Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Psychology alevel > research methods > Flashcards

research methods Flashcards

You may prefer our related Brainscape-certified flashcards:
Psychology 101 flashcards GRE® Psychology flashcards AP® Psychology flashcards
1
Q

qualitative data

A

detailed data in the form of description

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

quantitative data

A

numerical data that can be turned into statistical form

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

longitudinal study

A
  • study same group / person over time
  • tracks development of behaviour
  • collects multiple sets of data
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

snap-shot data

A
  • concluded at one point in time
  • collects one set of data
  • doesn’t track development of bhv
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

ecological validity

A

whether the task and setting are representative of real life

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

experiment

A

setting up a situation and studying behaviour

  • lab
  • quasi
  • natrual
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

observation

A

watching people with or without knowledge usually looking for certain pre-decided behaviour

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

self-reports

A

asking ppts about their behaviour by using questionnaires or interviews

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

correlations

A

looking at how 2 variables are related

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

free will

A

human beings are entirely free to act as they chose and bare responsibility for the outcome of their behaviour

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

determinism

A

suggests we lack control of our behaviour and it is pre-determined by factors such as genes and past experiences

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

usefullness

A

research that enhances our knowledge and can be applied to real life situations

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

limited usefulness

A

research that may lack credibility, generalisability and understanding or be difficult to apply to real life

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

nature

A

behaviour is due to biological factors such as genetics, nervous systems

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

nuture

A

sees behaviour as learnt or aquired through experiences in the environment

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

interactionist

A

accepts both nature and nurture as being interconnected and human behaviour is a product of both

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

reductionism

A

attempts to break down behaviour into constituent parts and uses single factors to account for a given behaviour eg genes

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

holism

A

sees behaviour as too complex to be reduced and there are many factors contributing to behaviours

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

individual

A

looks to a persons personality and dispositions as the cause of behaviour

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

situational

A

behaviour is caused by situations around individuals eg group members or environmental context

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

internal reliability

A

the extent to which we can replicate the procedure

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

what does internal reliability concern

A

procedure (all, always, same)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

how is internal reliability increased

A

standardisation - keeping the procedure the same

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
24
Q

how is internal reliability checked

A

split-half method

test-retest method

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
25
Q

external reliability

A

the extent to which we can replicate the findings and achieve consistency

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
26
Q

what does external reliability concern

A

findings

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
27
Q

how do we increase external reliability

A

quantitative data - makes comparison easier

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
28
Q

how is external reliability checked

A

split- half method

test-retest method

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
29
Q

inter-rater reliability

A

whether 2 or more researchers find and conclude the same thing

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
30
Q

when is inter-rater reliability used

A

mainly observations

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
31
Q

how is inter-rater reliability increased

A

pilot studies

use behavioural categories

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
32
Q

internal validity

A

whether the study measures what it se out to measure

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
33
Q

what does internal validity concern

A

procedure

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
34
Q

what needs to be controlled to increase internal validity

A

extraneous variables

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
35
Q

external validity

A

whether that findings can be generalised to real life

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
36
Q

what does external validity concern

A

findings

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
37
Q

test-restest validity

A

when ppts are tested more than once on separate occasions in the same condition

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
38
Q

split half method

A

2 halves of a questionnaire are similar

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
39
Q

population validity

A

whether the sample makes findings applicable to real life

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
40
Q

ecological validity

A

task or setting true to real life meaning findings can be generalised

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
41
Q

construct validity

A

extent to which a test measures all aspects of particular behaviour

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
42
Q

criterion validity

A

the scores on one measure are able to predict the outcome on another related measure

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
43
Q

predictive validity

A

a measure can predict future behaviour or attitude

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
44
Q

3 types of extraneous variables

A

situational variables - env
individual variables - ppts
researcher effects

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
45
Q

situational variables

A

lighting
distractions
time of year / day / month
location

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
46
Q

individual differences

A

gender
age
personality
cognitive ability

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
47
Q

researcher effects

A

facial expressions
body language
conscious/unconscious cues

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
48
Q

face validity

A

extent something looks as if it will measure what its supposed to

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
49
Q

measure of central tendency

A

measure of averages

eg mean median mode

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
50
Q

mean equation

A

∑(x÷n)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
51
Q

+ of using mean

A

all data is used
accurate rep of the data
first choice of central tendancy

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
52
Q
  • of mean
A

anomalous result can distort the values

not appropriate if data is skewed

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
53
Q

+ of the median

A

extreme scores don’t distort the value

use if data set is skewed by extreme values

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
54
Q
  • of the median
A

difficult and time consuming to calculate

less representative of all values

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
55
Q

+ of mode

A

can use when data is not numerical

allows analysis for most occurring category

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
56
Q
  • of the mode
A

may not accurately reflect data set

if no most popular answer , not useful

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
57
Q

discrete data

A

can be placed into categories

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
58
Q

continuous data

A

can be placed on a number line

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
59
Q

bar chart

A

used for discrete data only

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
60
Q

histogram

A

used for continuous data

area of columns = frequency

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
61
Q

frequency equation

A

frequency x class width

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
62
Q

frequency density equation

A

frequency ÷ class width

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
63
Q

pie chart

A

used for discrete data

shows relative contribution to overall total

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
64
Q

line graph

A

continuous data
continuous scale
shows change over time

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
65
Q

scatter graph

A

continuous data

measures relationship between 2 variables

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
66
Q

operationalisation

A

how you make a variable measurable

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
67
Q

what variables are used in experiments

A

independent variable
dependent variable
extraneous variables

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
68
Q

what variables are used in observations

A

co-variables (2 behaviours being measured)

extraneous variables

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
69
Q

independent variable

A

manipulated / changed by the researcher

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
70
Q

dependent variable

A

behaviour being measured

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
71
Q

alternate hypothesis

A

statement of prediction between variables

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
72
Q

correlational hypothesis

A

predict the relationship between 2 variables

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
73
Q

one-tailed hypothesis

A

predicts specific direction of resulty

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
74
Q

two-tailed hypothesis

A

predict a difference will be found but are non-directional in terms of what will be found

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
75
Q

null hypothesis

A

states no difference will be found

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
76
Q

target population

A

the group of people the psychologists want to be able to generalise their findings

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
77
Q

4 methods of sampling

A

opportunity
volunteer
random
snowball

78
Q

opportunity sampling

A

most common method

uses people who are readily available

79
Q

strength of opportunity sampling

A

easiest
quickest
most economical

80
Q

weaknesses of opportunity sampling

A

likely to produce bias

not very representative

81
Q

volunteer sampling

A

psychologist makes an advert

people who want to participate volunteer

82
Q

strength of volunteer sampling

A

wide range of access to ppts
ethical => informed consent
convenient

83
Q

weaknesses of volunteer sampling

A

unrepresentative

people tend mot to respond unless interested

84
Q

random sampling

A

everyone from TP has equal chance of selection

all names are entered into a draw and randomly selected

85
Q

strengths of random sampling

A

least biased method

86
Q

weaknesses of random sampling

A

very difficult and time consuming

limits TP

87
Q

snowball sampling

A

used if population isn’t easily contacted

ask someone to ask someone else

88
Q

strength of snowball sampling

A

possible to use members of groups where no lists or identifiable clusters exist

89
Q

weaknesses of snowball sampling

A

no way of knowing if sample is representative of the population

90
Q

ethical guidelines

A

rules which help to keep principles

91
Q

4 principles

A

respect
responsibility
integrity
competence

92
Q

principle of respect

A

respecting a persons individual rights

93
Q

respect guidelines

A

informed consent
confidentiality
right to withdraw

94
Q

strength of breaking ‘informed consent’

A

allows further insight and prevents demand characteristics

95
Q

strength of breaking confidentiality

A

may be able to offer therapy or treatment

96
Q

strength of breaking right to withdraw

A

see the true behaviour and increases insight

97
Q

principle of responsibility

A

upholding responsibility keeps participants safe

98
Q

responsibility guidelines

A

protection from harm (physical and psychological)

debrief

99
Q

strength of breaking protection from harm

A

increases insight

can lead to ground breaking discoveries

100
Q

principle of integrity

A

being honest and truthful in research

101
Q

integrity guidelines

A

deception

102
Q

principle of competence

A

followed all guidelines properly or made suitable adjustments that are broken

103
Q

competence guidelines

A

all of them

104
Q

experimental design

A

control the influence of ppt variables in an experiment

105
Q

types of experimental design

A

independent measures
repeated measures
matched patricipants

106
Q

independent measures design

A
  • 2 or more experimental conditions
  • different ppts take part in each condition
  • ppts are randomly allocated to one of the experimental conditions
107
Q

strength of independent measures design

A

eliminates order effects eg practice, boredom, fatigue

108
Q

weaknesses of independent measures design

A
  • risk of individual differences

- more ppts needed

109
Q

repeated measures design

A
  • 1 group of ppts

- all ppts take part in all experimental conditions

110
Q

strength of repeated measures design

A
  • less risk of individual differences

- more economical, less ppts needed

111
Q

weakness of repeated measures design

A

increased risk of order effects

112
Q

matched participants design

A
  • 2 different groups of ppts

- researcher allocates ppts to each group so that they all match in terms of key characteristics

113
Q

strength of matched ppts design

A
  • eliminates order effects

- controls for individual differences

114
Q

weaknesses of matched ppts design

A
  • time consuming

- ppts can’t be fully matched

115
Q

over come order effects

A

counter balancing => ABBA

116
Q

how to control situational variables

A

lab experiment

117
Q

how to control individual differences

A

repeated measures or matched ppts design

118
Q

control researcher effects

A

double blind trial or training researchers

119
Q

demand characteristic

A

changing behaviour to fit the aims of the experiment

120
Q

how to control demand characteristics

A

use single blind procedure
field procedure
im or mp

121
Q

social desirability bias

A

personality traits / bad habits hidden as not socially accepted

122
Q

single-blind procedure

A

ppts not told the aims of the study

123
Q

double-blind procedure

A

ppts not told the true aims of the study and experimenter not aware of which condition ppt is in

124
Q

the aim of MY OWN experiment

A

to investigate whether males are better than females at visual spatial tasks

125
Q

what is the iv and dv in MY OWN experiment

A 
IV = gender - male vs female
DV = time - time taken in mins to complete maze
126
Q

sample used in MY OWN experiment

A

opportunity sample

19 students from 6th form college ages 16 - 17

127
Q

experimental method used in MY OWN experiment

A

quasi

128
Q

experimental design used in MY OWN experiment

A

independent measures design

129
Q

conclusion from MY OWN experiment

A
  • males completed the maze quicker than females on avg

- men are better than women at visual spatial tasks

130
Q

naturalistic observation

A

observation carried out in a natural environment

focuses on ppts naturally occurring bhv

131
Q

controlled observation

A

conducted in a lab
allows control of env
mostly observe bhv through a one way mirror

132
Q

participant observation

A

observer becomes part of the group / situation

produces qualitative data of bhv

133
Q

non-participant observation

A

observer remains external - watches from a distance without ppts knowledge

134
Q

overt (disclosed) observation

A

ppts are aware they’re being observed

135
Q

covert (undisclosed) observation

A

ppts don’t know they’re being observed

136
Q

unstructured observation

A

researcher write bhv as they see it

analyse later by looking for patterns of behaviour

137
Q

range

A
  • how dispersed data is
  • large range => very spread out
  • small range => close together
138
Q

equation for the range

A

highest score - lowest score + 1

139
Q

variance

A

spread of scores around the mean

  • large variance => scores are inconsistent and far from the mean
  • small variance => sores are consistent and close to the mean
140
Q

population variance equation

A

(∑(x-μ)^2 )÷n or 1÷n ∑(x-μ)^2

141
Q

how to calculate variance (step by step)

A
  1. calc mean
  2. work out difference between each score and mean
  3. square the differences
  4. find sum of squared values
  5. ÷ sum of differences by N an - 1
142
Q

standard deviation

A

average amount numbers that differ from the mean

  • large SD => scores are varied
  • small SD => scores are consistent
143
Q

strength of using the variance

A

takes all scores into account

more precise and representative of dispersion

144
Q

weakness of variance

A

mat hide characteristics of data which could skew it

145
Q

strength of Standard deviation

A
  • takes all scores into account - more precise and rep

- returns the units to the same figure as the mean , easier to make direct judgements about data sets

146
Q

what is skewed distribution

A

when data is skewed to one side of the bell curve

- mean, median and mode aren’t equal.

147
Q

what causes positive skew

A
  • extremely high scores pull mean to the RIGHT

- there will be a long tail to the right

148
Q

what causes a negative skew

A
  • extremely low scores pulls the mean to the left

there will be a long tail to the left

149
Q

levels of measurement

A

how the data was measured and how precise it is

150
Q

3 levels of measurement

A
  1. nominal data
  2. ordinal data
  3. interval data
151
Q

nominal data

A
  • data in categories

- shows number of times bhv occurred

152
Q

ordinal data

A
  • data put in rank order

- the differences between each rank isn’t known and doesn’t have to be equal

153
Q

interval data

A
  • objective scale
  • data comes from a scale of = or known units with equal intervals
  • uses precise mathematical units
154
Q

strength of nominal data

A
  • easy to generate

- large amount of data can quickly be collected; reliable

155
Q

weakness of nominal data

A
  • without linear scale = ppts unable to express degrees of response
  • can only use the mode as measure of spread
156
Q

strength of ordinal data

A

indicates relative values on a linear scale instead of just total
=>more informative then nominal data

157
Q

weakness of ordinal data

A

dont know the size of the gap between values or if the gaps are equal

158
Q

strengths of interval data

A
  • more informative => points directly comparable because equal value
159
Q

probability

A
  • how likely something is going to happen

- assesses the probability of an event; if the data is due to chance or not

160
Q

significance

A

when low prob the difference between variables were due to chance => can accept the alternate hyp

161
Q

link between significance and probability

A

probability of the results being due to chance increases => significance of results decreses

162
Q

usual probability significance level

A

p<0.05

probability results were due to chance is less than or = to 0.05/5%

163
Q

type 2 error

A

when you wrongly accept the null hypothesis

probabilities are too strict (p<0.01, p<0.001)

164
Q

implications of type 2 error

A

null hypothesis is wrongly accepted => think there’s no effect when there actually was

165
Q

type 1 error

A 
wrongly accept the alternate hypothesis 
lenient probability (p<0.01/0.3)
166
Q

implications of type 1 error

A

wrongly accept alternate hypothesis when null should be accepted

167
Q

5 non-parametric hypothsis tests

A
  1. mann Whitney-u
  2. chi squared
  3. binomial sign test
  4. wilcoxon signed rank
  5. spearman’s rho
168
Q

assumptions of parametric tests

A
  • population should be ND
  • var of pop should be approximately equal
  • should have at least interval or ratio data
  • no extreme scores
169
Q

assumtions of non-parametric tests

A
  • population isn’t ND
  • var of populations are unequal
  • any level of data
  • can incl extream scores
170
Q

which test to use (checklist)

A
  1. type of data
  2. experimental design
  3. difference in conditions
  4. relationship or not
171
Q

what does the mann Whitney-U measure

A

difference of 2 conditions of an IV

ordinal or interval

172
Q

checklist for mann Whitney-u

A
  • DV produces ordinal or interval data
    2. independent measures design
    difference between each condition
173
Q

equations used in mann whitney-u

A 
Ua = NaNb+((Na(Na +1)÷2)-Ra
Ub = (NaNb)-Ua
174
Q

steps for mann whitney-U

A
  1. rank scores as one data set
  2. add ranks for each group => Ra and Rb
  3. Use formula to find Ua and Ub (Ub = observed value)
  4. critical U values table (no. ppts in A on one axis B on other)
175
Q

when is research significant in mann whitney-u

A

research is significant if observed value of U is EQUAL OR LESS than cv, at 5% sig level

[U = cv or U < cv] =>significant

176
Q

when can null hypothesis be rejected

A

if U ≤ / < CV => null hypothesis is rejected

U > CV => null hyp can be accepted

177
Q

experimental design used in wilcoxon signed rank

A

repeated measures

178
Q

checklist for wilcoxon signed rank

A
  1. DV produces ordinal or interval data
  2. rm design
  3. looking for difference between each condition
179
Q

steps for wilcoxon signed rank

A
  1. find difference between groups (add + or - )
  2. rank differences (ignore signs and zeros)
  3. count number of + & - separately
  4. add ranks of less frequent sign (= observed value)
  5. add number of differences (ignore 0’s)
  6. match n value on critical value table at 5% sig level, 2-tailed
180
Q

when are results significant in wilcoxon signed rank

A

observed value should be LOWER than cv for significant results

  • obs < cv =>reject ho
  • obs > cv => accept ho
181
Q

what does chi squared test measure

A

compares frequencies with occurring frequencies

182
Q

checklist for chi squared test

A
  1. DV produces nominal data
  2. independent measures design
  3. explores a difference between each condition / association
183
Q

steps for chi squared test

A
  1. add total for columns and rows
  2. find expected values ((row T x columnT)÷overall T)
  3. calculate chi squared using formula
  4. calc DoF => (rows-1) x (columns-1)
  5. find cv at 0.05 sig level using DoF
184
Q

when is results significant for chi squared test

A

observed chi squared value is BIGGER or EQUAL TO cv

obs ≥/=cv =>significant, reject ho
obs < cv =>insignificant, accept ho

185
Q

checklist for binomial test

A
  1. dv produces nominal data
  2. repeated measures design
  3. exploring a difference between each condition
186
Q

steps for a binomial sign test

A
  1. add + &- ignore 0’s
  2. count each + and - (T+, T-)
  3. smallest T value = observed
  4. level of sig, no. of ppts (ignore 0’s)
  5. 0.05 for 1 tailed
187
Q

when is binomial test significant

A

when observed value is SMALLER or EQUAL to the cv (T value)

obs significant, reject ho
obs > cv => not significant, accept ho

188
Q

what does spearmans rho look at

A

examine the relationship between co-variables

uses ranks

189
Q

checklist for spearmans rho

A
  • variables that produce ordinal data
  • explores relationship between co-variables
  • correlational design
190
Q

spearmans rho steps

A
  1. rank each column individually
  2. rank scores for each group separately
  3. find difference between ranks for each data set
  4. square differences and add them
  5. use formula to calc spearmans rho
191
Q

when are results for spearmans rho significant

A

when calculated spearmans rho value is BIGGER than the critical value, results are significant

obs > cv =>significant, reject ho
obs insignificant, accept ho

Psychology alevel (7 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions