8. Quantitative Method Design and Data Analysis

This chapter delves into the world of quantitative data analysis, covering essential techniques such as data cleaning, descriptive statistics, hypothesis testing, and multivariate analysis. It explores the advantages of quantitative research, including objectivity and generalisability, while also addressing its limitations, such as inflexibility and potential bias. The text highlights how AI and machine learning can complement traditional statistical methods, enhancing predictive modeling and hypothesis generation. Practical examples illustrate the application of these techniques in management research, providing a clear and concise overview of the process from data collection to interpretation. The chapter also discusses various statistical tests, including paired sample t-tests, independent sample t-tests, one-way ANOVA, and MANOVA, offering insights into their assumptions, applications, and interpretations. Additionally, it covers logistic regression analysis, a powerful tool for predicting binary outcomes. The text concludes with a discussion on the role of AI in augmenting quantitative analysis, demonstrating how these advanced techniques can provide deeper insights and more accurate predictions. This comprehensive guide is an invaluable resource for professionals seeking to enhance their analytical skills and apply advanced statistical methods in their research.