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Psychology > Research Methods > Flashcards

Research Methods Flashcards

1
Q

aim

A

what the researcher intends to investigate

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2
Q

hypothesis

A

statement stating relationship between IV & DV

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3
Q

state the 3 types of hypothesis

A

null, directional, non-directional

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4
Q

null hypothesis

A

nothing will happen

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5
Q

directional hypothesis

A

1 tailed, one specific group will do better than the other

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6
Q

non-directional hypothesis

A

2 tailed, predicts something will happen but not ‘direction’ of the effect

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7
Q

independent variable

A

manipulated by researcher so DV can be measured

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8
Q

dependant variable

A

measured by researcher

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9
Q

what do you have to do once you have made the aims and hypothesis

A

operationalise variable e.g. turn UV & DV into something we can measure

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10
Q

name all research issues

A

extraneous variable, confounding variable, demand characteristics, investigator effects

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11
Q

research issues - extraneous variable

A

variable affecting DV
additional/unwanted - should be identified + have steps taken to minimise effects
doesn’t vary systematically with IV

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12
Q

research issues - confounding variable

A

varies systematically with IV so can’t tell is change in DV is due to IV or varaible

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13
Q

research issues - investigator effects

A

effect of investigator’s behaviour on research outcome
e.g. leading questions
Coolican: include expectancy effects + unconscious bias - actions of researcher related to study design (selection of PPs, instructions)

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13
Q

research issues - demand characteristics

A

cues from researchers / situation that may be interpreted as revealing purpose

act in way they think expected + over-perform (please-U effect)
underperform to deliberately sabotage (screw-U effect)

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14
Q

research issues (combat) - randomisation

A

control investigator effects through use of chance methods to reduce researcher’s unconscious biases

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15
Q

research issues (combat) - standardisation

A

using exactly the same formalised procedures / instructions for all PPs in a research study

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16
Q

case studies

A

in-depth investigation, description, analysis of single individuals, group, institution, event

tend to take place over long time (longitudinal)

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17
Q

content analysis

A

research technique enabling indirect study of behaviour by examining communication that people produce e.g. in texts, emails, TV, film

aim to summarise & describe communication in systematic way so overall can be drawn

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18
Q

coding

A

stage of content analysis - categorise large sets of info into meaningful units

may involve counting up number of times particular word/phrase appears to produce quantitative data

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19
Q

thematic analysis

A

inductive & qualitative approach to analysis that involves identifying implicit/explicit ideas within data

themes often emerge once data been coding

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20
Q

evaluate case studies

A

+ rich, detailed insights may shed light on unusual + atypical forms of behaviour - preferred to more ‘superficial’ forms of data from experiment/questionnaire

+ contribute to understanding of ‘normal’ functioning e.g. HM significant as it demonstrated ‘normal’ memory processing

+ generate hypotheses for future study + 1 solitary contradictory instance may lead to revision of entire theory

  • generalisation when dealing with small sample sizes
  • information in final report based on subjective selection & interpretation
  • personal accounts prone to inaccuracy & memory decay (low validity)
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21
Q

evaluate content analysis

A

+ useful - gets around ethnical issues

+ most material already exists within public domain - no issues obtaining permission

+ high external validity

+ flexible - produce both qualitative & quantitative data

  • people tend to be studied indirectly so communication they produce usually analysed outside context within which it occurred
  • researcher may attribute opinions
  • lack objectivity especially when more descriptive forms of thematic analysis employed
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22
Q

assessing reliability

A
  1. test retest: questionnaire, testing over time, correlation -0.8+
  2. inter-observer reliability: multiple observers - overcome bias 0.8+ pilot study
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23
Q

assessing reliability - test retest

A

method assessing reliability of questionnaire/psychological test by assessing person on 2 separate occasions

shows to what extent test produces same answers i.e. is reliable

must be sufficient time between test & retest to ensure PP can’t recall answers but not so long attitudes, opinions, abilities changed

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24
Q

assessing reliability - inter-observer reliability

A

extent to which there is agreement between 2+ observers

measured by correlating observations of 2+ observers

(total number of agreements)/(total number of observations) >+0.80 data have higher inter-observer reliability

issue: everyone has own unique way of seeing world - relevant observational research as researcher’s interpretations may differ - introducing subjectivity, bias, unreliability

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25
Q

reliability

A

how consistent findings are - measuring device said to be reliable if it produces consistent results

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26
Q

improving reliability - questionnaires

A

test-retest method
if produces low test-retest reliability may require some items to be ‘deselected’/rewritten (if questions complex may be interpreted differently) solution replace some of open questions with fixed choice alternatives

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27
Q

improving reliability - interviews

A

ensure reliability best by use same interviewer - if not possible interviewers must be properly training so no asking leading/ambiguous questions

easily avoided in structured interviews where interviewer’s behaviour more controlled by fixed questions

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28
Q

improving reliability - experiments

A

lab experiment reliable by struct control on procedure

precise replication of method rather than demonstrating reliability of findings

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29
Q

improving reliability - observations

A

reliability improved by making sure behavioural categories properly operationalised + measurable + self-evident - categories shouldn’t overlap + all possess behaviours should be covered

if categories not operationalised well different observers have to make own judgements of what to record + end up with differing and inconsistent records

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30
Q

validity

A

extent observed event genuine (does it measure what supposed to) (can it be generalised)

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31
Q

face validity

A

a measure scrutinised to determine whether it appears to measure what its supposed to

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32
Q

concurrent validity

A

extent psychological measure relates to existing similar measure

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33
Q

ecological validity

A

extend findings can be generalised to settings/situation

form of external validity

if task used to measure DV is not ‘like everyday life’ (i.e. low mundane realism can lower eco valid)

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34
Q

temporal validity

A

extend findings can be generalised to other historical times

form of external validity

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35
Q

internal validity

A

refers to whether effects observed due to manipulation of IV and not another factor

major threat to IV is if PPs respond to demand characteristics e.g. Milgram ‘played along’ + didn’t believe they were administering shocks

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36
Q

external validity

A

relates to factors outside investigation e.g. generalising to other settings, populations, eras

ecological

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37
Q

validity - qualitative methods

A

high eco valid than quanti (less interpretive) because depth & detail associated with case studies and interviews (better reflect PPs reality)

validity further enhanced through triangulation - use number of different sources as evidence e.g. interviews, diary, observation

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38
Q

type 1 error

A

reject null hypothesis when we shouldn’t - if probability level too loose/lenient (e.g. 0.1 an ‘optimistic error’

incorrect rejection of true null hypothesis (false +tive)

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39
Q

type 2 error

A

accepting null hypothesis when we shouldn’t - if probability level too tight/stringent (e.g. 0.01 a ‘pessimistic error’

failure to reject false null hypothesis (false -tive)

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40
Q

levels of measurement

A

quantitative data classified into types/levels of measurement
e.g. nominal, ordinal, interval

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41
Q

chi-squared

A

test for association (difference or correlation) between 2 variables/conditions

data should be nominal level using unrelated design

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42
Q

Mann-Whitney

A

test for significant difference between 2 sets of scores

data should be ordinal level using unrelated groups

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43
Q

Pearson’s r

A

parametric test for correlation when data at interval level

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44
Q

Related t-test

A

parametric test for difference between 2 sets of scores

data must be interval with related design

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45
Q

sign test

A

statistical test analyse difference in scores between related items i.e. same PPs tested twice

nominal

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46
Q

spearsman’s rho

A

test for correlation when data at least ordinal level

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47
Q

unrelated t-test

A

parametric test for difference between 2 sets of scores

data interval with unrelated design

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48
Q

Wilcoxon

A

test for significant difference between 2 sets of scores

data ordinal level with related design

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49
Q

experimental design

A

related design: repeated, matched
unrelated design: independent

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50
Q

table to work out test

A

Carrots Should Come
Mashed With Swede
Under Roast Potatoes

51
Q

nominal data

A

presented in form of categories

52
Q

ordinal data NO UNITS

A

ordered in some way e.g. scale of 1-10
doesn’t have equal intervals
lacks precision - based on subjective opinion
due to unsafe nature, ordinal data not used as part of statistical testing

53
Q

interval data

A

numerical scales include units of equal size
HAS UNITS
more detail
public scales of measurement produce data based on units of measurement (time, temperature weight)
most precise & sophisticated

54
Q

probability

A

measurement of likelihood that particular event will occur where 0 indicates statistical impossibility + 1 statistic certainty

55
Q

significance

A

how sure we are that difference or correlation exists

significant result can reject null hypothesis

56
Q

levels of significance and probability

A

usual significance in psychology is 0.05 (5%) -> p <= 0.05

can never be 100% certain about result -> psychology settled on convectional level of probability where prepared to accept results may have occurred by chance

57
Q

order to psychological investigations

A

abstract, introduction, method, results, discussion, references

58
Q

psychological investigations: abstract

A

key details of research project
short summary (150-200 words)

59
Q

psychological investigations: introduction

A

look at past research on similar topic
literature review following logical progression - begin broadly + gradually become specific until aims & hypothesis presented

60
Q

psychological investigations: method

A

description of what researchers did
split into several subsections
sufficient detail so other researchers able to replicate
design clearly stated and reasons/justification
sample -> information related to people involved: how many, biographical/demographic information + sampling method & target population
apparatus/materials
procedure -> list everything that happened - briefing, standardised, instructions, debriefing
ethics -> explanation of how these addressed

61
Q

psychological investigations: results

A

description of what researcher found
summarise key findings
inferential statistics include choice of statistical test, calculated & critical values, level of significance + final outcome
any raw data collected

62
Q

psychological explanations: discussion

A

consideration of what results can tell us in terms of psychological theory
summarise results/findings in verbal form
mindful of limitations + discuss
wider implications considered

63
Q

psychological explanations: referencing

A

author(s), date, title of book (in italics), place of publication, publisher

64
Q

paradigm

A

set of shared assumption + agreed methods with scientific discipline

65
Q

paradigm shift

A

result of scientific revolution: significant change in dominant unifying theory within scientific discipline

66
Q

empirical method

A

scientific approaches based on gathering evidence through direct observation & experience

67
Q

falsifiability

A

principle that theory can’t be considered scientific unless it admits possibility of being proved untrue

Popper: genuine scientific theories should hold themselves up for hypothesis testing + possibility of being proven false - theory of falsification: even when scientific principle successfully tested no necessarily true just not proven false yet - theories that survive most attempts to falsify become strongest

reason for null hypothesis

68
Q

paradigms & paradigm shifts

A

Kuhn: suggested what distinguishes scientific disciplines from non-scientific disciplines in a shared set of assumptions & methods - suggested social sciences lack universally accepted paradigm + best seen as ‘pre-science)

psychology marked by too much international disagreement + has too many conflicting approaches to qualify as a science + therefore is pre-science

Kuhn: progress within established science occurs where there’s scientific revolution - handful researchers question accepted paradigm, critique gather popularity + eventually paradigm shift when too much contradictory evidence to ignore

e.g. Kuhn cited change from Newtonian paradigm in physics towards Einstein’s theory of relativity as paradigm shift

69
Q

theory construction & hypothesis testing

A

theory: set of general laws/principles that have ability to explain particular events/behaviours

theory construction occurs gathering evidence via direct observation

should be possible to make clear + precise predictions on basis of theory

theories should suggest number of possible hypothesis

hypothesis tested using systematic & objective methods to tell if it should be supported/refuted

deduction: process of deriving new hypotheses from existing theories

70
Q

replicability

A

if scientific theory ‘trusted’ findings must be shown to be repeatable across a number of different contexts & circumstances

replication important role in determining validity of finding

replication in determining reliability of method used

Popper: by repeating study over different contexts & circumstances can generalise

71
Q

objectivity & the empirical method

A

researchers must strive to maintain objectivity - must keep ‘critical distance’ during research

must not allow personal opinions/biases to ‘discolour’ data they collect/influence behaviour of PPs

lab experiments - more control - most objective

objectivity basis of empirical methods e.g. experimental methods, observational method

theory can’t claim to be scientific unless it’s been empirically tested & verified

72
Q

the scientific cycle

A

science based on concept of ‘empiricism’ - belief knowledge gained from experience - leads to idea evidence must inform theories

systematic and objective evidence leads to formation of a theory

once evidence no longer fits theory - theory should be abandoned

73
Q

aim & hypothesis

A

aim: general statement of what researcher intends to investigate; purpose of study

hypo: clear, precise, testable statement, that states relationship between independent and dependent variable (prediction)

74
Q

types of extraneous variable

A

situational variables: relating to the environment; time of day, temperature, lighting, instructions

participant variables: intelligence, age, gender and personality - controlled through experimental design and random assigning

75
Q

confounding variables

A

vary systematically with IV
not able to tell if change in DV due to IV or confounding variable

76
Q

demand characteristics

A

any cue from researcher or situation that may be interpreted by PPs as revealing purpose

leading to PP changing behaviour - may act in way they think expected/over-perform ‘please-U effect’ or under-perform to sabotage ‘screw-U effect’

participant reactivity significant extraneous variable

77
Q

investigator effects

A

any effect of investigator’s behaviour on research outcome

Coolican: can include expectancy effects + unconscious cues - any actions of researcher that are related to study design

leading questions

78
Q

randomisation

A

using chance methods to reduce investigator effect and demand characteristics (control effects of bias)

79
Q

standardisation

A

using exactly the same formalised procedures + instructions for all PPs in research study

80
Q

extraneous variable

A

variable that affects the DV
unwanted should be identified at start of study and steps taken to minimalize influence
don’t confound with findings

81
Q

independent groups design

A

PPs allocated to different groups where each group represents 1 experimental condition

when 2 separate groups experience 2 different conditions - performance of 2 groups compared

82
Q

repeated measures

A

all PPs take part in all conditions of the experiment

2 mean scores from both conditions would be compared

83
Q

matched pairs design

A

pairs of PPs first matched on some variables that may affect DV - then 1 member of pair assigned to condition A or condition B

attempt to control confounding variable + PP variables

84
Q

evaluate independent groups

A
  • PPs in different groups not same in terms of PP variables (act as confounding variable reducing validity) (deal with this using random allocation)
  • less economical each PP only contributes single result only

+ order effects no a problem

85
Q

evaluate repeated measures

A
  • each PP has to do at least 2 tasks so order of tasks may be significant (use counterbalancing)
  • order effects could create boredom + fatigue might cause deterioration in performance (confounding variable)
  • demand characteristics

+ PP variables controlled (higher validity)

+ fewer PPs needed

86
Q

evaluate matched pairs

A

+ PPs only take part in single condition so order effects + demand characteristics less of problem

  • PPs never matched exactly
  • matching may be time consuming + expensive, less economical
87
Q

laboratory experiment

A

controlled environment within which researcher manipulates IV + record effect on DV, whilst maintaining strict control of extraneous variables

88
Q

field experiment

A

natural setting within which researcher manipulates IV + records the effect

89
Q

natural experiment

A

change in IV not brought about by researcher but would have happened even if researcher not been there

researcher record effect on a DV they have decided on

90
Q

quasi-experiment

A

study that’s almost experiment but lacks key ingredients

IV hasn’t been determined by anyone - the ‘variables’ simply exist (being old/young)

91
Q

evaluate lab experiments

A

+ high control over confounding + extraneous variables TMT ensure any effect on DV likely to be result of manipulation of IV (more certain about cause + effect (high internal validity))

+ easy to replicate

  • lack generalisability (artificial task) - PPs behave unusual in unfamiliar context (low external validity) - PPs aware they being tested so demand characteristics - tasks not represent everyday life (low mundane realism)
92
Q

evaluate field experiments

A

+ high mundane realism because natural environment - produce behaviour more valid/authentic because PPs unaware they being studied (high external validity)

  • loss of control of confounding + extraneous variables TMT cause + effects more difficult to establish + precise replication often not possible
  • ethical issues - PPs unaware being studies + can’t consent
93
Q

evaluate natural experiments

A

+ provides opportunities for research that may not be undertaken for practical/ethical reasons e.g. Romanian orphans

+ high external validity - involve study of real-world issues

  • naturally occurring event may occur rarely
  • PPs may not be randomly allocated to experimental conditions if there is an independent groups TMT less sure whether IV affected DV
  • may be conducted in lab - lack realism + demand characteristis
94
Q

evaluate quasi-experiments

A

+ often controlled

  • can’t randomly allocate PPs to conditions + therefore may be confounding variables
95
Q

common ethical issues

A

informed consent, deception, protection from harm, confidentiality and privacy, right to withdraw

96
Q

BPS code of Ethics and Conduct

A
  1. respect (upholding dignity of others (privacy, consent + making PPs aware of rights))
  2. competence (completing work to high, professional standard)
  3. responsibility (to clients/PPs/public (providing robust evidence) + to psychology (upholding its scientific nature)
  4. integrity (transparency over bias + limitations)
97
Q

ethical issues - consent

A

need to be aware of following details in order to give fully informed consent:
statement participation voluntary, purpose, risks + discomfort, procedures, benefits to society and individual, length of time subject expected to participate

98
Q

ethical issues - combat consent

A

BPS: if don’t fully disclose before asking consent additional measures must be in place

presumptive consent using similar sample - fully debrief at earliest opportunity

children struggle understand what they consenting to require special consideration - using guardian/carer

independent advisor must approve anything that might result in negative consequences

cost-benefit analysis: researchers must weigh up potential benefits of study against potential negatives

99
Q

naturalistic observation

A

watching + recording behaviour in setting within which it would normally occur

100
Q

controlled observation

A

watching + recording behaviour within structured environment

101
Q

covert observations

A

PPs behaviour watched + recorded without their knowledge/consent

102
Q

overt observations

A

PPs behaviour watched + recorded with their knowledge/consent

103
Q

participant observations

A

researcher becomes a member of group whose behaviour they’re watching + recording

104
Q

non-participant observation

A

researcher remains outside group whose behaviour they are watching + recording

105
Q

evaluate all observations

A

+ give special insight into behaviour

  • observer bias (observer’s interpretation of situation may e affected by their expectations) (may be reduced by using more than 1 observer)
  • can’t remonstrate casual relationships
106
Q

evaluate naturalistic and controlled observations

A

+ high external validity (generalised to everyday life because behaviour studied within environment it would normally occur)

  • lack of control (can’t replicate)
  • uncontrolled confounding/extraneous variables more difficult to judge any pattern of behaviour
  • controlled: not easily applied

+ controlled: easier to replicate

107
Q

evaluate covert and overt observations

A

+ natural behaviour (PPS don’t know being watched remove demand characteristics increase internal validity)

  • ethics questioned (right to privacy)

+ overt: ethically acceptable BUT knowledge being observed may significantly influence behaviour

108
Q

evaluate PP and non-PP observations

A

+ PP: researcher experience situation as the PP’s do giving increased insight into lives of studies increase external validity

  • PP: danger researcher come to identify too strongly with those studied + lose objectivity - called ‘going native’ when line between being researcher + PP become blurred
  • non-PP: maintain objective psychological distance
  • non-PP: lose valuable insight as they are too far removed from people + behaviour
109
Q

observational design - behavioural categories

A

when target behaviour broken up into components that are observable + measurable (operationalisation)

110
Q

observational design - event sampling

A

target behaviour/event first established then researcher records this event every time it occurs

111
Q

observational design - time sampling

A

target individual/group first established then researcher records their behaviour in fixed time frame

112
Q

evaluate unstructured versus structured observations

A

+S: use beha cat recording data easier + systematic - data numerical TMT analysing straightforward

-U: produce quali
+ more rich detail - appropriate small-scale
- greater risk observer bias

113
Q

evaluate behavioural categories

A

+ make data collection more structured + objective

114
Q

evaluate sampling methods

A

+ E: useful when target event happens infrequently + could be missed if T used
- if specified event too complex observer overlook

+ T: reduces number observations have to be made
- instances when behaviour is sampled unrepresentative

115
Q

self-report techniques - questionnaires

A

PPs answer set pre-written questions

designing careful to work: clarity (avoid ambiguity) and bias (avoid leading ?)
open ?: provide own answer + quali - more insight + detail - PPs not articulate struggle - harder analyse

closed ?: pre-determined responses + quanti - easier analyse - forces PPs make choice so doesn’t always give actual answer (lack validity)

+ cheap + easy distribute
- only completed by people who can read/write + have time
- hard to get right difficult to design + if not done right can be meaningless

116
Q

questionnaires design

A

likert scales (PPs indicate agreement using scale)

rating scales (get PPs identify with a value representing strength of feelings about topic)

fixed choice option (include list of possible options which PPs tick)

117
Q

self-report design - interviews

A

structured: predetermined questions - standardised easily repeated - easy analyse - require trained interviewer

unstructured: set of ideas predetermined with some questions but questions developed as interview progresses its responsive - more detail - harder analyse - require highly trained interviewer expensive

semi-structured: list of pre-determined but interviewer free to ask follow up

  • can lie (social desirability bias + demand characteristics undermine internal validity)
  • may not know how they feel/remember event accurately
  • only certain types people willing - unrepresentative sample low external validity
118
Q

meta-analysis

A

researcher looks at findings from many different studies + produce statistic to represent overall effect - researcher will use secondary data

+ large varied sample, results generalised to larger pop increase validity
- conclusions biased as researcher purposely exclude negative or non-significant findings

119
Q

correlations

A

investigate relationship between co-variables

show strength of association between co-variables by plotting each pair of points on scatter graph

no manipulation so can’t establish cause and effect

120
Q

measures of central tendency

A

descriptive analytics can analyse data

involve graphs, tables allow identify trends

mean, median, mode

121
Q

measures of dispersion

A

indicate how data spread out

range + standard deviation
range skewed by large data points
standard shows to what extent values deviate from mean arguably more representative than mean - low SD indicate consistency: IV more consistent effect - larger SD indicate variability

122
Q

bar chart

A

data in categories (nominal/ordinal)

discrete (in categories) so bars separate

123
Q

historgram

A

present continuous data on interval/ratio scales of measurement

columns equal width per equal category

all categories represented

124
Q

scattergran

A

used when doing correlational analysis

visual picture of relationships

125
Q

distributions

A

spread of data

mean, median + mode occupy same midpoint of curve

positive skew - most distribution concentrated toward right

negative skew - most distribution concentrated towards right

126
Q
A

Psychology (5 decks)

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