My knowledge in various HR topics started to expand when I worked at Stantec. Later on I applied inferential and predictive analysis to a public dataset to practice these scenarios.
After employees are hired, a key activity that an HR analytics wants to conduct is to explore what factors predict post-hire employee performance the best. One reason to do this is to test how valid the interview processes are; a second reason is to identify the factors that will predict who is likely to turn […]
When it comes to recruitment and selection of new employees, one of the key focuses at the forefront of an HR analyst’s priorities is to minimize bias as much as possible. In this post, we will examine diversity-related analysis on recruitment and selection activities. For example, what are the gender – male and female ratios […]
In this post we will predict the factors that affect the individual employee turnover. Because our response variable is the turnover status, and its value in the dataset is binary type (1=leave, 0=stay), we will apply logistic regression to investigate the data.
Employee turnover usually refers to all “leavers” of an organization – those who leave voluntarily or involuntarily. It includes those who resign, are made redundant, take retirement, or exit for any other reason. In this article we are only concerned with voluntary turnover and its possible causes.
Last week we analyzed the survey question validity and construction provided by an external survey provider, now we are ready to test if the team engagement level is influenced by the work location and the group function.
While Principal Component Analysis (PCA) and reliability test should be conducted on individual-level responses, this information is usually not available if the survey is conducted by an external survey provider. What we can do first is to apply PCA and reliability test to validate how the survey questions are constructed.
Employee engagement is not a tangible thing and can’t be measured easily. However, based on the definition we choose, we then can ask a range of questions that will indicate how engaged the person is.
In the previous post, we learned from a financial institution that the type of the team – sales or professional service has an impact on the prevalence of the minority groups. The conclusion was that the proportion of the staff with different ethnic backgrounds is significantly lower in sales than in the professional service. In […]
In this example, we have team-level data from a UK-based financial company. The company has 29,976 employees in 928 teams which are divided in two main functions – sales and professional service. In short, sales staff are customer-facing employees; whereas professional service staff are non customer-facing employees such as product development, finance, marketing.
Besides descriptive data, HR can use other methods to interrogate data in more detail. For example, HR might explore a dataset of gender and job role, and find that as job role status increases, the number of female employees decreases. See the figures below. To ensure that the finding is not just a coincidence,
This article provides an analysis and forecast of a future three-day demand for the public transport system in Santiago de Chile. Before the company can invest resources to reconstruct the public transit system, they need a reliable prediction of the future demand.