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Research Methods Flashcards

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1
Q

What do aims come from?

A

Theories

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2
Q

What are aims?

A

General statements that describe the purpose of the investigation

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3
Q

What is a hypothesis?

A

A clear, precise, testable statement at the start of the study that clearly describes the relationship between the variables of the theory

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4
Q

What makes a hypothesis directional?

A

If there is previous research on the subject that is being investigated that suggests a direction

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5
Q

What makes a hypothesis non-directional?

A

If there is no previous research on the subject being investigated, or if previous research does not suggest a direction

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6
Q

How do we write a non-directional hypothesis?

A

There will be a difference in DV between IV (experimental condition) and IV (control condition)

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7
Q

How do we write a directional hypothesis?

A

There will be an increase in DV between IV (experimental condition) and IV (control condition)

OR

There will be an decrease in DV between IV (experimental condition) and IV (control condition)

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8
Q

How do we write aims?

A

To investigate…

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9
Q

What is an experimental hypothesis?

A

A hypothesis that predicts some difference between the results that has not occurred due to chance

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10
Q

What is operationalisation?

A

Making concepts testable by making them scientific and quantifiable
e.g. “The number of…”

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11
Q

What is the IV?

A

The Independent Variable
This is what the experimenter manipulates

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12
Q

What is the DV?

A

The Dependent Variable
This is what the experimenter measures

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13
Q

What are Variables?

A

Anything that varies or changes in an investigation

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14
Q

What are Extraneous Variables?

A

Variables other than the IV which could potentially affect the DV if they are not controlled

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15
Q

What are Confounding Variables?

A

Variables other than the IV which may have affected the DV
They make it difficult to see what has caused changes to the IV, so it is hard to establish clear cause and effect

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16
Q

How many Experimental Methods are there?

A

4

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17
Q

How many Experimental Conditions are there?

A

2 levels

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18
Q

What are the 2 levels of Experimental conditions?

A

The control condition
The experimental condition

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19
Q

What are the types of experiment?

A

Lab Experiment
Field Experiment
Natural Experiment
Quasi Experiment

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20
Q

What is a Lab Experiment?

A

An Experimental Method that uses a controlled environment
The researcher manipulates the IV and records the effects on the DV

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21
Q

Evaluate Lab Experiments

A

Good - high control
- allows for replicability
- can be certain of cause and effect
- minimises extraneous variables

Bad - low mundane realism
- lacks generalisability
- artificial tasks may lead to artificial behaviour/demand characteristics

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22
Q

What is a Field Experiment?

A

An experimental method that uses a real world setting
The researcher manipulates the IV and records the effects on the DV

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23
Q

Evaluate Field Experiments

A

Good - higher mundane realism
- real setting
- high external validity
- lower demand characteristics

Bad - less control
- harder to find cause and effect
- hard to replicate

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24
Q

What is a Natural Experiment?

A

An Experimental Method where the IV is naturally occurring, and would have occurred even if the researcher wasn’t there
The researcher records the effects on the DV

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25
Q

Evaluate Natural Experiments

A

Good
- Creates opportunities for studies that might not have been possible (earthquakes, volcano eruptions)
- High ecological validity due to using natural problems and real world issues

Bad
- Rare to find a naturally occurring event
- Limits generalisation
- No control as IV exists already
- Participants may not be randomly allocated

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26
Q

What are Quasi Experiments?

A

An Experimental Method where the variables already exist, so the IV has not and cannot be determined by anyone
e.g. twins, adoption - cannot control who is a twin and who has been adopted

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27
Q

Evaluate Quasi Experiments

A

Good - controlled conditions
- good replicability
- can be certain of cause and effect

Bad
- Possible confounding variables as you cannot randomly allocate participants
- Cannot claim the IV has caused any observed change as it has not deliberately been changed by the researcher

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28
Q

What are 6 Research Issues?

A

Demand Characteristics
Confounding Variables
Extraneous Variables
Standardisation (lack of)
Investigator Effects
Randomisation

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29
Q

What are Demand Characteristics?

A

When participants try to work out what is going on in the experiment (the aim), and change their behaviour so it is no longer natural

They can show the ‘please you’ or ‘screw you’ effect where they deliberately please the experimenter or sabotage the experimenter

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30
Q

What are Investigator effects?

A

Unconscious actions or Unstandardised procedures that may influence the research outcome

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31
Q

What are Internal Extraneous Variables?

A

Participant Variables
They are the differences between participants such as age, gender or personality

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32
Q

What are External Extraneous Variables?

A

Situational Variables
They are features of the experimental situation such as noise, temperature and weather

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33
Q

How many Experimental Designs are there?

A

3

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34
Q

What are the Experimental Designs?

A

Independent Groups
Repeated Measures
Matched Pairs

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35
Q

What is Independent Groups Design?

A

An Experimental Design where two separate groups of participants experience two different conditions of the experiment

  • all participants experience only one level of the IV
  • one group would do the control condition and one would do the experimental condition
  • performance of the 2 groups would be compared
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36
Q

Evaluate Independent Groups Design

A

Good
- Less chance of demand characteristics as participants only complete one so cannot figure out the aim
- No order effects as they have only done one
- No practice effects as they have only done one
- Can use random allocation

Bad
- Participant variables might make it difficult to establish cause and effect - there may be confounding variables
- Need a larger sample
- Takes longer/costs more

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37
Q

What is Repeated Measures Design?

A

An Experimental Design where all participants experience both conditions of the experiment

  • Each participant completes one condition (either experimental or control)
  • Participants then swap and complete the other condition afterwards

The scores from both conditions would be compared to see the differences

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38
Q

Evaluate Repeated Measures Design

A

Good
- Counterbalancing means that participant effects are minimised as every participant is completing every condition
- Fewer people are needed as they take part in both conditions
- Allows for Random Allocation

Bad
- Practice effects - participants may be used to the task, or may work out the aim if they complete it more than once
- Order effects - participants may be tired after the first condition, or may care less and not try as hard on the second condition

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39
Q

What is Matched Pairs Design?

A

An experimental design where participants are matched based on possible participant variables that may affect the DV
e.g. 2 people with glasses, 2 people with hearing aids, 2 people aged 22 etc.

  • one person from the pair completes one condition and the other person completes the other
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40
Q

Evaluate Matched Pairs Design

A

Good
- Reduces Participant Variables by having similar people in each condition
- Avoids practice effects - demand characteristics are less likely
- Avoids order effects

Bad
- Participants can never be matched exactly, even if they were identical twins
- Matching might be time-consuming and expensive

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41
Q

What is a Population?

A

The large group of individuals a researcher is interested in studying
Also called the target population

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42
Q

What is a Sample?

A

The smaller group who take part in the research

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43
Q

How many sampling techniques are there?

A

5

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44
Q

What are the sampling techniques?

A

Volunteer Sampling
Random Sampling
Opportunity Sampling
Stratified Sampling
Systematic Sampling

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45
Q

What is Random Sampling?

A

Every member of the target population has an equal chance of being selected
e.g. names in a hat, random number generator

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46
Q

Evaluate Random Sampling

A

Good
- Should represent the target population
- Should eliminate a sampling bias

Bad
- Difficult to achieve (effort, money)
- Time consuming - some people might say no

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47
Q

What is Opportunity Sampling?

A

The researcher selects participants from whoever is available at the time

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48
Q

Evaluate Opportunity Sampling

A

Good
- Quick
- Easy
(cost effective)

Bad
- Might be biased
- Might not provide a representative sample

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49
Q

What is Volunteer Sampling?

A

Participants put themselves forward to be put in the sample
They self-select

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50
Q

Evaluate Volunteer Sampling

A

Good
- Easy to find people willing to participate (less time consuming)

Bad
- Might not provide a sample representative of the whole population
- Might not have enough participants
- Might suffer Volunteer Bias if they are too keen to participate (please you effect), or they might all be the same type of person

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51
Q

What is Stratified Sampling?

A

The target population is broken down into smaller groups, and these are then sampled from
The sample is a proportional representation of the target population

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52
Q

Evaluate Stratified Sampling

A

Good
- Avoids researcher/sampling bias
- Can generalise results as sample should be representative of the population

Bad
- Takes a lot of time
- Difficult to do, and some people might say no, so you’d have to start again

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53
Q

What is Systematic Sampling?

A

Every nth member of the target population is selected

e.g. every 8th house on the street
every 5th person on the register

54
Q

Evaluate Systematic Sampling

A

Good
- Should provide a representative sample as it is a set selection across the whole target population
- Reduces researcher bias

Bad
- Difficult to achieve (time, effort, money)
- There might not be enough diversity to represent the whole population

55
Q

What are Correlations?

A

The strength and direction of an association between co-variables (the relationship between them)

56
Q

How are Correlations plotted?

A

Using a Scattergram
One Co-Variable is on the X axis, and one is on the Y axis
Each point on the graph is the X and Y position of each co-variable

57
Q

What are the types of Correlation?

A

Positive correlation - as one variable increases so does the other

Negative correlation - as one variable increases, the other rises

No correlation - there is no pattern between the variables

58
Q

Why can’t we assume a cause and effect from a Correlation?

A

Correlation does not equal causation
It simply points out the relationship between the variables

59
Q

How can we work out the strength of a Correlation?

A

Using a Correlation Coefficient
Between 0 and +1 shows a Positive Correlation
Between -1 and 0 shows a Negative Correlation
For a Correlation to be strong, it must have a Coefficient of at least .8
(-0.8 or +0.8)

60
Q

What do Descriptive Statistics Include?

A

Measures of Central Tendency
Measures of Dispersion

61
Q

What are Measures of Central Tendency?

A

Measures that give us averages about the typical values in a set:
Mean
Median
Mode

62
Q

What are Measures of Dispersion?

A

Measures that are based on the spread of scores and how they vary and differ from one another:
Range
Standard Deviation

63
Q

What is the Mode?

A

The most frequent value
There can be 2 modes (bimodal)
There can be no mode

64
Q

How do we calculate the Mode?

A

See which value is the most frequent

65
Q

Evaluate the Mode

A

Good
- Easy to find

Bad
- May be more than one answer
- May not represent the whole results

66
Q

What is the Median?

A

The Middle Value when results are placed in order
Used for Ordinal and Interval data

67
Q

How do we calculate the Median?

A

Place results in order
Find the Middle Value

68
Q

Evaluate the Median

A

Good
- Easy to calculate for small data sets
- Not affected by anomalies (outliers)

Bad
- Not a true representative of all data
- Can be difficult for larger data sets

69
Q

What is the Mean?

A

The overall average of a data set

70
Q

How do we calculate the Mean?

A

Add up all the results
Divide by the number of results there are

71
Q

Evaluate the Mean

A

Good
- Uses all the data

Bad
- Can be distorted by outliers

72
Q

What is the Range?

A

The largest value minus the smallest value
It tells us how spread out the data is

73
Q

How do we calculate the Range?

A

Subtract the Smallest Value from the Largest Value

74
Q

Evaluate the Range

A

Good
- Easy to calculate

Bad
- Affected by extreme data

75
Q

What is Standard Deviation?

A

A measure of Dispersion that tells us how far the data is from the mean

76
Q

Evaluate Standard Deviation

A

Good
- Accurate

Bad
- Difficult

77
Q

How do we ensure good design of self-report techniques?

A

Avoid Jargon
Avoid Emotive Language
Avoid Leading Questions
Avoid Double-Barrelled Questions
Avoid Double Negatives

78
Q

What is a Peer Review?

A

The assessment of scientific work by others who are specialists in the same field.
They should be objective and unknown by the Researcher.

79
Q

What are the main aims of Peer Review?

A

1) Decide if a proposed research project should receive funding

2) Assess research for quality and accuracy by validating the quality of the hypotheses, methodology, statistical tests, and conclusions

3) To suggest amendments or improvements, or even suggest it is unsuitable for publication

80
Q

Evaluate Peer Reviews

A

Good
- Spots mistakes and prevents invalid or inappropriate data from being published

Bad
- Some reviewers may use their anonymity to criticise rival researchers on purpose
- Burying groundbreaking research - reviewers might reject research that goes against mainstream theory or the status quo
- Publication bias - publishers may want research that will make good headlines to increase the readers, or they might want positive results
- Takes a long time
- Sometimes fraud can be missed if results are fabricated
- Bias - peer reviewers might know the person and dislike them or their university
- File Drawer Phenomenon - work might be left for too long

81
Q

What is Reliability?

A

Consistency
- you get the same results every time you complete the research

82
Q

How can we test Reliability?

A

1) Test Re-Test (inter-rater reliability)
- Carry out the research once
- Carry the same research on the same participants at a later date
- Calculate the correlation to check how similar the results are
- If the coefficient is 0.8 or above, it is reliable

2) Inter-Observer Reliability
- Carry out the research once
- Have a separate second researcher carry out the research
- Correlate the results
- If the coefficient is 0.8 or above, it is reliable

83
Q

When do we need to improve Reliability?

A

If the Correlation Coefficient is less than .8

84
Q

How can we improve Reliability?

A

Questionnaires
- remove some questions
- re-write some questions
- replace open questions with closed questions to reduce ambiguity

Interviews
- use the same interviewer each time or train all interviewers to standardise the procedure
- avoid leading questions
- use a structured interview for more control

Experiments
- use a lab experiment for more control
- ensure it is standardised for replicability

Observations
- operationalise behavioural categories
- train observers - standardise

85
Q

What is Internal Validity?

A

Whether we are measuring what we set out to measure

  • there are no confounding or extraneous variables that could have affected what we are measuring
86
Q

What is External Validity?

A

How generalisable the findings are beyond the research setting

  • e.g. to the real world (ecological validity), or across different eras (temporal validity)
87
Q

What is Face Validity?

A

It looks like the experiment measures what it is supposed to be measuring

88
Q

What is Concurrent Validity?

A

If the results from this study are close to the results from another already established study

89
Q

What is Ecological Validity?

A

The extent to which findings can be generalised to other settings and situations

90
Q

What is Temporal Validity?

A

The extent to which findings can be generalised to other historical times and eras

91
Q

How can we assess Face Validity?

A

The researcher or another expert will look at the test and see if it seems to measure what it is supposed to be measuring

92
Q

How can we assess Concurrent Validity?

A

Test your participants with your test
Test them again with an already established test
Collect Results and Correlate the scores
High concurrent validity will have a correlation coefficient of .8 or more

93
Q

How can we Improve Validity?

A

Questionnaires
- put in a lie scale to test consistency of results
- make it anonymous to reduce social desirability

Experiments
- control extraneous variables by using a control group for comparisons
- standardise procedures
- use a double blind to reduce investigator effects

Observations
- use covert observations so behaviour is authentic
- ensure behavioural categories are not too broad or ambiguous

Aim to use quantitative methods
If using qualitative methods, use quotes or triangulation (different sources of the same information)

94
Q

What are the 3 levels of data?

A

Nominal
Ordinal
Interval

95
Q

What is Nominal Level Data used for?

A

Categories
Lowest and most basic data such as tally charts
Discrete data (1 item can only appear in 1 category)

96
Q

What is Ordinal Level Data used for?

A

Ordered Data
The rank/place/rating
Lacks precision as it is subjective and there is not a set interval between each unit
e.g. 1st, 2nd, 3rd

97
Q

What is Interval Level Data used for?

A

Based on Numerical Scales
Includes units of equal, precisely defined size
A standardised and operationalised unit of measurement

98
Q

What Descriptive Statistics do we use for Nominal Data?

A

The Mode
It is the most often in all categories

99
Q

What Descriptive Statistics do we use for Ordinal Data?

A

Median
Range

100
Q

What Descriptive Statistics do we use for Interval Data?

A

Mean
Standard Deviation
It is the most precise measurement for the most precise level

101
Q

How do we complete an Inferential Statistics Test?

A

1) Test of Difference or Test of Association?
2) What Experimental Design is used?
3) What is the Level of Measurement?

102
Q

What is Insignificant Data?

A

In 95% of cases the results would have happened anyway

103
Q

What is Significant Dats?

A

In 5% or less of cases the results would have happened anyway
This suggests the IV has caused the DV

104
Q

What are the Features of Science?

A

Objectivity
Empirical Method
Replicability
Falsifiability
Theory Construction
Hypothesis Testing
Paradigms and Paradigm Shifts

105
Q

Who theorised the Features of Science?

A

Karl Popper

106
Q

What is Objectivity? (FOS)

A

Not allowing personal biases to affect our data

107
Q

What is the Empirical Method? (FOS)

A

Approaches based on gathering evidence through direct observation and experience

108
Q

What is Replicability? (FOS)

A

The extent to which procedures and findings can be repeated across different circumstances and contexts
Important to find validity and reliability

109
Q

What is Falsifiability? (FOS)

A

Scientific theories should hold themselves up for hypothesis testing and the possibility of being disproven
This is why we always have a null hypothesis
Strong theories are those that have been repeatedly tested and not falsified

110
Q

What is Theory Construction? (FOS)

A

Gathering evidence to form a theory - a set of general laws or principles that can explain behaviours
- could be a hunch that leads to experiments to develop a theory

111
Q

What is Hypothesis Testing? (FOS)

A

Testing a theory through empirical methods
Can support and strengthen a theory
Can disprove a theory

112
Q

What are Paradigms and Paradigm Shifts? (FOS)

A

Paradigm
- a set of shared beliefs and assumptions
- Kuhn suggested psychology has too many conflicting approaches to be a science

Paradigm Shift
- science progresses through scientific revolution
- researchers may begin to question the accepted paradigm, and this gathers pace until a paradigm shift occurs
- this is when contradictory evidence cannot be ignored, so there is a shift to the new belief/paradigm

113
Q

What are the Sections of a Scientific Report?

A

Abstract
Introduction
Method
Results
Discussion
References

114
Q

What is the Abstract? (SR)

A

A short summary
It includes all major elements of a report
- aims
- hypotheses
- method
- procedure
- results
- conclusion
It lets others know if they want to read it in full

115
Q

What is the Introduction? (SR)

A

A Literature Review of the past research and theories that relate to their study
It progresses from general to specific and ends on the most relevant aims and hypotheses

116
Q

What is the Method? (SR)

A

A section including
- Design
- Sample/Participants
- Apparatus/Materials
- Procedure
- Ethics
Should be detailed enough to allow replication

117
Q

What are the Results? (SR)

A

Summarising key findings
- Descriptive Statistics
- Inferential Statistics
- Qualitative results and findings

Raw data goes in an appendix

118
Q

What is the Discussion? (SR)

A

Summarising the results in verbal form
Discusses limitations
Suggests how they can be modified for future studies
Consider the wider implications

119
Q

What are the References? (SR)

A

Adds any details of source materials

120
Q

How are References written for Journal Articles?

A

Surname, Initials (Date) Title of article, Journal title, edition. Page numbers

121
Q

How are References written for Books?

A

Author, Surnames and initials (Date) Title pf book, place of publication, publisher

122
Q

What does Content Analysis do?

A

Turns Qualitative Data into Quantitative Data

123
Q

What is the process of Content Analysis?

A

1) Coding
- Categorise the data into meaningful units (codes)
- These could be categories, themes, phrases, key words

2) Count
- Count how many times these codes occur
- Highlight the transcripts to count for occurrences

There is your quantitative data

124
Q

What does Thematic Analysis do?

A

Leaves Qualitative Data as Qualitative Data but makes it easier to analyse

125
Q

What is the process of Thematic Analysis?

A

1) Identify Themes
- identify key ideas that are recurrent

Could put these into broader categories

2) Directly quote these to illustrate each theme in the final report

126
Q

How do you calculate the Percentage Something is of another Value?

A

Divide by the total value
Multiply by 100

127
Q

How do you calculate Percentage change?

A

Change/Original x 100

128
Q

How do you calculate Percentage Increase?

A

Calculate the Change
Find the change as a % of the original value

129
Q

How do you calculate Percentage Decrease?

A

Calculate the Change
Find the change as a % of the original value

130
Q

What is a Type 1 Error?

A

When the Null Hypothesis is rejected and the alternative is accepted when the Null Hypothesis is actually true

e.g. saying a fat man is pregnant
- He could not be pregnant
- As people have accepted he is pregnant although he could not be, the alternative hypothesis has been accepted when the null hypothesis is actually true

131
Q

What is a Type 2 Error?

A

When the Alternative Hypothesis is Rejected and the Null Hypothesis is Accepted although the Alternative Hypothesis is actually true

e.g. saying a heavily pregnant woman is not pregnant
- She is pregnant
- As people have accepted she is not pregnant although she is, the null hypothesis has been accepted when the alternative hypothesis is actually true

132
Q

Why do we test at the 5% level?

A

To try to get a balance between Type 1 and Type 2 errors to reduce the risk of both of them

Psychology Paper 2 (6 decks)

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