ORG 3630 People Analytics
ORG 3630 People Analytics
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.
After finishing this course, students should understand:
- The opportunities and limitations connected to the use of statistics as a source of knowledge and a foundation for decisions
- The difference between randomized experiments and non-experimental (correlational/observational) investigations, and the strengths and weaknesses of these approaches
- Important principles related to the planning and execution of quantitative surveys
- The relationship between the choice of independent and dependent variables and the choice of analysis
- What statistical significance entails, and how this differs from practical significance
- What effect size entails
- What statistical power entails
After finishing this course, students should be able to:
- Plan and perform data collection to answer simple research questions
- Choice of research question, and when it is appropriate with an experimental vs. non-experimental approach
- Reflect on survey design, effect size, statistical strength, and sample size
- Perform different statistical analyses using common statistical software (primarily Jamovi, with some additional analysis in G*Power)
- Interpret results from common forms of statistical analysis
- Present different results visually
After finishing this course, students should have:
- Developed their thinking about the value of quantitative data in answering important questions related to the workplace
- Increased their understanding of how (and when) experimental methods can give insights into causal relationships, and how they can be used to foster innovation
- Increased their ability to perform different statistical analyses
- Increased their ability to present results visually
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
The course will be taught through a series of lectures covering the curriculum. Large parts of the lectures will focus on the practical use of statistical software like jamovi and other relevant programs (e.g., G*Power). Students will also be working in groups of 1-3 people, with 3 practical course requirements that will be relevant for the final exam. 2 of these must be approved to be able to take the exam. Each student will also be tasked with collecting data that will be used in the analysis in the third part of the course. This implies getting 3-5 people who are NOT students at BI to fill out a questionnaire online.
The course will be concluded with a final written exam that will be delivered individually.
Re-sit examination
Students that have not gotten approved the coursework requirements, must re-take the exercises during the next scheduled course.
Students that have not passed the written examination or who wish to improve their grade may re-take the examination in connection with the next scheduled examination.
Higher Education Entrance Qualification
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
Introductory course in statistics and the course Methods and Insight (or equivalent)
| Mandatory coursework | Courseworks given | Courseworks required | Comment coursework |
|---|---|---|---|
| Mandatory | 3 | 2 |
| Assessments |
|---|
Exam category: School Exam Form of assessment: Written School Exam - digital Exam/hand-in semester: First Semester Weight: 100 Grouping: Individual Support materials:
Duration: 3 Hour(s) Exam code: ORG 36301 Grading scale: ECTS Resit: Examination every semester |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 30 Hour(s) | |
Feedback activities and counselling | 15 Hour(s) | |
Student's own work with learning resources | 87 Hour(s) | |
Group work / Assignments | 65 Hour(s) | |
Examination | 3 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.