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Research Methods Flashcards

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1
Q

Independent variable (IV)

A
  • In experiments
  • Manipulated by the experimenter in order to observe effects on dependent variable
  • E.g. what are the two conditions of the experiment? - male or female
  • This is used to establish cause and effect
  • The one that is CHANGED
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2
Q

Dependent variable (DV)

A
  • In experiments
  • The DV depends in some way and the IV
  • How the effect of the change in the IV is measured
  • E.g. different scores, different times
  • The one that is MEASURED
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3
Q

Co-variables

A
  • In correlations
  • When using a correlation, it is impossible to directly manipulate the variables
  • In this circumstance we would have to measure two separate variables and then compare them to each other
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4
Q

Operationalisation

A

Making something measurable

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5
Q

Examples of operationalisation of variables

A

1 - stating the method of measuring the DV
DV is time = not operationalised
DV is time takes to do task in seconds = operationalised

2 - Using a questionnaire to measure happiness

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6
Q

Extraneous variables

A

Any variables that might affect the DV so we try to keep them the same to ensure a fair test

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7
Q

Confounding variables

A

Variables which have not been kept the same (controlled) so may have affected the DV

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8
Q

Experiments

A

Involve manipulation of an independent variable to measure the effect on a dependent variable. There are 4 types of experiment.

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9
Q

Lab experiment

A
  • IV is directly controlled by the experimenter
  • Takes place in tightly controlled, artificial situations
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10
Q

Strengths of lab experiments

A
  • Easy to replicate
  • High internal validity due to good control of variables
  • Can establish cause and effect
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11
Q

Limitations of lab experiments

A
  • Lacks ecological validity as tasks are more artificial
  • Participants may try to guess the aim of the study
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12
Q

An example of a lab experiment

A

Lotus and Palmer

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13
Q

Field experiment

A
  • IV is deliberately manipulated by the researcher
  • A controlled experiment
  • It is conducted in a more “ordinary” environment
  • The “field” is anywhere outside of the laboratory
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14
Q

Strengths of field experiments

A
  • Participants are usually not aware they are participating in an experiment so behaviour may be more natural as they are not responding to demand characteristics
  • Participants may be more relaxed
  • Higher external validity due to greater mundane realism
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15
Q

Limitations of field experiments

A
  • Lower internal validity (more difficult to control extraneous and confounding variables)
  • Ethical issues as participants may not know they are being studied
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16
Q

Natural experiment

A
  • Conducted when it is not possible, for ethical or practical reasons, to deliberately manipulate and IV
  • The IV varies naturally and would vary whether or not the researcher was interested
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17
Q

Strength of natural experiments

A
  • Enables psychologists to study “real” problems such as the effects of a disaster on health (mundane realism and ecological validity)
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18
Q

Limitations of natural experiments

A
  • Cannot demonstrate causal relationships because IV is not directly manipulated
  • Random allocation is not possible so confounding variables may impact internal validity
  • Can only be used where conditions vary naturally
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19
Q

Quasi experiment

A
  • Studies that are “almost” experiments
  • IV is actually not something that varies at all - it is just a condition that exists e.g. age and gender
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20
Q

Strength of quasi experiments

A
  • Allows comparisons between types of people
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21
Q

Limitations of quasi experiments

A
  • Participants may be aware of being studied, creating demand characteristics and reducing internal validity
  • Dependent variable may be a fairly artificial task, reducing mundane realism
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22
Q

Example of a field experiment

A

Bickman (IV - what they were wearing (dressed as a milkman, security guard and ordinary clothes) DV - obedience)

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23
Q

Example of a natural experiment

A

Effects of institutionalisation in Romanian orphanages
Rutter (IV - age of adoption, DV - IQ)

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24
Q

Example of a quasi experiment

A

Miller (1956)
How age affects short-term memory (digit span test)

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25
Q

What chart is used for experiments?

A

Bar chart

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26
Q

High graph is used for correlations?

A

Scattergraph

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27
Q

What do correlations not look at?

A

Whether an IV affects a DV

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28
Q

Correlations

A

Allow a psychologist to examine the relationship between two co-variables. These co-variables exist and are not manipulated by the psychologist.

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29
Q

Correlation coefficient

A

Tells us whether a relationship is positive or negative, and tells us how strong the relationship is. It is a number between -1 and +1.

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30
Q

Strengths of correlations

A
  • Psychologists can study a topic where it would be unethical to carry out an experiment
  • The strength of a relationship can be found by using scattergraphs and calculating the correlation coefficient
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31
Q

Limitations of correlations

A
  • Can’t establish cause and effect between the co-variables i.e. we can’t say that changing one variable causes the other variable to change
  • Another variable may cause both variables to change
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32
Q

Observational study

A

A study that involves observing actual behaviours

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33
Q

Controlled observation

A

The setting for the observation is structured and controlled e.g. Ainsworth’s strange situation

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34
Q

Naturalistic observations

A
  • Setting for an observation is natural
  • Designed to examine behaviour without the experimenter interfering with it
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35
Q

What might a naturalistic setting do to demand characteristics?

A

Reduce them as people will act more naturally

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36
Q

Strength of naturalistic observations (will be the opposite for controlled observations)

A
  • People tend to behave more naturally than for controlled observations (providing higher external validity)
  • The information gathered tends to be richer and fuller than experimental methods
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37
Q

Limitations of naturalistic observations (will be the opposite for controlled observations)

A
  • Researcher has no control over the situation. Low internal validity and reliability, compared to controlled observations
  • Difficult to replicate
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38
Q

Covert observations

A

Participants’ behaviour is watched and recorded without their knowledge and consent

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39
Q

Overt observations

A

Participants’ behaviour is watched and recorded with their knowledge and consent

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40
Q

Strength of covert observations (will be the opposite for overt observations)

A

Should be higher in validity as people may change their behaviour if they are being observed

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41
Q

Limitation of covert observations (will be the opposite for overt observations)

A

Raise significantly more ethical issues regarding privacy as you shouldn’t observe covertly where people would not expect to be seen

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42
Q

Hawthorne effect

A

People change their behaviour when they know they are being observed

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43
Q

Participant observations

A

The researcher observes the participants from within the group that they are observing

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44
Q

Non-participant observations

A

The researcher observes the group of participants from a location away from the group

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45
Q

Strength of participant observations (will be the opposite for non-participant observations)

A

They give a greater insight into the behaviour of the group

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46
Q

Limitation of participant observations (will be the opposite for non-participant observations)

A

They are less likely to be objective

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47
Q

Why might researchers lose objectivity in a participant observation?

A

Researchers may form relationships with those they are observing

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48
Q

Unstructured observations

A

The researcher records all relevant behaviour but has no system

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49
Q

Problems with unstructured observations

A
  • There may be too much to record
  • The behaviours recorded will often be those which are most visible or eye-catching to the observer but these may not be the most important or relevant behaviours
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50
Q

A situation where an unstructured observation would be used

A

Where research has not been conducted before as a kind of pilot study to see what behaviours might be recoded using a structured system

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51
Q

Structured observations

A
  • Aim to be objective and rigorous
  • A researcher uses various systems to organise observations such as behavioural categories and sampling procedures
  • More common in psychology
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52
Q

Behavioural categories

A

Dividing a target behaviour (e.g. stress) into a subset of specific and operationalised behaviours

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53
Q

Behavioural categories should…

A
  • Be objective (no inferences need to be made about behaviours)
  • Cover all possible component behaviours and avoid a “waste basket” category
  • Be mutually exclusive (you should not have to mark two categories at one time)
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54
Q

Event sampling

A

Counting the number of times a certain behaviour (event) occurs in a target individual or individuals e.g. counting how many times a person smiles in a 10 minute time period

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55
Q

Time sampling

A

Recording behaviours in a given time frame e.g. noting what a target individual is doing every 30 seconds or some other time interval. At that time to observer may tick one or more categories from a checklist

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56
Q

Interviews

A
  • A face-to-face interaction that results in the collection of data
  • Questions can be predetermined or created in response to answers
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57
Q

Strengths of interviews

A
  • A lot of data can be collected
  • Face-to-face can assess emotions
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58
Q

Limitations of interviews

A
  • Social desirability bias and interview bias
  • Requires skilled personnel
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59
Q

Social desirability bias

A

People want to look good so they may be dishonest

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60
Q

Interviewer bias

A

The way the interviewer treats the participants might affect their answers
A biased interviewer may ask leading questions or otherwise affect the behaviour of participants, reducing validity

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61
Q

Structured interview

A

Pre-determined questions i.e. a questionnaire that is delivered face-to-face with no deviation from the original questions

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62
Q

Unstructured interview

A

New questions are developed as you go along

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63
Q

Strength of a structured interview

A

Easier to compare people’s answers

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64
Q

Strength of an unstructured interview

A

More natural so people may reveal more

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65
Q

Recording the interview

A
  • Taking notes may interfered with the interviewer’s listening skills
  • Taking notes may also make the respondent feel like they’re being evaluated especially if the interviewer doesn’t write everything down
  • Best to record the interview and transcribe later
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66
Q

What effect can having an interviewer who is interested in the respondent’s answers have?

A

It may increase the amount or the quality of the information

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67
Q

Questionnaire survey

A
  • Data that is collected using a set of written questions
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68
Q

Strengths of questionnaires

A
  • A lot of data collected
  • Does not require specialist administrators
  • More anonymous
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69
Q

Limitations of questionnaires

A
  • Leading questions and social desirability bias
  • Biased samples
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70
Q

Clarity (questionnaires)

A

The respondent needs to know exactly what is being asked. There should be no ambiguity.

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71
Q

Bias (questionnaires)

A

The questions should not lead the respondents to a particular answer e.g. social desirability bias

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72
Q

Analysis (questionnaires)

A

The answers need to be easy to analyse

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73
Q

Open questions

A

Harder to analyse due to the wide range of answers

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74
Q

Closed questions

A

Have a limited range of answers e.g. yes or no, scales etc. and are therefore easy to analyse

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75
Q

Filler questions (questionnaires)

A

Including some irrelevant questions helps distract respondents from the true purpose of the study
Prevent demand characteristics

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76
Q

Sequence for the questions (questionnaires)

A

It is better to start with easier questions as it puts respondents at ease

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77
Q

Sampling technique (questionnaires)

A

Questionnaires often use stratified sampling, which makes the answers more representative

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78
Q

Pilot study (questionnaires)

A

Test the questionnaires on a small group of people so they can be refined later

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79
Q

Case studies

A

An in depth study that gathers a lot of detail about one person or a small group

80
Q

Examples of case studies

A
  • Phineas Gage
  • HM
  • Tan
81
Q

Why is data triangulated?

A

To form a consistent conclusion about the case

82
Q

Strengths of case studies

A
  • Good ecological validity (real life application)
  • A more ethical way of studying psychology (can’t inflict pain or trauma so take the opportunity)
83
Q

Limitations of case studies

A
  • Cannot generalise
  • Cannot replicate
  • Time consuming/ have to employ people for an extended period of time
84
Q

Content analysis

A
  • A technique for systematically summarising and describing any form of content-written, spoken or visually
  • It converts qualitative data to quantitative data
85
Q

5 steps for content analysis

A
  • Data is collected
  • Researcher reads through or examines the data, making themselves familiar with it
  • The researcher identifies coding units
  • The data is analysed by applying the coding units
  • A tally is made of the number of times that a coding unit appears
86
Q

Thematic analysis

A
  • The researcher closely examines the data to identify common themes, topics, ideas and patterns of meaning that come up repeatedly
  • Psychologists may select quotes from the material they are analysing to exemplify the themes they’ve identified
87
Q

Strengths of content analysis

A
  • Reliable way to analyse qualitative data as the coding units are not open to interpretation and so are applied in the same way over time and with different researchers
  • It is an easy technique to use and is not too time time consuming
  • It allows a statistical analysis to be conducted if required as there is usually quantitative data as a result of the procedure
88
Q

Limitations of content analysis

A
  • Causality cannot be established as it merely describes the data
  • As it only describes the data it cannot extract any deeper meaning or explanation for the data patterns arising from
89
Q

Aim

A

A statement of what you want to find out e.g. are males or females better drivers?

90
Q

Hypothesis

A

A formal prediction of what you think will happen. It must include the operationalised IV and DV e.g. male drivers will have fewer faults on a driving test than female drivers

91
Q

A directional hypothesis/one tailed

A

Predicts the kind of difference between two conditions in an experiment or the direction of the correlation
E.g there will be a positive correlation between number of homeworks completed and final grade

92
Q

When do we use a directional hypothesis?

A

When there is previous research to support the prediction

93
Q

A non-directional hypothesis

A

Simply predicts that there will be a difference or a correlation but doesn’t state what the direction will be e.g. there will be a difference between number of error made by males and females in a driving test

94
Q

When do we use a non-directional hypothesis?

A

When there is no previous research or when there are mixed findings

95
Q

Target population

A

A group of people that are the focus of the researcher’s interest

96
Q

Sample

A

The group who take part in the research. We want the sample to be representative of the target population

97
Q

Random samples

A

Everyone in the entire target population has an equal change of being selected
Put names in a hat and draw out without looking

98
Q

Strengths of random sampling

A
  • It is widely accepted that since each member has the same probability of being selected, there is a reasonable chance of achieving a representative sample
  • It is an unbiased method
99
Q

Limitations of random sampling

A
  • It can be impractical (or not possible) to use a completely random technique e.g. the target group may be too large to assign numbers to
  • The people you select may not want to take part
100
Q

Opportunity sampling

A

Uses people from target population available at the time a willing to take part. It is based on convenience.

101
Q

Strength of opportunity sampling

A

It is easy and inexpensive to carry out

102
Q

Limitation of opportunity sampling

A

The consequent sample may not be representative as it could be subject to bias - you are only likely to approach certain types of people to ask if they want to take part in

103
Q

Systematic sampling

A

Chooses subjects in a system (i.e. orderly/logical) way from the target population e.g. every nth participant on a list of names

104
Q

Strength of systematic sampling

A

Assuming the list order has been randomised, this method offers an unbiased chance of gaining a representative sample

105
Q

Limitation of systematic sampling

A

If the list has been assembled in any other way, bias may be present e.g. if every 4th person in the list was male , you would only have males in your sample

106
Q

Volunteer sampling

A

Individuals choose to be a part of the study e.g. they respond to an advert

107
Q

Strength of volunteer sampling

A
  • Often achieves a large sample size through reaching a wide audience e.g. with online advertisements
  • The people want to take part
108
Q

Limitations of volunteer sampling

A

Those who respond to the call for volunteers may all display similar characteristics (such as being more trusting or cooperative than those who did not apply) thus increasing the chances of yielding an unrepresentative sample

109
Q

Stratified sampling

A

The researcher identifies the different types of people that make up the target population and works out the proportions needed for the sample to be representative

110
Q

Strength of stratified sampling

A

It is the only way to get a true representative sample

111
Q

Limitations of stratified sampling

A
  • It takes more time and resources to plan
  • Care must be taken to ensure each key characteristic present in the population is selected across strata, otherwise this will design a biased sample
112
Q

Experimental design

A

Refers to how psychologists use participants within an experiment

113
Q

Repeated measures

A

Involves using the same subjects in each condition of an experiment e.g. giving a group of subjects no alcohol followed at a later time by the same test after a pint of lager

114
Q

Strengths of repeated measures

A
  • No participant variables (e.g. both groups have the same driving ability as they are the same people)
  • Less participants are needed
115
Q

Limitations of repeated measures

A
  • Order/practice effects (participant may get better second time due to practice)
  • Demand characteristics
116
Q

Counterbalancing

A

An attempt to control order effects in a repeated measures design. Half the participants take part in A then B then the other half do B then A

117
Q

Independent measures

A

Involves using DIFFERENT subjects in each condition of the experiment e.g. giving one group of subjects a driving test with no alcohol and a different group of subjects the same test agates a pint of lager

118
Q

Strengths of independent measures

A
  • No order effects
  • less demand characteristics
119
Q

Limitations of independent measures

A
  • Participant variables
  • More participants needed to
120
Q

Matched pairs

A

Involves using different but similar subjects in each condition of experiment. An effort is made to match the subjects in each condition in any important characteristics that might affect performance e.g. in driving ability, alcohol tolerance etc.

121
Q

Strengths of matched pairs

A
  • No order effects
  • Less demand characteristics
  • Less participant variables
122
Q

Limitations of matched pairs

A
  • Time consuming
  • Difficult to do well
123
Q

Order effects

A

Order effects can occur in a repeated measures design and refers to how the positioning of tasks influences the outcome e.g. practice effect or boredom effect on second task

124
Q

Non-experimental design

A

How we design observations, interviews and questionnaires

125
Q

Consistency

A

The same people doing the same thing should get the same result

126
Q

A reliable observation should have…

A

A positive correlation between 2 observers watching the same thing at the same time

127
Q

Pilot studies

A
  • A small scale trial to help forsee any problems with a study
  • Allow us to check that participants know what they have to do and that the procedure is fit for purpose
  • Researchers can ask participants of pilot studies for feedback
  • Potentially saves time and money in the long run that could’ve been wasted on a badly designed study
128
Q

Demand characteristics

A

Where participants guess the aim and act up to the situation. Participants behave how they think you want them to in order to be a ‘good participant’

129
Q

Screw you effect

A

Participants acting in a way that researchers do not want on purpose

130
Q

Solution for demand characteristics

A

Deception/single blind technique

131
Q

Investigator effects

A

How the researcher’s behaviour might affect the participants. They could show bias as they want to see the result they expect

132
Q

Solution for investigator effects

A

Double blind technique where a 3rd party conducts the research who doesn’t know the aims.

133
Q

Ethical issue

A

These arise when there is a conflict between the rights of the participant and the goal to produce authentic results

134
Q

Ethical guidelines

A

Produced by the BPS to help researchers deal with the dilemmas.

135
Q

Informed consent

A

Making participants aware of the aims, procedure and their rights before the study goes ahead

136
Q

Ways of dealing with informed consent

A
  • All participants should sign a consent form
  • Parental consent for those under 16

Not ideal methods:
- Presumptive consent (ask a similar group of people)
- Prior general consent (give consent to a number of studies)
- Retrospective consent (get consent after the study)

137
Q

Deception

A

Deliberately misleading or withholding information from participants. This makes informed consent impossible.

138
Q

Ways of dealing with deception

A

Debrief participants at the end (inform them what happened, what their data will be used for and allow opportunity to withdraw results)

139
Q

Protection from harm

A

Participants should not be placed at any more risk than normal daily life - this includes stress and embarrassment

140
Q

Ways of dealing with protection from harm

A
  • Reassure participants that their behaviour was normal
  • Offer counselling if effects are extreme
  • Follow up
141
Q

Privacy and confidentiality

A

This is in line with data protection laws which is the right to have personal data protected

142
Q

Ways of dealing with privacy and confidentiality

A
  • Maintain anonymity - don’t record names - use numbers
  • Don’t share data with other researchers
143
Q

Ethics committee

A

Group of psychologists who weigh up the costs and benefits of a research proposal

144
Q

Consent forms

A
  • Participants need to sign a consent form to take part in a study
  • Set out as a formal letter
  • Includes procedural details and ethical rights
145
Q

Scientific method

A

Observe - develop a theory - create a precise testable hypothesis - research - accept or reject hypothesis - change or support theory

146
Q

Replicable

A

Easily repeatable

147
Q

Objective

A

Free from bias/emotion

148
Q

Controlled

A

Fair testing

149
Q

Title

A

Concise, precise and to the point

150
Q

Abstract

A
  • 100-150 word summary of aim, method, results and conclusion
  • Quick to read and understand
151
Q

Introduction

A
  • Often the longest section
  • Literature review of previous research in that area
  • States aim and hypothesis at the end of
152
Q

Method

A
  • Should be in enough detail to allow someone to replicate the research
  • Outlines design, sample, materials and procedure
153
Q

Results

A
  • A summary of the data, section of descriptive statistics, analysis of results of texts
  • Finishes with rejection or acceptance of null hypothesis
154
Q

Discussion

A
  • Discusses anything that went wrong or the limitations of the study
  • Suggests how it could be improved if it were to be repeated
  • Suggestions for alternative studies and future research are explored
  • Ends with a paragraph summing up what was found and assessing the implications of the study and any conclusions that can be drawn from it
  • Discusses how research compares to previous research
155
Q

References

A
  • Look through the report and include a reference for every researcher mentioned so people can find the original sources
  • Should be in alphabetical order
156
Q

Order for writing references

A

Name, date, title, book/journal published

157
Q

Appendices

A
  • Comes at the end of a report
  • Contains any additional information e.g. raw data or interview transcripts
  • Information is relevant but is too long or too detailed to include in the main body of your work
158
Q

Peer review

A

The way that psychological research findings are shared with other scientists and are held up for scrutiny

159
Q

Steps of peer review

A

1) A research paper is submitted to a journal is considered to be worthy of publication
2) The editor sends this paper to other experts in the field who critically appraise all aspects of the study then return it with their recommendations as to whether the work is of acceptable quality
3) If not, researchers revise their work and resubmit their paper

160
Q

Limitations of peer review

A
  • Peer review can act to maintain the status quo and prevent potentially revolutionary research from being published because science is conservative and resistant to large changes changes in opinion. If a study doesn’t fit with accepted existing knowledge it can be rejected
  • A reviewer may strongly support an opposing view making them less likely to provide an unbiased opinion of the work
  • Not necessarily anonymous as the research world is very small
  • Institution and gender bias
  • The ‘file-drawer’ problem - a bias towards publishing studies with positive results (supporting the hypothesis) For every positive finding there could be hundreds of negative results
161
Q

Institution bias

A

Research from prestigious universities is favoured

162
Q

Gender bias

A

Male researchers tend to be favoured

163
Q

Qualitative

A

Describes qualities and characteristics

164
Q

Examples of qualitative data

A

Interviews, unstructured observations

165
Q

Weakness of qualitative

A

Less easy to analyse
Hard to draw conclusions

166
Q

Quantitative

A

Numerical

167
Q

Example of quantitative data

A

Reaction time
Number of mistakes

168
Q

Strengths of quantitative data

A

Objectivity
Clarity
Predicative capabilities

169
Q

Weaknesses of quantitative data

A
  • Oversimplify or ignore meanings
  • Potential for researcher bias
  • Limited ability to capture complex human experiences
170
Q

Primary data

A

Collected first hand for the purpose of an investigation

171
Q

An example of primary data

A

Milgram

172
Q

Strengths of primary data

A

More accurate and reliable because it comes from a direct source
Can be collected in real time
Faster to collect

173
Q

Weaknesses of primary data

A

Time and effort

174
Q

Secondary data

A

Collected for a different purpose
Collected by someone other than the secondary user

175
Q

An example of secondary data

A

Government publications

176
Q

Strength of secondary data

A

Inexpensive
Less effort

177
Q

Weaknesses of secondary data

A

Lack of relevance - rarely provides the answers needed
Lack of accuracy

178
Q

Meta analysis

A

Examination of data from a number of independent studies of the same subject, in order to determine overall trends

179
Q

An example of a meta analysis

A

Van Ijzendoorn and Kroonenberg (Cultural variation in attachment)

180
Q

Strength of meta analysis

A

Increases validity of conclusions — much larger sample size

181
Q

Weaknesses of meta analysis

A

Publication bias - not all studies will be selected

182
Q

Descriptive statistics

A

Describe the data collected

183
Q

Measures of central tendency

A

Averages (mode, median, mean)

184
Q

Measures of dispersion

A

How spread out the data is (range, standard deviation)

185
Q

How to find the median

A

Put in size order and find the middle value

186
Q

Normal distribution curve

A

Mean, mode and median are the same

187
Q

Negative skew

A

Mode is greater than the mean (left foot)

188
Q

Positive skew

A

Mode is less than the mean (right foot)

189
Q

The bigger the standard deviation…

A

The more spread out the data is

190
Q

What percentage of people lie within 1 standard deviation of the mean?

A

68%

191
Q

What percentage of people lie within 2 standard deviation of the mean?

A

95%

192
Q

Inferential statistics

A

Tell us if the difference or correlation we have found in our research significant or if it is just a fluke/chance result

193
Q

When do we accept the hypothesis? (Inferential statistics)

A

If the results are significant at the 5% level (i.e. there is a 5% or less chance they were a fluke)

194
Q

Things to consider when using a critical value table

A
  • Is the hypothesis directional or non-directional?
  • Number of participants
  • Look down the 5% (0.05) column
195
Q

How do you know if the findings are significant?

A

If the critical value is equal to or less than 1

196
Q

Sign test step by step

A

1) Cross out any no difference scores and discount these participants from n
2) Count the number of + and -
3) Find out whether the number or + or - are the lowest
4) The lowest value is the S value
5) Use the critical value table to find the critical value for this data set at the 5% level
6) If the S value is equal to or less than the critical value then the research is significant

psychology (13 decks)

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