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Research Methods A Flashcards

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1
Q

Hypothesis

A

Derived from theories, they are testable predictions

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2
Q

Independent variable

A

A variable varied by the experimenter in order to examine the effects of the dependent variable (Is tested on)

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3
Q

Dependent variable

A

A variable liable to be influenced by the independent variable (what is measured in the experiment)

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4
Q

Which is independent and dependent variable?…

“Eating carrots improves eyesight”

A

Independent: Eating carrots
Dependent: Eyesight

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5
Q

Three problems with research

A

Can be bias
Can breach ethics
Confounding variables

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6
Q

Confounding variable

A

An extraneous variable that has interfered with the results of the experiment

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7
Q

Three ways to avoid bias in an experiment

A

Single blind study
Double blind study
Use a placebo for a group

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8
Q

Single blind study

A

The participants are kept in the dark about specific elements of the study

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9
Q

Double blind study

A

The participants and the researcher conducting the experiment are kept in the dark about specific elements of the study

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10
Q

What Clever Hans tells us about bias

A

That it is better to conduct a double blind study so the examiner can’t give unconscious physical clues as to the correct or preferred answer

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11
Q

Reactivity

A

When the knowledge that a participants is being observed or measured influences their behaviour

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12
Q

Three types of research methods

A

Non-experimental
Experimental
Quasi - experimental

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13
Q

Four types of non-experimental research

A

Observational
Case study
Survey
Correlational research

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14
Q

Difference between non-experimental and experimental research

A

Non-experimental research is descriptive whereas experimental is explanative and contains control factors

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15
Q

How are observational experiments carried out?

A

Mainly through categorization with as little disturbance as possible

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16
Q

Example of observational experiment

A

Eibl-Eibesfeldt’s cross-cultural eyebrow raising during greeting observations

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17
Q

Two problems of observable methods

and how they are solved

A

-Reliability of categorisation (due to subjectivity)
Solved by comparison with other researchers
Reactivity of subjects
Solved with observers undercover as participants

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18
Q

How are case studies carried out?

A

Observation of a single person or particular group, often with a unique quality

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19
Q

Three problems with case studies

A

Generalisations
Reproducibility
Lack of cause and effect understanding

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20
Q

A solution to the problems with case studies

A

Deviant case analysis: create a situation similar to the case study with a distinct difference to work out the cause effect relationship

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21
Q

Three types of surveys

A

Questionnaire
Interview
Diary study

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22
Q

Four problems with surveys

A

Reactivity
Validity of questionnaire
How to quantify
Participant’s memory

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23
Q

Benefits of a structured interview (three)

A

Easily quantified
Comparable across participants
All topics covered

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24
Q

Three costs of a structured interview

A 
Rigid structure
Not personally adaptable
Surface information (can't probe deeper depending on participants answers)
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25
Q

Two benefits of an unstructured interview

A

More in-depth information

Personalised to participant

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26
Q

Two costs of an unstructured interview

A

Generalisability

Analysis can be time consuming (especially for big groups)

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27
Q

Purpose of correlational research

A

Determine the relationship between two variables without manipulation

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28
Q

Problems with correlational research (four)

A

Confounding variables (secondary causes etc)
Can often be unclear
Can be coincidental
Correlation is not proof of any causation

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29
Q

What is an experiment?

A

Manipulate the independent variable to test the effects on the dependent variable

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30
Q

Null hypothesis

A

The idea that there is no relationship, nothing happening in the study. Is always assumed while conducting the study

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31
Q

Nuisance variable

A

Additional factor that may effect the dependent variable (the results)

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32
Q

How to stop nuisance variables turning into confounding variables

A

Either turn it into a control variable or intentionally make it another independent variable

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33
Q

Control variable

A

Variables kept the same or otherwise made sure to not interfere with the dependent variable

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34
Q

How to solve nuisance variables across participants (E.g. sex, age etc.)

A

Either separate systematically or spread randomly

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35
Q

Why including multiple independent variables in one experiment rather than multiple experiments is better (three)

A

More efficient
More control over nuisance variables
Can see the relationships of independent variables

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36
Q

How to carry out a multiple independent variable study

A

Make sure to include groups for each possible combination of independent variables

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37
Q

How to tell on a graph if there is interaction between independent variables or not

A

If there is an interaction, the lines for each independent variable wont be parallel, if there isn’t an interaction, they will be parallel.

The more unparalleled the lines the stronger the interaction

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38
Q

Common example of multiple dependent variables in an experiment

A

Speed and accuracy (has to be trade off)

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39
Q

Advantages of experiments (two)

A

Stronger test of causality

Possibility of a variety of manipulated controls

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40
Q

Disadvantages of experiments (three)

A

Unnatural setting/task causes more reactivity
Some phenomena cannot be studied this way (E.g. social interaction)
Ethical limitations

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41
Q

What happens to a nuisance variable when it is not dealt with by the experimenter?

A

Becomes a confounding variable

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42
Q

A within subject design experiment

A

All participants receive all levels of the independent variable

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43
Q

A between subject design experiment

A

Different groups of participants receive different levels of the independent variable

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44
Q

Between subject design advantages (3) and disadvantages (2)

A

Adv: No order effect, essential for some experiments, naïve participation
Disadv: Lots of participants, characteristics between groups may differ (can be solved)

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45
Q

Within subject design advantages (2) and disadvantages (2)

A

Adv: Fewer participants, reduces individual differences
Disadv: Carryover effects (into next IV tested), order effects

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46
Q

How to counteract order effects in within subject design experiments

A

Randomise, or better: The Latin Square Design… make each order occur equally often (cant have too many variables though!)

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47
Q

Quasi experiment (and example)

A

When one (or more) independent variables are selected - not manipulated
Eg) Education relation to memory
IV: university degree YES/NO
DV: score in memory test

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48
Q

Advantage and disadvantage of Quasi experiment

A

Adv: Can examine otherwise unethical variables (as no manipulation)
Disadv: Possibility of confounding variable means no strong causal conclusions

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49
Q

Three types of sample

A

Random
Stratified
Quota

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50
Q

A random sample

A

Everybody in cohort has an equal chance of being selected

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51
Q

Why is random sampling particularly difficult

A

Always depends where you are to select people (choosing people ‘at random’ outside tennis court would be opportunity sampling)

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52
Q

Stratified sample

A

Random selection of each subgroup of the population/cohort

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53
Q

Quota sampling

A

Represents prechosen proportions

54
Q

Four psycho-physiological measurements

A

Muscle activity
Eye movement
Blink rate
Brain imaging

55
Q

Advantages (2) and disadvantages (3) of EEG scanner

A

Adv: excellent temporal resolution, relatively inexpensive
Disadv: Poor spatial resolution, surface activity of brain, artefacts (confounding variables in brain activity)

56
Q

Advantages (2) and disadvantages (4) of FMRI scanner

A

Adv: excellent spatial resolution (2-3mm), Accesses all brain areas
Disadv: Poor temporal resolution (5sec lag), expensive, participants cannot move, those with claustrophobia may refuse

57
Q

Milgram’s obedience study

A

Participants instructed by man in white coat to give an actor an electric shock
(thought unethical due to psychological damage to participants)

58
Q

When can ethics be deprioritised

A

When the potential research could have a large positive impact on society

59
Q

Why is fully informed consent of the participant not always possible? And how is this made up for?

A

Due to potential reactivity from the subject

Made up for with a full debrieft afterwards

60
Q

Three rights of the participant

A

To leave at any time
To have their privacy valued
No sharing of their information without consent

61
Q

What groups of people have different ethics applied to them?

A

Children, seriously ill people, prisoners (feel a pressure to cooperate for benefits)

62
Q

By who is all national research ethically monitored by?

A

The British Psychological Society (BPS)

63
Q

Why do researchers experiment on animals? (2)

A

They are models of humans

Generally viewed as less valuable than a human

64
Q

Guidelines to animal research are imposed by… (2)

A 
Animal Act 1986
The BPS (very strict)
65
Q

Problems with measuring variables (2)

A

Subjectivity (eg, mood or intelligence)

Testability (eg, mood or anxiety levels)

66
Q

Types of measurement (4)

A

Nominal scales
Ordinal scales
Interval scales
Ratio scales

67
Q

Nominal scale and example

A

Numbers are labels, no relationship between the size and attribute measured (eg, bus numbers)

68
Q

Ordinal scales and example

A

The order of the size of the number equals order of the size of the attribute measured (distance between scores vary) (eg, IQ score, medal table)

69
Q

Interval scales and example

A

Equal interval on scale is the same as the equal interval in property measured (eg, degrees C)
(can have negatives)

70
Q

Ratio scale

A

Equal interval on scale is the same as the equal interval in property measured, and 0 denotes an absolute absence (eg, time taken…)

71
Q

Difference between interval and ratio scales

A

Interval can be negative, ratio cant

72
Q

The mean

A

Sum of scores / number of scores

73
Q

The median

A

Midpoint of sample

if n is even, is the mean of the middle two scores

74
Q

When to use the mean and the median and mode

A

Mean: no or little anomalies
Median: Some anomalies, ordinal data
Mode: Nominal data

75
Q

Mode

A

Most frequently occurring

76
Q

Bimodal

A

Two modes in the data

77
Q

Limitations of the mode (3)

A

Some data doesn’t have one
Some data is bimodal (or more)
Can be atypical (not typical of data)

78
Q

Alpha value

A

Generally when the p value is 5% (0.05), it is the point at which researchers reject the null hypothesis
(there is a 5% chance of their hypothesis being incorrect - probably not down to chance!)

79
Q

p value

A

Value generated for the probability of the results being due to chance

80
Q

When do researchers change the alpha value

A

Will lower it when the consequences of the results are serious and raise it when they are trivial

81
Q

Measures of spread (3)

A

Range
Interquartile range
Standard deviation

82
Q

Range

A

Maximum value - Minimum value

83
Q

Interquartile range

A

Measure of spread between the middle 50% of scores (Third quarter (Q3) - first quarter (Q1))

84
Q

Standard deviation

A

Measure of variation around the mean (the higher the value the larger the spread)

85
Q

How to calculate standard deviation

A
  • Find the mean
  • Find the deviation (how far each score is away from the mean)
  • square all the deviation scores (makes all positive)
  • divide by n
  • square root (to reverse initial squaring)
86
Q

The variance

A

standard deviation squared

87
Q

Equation for standard deviation

A

sqr rt of: sum of (x - mean)^2 / N

88
Q

Why use graphs (3)

A

Indicates data patterns
Helps decide how to use data
Illustrates findings to others

89
Q

When are bar graphs good to use

A

Ordinal or nominal data

90
Q

Advantages of histograms (2)

A

Area shows frequency of data

Clearly shows the mode and outliers

91
Q

How to do a stem and leaf diagram

A

Stem represents the start of number, leaf is the end

92
Q

What do box plots show (6)

A 
Minimum value
Q1
Median
Q3
Maximum value
Any outliers
93
Q

How are outliers defined in box plots

A

1.5 x above or below Q3 and Q1 respectively

94
Q

What do scatterplots show

A

The relationship between variables

95
Q

What does a perfect correlation on a scatterplot mean (2)

A

Either fake data or the same thing is being measured in different ways (measuring height in cm and inches)

96
Q

Purpose of correlation analysis (3)

A

Determine the nature, direction and strength between two variables

97
Q

Two correlation coefficients

A

Pearson (r)

Spearman (r s)

98
Q

Does changing the units of measurement effect correlation

A

No

99
Q

What does a non linear correlation look like

A

Line of best fit is curved

100
Q

Example of non linear correlation

A

Relationship of stress and resilience

101
Q

How is the Pearson correlation coefficient calculated

A

Directly from the raw scores

102
Q

When to use Pearson correlation coefficient (3)

A

Suitable for interval and ratio data
Little to no outliers (is highly affected)
Not for skewed data

103
Q

How us the Spearman correlation coefficient calculated

A

Ranking of the raw scores

104
Q

When to use Spearman correlation coefficient

A

Suitable for ordinal data
Can be used with outliers (marginally effected)
Suitable for skewed data

105
Q

Density curve

A

Basically a histogram with a curve of best fit from each top point (shows distribution of population)

106
Q

When are density curves good to use

A

With lots of participants (generalise the population)

107
Q

What does positively and negatively skewed data look like

A

Lump on the left for positively skewed data and the right for negatively skewed data

108
Q

What does the area under the distribution curve equal (2)

A

1 or 100%

109
Q

Finding the median, upper quartile and lower quartile in distribution curve

A

Median splits area underneath in half

Quartiles split it in quarters either side (Q1 / Q3)

110
Q

Normal distribution

A

A lot of naturally occurring data (height) is distributed symmetrically around a single central dendency

111
Q

Symbol for: 1) sample and 2) population mean

3) Standard deviation, 4) population standard deviation

A

1) x bar
2) myou (u)
3) S
4) theta (o)

112
Q

How are the location and shape of the normal distribution curves determined

A

Location by population mean (u) and shape by population standard deviation (o)

113
Q

Where does the tail of the normal distribution curve meet the x axis

A

At infinity (or never)

114
Q

What do statistical tests assume about the distribution of data

A

Normally distributed

115
Q

If data is not going to be normally distributed, what kind of test is needed

A

Non-parametric tests

116
Q

Why normal distribution curves are useful

A

Can compare different data sets (eg two exams)

117
Q

How do you compare normal distribution curves

A

Translate them into standardised normal distribution curves and calculate the z-scores, then plotting them onto standardised curve

118
Q

What do z scores show

A

The number of standard deviations that the score is away from the mean

119
Q

z-score = …

A

x - mean / S

120
Q

What does the mean and S equal in a standardised normal distribution curve

A 
Mean = 0
S = 1
121
Q

What do you do after plotting the z-score on the standardised normal distribution curve

A

Look up in table on on SPSS the decimal area to the left of the point’s vertical line from the x axis.
(minus from 1 to get the area on the right)

122
Q

Type 1 error

A

Rejecting the null hypothesis when it should not be rejected

123
Q

Why wouldn’t we always lower the alpha level to 0.01 to avoid type 1 errors?

A

Because of type 2 errors

124
Q

Type 2 error

A

Failing to reject the null hypothesis when we should reject it

125
Q

Generally, what is the probability of a type 1 error

A

5%, assuming the alpha value is 0.05

126
Q

Generally, if the null hypothesis is accepted, whats the probability of a type 2 error

A

= p. E.g. if alpha = 0.05 and p = 0.08, the probability that we should have rejected the null hypothesis is 8%

??

127
Q

Directional hypothesis (one tailed)

A

Specifies which way the results will go (should be based off prior research)

128
Q

Non-directional hypothesis (two tailed)

A

Only predicts some form of difference / relationship

129
Q

Does the null hypothesis change depending on if a directional or non-directional hypothesis is chosen

A

No it doesn’t, is always the general alternative to hypothesis (there will be no relationship…)

130
Q

What can samples show about a population, and how is this accounted for

A

Can only show an inference, a margin of error often calculated to account for this

Psychology (9 decks)

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