Principles and Strategies of Quantitative Data Analysis

1. Principles and Strategies of Quantitative Data Analysis SLC515 Research Methods for Socio-Legal Studies and Criminology 2007/2008

2. Outline • Foundation of QR: Positivism • Validity and reliability in QR • Core issues of concern in QR • Critique of QR

3. Outline • Descriptive and inferential statistics • Inferential Statistics • Crosstabs • Correlation • Regression

4. Positivism - Historical Facts • 16/17th century: blooming of European thought • Beginning of modern science • Auguste Comte (1798-1857): • Sociology as the “queen” of social sciences • “Social physics”; idea of progress, social science works like natural science • Precise and certain methods, basing theoretical laws on sound empirical observation • Knowledge is derived from empirical evidence • 19th century: natural sciences gain influence  impacts thinking in social science

5. Positivism - Emile Durkheim (1858-1917) • Human and material phenomena are equally real but human phenomena cannot be reduced to pure material facts. • Social facts - society as a moral reality, expressed in institutions such as law, religion etc. which are external to us and constrain us. • Sociologists should describe characteristics of facts and explain how they came into being. • Explanation of social facts by causes: single cause  effect, law-like relationship • Same general methods of scientific inquiry can be used.

6. Positivism - Key Elements • Social research as ‘science’ • Universal laws - testing theories • Cause and effect relationships between variables • Solid methods • Value neutral • Objectivity

7. Science is … Adapted from Sarantakos (1993) Table 2.2, page 38.

8. Purpose of research … Adapted from Sarantakos (1993) Table 2.2, page 38 & 39.

9. Theories, Hypotheses and Research Design • Deductive approach • Theory testing • Derive hypotheses from theory (if… then… sentences) and test them • Research design: • Cross-sectional survey • Longitudinal design • Case study design • Comparative design

10. Operationalisation • Translation of a theoretical concept into something that can be measured • Example (natural sciences): temperature - degrees Celsius, velocity - km/h • How do you measure the frustration caused by unemployment or the level of alienation in a society or a society’s satisfaction with its government? • Operational definition through indicators

11. Indicators • Difference between a measure and an indicator (quantities versus complex concepts) • An indicator is employed as though it were a measure of a concept. • Example: job satisfaction

12. Research Sites and Subjects • Depending on research design, methods and sources of data • Establish an appropriate setting: • Decisions are involved: where? and who? • Sampling strategies • Probability and non-probability sampling • Representative sample - generalizability

13. Collecting and Processing Data • Depends on the chosen research design: • Experiments: pre- and post-testing • Survey interviews: questionnaire and interviews • Etc. • Gathered information is then transformed into ‘data’ • Information will be quantified - coding - to be processed by a computer

14. Analysing Data and Research Findings • Statistical techniques/analysis, special software • Results/findings have to be interpreted based on theoretical reflections in the beginning (verification/falsification of hypotheses) • Objectives of QR: • Support or reject theoretical concepts or findings of other studies • Detecting trends, patterns • Uncover common sense knowledge • Building typologies

15. Writing up Findings and Conclusion • Results enter the public domain • Conference paper, article, report, thesis, book • Significance and validity of findings • Implications? (policy advice etc.) • Presentation of quantitative data is different than in qualitative research

16. Validity and Reliability • Validity, reliability and generalizability are measures of the quality, rigour and wider potential of research • Validity = are you observing what you want to observe (construct validity) • Is your set of indicators really measuring what you want to measure? • Reliability = are the measures, devised for the concept, concise (stability of measure) • Stability over time, consistency of indicators (internal reliability) and observers (inter-observer consistency)

17. Core Issues of Concern in QR • Measurement • Causality - Explanation (dependent and independent variable) • Generalisation (representative sample) • Replication • Testing theory

18. Critique of QR • Positivism vs. interpretive social sciences • Objectivity? • Generalization - but too simplistic? • Causality

19. Quantitative Numbers Researcher’s view Researcher distant Theory testing Static Structured Generalization Hard, reliable data Macro Behaviour Artificial setting Qualitative Words/Text Participant’s view Researcher close Theory emergent Process Unstructured Contextual Rich, deep data Micro Meaning Natural setting Contrasting Qualitative and Quantitative Research

20. Descriptive and Inferential Statistics • Univariate • Descriptive analysis of one variable (column in data set) • Bivariate • Relationship between two variables • Dependent and independent variables • Relation between dependent and independent variables • Differences between dependent and independent variables

21. Bivariate Analysis • Questions we can ask: • Is the relationship significant? • If so, how strong is the relationship? • In which direction does the relationship go? • Positive relationships • Negative relationships • Some statistical tests: • Crosstabulation • Correlation • Regression analysis

22. Cross Tabs • All levels of measurement are allowed • Cross tabs express common frequencies of the categories of two different variables • Significance test: CHI Square • How strong?: lambda, gamma, r2 • Which direction?: gamma, tau

23. Example

24. Example

25. Correlation • Correlations measure statistical associations, but do not allow any inferences about causal patterns • Requirement: • Ordinal and interval data • Normally distributed and linear relation • Coefficient: • Pearson’s r (interval data) • Spearman’s correlation coefficient (ordinal data)

26. Example

27. Regression Analysis • You can visualize correlation in a scatter diagram • Regression line • Regression coefficients • Formula: y=a+b(x) • Requirements: • Interval data • Normally distributed • Linear relationship

28. Example

29. Controlling for Variables • Purpose of controlling for variables: e.g. exploration of variables that intervene in the relationship between other variables • Example: examination of the relationship between GDP and literacy rates in different regions of the world • Independent variable: GDP • Dependent variable: literacy • Control variable: regions

30. Example • Pearson’s r was used to measure the correlation between GDP and literacy rates in three regions of the world • OECD countries r=0.616 • Latin America r=0.608 • Africa r=0.421 Conclusion: The strength of the association between GDP and literacy rates varies between different regions. In some, GDP is a better predictor of literacy rates than in others.