1. Theme 4 Elementary Analysis
Babbie & Mouton, The Practice of Social Research. Cape Town: Oxford

2. Multivariate Analysis
Most soc. Science analysis falls within multivariate analysis (general term not a form of analysis) – examination of several variables simultaneously
Techniques for conducting multivariate analysis:
Factor analysis,
Smallest-space analysis,
Multiple correlation,
Multiple regression, and
Path analysis
Basic logic of multivariate analysis is by means of contingency tables / cross-tabulations
Understand multivariate analysis by fundamental analytic modes:
Univariate analysis
Bivariate analysis

3. Univariate Analysis
Examination of distribution of cases on only one variable at a time (Describe units of analysis for inferring to larger population)
The logic is as follows:
Distributions – report all individual cases – listing the attributes for each case under study terms of the variable in question
Central tendency – presenting data in the form of summary averages/measures of central tendency – calculating either the mode, arithmetic mean or median (see figure 15.2 p. 425)
Dispersion – measuring the range & standard deviation–the distance separating the highest from the lowest value
Continuous & discrete variables – e.g age & military rank – nominal & ordinal variable
Detail vs manageability – two goals when reporting data

4. Subgroup Comparisons
Bivariate & multivariate analyses aimed at explanation
Purpose of subgroup description is comparative
Table-formatting issues:
“Collapsing” response categories – combine/collapse two ends of the range of variation e.g very good & good; very poor & poor
Handling “don’t knows” – analyse with and without as well as reporting on both – so readers can draw their own conclusions

5. Bivariate & Multivariate Analysis
Contrast to univariate as two variables are involved
Largely descriptive – independently describing subgroups with comparison
Adds relationships amongst the variables themselves
Thus involves describing the variables instead of the unit of analysis
Tables constructed from several variables
Reading a table can be as follows:
Read across if table is percentaged down, or
Read down if table is percentaged across
Multivariate - same as bivariate tables except we now use more tan one independent variable

6. Theme 4 The Elaboration Model
Cornel Hart
March 2007

7. The Elaboration Model
Also known as interpretation method, Columbia school or Lazarsfeld method
Aims at elaborating on empirical relationship among variables i.o.t. interpreting that relationship
Provides clearest picture of the logic of causal analysis in research
Understanding the relationship between two variables through controlled simultaneous introduction of additional variables by means of contingency tables

8. Elaboration Paradigm (Table 16.5)
Technical notations – replication, explanation, interpretation & specification
Know whether the test variable is antecedent (prior in time) to the other two variables or intervening between them
Antecedent – test variable affects both the ‘independent’ & ‘dependent’ variables; are related to each other but no causal link between them (figure 16.2)
Intervening – independent variable affects the intervening test variable which in turn affects the dependent variable (figure 16.1)

9. Technical Notions
Replication – original relationship has been replicated among different groups with still the same result thus original relationship is genuine & general one
Explanation – spurious relationship – original relationship explained through introduction of test variable
Interpretation – Similar to explanation except for time placement of test variable & implications that follow from that difference
Specification – used regardless of whether the test variable is antecedent or intervening – have specified particular conditions under which original relationship holds
Ex Post Facto Hypothesizing - Developing a hypotheses “predicting” relationships that have already been observed – invalid science
Elaboration model provides logical tools for testing those hypotheses within the same body of data - replication