Students will acquire the skills and techniques required to analyse and manage data, interpret results, and report data analysis methods and findings in a business environment. Qualitative and quantitative research approaches are examined to consider their respective contributions, discretely and in combination, to knowledge development through empirical research. The quantitative component covers basic statistical thinking and data analysis techniques.

A strong emphasis will be placed on the logic underlying statistical concepts such as probability and probability distributions, normal distribution, sampling distributions, parameter estimation, and hypothesis testing. A range of data analysis techniques will also be covered, including t-test, Analysis of Variance, cross tabulation, regression, correlation, and factor analysis. There is a strong emphasis on the application of statistical techniques to practical research problems in a business context.

The statistical computer package SPSS will be used for the statistical analysis of data. The qualitative component examines principles and techniques for organising, analysing and reporting qualitative data. The central principle of this component is the execution of rigorous qualitative data analysis through ‘good housekeeping’ –undertaking, recording and demonstrating careful, rational decision-making in qualitative data analysis (Marshall, 1999). Consequently, strategies for undertaking and reporting analysis of qualitative data are equally emphasised. Strategies for data analysis will include techniques for organising, searching, retrieving and interpreting qualitative data to develop and test theoretical conclusions.

Strategies for reporting analytical processes will incorporate techniques for recording and describing data analysis, including the articulation of theoretical conclusions and the use of qualitative data to illustrate and support conclusions drawn. Data analysis processes will be undertaken using NVivo, a computer software program for qualitative data analysis.

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