1. Quantitative Data Analysis
Leonardo Veliz, Ph.D

2. Analysing quantitative data
Overview of descriptive statistics
- Measures of central tendency
- Measures of variability
Overview of inferential statistics
- choosing a test to test hypotheses
- parametric vs. non-parametric tests
Identifying a relationship & comparing means

3. Measures of central tendency
Mean : the average of a distribution (we add all values and divide by number of observations)
Median: The mid-point in a distribution of values
Mode: The value that occurs most frequently in a distribution
Note: sometimes quantitative researchers prefer to disregard some extreme values rather than use the median or mode.

4. Measures of variability
Range: distance between the highest and the lowest value
Standard Deviation: the average amount of deviation from the mean
Variance: the standard deviation squared
Where data are not normally distributed (e.g. income and wealth), measures of dispersion are very useful (quartiles, deciles, percentiles)

5. Small task1 Please use the hypothetical data in your handouts to
Calculate the mean, median and mode: which ones best represents your data?
Calculate the range of the values, the variance and the standard deviation

6. Some tests for comparing averages
Independent T-test: comparing 2 groups
(male vs. female)
Paired sample T-test: one group on 2 occasions
(BAES cohort tested in Sept and then in June)
ANOVA: When the independent variable has more than 2 levels
(teachers with 1-5, 6-10, 11-15, 16+ years of experience)
MANOVA: when we have more than 1 dependent variables (We are interested in the interaction (the multivariate effect)

7. Introducing SPSS Very powerful tool for analysing quantitative data
Looks like Excel (rows as cases & columns as variables)
Data view window: where you enter your data
Variable view window: where you define your variables
Output window: it reports the results of your analysis
(PGCE survey example)

8. Quantitative data analysis references
Diamond, I. (2001). Beginning statistics. London: Sage.
Hinton, P (1995) Statistics explained. London: Routledge
Salkind, N. (2008) Statistics for people who (think they) hate statistics. London: Sage.
Tabachnick, B. & Fidell, L. (2001). Using multivariate statistics. Boston, MA: Allyn & Bacon.

9. Quantitative data analysis using SPSS
Colman. A (2008). A crash course in SPSS for windows 14, 15 and 16. Chichester: Willey.
Field, A. (2005) Discovering statistics using SPSS. London: Sage.
Kinnear, P. & Gray, C. (2009). SPSS 16 made simple. Hove: Psychology Press.
Miller, R. et al (2002). SPSS for Social Scientists. Bristol: Palgrave.
Pallant, J (2007) SPSS survival manual. Maidenhead: OUP