People Analytics

Introduction

People analytics, also known as HR analytics, concerns itself with how quantitative data from an organisation can be used for purposes such as mapping factors that influence productivity or cooperation, how job satisfaction can be increased, finding the right person for a position, etc. This course delves deeper into different techniques for statistical analysis, with a focus on the practical application of these techniques to solve relevant issues.

Students will learn how experiments can be used to investigate whether different interventions are effective, to help innovation, and to establish causality, and when it is more useful to conduct non-experimental surveys. Terms like statistical significance (p-values), effect size and statistical power are central. Students will have access to relevant datasets, and will practice performing and interpreting different statistical analysis like different variants of ANOVA, correlation, and multiple regression. This also includes that students will learn to communicate results both in written and visual form (graphs/diagrams). The course emphasizes interpretation, practical understanding, and application, rather than knowledge about the mathematics behind the analyses.

Course content

This course prioritizes practical experience with analysis and use of quantitative data in an organizational context. The course will have an introductory part with a focus on theory, a middle part concerned with practical demonstration of different types of analysis, and a final part including a data collection, followed by analysis, interpretation, and reporting.

The first part of the course is titled “Data and statistics in organisations: why and how”, and the lectures will be dealing with the following subjects:

• About people analytics
• Experimental and non-experimental approaches
• Basic concepts (p-values, NHST)
• Introduction to jamovi

The second part of the course is titled “The statistical toolbox: common analyses, 'new' approaches, and data visualization”, and will deal with the following subjects:

• Common analyses: correlation, regression, t-test, ANOVA, reliability analysis
• Effect sizes and statistical power
• Data visualization

The third part of the course is titled “People analytics in practice” and will look closer at the following subjects:

• Research questions, design, and data collection
• Choice of analysis
• Reporting and visualization