CHAPTER 10

Analysing quantitative

data and formulating conclusions

Learning outcomes• Make sense of basic terminology in

quantitative data analysis• Undertake an initial analysis of your data• Identify and implement appropriate statistical

tools to help you interpret your data• Reflect on the significance of the results of

your analysis• Interpret your data to formulate appropriate

conclusions

Analysing quantitative data and formulating conclusions

• Variable– independent and dependant variable

• Bivariate analysis• Probability• Significance• Hypothesis

– null hypothesis– one-tailed hypothesis and two-tailed

hypothesis• Parametric data

Key terms in quantitative data analysis

Steps and options for quantitative data analysis

Scattergram produced using Excel

Chi-square example

Mann-Whitney U output

Principal component analysis: scree plot

Test and purpose Types of data Notes

Test of association – cross tabulation

All types – useful for nominal (category) data

How significant is the association?

Test of association – scattergram

Interval or ratio scale data only

How strong is the association?How significant is the association?

Assessment of significance of association (chi-square)

All types

Test of difference – t-test or Mann-Whitney test

Never for nominal data What is the probability that this result is due to chance?

Test of correlation –Spearman’s rho or Pearson’s correlation

Never for nominal data What is the probability that this result is due to chance?

Principal component/factor analysis

Never for nominal data

Analysis options and choices