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Research Methods 2 Flashcards

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1
Q

Features of a science

A

Theory construction
hypothesis testing
empirical method
paradigms
replicability
objectivity
falsifiability

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2
Q

what is a science

A

knowledge and evidence
using scientific method

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3
Q

scientific method

A

make an observation

develop an explanation

test empirically

do findings fit theory

if not then develop new explanation

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4
Q

paradigms

A

set of assumptions, theories, methods, terminology shared by psychologists e.g. approaches

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5
Q

Paradigm shift

A

When an established paradigm is challenged to the point that a new one takes its place

Thomas Kuhn - normal science- theory is dominant
-scientific revolution - caused by disconfirming evidence for normal
-paradigm shift - normal is overthrown - theories and methods change

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6
Q

social sciences - paradigms

A

Kuhn - science has single paradigm

social sciences lack universally accepted paradigms due to too many conflicting approaches so psychology is deemed pre-science

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7
Q

Falsifiability

A

theory cannot be considered scientific unless possible for it to be proven untrue

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8
Q

Karl Popper

A

all swans are white - no matter how many instances of white swans we observe, this does not justify conclusion that all swans are white

should seek disproof rather than examples that confirm theory

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9
Q

null hypothesis

A

e.g. not all swans are white

researchers should aim to reject null e.g. if no black swans are sighted this strengthens theory

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10
Q

pseudoscience

A

Popper - cannot be falsified e.g. freud - unscientific

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11
Q

Theory construction

A

set of general laws or principles that explain a specific behaviour

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12
Q

Hypothesis testing

A

testable expectations

if scientist fails to support hypothesis then the theory requires modification

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13
Q

empirical method

A

refers to any methods that provide evidence based on direct experience rather than unfounded belief

reports on world how it really is

well controlled and objective

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14
Q

empirical method in psychology

A

the variables that are measured in psych can be difficult to directly observe e.g. happiness/intelligence levels

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15
Q

Objectivity

A

removal of any bias - results collected in a way that is independent of researcher

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16
Q

why objectivity is important feature of science

A

builds confidence that findings represent real effect rather than views of investigator

helps ensure methods used were well controlled and high internal validity

identify scientific fraud

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17
Q

Replicability

A

repeatability to determine validity and reliability

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18
Q

why replicability is important feature of science

A

ensures results are reliable and builds confidence that finding is trustworthy and represents real effect

ensures methods are standardised improving validity

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19
Q

Abstract

A

overview of entire investigation

allows reader to decide whether or not to read rest of report

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20
Q

Introduction

A

review of previous research so reader knows what research has already been done

should follow logical flow of ideas to persuade about reasons for carrying out the study

also state aims and hypothesis

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21
Q

Method

A

design, participants, apparatus, procedure, ethics

should be enough detail for someone to replicate the study

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22
Q

Results

A

descriptive stats and inferential stats

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23
Q

How to write results section

A

1) always be very clear on precisely what the findings show with fully operationalised names to conditions and measurements

2) give full information including all numbers and all details that led you to choose critical value

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24
Q

Discussion

A

1)summary of results - statistically significant?
2)relationship to previous research
3)consider methodology and improve suggestions
4)implications for psychological theory
5)suggestions for practical applications
6)suggestions for future research

Selling Peanuts (to) Monkeys In Pretty Fleeces

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25
References
full details of any books, journals or websites mentioned in report
26
Case studies
investigation of single individual, group, institution or event
27
Case study - advantages
+provide rich detailed data complex interaction of many variables - helps identify what may have been overlooked +allows behaviour that would be unethical to deliberately manipulate to be studied e.g. clive wearing
28
Case study - disadvantages
-may lack validity population validity due to focus on small sample prone to social desirability bias -ethical issues informed consent e.g. not able to fully comprehend or too young e.g. clive wearing and little hans confidentiality e.g. easily identifiable due to unique characteristics
29
Content analysis
observational study where people are observed indirectly - focuses on communications people have produced such as speeches, diaries, films, books etc coding system is used to convert qualitative to quantitative
30
Sampling method for content analysis
Selected randomly or identify characteristics Every page or every nth page Time sampling or event
31
Coding
Placing into clearly operationalised categories and use tallying (quantitative) or describing examples in each category (qualitative)
32
How to conduct content analysis
1) gather materials to be analysed using sampling method 2) read and reread to familiarise 3) form key categories and operationalise 4) tally up number of times each one occurs 5) draw conclusions
33
Content analysis advantages
+easy to replicate +high ecological validity
34
Content analysis disadvantages
-suffers observer bias -likely to be ethnocentric - culture influences interpretation
35
Thematic analysis
Identifying recurring themes that emerge from the data and organising them Allows data to be summarised - remains qualitative
36
How to conduct thematic analysis
1) familiarise 2) break data into meaningful units - small bits of text that convey meaning equivalent to sentences or phrases 3) assign label to each unit 4) combine labels into broader themes 5) check by collecting new set of data and applying themes 6) write up report with themes as headings
37
Thematic analysis advantages
+maintains much of detail from original material +high ecological validity
38
Thematic analysis disadvantages
-Time consuming -suffers observer bias
39
Assessing validity - face
Whether it looks like it is measuring what the researcher intends to
40
Assessing validity - concurrent
Whether findings match those from a more recognised and established test of same topic Must correlate strongly +0.8
41
Improving validity
Questions removed, revised or rewritten
42
Assessing reliability - test-retest
Same test or measurement given to same participants on two separate occasions 0.8 correlation
43
Assessing reliability - inter-observer reliability
The extent to which there is agreement between two or more observers 0.8 correlation
44
Improving reliability
Inter observer - behavioural categories weren’t operationalised clearly enough Test-retest - questions were ambiguous or too complex
45
Nominal data
Named categories where frequencies are counted in each category Discrete Mode Bar chart +basic and simple -less precise
46
Ordinal data
Data placed in an order with no fixed intervals between units Subjective E.g. ranking, rating, test score Median Histograms and line graphs +more precise than nominal -less precise than interval
47
Interval data
Data based on scale that has fixed intervals between each unit E.g. time in seconds, distance in metres Mean Histograms and line graphs +More objective and precise -not basic and simple
48
Probability
Numerical measure of the chance that certain events will occur Number of particular outcomes divided by number of possible outcomes
49
Significance
A result that is strong enough for us to be confident it represents a real effect
50
Stringent level
P < 0.01 99% Medicine
51
Standard level
P< 0.05 95% Psychology
52
Lenient level
P < 0.1 90% Pilot study
53
Type 1 error
Optimistic - false positive Accepting a hypothesis that is false Likelihood increased when lenient significance level
54
Type 2 error
Pessimistic - false negative Rejecting a hypothesis that is true Higher likelihood when stringent significance level
55
Choosing a stats test
Difference Relationship Unrelated Related Related Nominal: Chi squared Sign test Chi squared Ordinal : Mann- Whitney Wilcoxon Spearman’s Interval: Unrelated T Related T Pearson’s Chi squared - association + unrelated
56
Sign test
Number of pluses = Number of minuses = Lower number = calculated s value S value must be less than critical to be significant
57
Chi squared - degrees of freedom
Rows x columns Calculated value must be more than critical value to be significant
58
One tailed hypothesis
Directional
59
Two tailed hypothesis
Non directional
60
Template stats answer
The result of the statistical test is/isn’t significant Therefore the null is rejected/accepted And the hypothesis is accepted/rejected This is because the calculated value (X) is greater than the critical value (X) At the p=0.05 level of significance when N/Df = (X) for a one/two tailed test This means there is/isn’t a significant ————— between X and Y in terms of their Z
61
Chances of making type one error
The given level of significance (e.g. P=0.05) tells us the chance (e.g. 5%)
62
Chances of making a type 2 error
If results aren’t significant look across table to whether significant at a more lenient level
63
Brief
Read to participants before hand Asking for consent
64
Debrief
Read after Thank them Reveal true aim Ethical considerations Any questions?

Psychology (7 decks)

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