less we can apply our results to the real world)
26
Field experiment
1. Experimenter manipulates the IV 2. Conducted in the real world
27
Strength of Field experiment
- Better ecological validity
28
Weakness of Field experiment
- Lack of control (can no longer see cause and effect)
29
Natural experiment
1. When the researcher measures the effect of an IV on a DV
30
Strength of a Natural experiment
- Have high external validity (as they involve the study of real- world issues/ problems)
31
Weakness of a Natural experiment
- May only happen very rarely, reducing opportunities for research - Participants may not be randomly allocated to experimental conditions (ONLY independent group design)
32
Quasi experiment
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.
33
Strength of a Quasi experiment
- Often carried out under controlled conditions e.g replication
34
Weakness of a Quasi experiment
- Cannot randomly allocate participants to conditions and therefore there may be confounding variables
35
Difference between an aim and hypothesis
- 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
36
Directional hypothesis
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**
37
Non- directional hypothesis
When you think there will be a difference- but not sure which one will do better. **No previous research exists**
38
Null hypothesis
There will be no difference- the IV will have no effect
39
Difference between population and sample
- Population is the entire group that you want to draw conclusions about. - Sample is the specific group that you will collect data from.
40
Opportunity sample
Researchers select anyone who happens to be willing and available
41
Strength of Opportunity Sample
Quick and easy method -it is using people who are **readily available** (to use)
42
Weakness of Opportunity Sample
Biased - sample is unrepresentative of target population so findings cannot be generalised - drawn from specific area
43
Volunteer sample
When participants select themselves to be part of the sample
44
Strength of Vounteer sample
Participants are willing - participants are less likely to want to jeapordise the study and its results
45
Weakness of Volunteer sample
**Volunteer bias** - participants may share certain traits so results cannot be generalised to target population
46
Random sample
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 3. A set of numbers is randomly generated using a random number/name generator. 4. The individuals corresponding to the selected numbers are chosen to take part in the study.*
47
Strength of Random Sample
There is an unbiased selection - **representative** of target population so results can be generalised
48
Weakness of Random Sample
Time consuming and impractical- some participants may **refuse to take part**
49
Systematic sample
Is when every nth member/ participant of the target population is selected
50
Strength of Systematic sample
Unbiased selection - The first set of participants are selected at random - objective method
51
Weakness of Systematic sample
Time consuming - A complete list of the population is required
52
Stratified sample
Composition of the sample reflects the **proportions of people** in certain subgroups within the Target population OR Wider population
53
Strength of Stratified sample
Representative method - represents characteristics of target population - can be generalised
54
Weakness of Stratified sample
Time consuming - knowing subgroups and dividing population into categories
55
Descriptive statistics
Describing data (patterns, range)
56
What is central tendency?
Averages
57
Examples of measures of central tendency
Mean Median Mode
58
Mean
Strength- Most sophisticated measure because it uses all the data Weakness- Sensitive to outliers/ extreme data
59
Median
Strength- Not affected by outliers/ extreme scores Weakness- Not as sensitive as it doesn't use all the data
60
Mode
Frequencies, most simple measure
61
What is measures of dispersion?
How spread out the data is
62
Examples of dispersion
Range spread Standard deviation- how close is the data to the mean- variability of the data
63
External validity (generalisability)
Whether we can generalise results outside of study, e.g other groups of people
64
Internal validity (demand characteristics, bias)
Design of your study
65
Measuring reliability
If we did the study again would we get the same results
66
Improving reliability
Re- write behaviour categories- make more specific Re- train observers
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