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Research methods Flashcards

Introduction to RM, descriptive stats, presenting data, research approaches, organisational designs, probabilities

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1
Q

methodology

A

the study of methods

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2
Q

method

A

how to reach a certain goal

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3
Q

epistemology

A

what is knowledge

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4
Q

ontology

A

what is reality

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5
Q

philosophy of science

A

what is science?

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6
Q

why do i need research methods?

A

limit bias of perception, imperfect memory (cognitive),
prior experience and learning,
create a scientific method

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7
Q

challenges in psychological research

A
  1. unobservable object of investigation
  2. subjectivity of object of investigation
  3. social construction (phenomena occurs in environment)
  4. ethics
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8
Q

types of research

A

basic research - to increase the stock of knowledge.

applied research - to increase the use of knowledge to devise new applications.

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9
Q

stats in psychology

A

clinical, diagnostics, development, neuroscience and cognition

allow for discovery, modelling and scientific proof.

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10
Q

the difference between a belief and knowledge

A

belief- subjectively true

knowledge- objectively true

the research method is a way to check for proofs and obtain knowledge

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11
Q

scientific research

A

building knowledge using specific research methods to check for proofs

should be
- transparent
- share expertise

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12
Q

hypotheses vs conclusions

A

a hypothesis is an unproven provisional statement

a conclusion is a proven proposition

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13
Q

scientific proof

A

logical proof- does it make sense (rationalism)

empirical proof- is there evidence (empiricism)

the process - logical arguement - empirical test - logical argument

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14
Q

logical argument (causal influence)

A

3 types of causal influence:
- necessary cause- the cause is to produces the effects
- sufficient cause- the cause alone produces the effect
- contributory cause - this contributes to the cause producing the likelihood or strength.

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15
Q

criteria of scientific propositions

A
  • logical consistency
  • testability
  • scope
  • fruitfulness
  • novelty
  • simplicity
  • conservatism
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16
Q

logical consistency

A

3 types of logical inference:
- deduction - infer info about a single case
- induction - infer a general statement from statements about single cases
- abduction - infer cases the most likely best explanation

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17
Q

testability

A

how well it can be tested

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18
Q

scope

A

general validity

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19
Q

fruitfulness

A

implications beyond the research question

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20
Q

novelty

A

information which is new and creating propositions

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21
Q

simplicity

A

parsimony - minimise assumptions by creating the most simplistic and scientific explanation

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22
Q

conservatism

A

minimise new assumptions that contradict existing knowledge. propositions that integrate with existing knowledge and more likely because it is unlikely to be wrong.

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23
Q

theory before the empirical test

A

theory - research question - assumption - hypothesis - prediction

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24
Q

theory after empirical test

A

data analysis - conclusion - implications.

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25
Q

level of measurement of data

A

nominal, ordinal, interval or ratio

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26
Q

nominal data

A

categorical data of groups, have to be mutually exclusive (not part of two groups), can’t order the data

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27
Q

ordinal data

A

orders people, objects or events along some continuum (various rankings), no information is given about the differences between the points on then scale

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28
Q

interval data

A

equal intervals between objects represents equal differences, interval scales do not talk about ratios (zero is arbitrary)
eg temperatures

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29
Q

ratio data

A

ratio data has a true zero point eg the absence of something being measured eg weight

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30
Q

descriptive statistics

A

goal to characterise a numerical set of data efficiently and representatively, condense and render meaningful information, minimise error involved in condensing information

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31
Q

inferential statistics

A

the goal is to infer the characteristics of the population from a sample so generalise the results

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32
Q

measures of central tendency

A

mean, median and mode

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33
Q

mean

A

the average score, add up all the scores and divide by the number of scores. mean can be influenced by the anomalies. a histogram can show whether the mean is a good measure

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34
Q

median

A

middle score when all do the scores are rearranged in order from smallest to largest, not affected by extreme scores

(N+1)/2

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35
Q

mode

A

the value with the most frequent score, if two adjacent scores find the middle eg 4 and 5 = 4.5, if two non adjacent scores report the, both eg 4 and 7 (bimodal distribution)

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36
Q

measures of variability

A

the degree to which the values vary: range, interquartile range, variance and standard deviation

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37
Q

range

A

the difference between the maximum and minimum scores, measure of distance, easily distorted by outliers

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38
Q

interquartile range

A

percentiles - cut off that divides the data into percentage chunks
percentiles can try to avoid anomalies. 50th percentile splits the data into percentage hard so 50% scores above and below.

0.5(N+1) = 50th percentile
0.75(N+1) = 75th percentile

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39
Q

variance

A

how much the mean scores vary given in
terms of the distance from the mean

the average of each score’s squared deviation from, the mean score

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40
Q

sample vs population

A

population- use the population formula (divide by N)

sample- use the sample formula (divide by N-1)

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41
Q

experimental control

A

casual effect - is what’s wanted to be measured

noise - unwanted

confounders - unwanted

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42
Q

noise

A

random variation which is uncertain

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43
Q

signal

A

systematic, regular and informative

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44
Q

bias

A

unwanted or unexpected signal (e.t. systematic variation, systematic tendency) producing spurious results

consequence- measurements without (real) experimental effects differ from what you assume and expect. discrepancies between results and facts

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45
Q

types of bias

A

independent bias and dependent bias

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46
Q

independent bias

A

systematic errors,
systematic tendencies (signals) in measurements that are incorrect but limited to one variable
bias does not vary with the independent variable hence can’t be mistaken for causal effect (results)

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47
Q

dependent bias

A

cofounder
a confounder produces bias in the dependent variable (DV) that varies with the experimental conditions (IV)
this bias is correlated with both independent and dependent variables - interfere with causal effects (results)
produces an unwanted, spurious effect on measurements (DV)

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48
Q

systematic errors

A

unwanted signal bias that does not interfere

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49
Q

criteria of empirical evidence

A

noise - reduce reliability, reduce accuracy

systematic errors - reduce accuracy

confounders - reduce internal validity and reduce external validity

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50
Q

sources of noise and bias

A
  • method (stimulus material and procedure)
  • participant (variation of physical condition e.g. fatigue, perception, emotion and cognitive processing)
  • data recording (variation of experimenter or measurement instruments)
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51
Q

minimise systematic errors

A

easy to fix or compensate through CALIBRATION - comparison with and adjustment measure

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52
Q

why control noise?

A

noise decreases the accuracy and reliability of measurements:
- results of single measurements are inaccurate due to random variation
- more random variation the less likely it is to get the same results across measurements

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53
Q

aim of controlling noise:

A

maximise systematic variation (signal) due to causal effects relative to random variartion (noise) - single -to -noise -ratio

want to maximise the ratio

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54
Q

how to control noise?

A
  • minimise (aka maximise precision)
  • neutralise (e.g. maximise sample size)
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55
Q

minimise

A
  • instruments with high precision
  • tasks with high precision
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56
Q

neutralise

A

take an average of the frequencies - approach the frequencies based on probability

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57
Q

neutralise by sample size

A

if noise is uniformly random, increasing the sample sizes makes the average coverage to the expected value

expected value of noise is the same for all conditions

central tendency of larger datasets involve less noise

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58
Q

how to neutralise by sample size?

A

increase number of participants

increase number of measurements per participants

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59
Q

noise and statistical reliability

A

signal-to-noise-ratio
- the amount of desired signal (e.g. experimental effects) relative to the noise

  • the higher the SNR the higher the sensitivity and the specificity of your measurement
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60
Q

agency

A

human beings (agents) act upon and shape their environment

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61
Q

interpretation

A

people perceive, think and make choices to adapt their actions and behaviour

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62
Q

interaction

A

human beings act on other acting human beings - they mutually influence their behaviour

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63
Q

double-interpretation

A

one person interprets how another person interprets and acts accordingly, which may in turn influence the interpretation and action of the other person

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64
Q

social construction

A

interpretation and meaning exist because of the coordination with others rather than separately within each individual, based on mutual expectations

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65
Q

double-hermeneutic

A

concept from sociology, Giddens, 1970
participants interprets the researchers behaviour and setting of the study and adapt their behaviour

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66
Q

agency in psychology

A
  • investigators influence participants in studies
  • participants influence the interpretation by the investigator
  • not limited to direct communication
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67
Q

agency-based biases in research

A
  1. participant effects
  2. investigator effects
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68
Q

participant effects example

A

Hawthorne studies
- aim to increase performance of factory workers
- manipulation - changing lighting, cleaning workstations, clearing floors and relocating workstations

Hawthorne effect
- productivity gain as a result of increasing worker motivations because of the interest being shown in them

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69
Q

participant effects: general idea

A

participant reactivity
- the act of doing the research changes the behaviour of participants
- due to awareness of the, being observed by the researcher or by other participants
- response bias (unconsciously it could happen)

response bias
- systematic tendencies of participants to respond inaccurately or falsely, producing either a systematic error, or a confound (if mixed with investigated effects)
- participant reactivity - only one of different types of response biases due to the location of response keys

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70
Q

types of participant effects

A
  • participant expectancy
  • demand characteristics
  • social desirability
  • stereotype threat
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71
Q

participant-expectancy effects

A

participant expects a result and therefore unconsciously affects the outcome or reports the expected outcome

placebo effect
positive expectations about a treatment improve patient-reported outcomes

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72
Q

placebo effect

A

placebo is a substance or treatment, which is designed to have no therapeutic value

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73
Q

Nocebo effect

A

negative expectations about a treatment cause negative effects

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74
Q

demand characteristics

A

participants form an interpretation of the study’s purpose and subconsciously change their behaviour to fit that interpretation

please-you effect
- ppts try to fulfil the expectations of the researcher to please them

screw-you effects
- ppts are defiant and try to produce unexpected results that screw up the study

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75
Q

social desirability bias

A

tendency to answer questions in a manner that is expected to be viewed favourably by others and that produces or maintains a publicly acceptable image

examples:
- bradley effect: consider voting for a black candidate heavyset it is socially desirable but in the end they don’t vote for them

  • evaluation apprehension: when being observed ppts feel evaluated and try to convey a positive image
  • watching-eye effect: when being observed, ppts try to behave better than without observation
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76
Q

stereotype threat

A

responses are biased to conform to stereotypes about the ppts social group

negative effects:
negative stereotypes about your own group produce self-doubts and negative expectations towards yourself

positive effects:
stereotype boost: perceive yourself better because of positive stereotypes about your own group

sterotype lift:
perceive yourself better because of negative stereotypes about an outgroup

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77
Q

control of participant effects

A
  1. minimise
  2. assess
  3. account for
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78
Q

minimise participant effects

A
  1. blind procedures
    - information is withheld until after the experiment
    - unobtrusive manipulations and measures: conceal IV and DV so no clue on hypothesis

unobtrusive methods:
-unobtrusive observational data recording
- unobtrusive observation
ethical issues:
- no consent
- risks to ppt and researcher

deception: deceived ppt about one or more aspects of the research to conceal the hypothesis

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79
Q

assess participant effects

A
  1. post-experiment questioning
    - did they know what was expected
    - questionnaires e.g. Perceived Awareness of the Research Hypothesis (PARH) scale
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80
Q

account for participant effects

A
  1. control conditions
    - everything is the same as in the experimental condition except the experimental manipulation
    - baseline results: reference measurement for comparison with experimental conditions

control group
- everything same as in experimental condition except IV

randomised control trials (RCTs)
- typically ppts are randomly allocate to the groups in a randomised control trial - if not quasi

placebo control groups:
- create positive expectations with placebo to establish comparability with treatment groups

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81
Q

investigator effects

A

influence of researcher on results

primary observer effect- effects on participant responses

secondary observer effect- effects on data acquisition and interpretation

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82
Q

primary observer effect

A

researchers expectations about the findings of their research are conveyed to ppt and their responses

examples: experimenter-expectancy and self-fulfilling prophecies

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83
Q

clever hans effect

A

shows the primary observer effect
the horse could “count” but it was just the horse responding to the involuntary cues in the body language of the human trainer and audience

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84
Q

self-fulfilling prophecies

A

behaving so that what you expect will become true

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85
Q

pygmalion effect

A

one’s expectations become reality like pygmalion’s sculpture coming into life

golem effect- negative effects (low expectations lead to poor performance)

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86
Q

rats in the maze example

A

self fulfilling prophecy
- rats that were expected to be good actually performed better

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87
Q

secondary observer effect

A

the researchers select and handle the data in a subjective and bias
happen during data sampling, recording , analyses and interpretation

biases due to secondary observer effects:
sampling bias
observer bias
selection bias

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88
Q

observer bias

A

systematic divergence from accurate facts during observation and recording of data

  • confounders distort data
  • researchers observe and record information differently

types of observer bias:
cognitive biases
detection biases

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89
Q

cognitive biases

A

researchers cognitive bias distort data collection

types of cognitive bias include confirmation buas and halo effect

confirmation bias :tendency to search for, interpret, favour and recall information in a way that confirms their beliefs or hypothesis

halo effect:
tendency for the positive impressions and beliefs in one area to influence a researchers data recording or interpretation in other unrelated areas

detection biases:
focusing on some cases to the detriment of others (e.g. checking diabetes in obese people and not other patients

detection biases can involve cognitive biases (the researcher’s focus) and or errors in sampling

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90
Q

control of investigator effects

A
  1. minimise
  2. assess
  3. control
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91
Q

minimise investigator effects

A

double blind procedures
- ppt and researcher are unaware of allocation to experimental condition and cannot anticipate any result

^ counteracts demand characteristics and primary observer effects

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92
Q

assess investigator effects

A

observer reliability
- indicates how consistent or reliable measurements are

  • inter-observer reliability - across different observers
  • intra-observer reliability - within observer (retest-reliability
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93
Q

account for investigator effects

A

averaging across several researchers compensates for individual idiosyncrasies and biases, like when using a jury for ratings

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94
Q

summary link to validity

A

accuracy and reliability:
- systematic and unsystematic errors

internal validity
- measurements do not measure what they are meant to measure e.g. placebo effects

external validity
- results are not reproducible without experiment specific conditions

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95
Q

representativity of measurements (representativeness)

A

are measures of the experiment representative of the conditions you want to understand (external validity)

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96
Q

statistical population

A

the entire pool (or set) of people, items or events ability which a researcher wants to gain insight and draw conclusions in a study

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97
Q

statistical sample

A

a set of individuals or objects collected or selected from a statistical population by a defined procedure

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98
Q

sampling

A

the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population

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99
Q

representativity of a sample

A

complete sample:
- all members of the population, usually impossible to measure

representative sample:
- represents all relevant properties of the whole population so we can draw conclusions about the population based on measurements
- statical technique allows us to estimate the situation for the whole population based on the measured obtained from representative samples

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100
Q

sampling bias

A
  • systematic error (bias) in sampling causing members of the intended population to be less likely be included in the sample
  • undermined external validity - generalisation about the population
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101
Q

sampling bias

A
  • inclusion in measurements
  • miss important subpopulations in sample
  • in process of gathering sample
  • contradicts assumptions and research question
  • distorts data and conclusions
  • mainly external validity - undermines generalisation of findings beyond context of study
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102
Q

selection bias

A
  • inclusion in statistical analysis
    -discard outliers that are
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103
Q

selection bias

A
  • inclusion in statistical analyses
  • discard outliers which are meaningful
  • after measurements (analysis)
  • contradicts experimental design (control and manipulation)
  • distorts statistical results and conclusions (data is ok)
  • mainly internal - undermines interpretation of results within context of study (confound)
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104
Q

types of causal influence

A

necessary cause
sufficient cause
contributory cause

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105
Q

necessary cause

A

the cause is necessary to produce the effect (but might not be sufficient)

bananas must be ripe to taste good, but not all ripe bananas taste good

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106
Q

sufficient cause

A

the cause alone produces the effect, but it might not be necessary

bananas from kenya always taste good (but there are other good bananas)

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107
Q

contributory cause

A

the cause contributes to an effect by increasing its likelihood or strength

chocolate sauce increases the flavour of bananas, but if they are not ripe they are still not good, and there are ripe bananas that taste good even without chocolate

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108
Q

standard deviation

A

the square root of the average of each scores squared deviation from the mean score

the square root of variance
bigger value is more spread out

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109
Q

probability

A

ranges from 0-1
p(event A) = A/(A+B)

p(event A doesn’t occur) = B/(A+B)

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110
Q

mutually exclusive

A

occurrence of one event precludes the occurrence of another event

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111
Q

independent events

A

occurrence of one event has no effect on the probability of the occurrence of another event

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112
Q

additive rule for mutually exclusive events

A

the probability of occurrence of one event or another is equal to the sum of their separate probabilities

p(A or B) = p(A) +p(B)

clue OR

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113
Q

multiplicative rule for independent events

A

p(A and b) = p(A) x p(B)

clue AND

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114
Q

rules of and vs or

A

AND- multiply probabilities

OR- add their probabilities

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115
Q

addictive rule when not mutually exclusive

A

p(A or B) = p(A)+p(B)-p(A and B)
e.g. when picking a jack or a diamond have to take out the card which is both a jack and a diamond

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116
Q

multiplicative rule for non-independent events

A

p(A and B)= p(A) x p(B|A)
conditional probability - probability of one evnt given the occurrence of another p(B|A)

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117
Q

combinations

A

number of ways to arrange a subset of objects

NCr =(N!)/(r!(N-r)!

N is number of events
r is number of successes
! is multiply by all smaller integers

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118
Q

binomial distribution formula

A

calculates combinations and probability for any one combination of getting r out of N

when b number of events (N) is large the binomial distribution is equal to the normal distribution

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119
Q

binomial distribution formula

A

p(r) = N!/(r!(N-r)! p^r (1-p)^(N-r)

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120
Q

three ways of presenting results

A

text- verbal description - establish unambiguous logical relationships, could get lost in details

table- spatially organised representation of single, precise specifications - communicate large number of details, could get lost in details, arguments are not communicated

Graphics- visualisation of trends - focus and highlight main patterns of data, communicate different types of data - specific details e.g. decimals get lost - visual interpretation depends on viewer

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121
Q

data tables

A

organise information
communicate details

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122
Q

why use graphics?

A

Assess the quality of data:
- detect faults in experiment (bugs, incorrect data recording, misleading instructions, task too difficult)
- characterise data distribution (normal distribution, skew, kutosis)
- identify outliers (single data points outside the distribution that can be misleading

visualise results:
- highlight main results (test of main prediction)
- explain main results (reveal origin of results, expose spurious results, confounds)

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123
Q

How to make graphics?

A

1 Dimension - List, rank, line - no gain of visualisation

2D- Area - visualise patterns

3D - Relief, animation, interactive graphic - visualise complex relationships - difficult to interpret

4D - animation, interactive graphic - visualised complex relationships, difficult to interpret

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124
Q

how to use graphical tools?

A
  1. how many variables
  2. what types of variation
  3. what scale
  4. what resolution
  5. what pattern or relationship
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125
Q

number of variables

A

One variable (univariate)
- data quality
- distribution
- outliers

two variables (bivariate)
- independent and dependent variable
- simple relationship

more than two variables (multivariate)
- two or more factors
- complex relationships

126
Q

types of variation

A

independent variation - input to the empirical test, manipulated by experimenter, fixed in design - conditions of observation or measurements

Dependent variation - output of the empirical test depends on measurements and is expected to depend on the IV - observation/measurement

other variation (noise) - not part of testing logic, interferes with the tested variation and defines reliability of results

127
Q

where do the variation go on the graph

A

IV - X axis
DV - Y axis
Noise - error bars

128
Q

types of scales

A

Nominal, ordinal, interval and ratio

129
Q

resolution

A

least resolution:
Binary data
discrete sets
discrete categories
continuous numbers
full description
Most resolution

130
Q

decrease the resolution of data to avoid clutter

A

Nominal - categorisation
Quantitative - Aggregation and binning

131
Q

Patterns

A

trends and relationships

132
Q

Pie chart

A

Variable 1: continuous ratio-scaled circle (pie)
Variable 2: discrete data in sectors

+ relates sectors to whole circle
- limited to 2 sources of variation
- no error bars
- requires ratio scales but finite data to add up and define beginning and end of circumference

133
Q

Bar chart

A

Variable 1: discrete x-axis
variable 2: quantitative scale along y axis

noise - error bars

additional discrete variables: grouping and/or visual appearance

+ Grouping
+ Highlight distance from zero
+highlight difference across groups or conditions
+ visualise different groups and conditions

  • no continuous X
  • useless for continuous data on both axes
  • risks clutter when too many data points
134
Q

Line chart

A

Variable 1: continuous x axis, at least ordinal scale
variable 2: continuous y axis, at least ordinal

noise - error bars along y-axis

additional continuous variables: grouping of data on sperate lines as identified by visual appearance

+ functions and tends - highlight relationship between values along the x-axis
- covariation
- useless when multiple data points per x-value (non-bijective data)
- less efficient for grouping than bars

135
Q

scatter chart

A

variable 1 + 2: continuous at least interval

noise error bars possible along both axes

additional variables - colour (discouraged)

+ covariation
+ bivariate distribution
- lacks structure
- useless for discrete data - occlusion
- free-floating points
- high risk of clutter for 2+ variables

136
Q

aggregation by frequencies

A
  1. aggregation by counting each value
  2. summing up a binary value
  3. display in a frequency table
137
Q

frequency table

A

proportion: a part, share or number considered in comparative relation to a whole - relative frequency

percentage = relative frequency * 100

138
Q

What graphs to use for a nominal scale

A

Pie, Bar

139
Q

What graphs to use for an ordinal scale

A

Bar

140
Q

continuous data

A

Binning- transform continuous data into discrete data by allocating the continuous data to intervals, the bins

e.g. 140-150 = 2
150-160 = 5

this can be displayed in a stem and leaf diagram

which when turned on its side shows a histogram

141
Q

what graph should be used for continuous data?

A

Bar (histogram)

142
Q

histograms

A

symmetrical: no tails of data/2 tails
negative skew: left tail
positive skew: right tail

frequency density: frequency of data per equal interval = frequency / bin size
probability density: probability of data per value

143
Q

continuous data - cumulative frequency

A

cumulative sum: summing up progressively
(adding up all the previous frequencies to the next)

cumulative histogram: x axis: binned height, y-axis: cumulative frequency

purpose of cumulative histogram:
cumulative probabilities- cumulative frequencies are an estimate of cumulative probabilities

144
Q

what graph for a comparison of frequencies?

A

Bar graph

error bars with standard deviation are redundant in this case

standard deviation of binary variable only depends on proportions

square root (p*(1-p))

standard error:
square root (p*(1-p)/n)

145
Q

aggregation by central tendency

A

standard deviation and mean

the mean is the bar and the standard deviation is the error bar

only representative for symmetrical distributions

146
Q

asymmetrical distibutions

A

do a boxplot (box-and-whisker plot)

Median, quartiles, IQR shown

whiskers - 1.5*IQR
- the minimum and maximum of the data after excluding outliers

147
Q

what graph should be used for a discrete IV?

A

Line graph - highlights trends

148
Q

what graph should be used for a continuous IV?

A

Line graph

149
Q

what graph should be used for a bivariate distribution?

A

scatter graph

150
Q

Alhazen

A

Advocated observation to overcome subjectivity

151
Q

Bacon

A

Baconian method - systematic tests of ideas (hypothesis) through observations

152
Q

what did Alhazen and Bacon believe in?

A

Induction

153
Q

critical rationalism

A

rejection of induction - general propositions can never be proven e.g. all bananas are yellow

154
Q

critical rationalism: falsifiability

A

general propositions can be refuted = falsified

there is a red banana so not all bananas are yellow

155
Q

criterion for scientific propositions

A
  • testable = verified
  • scope (is it s general insight)
  • falsified (be refuted)

all scientific propositions (assumptions, hypothesis and conclusions) must be falsifiable

156
Q

hypothetico-deductive model of scientific method

A
  1. research question
  2. hypothesis: general statement or explanation that answers the research question and can be tested (falsifiable)
  3. predictions: deduce specific predictions from the hypothesis
  4. test: falsify (disprove the hypothesis)

between 2 and 3 = deduction

logical error (affirming the consequent) to consider a confirmation of the specific prediction as a positive proof of the general hypothesis

157
Q

operationalisation

A

translate general hypothesis into a specific prediction of the measurements obtained with your empirical test. the test defines the variables that you measure

hypothesis - operationalisation - prediction

by operationalising deduction is occurring

158
Q

scientific process

A

Karl Popper

scientific progress is based on challenging existing general propositions (assumptions) proposing new ones (hypotheses) which will then be challenged again, and so on

159
Q

scientific progress cycle

A

theory (rationalism)
deduction
empirical test
falsification

160
Q

generalisation

A

translate specific results of a test into a general statement that contributes to a theory

results - generalisation - conclusion

induction occurs at generalisation

161
Q

discussion

A

consider existing evidence and evaluate the contribution of your results to the general hypothesis

results - interpretation - discussion - generalisation - conclusion

162
Q

limitations to the hypothetico-deductive model

A
  • assumes absolute unidirectional relationships
  • criticism of indoor ornithology
  • research is often more complex
163
Q

uncertainty of premise

A

the premise may be uncertain e.g is it covid or the flu

164
Q

probabilistic relationships

A

the conclusion maybe about statistical likelihood, rather than a black-or-white (dichotomous) decision

e.g. swans are not necessarily white, but they are likely to be white

165
Q

Bidirectional relationships

A

the conclusion may be about how A depends on B, and how B depends on A.

how likely is it that a swan is white and that a white animal is a swan

either: identify swans or exclude non-swans

whiteness is neither necessary nor sufficient for animal to be a swan

166
Q

Bidirectional relationship: sensitivity and specificity

A

95% sensitivity - false negatives - 5% of cases are not detected

95% specificity - false positives - 5% of negative cases are falsely declared as positive

167
Q

descriptive propositions

A

cooccurrence relationships: If A happens (antecedent), B (consequent) also happens. no order in time. no change or variation of B depending on A

168
Q

explanatory propositions

A

casual relationships
- Effect happens due to cause
- Cause contributes to the state of effect - change and variation of effect depending on cause

169
Q

cooccurrence and causation

A

causation is difficult to determine, can only observe cooccurrences (observe B when A happens)

cooccurrence doesn’t imply causation
cooccurrence without causation is a spurious relationship

170
Q

causation in research

A

research aims to show causal relationships.
can only observe cooccurrences

171
Q

experiment

A

experimental manipulation - test how measurements change with and without the experimental condition

experimental control: keep all conditions the same with and without experimental manipulation

172
Q

experimental operationalisation

A
  • translate general hypothesis into variables (deduction)
  • specifies the operation that results in the production of an outcome
  • defines a DV in how its measured and IV in how it is controlled
173
Q

experimental manipulation

A
  • IV: variation across experimental conditions
  • DV: variation of the measurements depending on the experimental conditions
174
Q

experimental contol

A

confounder: condition/factor whose variation systematically affects the DV but which is not part of the conditions IV

noise: unsystematic random variation of measurements

175
Q

experimental control: confounder

A

confounding = mixing up cause and effect

confounders produce cooccurrence without causation = spurious relationships

176
Q

example of noise

A

grade varies independent of sleeping time

e.g. 4 different grades when students sleep 7 hours a night

noise does not produce spurious effects but ambiguity (uncertainty)

177
Q

how to control confounds

A

keep all conditions CONSTANT that are not part of the experimental test
the core of experimentation: it allows identifying effects on the DV that only occur due to the variation of the IV

178
Q

Control random noise

A

optimal: measurements of the DV are constant within a specific experimental condition
depends on control of conditions (IV) and control of measuring DV

179
Q

challenges to confounders

A

keep all confounding conditions constant:
- confounding conditions might escape your control
some confounding conditions might only be similar, but not exactly the same.

180
Q

challenges to noise

A

always some degree of noise in measurements even under optimal conditions
depending on other factors your measurements might not be as precise as they would under optimal conditions

181
Q

continuum of research methods

A

Least control
Observations
Quasi
True experiments
most control

182
Q

observational research

A

types: case studies, surveys, interviews, focus groups

conditions of measurements: situation and context choice, systematic organisation and structure of observations
no manipulation

measurement: qual or quant

183
Q

weaknesses of observational research

A

no test of causality
- no experimental manipulation and control
- only cooccurrence
- causality fully depends on interpretation by researcher

imprecision of DV
- uncontrolled conditions may interfere with measurement e.g walk in front of object being observed
- clutter: time-consuming, overflow of data

184
Q

true experiment

A

full control of IV and confounders

e.g. colour perception
everything constant

  • motivation, concentration and fatigue can’t be controlled
185
Q

quasi-experiment

A

operational IV and DV

non-equivalent conditions/groups:
systematic differences between conditions/groups due to:
- IV not fully independent (random assignments in true experiments)
- IV is not controlled and manipulated

186
Q

Quasi weaknesses

A

confounder in group comparison- systematic differences between groups may account for observed effects

sampling error or bias- samples not representative of population

confounder in experimental conditions - uncontrolled variation of experimental conditions can produce systematic or random variation of the DV

187
Q

Quasi purpose

A

necessary in field studies because not possible or not ethical to manipulate IV

188
Q

Types of quasi

A
  • controlled but non-equivalent determination
  • natural determination = natural experiments, IV is measured, but not fully controlled
189
Q

reliability

A

reliability of:
1. Research procedures: information necessary to reproduce the same conditions of a study

  1. reliability of results:
    - test reliability
    - statistical reliability
    - experimental reliability
190
Q

test-retest reliability

A

how reliable is your measurement instrument
low test-retest reliability: measurement is broken

191
Q

statistical relaibility

A

how much noise is in your data?
conditions vary too much

192
Q

experimental reliability

A

stable results across experiments
1. reproducibility
2. repeatability
3. replicability

193
Q

reproducibility

A

other researchers in another lap reproduce the results with the original data and analyses

quality and transparency of data analysis

194
Q

repeatability

A

results can be reproduced by the same researchers in the same lab by simply repeating the experiment and data analysis
certifies statistical reliability

195
Q

replicability

A

other researcher in another lab reproduce the same results by replicating the experimental conditions

196
Q

construct validity

A
197
Q

internal validity

A
  1. validity of experimental control
    - the data is not due to confounders e.g. spurious correlations
  2. construct and content validity
    - validity of operationalisation, measures the idea or concept (construct) for which we use it and the extent to which it represents that construct
198
Q

external validity

A

the extent to which the results of a study can be generalised so that conclusions apply beyond the context of the study

lack of external validity means that IV miss important causes

199
Q

two types of external validity

A
  1. ecological validity- result applies to different settings
  2. population validity - representativeness - the result for a sample of participants applies to the whole population
200
Q

relationships between different types of validity

A

accuracy and reliability are necessary but not sufficient for internal and external validity

valid instrument accuracy becomes equivalent to internal validity
internal validity contributes to external validity but could be a trade off as impossible to achieve both at same time

201
Q

trade off between internal and external validity

A

not all factors may be known yet
effects of factors may not be understood so that they are confounders

202
Q

lab study

A

artificial environment - control of IV and precise DV

+ full experimental control
+ maximise internal validity
- jeopardise external validity

203
Q

field study

A

real environment and setting including all relevant factors. different types e.g ppt observation, field experiment

+ high external validity (ecological and population)
- reduced experimental control (internal validity)
- ethical issues

204
Q

ppt observation

A

the researcher lives and participates with the informants
+ observation of maximal number of factors
- subjectivity of observation
- lack of experimental control

205
Q

field experiment

A

can feature full experimental control or more likely a quasi-experiment

e.g. football shirts, Levine et al, (2005) ppts more likely to help an injured jogger wearing a football shirt from a team they supported - real life setting many confounders

206
Q

what is noise?

A

random variation
- brightness of pixels an image vary randomly

207
Q

what is a signal

A

systematic, regular = informative

noise = random = uncertain

208
Q

bias

A

unwanted signal (systematic variation, systematic tendency) producing spurious results

in contrast to non-systematic random noise, conceals any kind of results

209
Q

bias examples

A

scale: should show zero when nobody is on it, if not it is bias

handedness bias: answering with left and right finger in task - right handers yield better performance with right hand

210
Q

2 types of biases

A
  1. Independent bias- systematic errors - signals in measurements which are incorrect - limited to one variable (DV usually)
  2. Dependent bias- confounder- produces bias in the DV varies with the IV
211
Q

criteria of empirical evidence

A

noise reduces reliability and accuracy

systematic errors reduce accuracy

confounders reduce internal and external validity

212
Q

sources of noise and bias

A

method (stimulus material and procedure)

participant (variation of physical condition, fatigue, cognitive processing, emotional responses)

Data recording- variation of experimenter or measurement instruments

213
Q

minimise systematic errors

A

calibration of equipment - compare with reference measure

214
Q

why control noise?

A

noise decreases the accuracy and reliability of measurements due to random variation. less likely to get the same results across measurements

aim of controlling noise: maximiser systematic variation due to causal effects relative to random variation (noise)

maximise the signal-to-noise-ratio

215
Q

how to control noise?

A

Minimise or neutralise
1. minimise by using instruments with high precision
2. minimise by using a task with high precision

  1. neutralise by sample size, if the noise is uniformly random increasing sample size makes the average converge to the expected value. noise isn’t absent but neutralised
    - increase number of ppts
    - increase number of measurements per ppt
216
Q

signal-to-noise-ratio

A

the amount of desired signal relative to the noise

increasing the ratio implies more information (reduction of uncertainty) of your measurement because noise is uncertainty

the higher the signal-to-noise-ratio, the higher the sensitivity and specificity of your measurements: less likely to get false negatives and false positives

217
Q

noise and inferential statistics

A

how likely are you to detect an effect (signal, systematic variation) in a statistical test?

all test statistics express a SN Ratio

false positive - type 1 error - alpha - specificity

false negative - type 2 error - beta - sensitivity

sensitivity to detect effects - statistical power

218
Q

why control confounders?

A

controlling confounders is fundamental for internal and external validity if results are due to a confounder

internal validity - conclusions are unwarranted - no validity

external validity - generalisation is misleading - no validity, results might turn out differently in real settings

219
Q

how to control confounders?

A
  1. fix
  2. randomise
  3. balance
  4. measure and model

2 and 3 are to neutralise

220
Q

how to fix confounders

A

keep all conditions constant that might potentially affect your measurements

when held constant, potential confounder is a control variable

fixing is not always possible - order effects - ppts characteristics will always vary across ppts - minimising is not sufficient as still systematic unwanted signal interfering

221
Q

how to randomise to control confounders?

A

converts confounder (systematic variation) into noise (uniform random variation)
biases become orthogonal (uncorrelated) to the IV
can be neutralised by central tendency equal across conditions

limitations
- only works if sample size is sufficient
- only works if confounder affects all conditions in the same way

222
Q

how to balance to control confounders?

A

dividing a set into two or more subsets that have roughly the same characteristics

  • equalise central tendency across conditions of IV, confounder on DV is neutralised

systematic neuralisation 0 values of confounder are distributed in equal frequencies across conditions of IV, because values are symmetrical because central tendency is unbiased

limitations:
- only works if confounder is known before hand
- only work if confounder affects all conditions in the same way

223
Q

how to fix confounders by measure and model?

A

measure potential confounder - it becomes a measured control variable (CV)

include CV in stats model when analysing data - test for interaction effects

confounder becomes an IV in stats model

results indicate whether or not there was a bias and how it affected the DV

stats model may account for effect making assumptions about the nature of the effect

224
Q

why have a research protocol?

A

specifying details of experimental design in written format
fix experimental conditions across measurements and across experiments

maximise consistency across measurements - reliability
minimise noise and confounders produced by research procedure, increasing test and experimental reliability

225
Q

content of a research proposal

A

operational definitions of conditions of IV, measurement of DV, experimental settings, such as lighting, distance from light…

instructions for experimenter- how to proceed and handle stimulus material

instructions for ppts - what to tell the ppts and how written/oral

method sections in scientific articles

226
Q

typical structure of experiments

A

trial= one completion of a task, providing one datapoint for each DV

Block= trials with a specific characteristic concerning the control of confounders and manipulation of IV

Session= may feature blocks for each task

Run= experiment may involve several sessions

each ppt completes one run of the experiment, providing one raw dataset

227
Q

manipulating IV

A

repeated measures design
between groups

228
Q

repeated measures design

A
  • same ppts in all conditions
  • the IV is manipulated within ppts
  • also called paired-sample when only two conditions
  • two ways to organise sequences of repeated measures (blocked, interleaved)
229
Q

Block design

A

green target block and blue target block

consequence: order effects due to:
- practice + learning
- anticipation
- fatigue

230
Q

counterbalancing

A
  • half of the ppts start with green, the others start with blue block
  • to control for order effects
231
Q

interleaved design

A

benefits
+ order effects controlled through randomisation across trials
+ no expectations towards condition of IV because ppts don’t know what target to look for

problems
- noise due to unexpected changes
- bias when effects of sequence differ between experimental conditions

232
Q

between groups design

A

A. Random groups:
- assign ppts randomly to each group/condition

B. matched-pair design - match ppts in characteristics that may be important, such as age

challenges of between groups design: potential noise and confounds because groups vary due to ppt characteristics, different approaches use randomisation and balancing

233
Q

strengths and weaknesses of repeated measures designs

A

+ reduced noise and confounds by excluding differences between groups
+ wanted when practice effects are desirable

blocked design
- order and carry over effects (if no counterbalancing)

interleaved design
- produce noise due to task switching and mistakes

both
- interaction effects with trial order (practice, fatigue)

234
Q

strengths and weaknesses between-group designs

A

between groups design
+ controls order and carry-over effects
+ wanted when inexperienced, naïve ppts needed

random groups
- large ppt samples needs to allow for neutralising by randomisation

matched group:
- failure to account for ppt variation
- vulnerable to dropouts

235
Q

Factorial design

A

multiple factors (IV’s)
multiple independent variables

manipulation of IV’s:
- between ppts
- within ppts
- both between and within ppts

mixed factorial designs:
- both between and within factors are manipulated together
- very common approach

236
Q

interaction effect

A

interaction between factors:
= effect of one factor depends on another factor
= multiplicative effects

quantitative = in the same direction for all conditions (change only in magnitude)

qualitative = in opposite directions some positive effects others negative (effects change direction)

interaction: the difference before and after depends on the type of therapy

quantitative interaction: both therapies improve happiness, but therapy 1 improves more than therapy 2

237
Q

factorial design advantages

A

internal validity:
+ more than one hypothesis can be tested because you are manipulating multiple IV
+ allows for assessing complex casual relationship, interaction effects
+ allows for including potential confounders in stats model

ecological validity
+ more complex designs can be more relevant to the real world

238
Q

multivariate design

A

multivariate = more than one DV (effect of walking on physical and mental health)
involves relationships between IV and DV

239
Q

historical scandals in ethics

A

Little Albert
Monster study
Milgram
Zimbardo
Harlow

240
Q

Little Albert

A

9mth orphan
conditioned albert to be fearful of rats by shouting and making loud noises

  • no attempts to conditioning afterwards
  • potential negative consequences on development
241
Q

monster study

A

22 children from Iowa soldiers orphans
half received negative treatment e.g. belittling the children for speech imperfection

  • no consent, no information, no debriefing and no help afterwards
  • negative psychological effects and speech problems for rest of life
242
Q

milgram

A
  • ppts gave consent but were deceived
  • no debrief
  • inflicted insight without being explained that his was part of the experiment
  • ppts did not feel well during the experiment
243
Q

zimbardo

A
  • stimulation of a prison environment where ppts took the role of prisoners or guards
  • study authority effects behaviour

criticisms
- did not obtain consent
- ppt suffered psychologically and physically during experiment
- hindered ppts to withdraw
- debriefing too late, leaving ppts under the impression of the experiment

244
Q

Harlow

A

isolation of infant and juvenile macaques
depression and social deprivation link
- unethical treatment of monkeys during experiment
- effect of monkeys beyond experiment

245
Q

the belmont report

A

respect for people:
- informed consent
- voluntary ppt
- no/minimal deception
- anonymised data
- confidentiality

Beneficence
- ppt welfare: beneficial, not harmful

justice
- fair selection of ppts

246
Q

ethical guidelines

A

BPS
principles: respect, scientific value, social responsibility, maximising benefit and minimising harm

risk assessment, informed consent, confidentiality, advice, justify deception, debrief and GDPR

247
Q

informed consent

A

A) consent - ppt voluntarily confirms willingness to participate in study

B) ppt first needs all information before deciding whether to participate

  • ppt information sheet, consent form
248
Q

debrief

A

purpose: ppts are fully informed about the study and not harmed in the process

especially important in social psychology that use deception

format - short interview, written debriefing statement

249
Q

review

A

application to ethics committee ethics committee checks:
risk assessment
information sheet
debriefing statement
consent form
debriefing statement
general information about your study
posters and adverts
recruitment methods

250
Q

agency

A

human beings act upon and shape their environment

251
Q

interpretation

A

people perceive think and make choices to adapt their actions and behaviour

252
Q

double interpretation

A

one person interprets how another person interprets and acts accordingly which may in turn influence the interpretation and action of the other person

253
Q

social construction

A

constructed understandings of the world form the basis for shared assumptions about reality

based on mutual expectations

unconscious and automatic

254
Q

double hermeneutic

A

from sociology

ppt interpret the researchers behaviour and the setting of the study and adapt their behaviour, which is then interpreted by the researcher
shapes and observes reality

255
Q

sampling methoods

A

define population
identify sampling frame
choose sampling method

256
Q

inclusion and exclusion criteria

A

depends on:
- population of research question
- prior knowledge and assumptions

257
Q

sampling frame

A

depends on resource
sampling frame can nevertheless bias research

258
Q

types of sampling methods

A

non-probability sampling
probability sampling

259
Q

non- probability sample

A

self-selecting / volunteer samples

opportunity / convenience samples

snowball samples - one ppt from a key group gives leads to others from this group

260
Q

sampling biases in non-probability samples

A

exclusion bias: exclusion of particular groups

self-selection bias: when ppt can decide whether to participate, people with specific characteristics are more likely to agree to take part in a study with others

261
Q

sampling biases in nonprobability samples

A

WEIRD ppts
Wester, Educated, Industrialised, Rich and Democratic sample frame

262
Q

probability sampling

A

convert systematic biases to noise through randomisation to obtains representative unbiased sample

Every unit in the population has a known nonzero probability of being sampled

random selection of units by weighting units according to their probability of selection

random sampling
systematic sampling
stratified sampling

263
Q

random sampling

A

each element of the frame has an equal probability of selection

the sampling frame is not subdivided or partitioned

all subsets of a sampling frame have an equal probability of being selected

264
Q

systematic sampling

A

also quasi-random sampling

starting from a random, every nth person from a list is selected

265
Q
A
266
Q

stratified sampling

A

stratum = sub-population
stratification = diving members of the population into homogenous subgroups before sampling

each stratum is sampled randomly as an independent sub-population

the number of individuals per stratum is proportional to the size of strata in the population

combines balancing and randomisation in ppt sampling

267
Q

representativity and validity

A

population validity= is the sample representative

ecological validity= is the stimulus representative and the task representative

experimental control - internal validity

representativity - external validity

268
Q

observational studies and subtypes

A

observational studies = IV is not under experimental control - correlation studies

subtypes:
Comparative research: comparisons between countries and cultures

Case-control study: comparing subjects who have that condition with patients who do not but are otherwise similar (the controls)

Case study: in depth detailed examination of a particular case

Longitudinal study: repeated observations of the same variables over short or long periods of time

269
Q

review and meta-analysis

A

review
summarises the current state of understanding on a topic or research question
uses mainly logical argument to integrate existing evidence

meta-analysis
aimed at drawing conclusions about evidence across existing studies
uses statistical analysis to combine the results of multiple exisiting empirical studies

270
Q

communication and dissemination

A

research report:
- scientific articles
- other publications

conference presentations

meetings
- invited speakers (colloquia, seminars)
- workshops, symposia
- informal chats and discussions

collaborations

271
Q

research report and scientific articles

A

introduction
- assumptions
- research question
- hypothesis
- prediction

method
- research design

result
- data analysis
- interpretation

discussion
- internal and external validity
- generalisation

conclusion

272
Q

interests

A

financial & material resources:
= research funding

includes:
- salary
- lab equipment and research expenses
- financial support for research institution

standing and reputation
- awards, invitations, professional roles and influence on decisions
- within institution
- across international scientific community
- across society

273
Q

currency of research: publication record

A

strong publication records to:
- get a job
- get research funding
- gain wider acceptance from scientific community

strong publication record means:
- many papers = good
- papers in prestigious, high profile journals = good

274
Q

problems: conflict of interest

A

journals can refuse to publish research that presents null results - failed experiment

strong incentive to obtain significant results from experiments - no incentive to obtain null results

bolder claims - higher risk to fail

275
Q

personal interest vs scientific quality

A

e.g Brian Wansink

eating behaviour

he manipulated data to fit theories

his article has been the subject of retractions

276
Q

concequences

A

discipline in crisis
- failed replications of studies so researchers have conducted experiments wrong?

discipline in progress
- better checks of data, method and conclusions
- open science: make all details including data available and transparent

277
Q

quality control

A
  1. resources
    - financed by a third party (usually tax payers)
    - should not be wasted with useless research
  2. ethics
    - participants must not be endangered or harmed
  3. dissemination
    - findings are sound and conclusive
    - otherwise: misleading, fake news!
278
Q

quality control via peer review

A
279
Q

data distribution

A

the distribution shape is the curve enclosing a histogram

280
Q

normal distribution

A

assume everything follows a normal distribution. symmetrical and bell-shaped.

281
Q

standard deviations affect the distribution

A

wider peak

282
Q

symmetry in distribution

A

mean=median=mode

283
Q

asymmetric data is skewed

A

range -infinity to +infinity

skew is greater than +/-2 the data is substantially skewed

direction of tail = direction of skew
negative skew- left tail
positive skew- right tail

284
Q

positive skew

A

mean > median > mode

285
Q

negative skew

A

mean < median < mode

286
Q

kutosis

A

peakedness (altitude) of this distribution
relative concentration of scores in the centre
-2 (flat) to +infinity (peaked)

mesokurtic (normal distribution)
platykurtic (flat, thick in the shoulders, wide peak)
leptokurtic (peaked, thick in the centre and tails, narrow peak)
multimodal- more than one peak

if the kurtosis is greater than 1.96, then the distribution of the data is significantly different from mesokurtic

287
Q

the normal distribution

A

bell shape distribution
skewness and kurtosis are 0
more stats tests assume data is normally distributed
data can be transformed to meet the assumption

288
Q

transformation

A

apply a mathematical transformation to each score e.g. multiply it by itself (square it)

289
Q

non-linear transformations to a positive skew data

A

moderate skew: square root X

Substantial skew: log(X)

severe skew: reciprocal transformation (1/X)

290
Q

non-linear transformation to negative skew

A

reflect the distribution (multiply by -1)
add a constant so the lowest value is 1.0

moderate skew square root X

substantial skew log(X)

severe skew reciprocal (1/X for each value)

291
Q

do nonlinear transformations change the shape of the distribution of scores?

A

YES!

292
Q

do linear transformations change the shape of distribution of scores?

A

NO!

293
Q

linear transformations

A

add, subtract, multiply or divide
distribution shape remains the same

e.g. Celsius to Fahrenheit

294
Q

link between the distribution of data and probability of obtaining a particular value

A

tabling the distribution can give estimates of probability

295
Q

Normal distribution

A

bell shaped (symmetrical)

standard normal distribution

mean= 0
standard deviation - 1

possible to transform any normal distribution

296
Q

transforming distributions

A

if we subtract a constant from each score, the mean of the distribution is reduced by that constant (subtracting the mean from all values in the distribution gives us a mean of 0)

when divide all values by a constant, we divide the SD by that constant giving us a SD of 1

297
Q

z-scores

A

z-score tells you how many SD units a particular score is from the mean
scores range from -infinity to +infinity although most are in the range of +/- 2

formula= (x-mean)/standard deviation

298
Q

z-scores

A

mean + standard deviation

mean - standard deviation

the answers to the formulas above give the extreme values so anything above or below are extreme

299
Q

95% of scores are within how many standard deviations of the mean

A

± 1.96

300
Q

the standard normal distribution, z-scores

A

mean = 0 and standard deviation = 1

b values greater than 1.96 or less than 1.96 are extreme (95%)

301
Q

what does a z-score tell you?

A

z-score indicates that the observation is greater or less than the mean

z-score magnitude tells you how far the score is from the mean

302
Q

how to calculate the z-score?

A

(score-mean of scores) / standard deviation

303
Q

how to calculate that z-scores are the same distance from 0 will have the same chance of occurring

A

probability of several shaded areas = sum of probability of the shaded areas

probability of non shaded areas = 1 - probability of shaded areas

304
Q

z-score tables

A

show the percentage above a certain z-score so if want to know the amount less than the score do 100 - percentage

305
Q

dealing with outliers

A

outliers are extreme scores

correcting outliers
- check original data
- remove from dataset
- Change the score

replace with the next highest score plus one

replace with mean plus or minus 2 x SDonly if it remains the highest or lowest score
95% o the data lie between 2 SD of the mean

306
Q

what does inferential statistics do?

A

infer from sample to population

measure statistics of samples

infer parameters of population

307
Q

sampling distribution

A

sample mean is unbiased estimator of the population mean, but rarely will it be the same because of error

mean of all possible sample means is equal to the population mean

308
Q

central limit theory:

A
  1. the sampling distribution of the mean will have a mean equal to population mean (all possible sample means)

sample mean usually ≠ population mean

309
Q

sampling distribution

A

the standard deviation of the sampling distribution of the mean is called the standard error of the mean

310
Q

standard error

A

measure of the amount that a sample mean could be different from the population mean

psych uni (4 decks)

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