ORG 3630 People analytics

ORG 3630 People analytics

Course code: 
ORG 3630
Department: 
Leadership and Organizational Behaviour
Credits: 
7.5
Course coordinator: 
Erik Løhre
Course name in Norwegian: 
People analytics
Product category: 
Bachelor
Portfolio: 
Bachelor of Organisational Psychology, HR and Leadership - Programme Courses
Semester: 
2022 Spring
Active status: 
Active
Level of study: 
Bachelor
Teaching language: 
Norwegian
Course type: 
One semester
Introduction

People analytics, also known as HR analytics, concerns itself with how quantitative data from an organisation can be used for purposes such as surveying and improving productivity or cooperation, finding the right person for a position, etc. This course builds on the knowledge students have gained in previous courses in methods and statistics, and delves deeper into different statistical techniques, 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 gain experience in 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.

Learning outcomes - Knowledge

After finishing this course, students should understand:

  • The opportunities and limitations connected to the use of statistics as a source of knowledge and a background for decisions
  • The difference between randomized experiments and non-experimental (correlational/descriptive) 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
Learning outcomes - Skills

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 effect size, statistical strength, and sample size
  • Perform different statistical analysis by using common statistical software (primarily SPSS, with some additional analysis in G*Power, Jamovi/JASP)
  • Interpret results from common forms of statistical analysis
  • Present different results visually (using Excel/Powerpoint or other alternatives)
General Competence

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
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
  • Introduction to SPSS

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

  • Common analysis: correlation, regression, t-test, ANOVA
  • -values and NHST (null hypothesis significance testing) and the problems surrounding them
  • Effect size 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 collection
  • Choice of analysis
  • Reporting and visualization
Teaching and learning activities

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 SPSS and other relevant programs (e.g., G*Power, Excel). 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.

Software tools
SPSS
Qualifications

Higher Education Entrance Qualification

Covid-19

Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.

Teaching

Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.

Required prerequisite knowledge

Introductory course in statistics and the course Methods and Insight (or equivalent)

Mandatory courseworkCourseworks givenCourseworks requiredComment coursework
Mandatory32
Mandatory coursework:
Mandatory coursework:Mandatory
Courseworks given:3
Courseworks required:2
Comment coursework:
Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
ORG 36301
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination every semester
100Yes3 Hour(s)
  • No support materials
Individual
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:100
Invigilation:Yes
Grouping (size):Individual
Support materials:
  • No support materials
Duration:3 Hour(s)
Comment:
Exam code: ORG 36301
Grading scale:ECTS
Resit:Examination every semester
Type of Assessment: 
Ordinary examination
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
39 Hour(s)
Student's own work with learning resources
93 Hour(s)
Group work / Assignments
65 Hour(s)
Examination
3 Hour(s)
Sum workload: 
200

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.