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Psychology > Research Methods > Flashcards

Research Methods Flashcards

1
Q

Aim

A

General expression of what the research intends to investigate.

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2
Q

Independent variable

A

The aspect of the experiment that the researcher changes or manipulates.

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3
Q

Dependent variable

A

The data that the researcher measures.
The data should only be affected by the IV.

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4
Q

Extraneous variable

A

A variable other than the IV that might affect the DV so therefore should be controlled.

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5
Q

Hypothesis

A

A prediction or testable statement about what the researcher thinks will happen.

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6
Q

Operationalisation

A
  • Operationalised variables are carefully stated, demonstrating exactly how they are to be measured.
  • This makes the hypothesis testable and measurable.
  • This is so that the target behaviour can be observed and recorded.
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7
Q

Null hypothesis

A
  • Predicts that there is no difference or relationship between the two groups.
  • If any difference is found it is due to chance.
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8
Q

Alternative hypothesis

A

Predicts a difference/relationship between groups/conditions.

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9
Q

Directional hypothesis (one tailed)

A

Predicts a difference/relationship between conditions and states the direction of the difference.

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10
Q

Non-directional hypothesis (two tailed)

A

Predicts a difference/relationship between conditions and doesn’t state the direction.

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11
Q

When do you use directional or non-directional hypothesis?

A

Previous research evidence - directional
No previous research that suggests which direction - non-directional

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12
Q

How to write a null hypothesis

A

There will be no difference in DV in IV1 compared to IV2.

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13
Q

How to write a directional hypothesis

A

Participants who IV1 higher/lower DV IV2.

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14
Q

How to write a non-directional hypothesis

A

There will be a difference in DV in IV1 compared to IV2.

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15
Q

Levels of IV

A

Numbers of conditions

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16
Q

Lab experiment

A
  • Carried out in an artificial environment
  • Controlled and standardised procedure
  • Researcher manipulates the IV to measure the effect on the DV
  • Participants know they’re taking part in a study
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17
Q

Field experiment

A
  • Conducted in a more natural environment
  • Researcher manipulates the IV to measure the effect on the DV
  • Participants do not know they are in an experiment
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18
Q

Natural experiment

A
  • Conducted in a natural environment
  • The IV is naturally occurring
  • IV: setting
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19
Q

Quasi experiment

A
  • Either lab or natural
  • IV: something that occurs within the person (a characteristic)
  • Not true experiments because you cannot randomly allocate participants to conditions
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20
Q

Standardised procedure

A
  • Ensuring that all participants are treated in exactly the same way.
  • Allows for reliable methodology
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21
Q

Reliability

A

Consistency

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22
Q

Internal validity

A

The extent to which it was the IV alone that caused a change to the DV.

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23
Q

Ecological validity

A

The extent to which the results can be generalised to another setting (e.g real life).

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24
Q

Mundane realism

A

The extent to which the task is representative of that behaviour in the real world.

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25
Q

Demand characteristics

A
  • Cues in the environment that may reveal the aim of the experiment, and so participants may change their behaviour as a result.
  • ‘Please you’ effect - changing your behaviour to try and ‘help’ the researcher.
  • ‘Screw you’ effect - changing your behaviour to go against what the researcher is trying to find.
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26
Q

Random allocation

A

Each participant has an equal chance of being put into either condition.

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27
Q

Order effects

A

An extraneous variable where the order in which conditions of the experiment take place effects the results (e.g practice effects or fatigue effects).

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28
Q

Lab experiments strengths (x2)

A
  • High control of extraneous variables (increases internal validity)
  • Replication is possible due to standardisation (can test if the findings are reliable)
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29
Q

Lab experiments limitations (x3)

A
  • Artificial environment (low ecological validity)
  • Artificial task (low mundane realism)
  • People know they are being tested (demand characteristics)
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30
Q

Field experiments strengths (x2)

A
  • Natural environment (higher ecological validity)
  • Participants do not normally know they are in an experiment (reduction in demand characteristics)
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31
Q

Natural experiments strengths (x2)

A
  • They provide opportunity for research that might not otherwise be undertaken for practical or ethical reasons
  • They often have high ecological validity because they study real-life events
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32
Q

Natural experiments limitations (x3)

A
  • Lack of controls (difficult to establish cause and effect)
  • A naturally occurring event may not happen very frequently (reducing the opportunities for research)
  • Participants may not be randomly allocated to conditions (you cannot be sure the IV is affecting the DV - reducing validity)
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33
Q

Quasi experiments strength

A

They are often carried out under controlled conditions (same strengths as lab experiments).

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34
Q

Quasi experiments limitation

A

The participants cannot be randomly allocated to conditions (there may be participant extraneous variables).

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35
Q

What is meant by experimental designs

A

How the participants are organised across the conditions.

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36
Q

Independent groups design

A

Each participant takes part in one condition only.

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37
Q

Random allocation - the lottery method (x3)

A
  1. Obtain a list of all the people in the sample
  2. Put all the names in a lottery hat
  3. Select the number of names required for condition A and put the next names into condition B
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38
Q

Random allocation - random number generator (x2)

A
  1. Number every member of the sample
  2. Use a computer program to to get a random number and allocate half into one condition
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39
Q

Matched pairs design

A

Each participant only takes part in one condition only, but the participants are matched on variables considered relevant (e.g age, sex, IQ).

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40
Q

How is a matched pairs design done? (x3)

A
  1. The researcher recruits a group of participants
  2. They match the participants on specific variables (such as age or IQ) often done with a questionnaire
  3. One of the pair gets randomly allocated into condition A, the other condition B
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41
Q

Repeated measures design

A

Each participant takes part in both conditions.

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42
Q

Independent groups strengths (x2)

A
  • Order effects are reduced as participants only take part in one condition
  • Demand characteristics are reduced (less likely to guess the aim of the study if only taking part in one condition)
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43
Q

Independent groups limitations (x2)

A
  • Participant extraneous variables between the groups (lowers the internal validity)
  • Less economical than repeated measures (need twice as many participants)
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44
Q

Repeated measures strengths (x2)

A
  • Participant extraneous variables are controlled for (reduced, never fully eliminated)
  • Less participants needed as they appear in both conditions
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45
Q

Repeated measures limitations (x2)

A
  • Order effects
  • Demand characteristics
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46
Q

Matched pairs strengths (x2)

A
  • Reduced order effects and demand characteristics (participants only take part in one condition)
  • Participant extraneous variables are reduced (not eliminated)
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47
Q

Matched pairs limitations (x2)

A
  • The participants cannot be truly matched
  • Time consuming and expensive, so less economical than the other designs
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48
Q

Fixing problems - independent groups

A
  • Participant variables and researcher bias: use random allocation
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49
Q

Fixing problems - repeated measures

A
  • Order effects: use counterbalancing (half do A then B, half do B then A)
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50
Q

Fixing problems - matched pairs

A
  • Participant variables: restrict the number of variables to match on
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51
Q

Experimental realism

A

Whether an experiment has psychological impact and ‘feels real’ to participants.

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52
Q

Confounding variables

A

Variables apart from the IV that have affected the DV.

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53
Q

Difference between extraneous and confounding variables

A

Extraneous - could affect
Confounding - have affected

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54
Q

Uncontrolled variables

A

Variables that cannot be controlled for (e.g weather) - they will become confounding variables.

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55
Q

Situational confounding variable

A

Features of the experimental situation

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56
Q

Participant confounding variable

A

Differences between the participants

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57
Q

Investigator effects

A

Where a researcher (consciously or unconsciously) acts in a way to support their prediction.
This is particularly a problem when observing effects that can be interpreted in more than one way.

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58
Q

Examples of direct effects (x3)

A
  • Non verbal communication
  • Spending more time with one group
  • Asking leading questions
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59
Q

Examples of indirect effects (x2)

A
  • Operationalised variables are designed in such a way that the desired result is more likely
  • Loose procedure effect: an investigator may not clearly state the standardised instructions which leaves room for the results to be influenced by the experimenter
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60
Q

Randomisation

A

Presenting any stimuli in an experiment in a ‘random’ manner to avoid it having an effect on the DV.
It reduces the chance of practice effects becoming a confounding variable.

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61
Q

Single blind test

A

Where participants do not know which condition of a study they are in.

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62
Q

Double blind test

A

When neither participant nor the investigators know which condition the participants are in.

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63
Q

Target population

A

The group of people the researcher wants to study.
They cannot study everyone so they have to select a sample.

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64
Q

Sample

A

A small group of people who represent the target population and who are studied.

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65
Q

Random sampling

A

Sampling technique in which every member of the target population has an equal chance of being chosen.

66
Q

How to do random sampling

A
  1. You need a sampling frame, which is a complete list of all members of the target population.
  2. All of the names on the list are assigned a number.
  3. The sample is selected randomly (e.g a computer-based randomiser or picking names from a hat).
67
Q

Random sampling strength

A

Lack of bias because everyone has an equal chance (more likely to be representative)

68
Q

Random sampling limitations (x2)

A
  • Impractical (takes time and effort)
  • Does not guarantee representativeness
69
Q

Opportunity sampling

A

A technique that involves recruiting anyone who happens to be available at the time of your study.

70
Q

How to do opportunity sampling

A

The researcher will go somewhere where they are likely to find their target population and ask people to take part.

71
Q

Opportunity sampling strengths (x2)

A
  • Simple and easy to conduct
  • For field and natural experiments, the researcher must use those available
72
Q

Opportunity sampling limitations (x2)

A
  • Unrepresentative sample
  • Researcher bias
73
Q

Volunteer sampling

A

People volunteer in response to an advert.
The researcher may then select only those who are suitable for the study.

74
Q

How to do volunteer sampling

A

Participants self-select by responding to an advert.

75
Q

Volunteer sampling strengths (x2)

A
  • Most convenient (economical method)
  • Reach a wide audience
76
Q

Volunteer sampling limitation

A

Biased sample (particular interests, altruistic (kind))

77
Q

Systematic sampling

A

Selecting names from the sampling frame at regular intervals.

78
Q

How to do systematic sampling

A
  1. A sampling frame is produced and organised (e.g into alphabetical order)
  2. A sampling system is nominated
  3. The researcher then works through the sampling frame until the sample is complete
79
Q

Systematic sampling strengths (x2)

A
  • Objective system so no researcher bias
  • Simple (as long as you have a sampling frame)
80
Q

Systematic sampling limitation

A

Not completely random, so not completely representative

81
Q

Stratified sampling

A

Participants are selected from different subgroups (strata) in the target population in proportion to the subgroup frequency in the population.

82
Q

How to do stratified sampling

A
  1. Identify the number of people in the target population
  2. Calculate the frequency of the subgroups
  3. Apply the frequency to the sample
83
Q

Stratified sampling strength

A

Representative

84
Q

Stratified sampling limitations (x2)

A
  • Knowledge of population characteristics is required
  • Time consuming
85
Q

Test-retest reliability

A

The reliability of a test measured over time (measures consistency).

86
Q

Inter-rater reliability

A

The degree of agreement amongst raters.

87
Q

Population validity

A

How well the sample can be generalised to the population as a whole.

88
Q

Temporal validity

A

Whether the findings are still valid today (high when research findings successfully apply across time).

89
Q

Construct validity

A

The degree to which a test measures what it claims, or purports, to be measuring.

90
Q

Concurrent validity

A

Asks whether a measure is in agreement with a pre-existing measure that is validated to test for the same (or a very similar) concept.
This is gauged by correlating measures against each other.

91
Q

Predictive validity

A

The degree to which a test accurately predicts a criterion that will occur in the future.

92
Q

Face validity

A

Where you apply a superficial and subjective assessment of whether or not your study or test measures what it is supposed to measure.

93
Q

Quantitative data

A

Data that is expressed numerically.

94
Q

Quantitative data strengths (x2)

A
  • Relatively simple to analyse (comparisons between groups can be easily drawn)
  • Data in numerical form tends to be more objective and less open to bias
95
Q

Qualitative data

A

Data that is expressed in words, and may take the form of a written description of the thoughts, feelings and opinions of participants (or a written account of what the researcher saw in the case of an observation).

96
Q

Qualitative data strengths (x2)

A
  • More richness in detail
  • Much broader in scope and gives the participant the opportunity to more fully report their thoughts, feelings and opinions on a given subject
97
Q

Quantitative data limitation

A

Much narrower in meaning and detail than qualitative data (may fail to represent ‘real life’)

98
Q

Qualitative data limitations (x2)

A
  • Often difficult to analyse (cannot be summarised statistically)
  • Conclusions often rely on the subjective interpretations of the researcher
99
Q

Primary data

A

Original data that has been collected specifically for the purpose of the investigation by the researcher.

100
Q

Primary data strength

A

Specific to the investigation

101
Q

Primary data limitation

A

Requires time and effort (planning, preparation and resources)

102
Q

Secondary data

A

Data that has been collected by someone other than the person who is conducting the research.

103
Q

Secondary data strengths (x2)

A
  • Quick and cheap to access
  • The researcher may find that the desired information already exists and so there is no need to conduct primary data collection
104
Q

Secondary data limitations (x3)

A
  • Variation in the quality and accuracy
  • May be outdated or incomplete
  • May not match the researchers needs or objectives (could challenge the validity of any conclusions)
105
Q

Case study

A

The detailed study of a single individual or a small group of people.

106
Q

Triangulation

A

Using more than one method to check the validity of the findings.

107
Q

Longitudinal study

A

Research carried out on an individual or group over a long period of time.

108
Q

Longitudinal study strength

A

Allow to look at changes over time

109
Q

Longitudinal study limitation

A

Participants may drop out (attrition rate) which can lead to a small sample size

110
Q

Case study strengths (x4)

A
  • High levels of validity (go into depth and give a rich insight)
  • They allow multiple methods to be used (triangulation) which increases validity
  • They allow researchers to study events or complex psychological areas they could not practically or ethically manipulate
  • Efficient as it only takes one case study to refute a theory
111
Q

Case study limitations (x3)

A
  • Researcher bias (researchers can become too involved and lose their objectivity)
  • Lack of control
  • Difficult to replicate so lack scientific rigour (each case study is unique)
112
Q

Observation

A

Simply observing behaviour and looking for patterns.

113
Q

Participant reactivity

A

Individuals modify an aspect of their behaviour in response to their awareness of being observed (slightly different to demand characteristics).

114
Q

Inter-rater reliability (observations)

A

There should be at least 2 observers to make the observational data more objective and unbiased.
They should then compare their data at the end, and the correlation should be as close to 1 (0.8 minimum) to have good reliability.

115
Q

Naturalistic observations

A

The observation of behaviour in its natural setting.

116
Q

Naturalistic observation strengths (x2)

A
  • High ecological validity
  • Participants are less likely to be affected by demand characteristics
117
Q

Naturalistic observation limitations (x2)

A
  • Little control over extraneous variables (hard to establish causality)
  • Replication is often not possible (cannot check the reliability of the findings)
118
Q

Controlled observations

A

An observation taking place in a controlled setting, usually behind a one way mirror so they cannot be seen.

119
Q

Controlled observation strength

A

Controlled environment (less risk of extraneous variables affecting behaviour)

120
Q

Controlled observation limitation

A

Artificial setting (results may lack ecological validity)

121
Q

Structured observations

A

The researcher creates a behavioural checklist before the observation in order to code the behaviour.
Behaviour can be sampled Using time or event sampling.

122
Q

Behavioural checklist

A

The researcher determines precisely what behaviours are to be observed before the observation.
The target behaviour is split up into a set of behavioural categories (behaviour checklist).

123
Q

Criteria for a behavioural checklist (x4)

A

The behaviours should:
- Be observable
- Have no need for interferences to be made
- Cover all possible component behaviours
- Be mutually exclusive / not overlap

124
Q

Pilot study

A

A small scale study carried out before the actual research.
It allows the researchers to practice using the behaviour checklist / observation schedule.

125
Q

Event sampling

A

Counting each time a particular behaviour is observed.

126
Q

Event sampling strength

A

Useful when the target behaviour or event happens infrequently and could be missed if time sampling was used.

127
Q

Event sampling limitation

A

If the situation is too busy and there is lots of the target behaviour occurring then the researcher may not record it all.

128
Q

Time sampling

A

Recording behaviour at timed intervals.

129
Q

Time sampling strength

A

The observer has time to record what they have seen.

130
Q

Time sampling limitation

A

Some behaviours will be missed outside the intervals - observations may not be representative.

131
Q

Structured observation strengths (x2)

A
  • The behavioural checklist allows objective, quantifiable data to be collected which can be statistically analysed.
  • Allows for more than one observer (due to checklist) which can increase the (inter-observer) reliability.
132
Q

Structured observation limitation

A

The pre-existing behavioural categories can be restrictive and does not always explain why the behaviour is happening.

133
Q

Unstructured observations

A

The observer notes down all the behaviours they can see in a qualitative form over a period of time (no behavioural checklist is used).

134
Q

Unstructured observation strengths (x2)

A
  • Can generate in-depth, rich qualitative data that can explain why the behaviour has occurred
  • Researchers are not limited by prior theoretical explanations
135
Q

Unstructured observation limitations (x2)

A
  • The observers can get down to eye catching behaviours that may not be representative of all behaviours occurring
  • More subjective and less comparable across researchers
136
Q

Overt observations

A

Participants are aware that their behaviour is being studied - the observer is obvious.

137
Q

Overt observation strength

A

Better fulfils ethical guidelines (compared to covert)

138
Q

Overt observation limitation

A

Participant reactivity - participants know they are being observed and so they may change their behaviour

139
Q

Covert observations

A

Participants are unaware that their behaviour is being studied - the observer is covered

140
Q

Covert observation strength

A

Behaviour is more likely to be natural (higher validity)

141
Q

Covert observation limitation

A

Can break ethical guidelines as deception is used (it may cause the participants some psychological harm)

142
Q

Participant observations

A

The observer becomes involved in the participant group and may not be known to other participants.

143
Q

Participant observation strength

A

Being part of the group can allow the researcher to get a deep understanding of the behaviours of the group (increasing validity).

144
Q

Participant observation limitations (x2)

A
  • The presence of the researcher might influence the behaviour of the group
  • The researcher may lose objectivity as they are part of the group
145
Q

Non-participant observations

A

The observer is separate from the participant group that are being observed.

146
Q

Non-participant observation strength

A

Researchers’ observations are likely to be more objective as they are not influenced by the group.

147
Q

Non-participant observation limitation

A

It is harder to produce qualitative data to understand the reasons for the behaviour.

148
Q

Self-reports

A

Methods of gathering data where participants provide information about themselves (e.g thoughts, feelings, opinions).

149
Q

Psychometric measure

A

Tests that have been assessed for validity and reliability.

150
Q

Self-report strengths (x4)

A
  • Provides rich qualitative data about complex human behaviour
  • Can help explain reasons behind behaviour
  • An easy way to gather a large amount of data (increases generalisability)
  • You can ask people hypothetically what they would do without having to set up an experiment and observe behaviour
151
Q

Self-report limitations (x5)

A
  • Social desirability bias (wanting to come across in a certain way)
  • Only useful if the participant is willing to disclose the information
  • Relies on participants having the introspective ability to understand their own thoughts and feelings
  • Acquiescence bias (people tend to agree with questions)
  • Participants may misinterpret the questions (subjective)
152
Q

Questionnaires

A

A written self-report technique where participants are given a pre-set number of questions to respond to.

153
Q

How should questions be designed in a questionnaire?

A
  • Questions should progress logically from least to most sensitive, and from more general to more specific
  • The researcher should ensure that the answer to a question is not influenced by previous questions
154
Q

Types of questions (x4)

A
  • Likert scales (strongly disagree…strongly agree)
  • Rating scales (1-10)
  • Closed questions (yes/no)
  • Open questions (describe…)
155
Q

Closed questions

A

There are only a certain amount of choices available to answer.

156
Q

Open questions

A

Allow participants to give a full, detailed answer and there is no restriction on what the participants can say.

157
Q

Standardised instructions

A

A set of written or recorded instructions that are given to ensure that all participants receive them in the same way.

158
Q

Filler questions

A

Questions put into a questionnaire or interview to disguise the aim of the study (to reduce demand characteristics p).

159
Q

Questionnaire strengths (x3)

A
  • Social desirability bias is reduced (no interviewer present, and often anonymous)
  • A large amount of data can be controlled very quickly (increase representativeness and generalisability)
  • Data can be analysed more easily than interviews (if mostly quantitative)
160
Q

Questionnaire limitations (x2)

A
  • Opinions given may not reflect the participant’s opinion and they may be forced into answering something which does not fit (lowering the validity of the findings)
  • The quantitative data produces less rich data than interviews
161
Q

Interview

A

A self-report technique that involves an experimenter asking participants questions (generally on a one-to-one basis) and recording their responses.

Psychology (14 decks)

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