1. 1 Relational Research This lecture ties into chapter 17 of Terre Blanche What is a hypothesis? Statement of the relationship between 2 or more variables Relationships tell us how things affect each other Hypotheses lead to interventions Understanding the relationship between variables can help us to create interventions

2. 2 Finding Relationships Relational research aims to find relationship between variables in the population Do this by looking at relationships in a sample Problem: what if the relationship exists in the sample but not the population? Statistics can tell us how likely this is; if it is unlikely (less than 5%) then it probably exists in the population too

3. 3 Example: Gulle et al on SAPS stress Gulle, G., Tredoux, C.G. & Foster, D. (1998) Inherent and organisational stress in the SAPS: an empirical survey in the Western Cape. South African Journal of Psychology, 28, pp. 129-134 Traditionally, policework was seen as stressful because of the danger Gulle et al asked: what about other stressors? Admin; ineffectiveness of the courts, etc

4. 4 Gulle’s Plan Mailed 200 questionnaires to policemen in the Western Cape Got 91 back; interviewed those people too Each questionnaire measured several variables Set hypotheses about relationships between various possible stressors & stress experienced Used Factor Analysis & Correlations to test those hypotheses

5. 5 Some of Gulle’s Findings Poor Salary was as stressful as seeing a colleague killed (87 out of 100) Ordinary job stresses affect policemen highly Eg. Competition for promotion, lack of recognition for good work Inherent (danger) stressors also affect Eg. High speed chases, exposure to deaths Social responsibility also affected stress Eg. Public criticism of police, increased responsibility, demands for high moral standards

6. 6 Overall: Gulle’s results Stress in the police comes from several sources Danger, red tape, high responsibility Policemen seem to have normal job stress plus extra stress Explains high rates of divorce & suicide High stress could be reduced Danger related stress cannot, but red tape can be reduced by reorganizing the police structure

7. 7 Analysis of Gulle et al Important aspects to note: Reasonably large n (not as big as in descriptive) Used questionnaires to increase reliability Collected only enough variables to study hypotheses Carefully analysed data (using statictics) to find relationaships

8. 8 Relational designs Very simple design O  X No sub groups, everyone is measured on the same variables. No interventions Observe relationships as the occur naturally IV/DV decided on basis of hypotheses Stress was the DV, stressors the IVs

9. 9 Sampling to find relationships Medium sized samples required Correlations do not need large samples (40-50) Factor analysis requires more (80-100) Careful sampling to ensure that results can be generalized If a relationship is found in a representative sample, it is more likely to exist in the population

10. 10 Gulle’s Sample Used volunteers Included various races, both genders, languages Medium sample (91) Probabilistic sampling method All policemen from a certain area approached; each one had the same odds of being selected

11. 11 Measurement Only as many variables as are necessary to evaluate the hypotheses Operationalisation & measurement of variables is highly important Poor reliability makes relationships harder to detect Poor validity establishes a false relationship (useless)

12. 12 Gulle’s measurement Established questionnaires Spielberger’s Police Stress Survey (Likert Items) Rate events on a 1-100 scale Few, directed variables Knew which variables they wanted to focus on Using established methods gave her a good idea of the reliability & validity of the measures

13. 13 Results & Analysis Statistical methods Correlations, Factor Analisys, Regression Provide an exact measurement of the strength & direction of the relationship Tests of statistical significance Ensure the relationship exists not only in the sample, but also in the population

14. 14 Summary: Relational Studies Aim: find relationships between variables in the population Ensuring validity: Large enough n Good measures Statictical significance Correlation, Regression or Factor Analysis for analysis Measure the strength & direction of relationship