Course Objectives
Certified HR Analyst
Course Methodology
This workshop is designed to be interactive and practical. It includes various learning methodologies that enable participants to immediately implement all the tools they learn during the workshop.
Case studies from the best multinational companies followed by practice sessions dominate the learning methods in this course which aims at strengthening participants’ analytical skillset. In addition, mindset-changing will build participants’ conviction about the paramount importance of data in all aspects of HR practices.
The software used for data analysis is SPSS.
Course Objectives
By the end of the course, participants will be able to:
Demonstrate deep understanding of the use of data analytics in HR disciplines
Implement data analytics tools and strategies to improve recruitment decisions, and predict employee turnover
Analyze the impact of learning and development provision on employee motivation using linear regression
Promote a culture of diversity and inclusion within their organization through significance statistical tests
Predict employee performance using data from employee engagement surveys
Apply HR data analysis strategies and tools in their own business environment
Target Audience
This course is targeted at HR professionals from all practices: learning and development, talent management, organization development, workforce planning, performance and rewards. HR business partners, and generalists would also benefit greatly from this workshop.
Target Competencies
Data analysis
Decision making
Storytelling
Data visualization
Recruitment and selection
Employee engagement
Performance management
Learning and development
Diversity and inclusion
Course Outline
Data-driven HR analytics
Definition of HR analytics
The analytics process – using data to influence business decisions
Data
Metrics
Analytics
Action
Information sources – HR data are not only found in HR departments
The most commonly used HR information systems and data analysis platforms
Basic statistics
Types of variables
Statistical significance
Descriptive data vs. data analysis
Modelling and predictive analysis
How data are reinventing the HR functions
HR professionals and data – how to synergize for the best of the business
Data analysis of recruitment and prediction of employee turnover
Dependent and independent variables
Categorical and continuous variables
Logistic regression analysis methodology - building predictive models
Removing guesswork from recruitment decisions - data-informed candidate selection decisions
Testing validity and reliability of candidate selection methods
Predicting rejection and shortlisting of candidates
Predicting employee turnover in your organization
Data-driven learning and development – the impact of training provision on employee motivation
Transforming answers of questionnaires into continuous data to expand analysis opportunities
Questionnaire design - testing internal consistency of questionnaires - Cronbach's alpha measure
Removing irrelevant answers from respondents (outliers) to questionnaires
Testing if your data is representative using normality test
Understanding the nature of relationship between business variables using Pearson's correlation
Examining the impact of training provision variables on employee motivation using linear regression
Simulating an alternative model to Kirkpatrick's model for evaluating training impact
Deep analysis of diversion and inclusion in the organization
The importance of diversion and inclusion (ethnic and gender) in organizations
Wrong ways of using descriptive data to present a case of organization bias
Significance p value and degrees of freedom
t-tests and chi square test - a simple mathematical notion
Analyzing gender bias in workforce and job grades using frequency tables and chi square
Exploring ethnic diversity across teams using descriptive statistics
Reporting gender-biased promotions using t-tests
Using multiple linear regression to model and predict ethnic diversity variation across teams
?Exploring relationships between employee performance, employee engagement, and profitability
How to measure employee engagement
Factor analysis to test the reliability of questions in an employee engagement survey
Analyzing data to explore the relationship between customer loyalty levels and employee engagement levels
Stepwise multiple regression - an effective tool to explore relationships among business variables
Using stepwise multiple regression to model employee performance
Revisiting multiple regression to predict employee sickness
Modeling change in performance of employees over time using stepwise multiple regression
Application of HR data analysis in business context - An eight-step methodology
Step 1: Linking business strategies to people strategies
Step 2: Identifying business challenges
Step 3: Forming your business hypothesis
Step 4: Gathering your data
Step 5: Choosing analysis tools and strategies
Step 6: Findings and decisions - turn data to insights
Step 7: Communicating your conclusion with storytelling and visualization
Step 8: Evaluating your analytical intervention