Research Methods Y2

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37 Terms

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content analysis
a form of observational research where people are studied indirectly via communications they have produced (media, written, spoken)

* aim is to systematically summarise so that conclusions can be drawn
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case studies
detailed study of a single individual, institution or event
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strengths and weaknesses of case studies
\- diff to generalise to rest of the target population

\+ produces rich detailed data
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strengths and weaknesses of content analysis
\- may lack objectivity as it may impose meaning of behaviour from preconceptions

\- reductionist

\- detailed qualitative data gets reduced to numerical figure

\+ allows for statistical procedures

\+ more objective and replicable
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thematic analysis
identifying/analysing patterns of meaning within qualitative data to organise into pre-identified themes to analyse an existing theory
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strengths and weaknesses of thematic analysis
\- time consuming

\- subjective

\+ allows systematic study and exploration of qualitative data
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procedure of content analysis

1. **Sampling**: researcher decides what material to use, what they will analyse and how they will collect the info
2. **Coding**: researcher must decide how to categorise the analysed materials
3. **Instances of each theme are gathered and categorised**
4. **Frequencies counted** numerically
5. **Reliability is tested** (+0.8 positive correlation)
6. Conclusions are drawn
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Reliability
a measure of consistency

* **Reliability ain’t great unless it’s +0.8**
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Ways of assessing reliability
* Test-retest
* Inter-observer reliability
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Test-Retest method
administer the same test/ questionnaire to the same person/people on diff occasions

used for questionnaires, interviews, tests (like IQ etc)

* reliable test should give similar results
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Inter-Observer reliability
observers should work in teams of 2, watch the same event and record separately

results should be correlated at the end to assess reliability
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Ways to improve reliability
Questionnaires

* use test retest method, low reliability = some items may need to be rewritten due to ambiguity or too complex

Interviews

* use same interviewer each time, structured interviews, no leading questions

Experiments

* lab for high control and standardisation

Observations

* behavioural categories = properly operationalised, measurable and self-evident
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Validity
a measure of legitimacy

* **internal validity**: whether the effects observed are due to manipulation of IVs or not
* **external validity**: factors outside of the investigation such as generalising to other populations, settings etc
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Ways of assessing validity
* **face validity**: does it look good?
* **concurrent validity**: compare current research to previously established research and see if they produce similar results
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Ways to improve validity
Experimental research

* single/double blinds, standardisation, control groups

Questionnaires

* lie scales to control social desirability bias, anonymity

Observations

* covert observations as behaviour more likely to be authentic, behaviour categories = properly operationalised

Qualitative

* interpretive validity = research must be reported coherently, triangulation = multiple diff sources as evidence
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hypothesis
precise and predictable statement about the world which can be tested

* **null hypothesis**
* **alternative hypothesis**
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significance
indicating that the research findings are sufficiently strong to enable a researcher to reject the null hypothesis

* 95% + = IV caused change
* higher for drug trials = 99%
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type 1 error
**rejecting** a null hypothesis which is true

* likely if significance level is too high (10%)
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type 2 error
**accepting** null hypothesis that is false

* likely to happen when significance level is too low (1%)
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Levels of measurement
* nominal
* ordinal
* interval
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Nominal
frequency data that consists of the # of pps falling into categories

eg. 7 ppl passed the test whilst 5 ppl didn’t
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Ordinal
data presented in rank order

eg. attractiveness ratings
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Interval
data measured in fixed units with equal distance between points on the scale

eg. temperature
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descriptive statistics
describe, show differences and pattern in the data set

* includes measures of central tendency and measures of dispersion
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inferential statistics
find out whether results are significant so they can be used to generalise to the rest of the population
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critical value
calculated value needs to be less than or equal to critical value to be significant
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parametric tests
* better able to detect a significant affect
* only used on interval data
* homogeneity of variance between conditions is needed
* unrelated t-test
* related t-test
* Pearson’s R
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non-parametric tests
* used for ordinal and norminal data
* less powerful
* no homogeneity of variance
* Spearman’s Rho
* Wilcoxon
* Mann-Whitney U-Test
* Chi-squared
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Mann Whitney U test
used when

* hypothesis predicts a diff between 2 sets of data
* independent groups design
* ordinal data
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Wilcoxon
used when

* hypothesis predicts difference
* repeated measures or matched pairs
* ordinal data
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Unrelated t-test
used when

* independent groups design
* interval data
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Related t-test
used when

* repeated measures or matched pairs design
* interval data
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Chi-squared
used when

* test of difference
* independent groups design
* nominal data
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Spearman’s Rho
used when

* test of relationship
* ordinal data
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Pearson’s R
used when

* test of correlation
* interval data
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Features of science
* **objectivity**
* **empirical method**
* **replicability**
* **falsifiability**
* **theory construction**: evidence needs to be collected before and knowledge cannot be based on beliefs
* **hypothesis**
* **paradigms and paradigm shift**
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structure of psychological studies

1. title
2. abstract
3. introduction
4. aim and hypothesis
5. method
* design of the investigation
* procedure
* use of participants
* resources used
6. results
7. discussion
* explanation of findings
* implications of study
* limitations and modifications of the study
* relationship to background research
* suggestions for further researcher
8. references
9. appendices