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 purposeTypes of dataNotes 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