Research Methods

1. Research Methods revision

2. Peer review • What do peer reviews achieve? • Allocation of funding: public bodies can peer review studies to gather whether or not they will be worthwhile • Publication of research in scientific journals and books: this gives scientists the opportunity to share the results of their research. Peer review process can be used to eliminate incorrect or faulty date entering the public domain. • Assessing the research rating of university departments: all university departments are expected to conduct research and this is assessed in terms of quality future funding. Good research = better funding.

3. What problems occur with peer reviews? • Unachievable ideal: is not always possible to find an expert to review a research proposal. Poor research could therefore be passed on. • Anonymity: gives people the chance to ‘settle old scores’ or bury rival research. • Publication bias: researchers may publish something if it will prove their own research. Also, positive results tend to be published more often. • Preserving the status quo: peer review has a preference for data that already follows what other current theories have found rather than dissenting. Peer review therefore can make the pace of development slower.

4. Specific research methods used: Experimental Observations Correlational analysis Surveys Interviews Case studies Pilot studies

5. 1. EXPERIMENTAL

6. 2. OBSERVATIONAL

7. 3. CORRELATIONAL ANALYSIS • This is the use of statistical correlation to evaluate the strength of the relations between variables.

8. 4. SURVEYS • This is a set of questions that can be written out and delivered to participants in order for them to fill out.

9. 5. INTERVIEWS

10. 6. CASE STUDIES • Focusing on one individual or a group of people and using different research methods to attain data.

11. 7. PILOT STUDY • A practice attempt before the real study to find if there is any problems with the upcoming experiment.

12. Sampling... Opportunity sampling Whoever is around at the time is chosen to take part in a study – based on availability. Volunteer sample Participants see an advert for a study and sign up.

13. Sampling... Random sample When everyone is given an equal chance (i.e. Picked out from a hat. Stratified sample Ensuring the ratio of people is equal. 50:50

14. Experimental designs:

15. RELIABILITY • INTERNAL RELIABILITY • Concerns the extent to which there is consistency. Different parts should give consistent results throughout. i.e. IQ tests should all have questions of similar difficultly • Split-half method: split the test in half by odd and even numbers – this can assess questionnaires. • EXTERNAL RELIABILITY • Concerns the extent to which there is a measure of something that is consistent over time. Test should produce consistent results regardless of when used. • This can be assessed with the test-retest method: repeating the test at a later date.

16. How do you improve reliability?

17. VALIDITY • INTERNAL VALIDITY • Concerns the extent to which the changing IV is entirely reliable for the DV and not an extraneous variable. Milgram’s study was valid as the participants believed the study was real. • Face validity: examines/assesses the test • EXTERNAL VALIDITY • The extent to which things can be generalised to other people, times, situations etc. Milgram’s study did not have it as it was set in a lab, not in a natural environment. • Predictive validity – two sets of scores are obtained at different times. Allows accurate prediction of future behaviour.

18. How do you improve validity?

19. GRAPHS! HISTOGRAM BAR CHART FREQUENCY POLYGON SCATTER GRAPH LINE GRAPH

20. Significance levels P < 0.01 Stricter level of significance. It means we are 99% sure the results are accurate and there is a real effect. Used more for drug studies as the results need to be accurate in order to be safe. This type of significance will lower the chances of a type 1 error (FAR) where you falsely accept the research hypothesis. P < 0.05 A conventional level of significance. This means you are 95% sure that it is a real effect. This level would be used more for studies that have smaller samples and are not too important. This type of significance will also lower the chances of a type 2 error (FAN) falsely accepting the null hypothesis.

21. TYPESOF DATA Nominal Where simple categories are used – i.e. Categorising people as smokers or non-smokers. A numerical value often cannot be assigned. You can normally count up frequencies within these categories (i.e. Number of women or number of men.) Ordinal Where numerical value is used, but based on ranks or ratings. i.e. Doctors may be ranked in preference by patients. Ordinal data is very subjective as based on personal opinions of people. Interval data This is when data is in the form of equal units e.g. Score on an objective test. In such a case, if one participant has scored 20 on one test, and another has scored 10, then it is correct in saying participant one performed twice as well as participant two. Ratio Interval data with an absolute value of 0 i.e. Distance or speed

22. Choice of statistical test What level of measurement is the data? Is the study about difference between two sets of data or a relationship between two variables? NOMINAL Chi-square What type of design was it? RELATIONSHIPS (CORRELATIONAL) Spearman’s Rho INDEPENDENT GROUP Mann Whitney REPEATED MEASURE Wilcoxen