Digital Transformation WalkMe TeamUpdated September 2, 2021

Applied Longitudinal Data Analysis Modeling Change and Event Occurrence: Before You Read

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Applied Longitudinal Data Analysis Modeling Change and Event Occurrence: Before You Read

Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence is a technical book that covers two of the most popular statistical methods used today – multilevel models for individual change and hazard/survival models for event occurrence.

The concepts covered in this book are useful for social, behavioral, and biomedical scientists.

In this article, we’ll look at a basic introduction to statistics concepts and discover how statistical methods, such as those covered in this book, can help managers improve performance outcomes.

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Applied Longitudinal Data Analysis Modeling Change and Event Occurrence: Before You Read

In the business world, data-driven techniques and statistical methods have become more popular in recent years.

This rise in popularity is due in part to technology-driven advancement, digitization, and digital transformation. That is, the more that technology advances, the more data we have access to, and the more we can use statistical methods to make use of that data.

Let’s take a look at how such methods can be applied in a business context:

Longitudinal Studies

Longitudinal studies, or surveys, repeatedly review the same set of variables, such as people, over a set time frame.

By observing how that variable changes, those conducting the study can gain insight into the causes behind the changes, how those changes will roll out over time, and more.

These types of studies are frequently used in social and clinical psychology, in order to track and understand behavioral changes.

Longitudinal studies are performed on the same set of variables over time, rather than on different variables that share common characteristics.

In a business context, longitudinal studies could be used to examine the relationship between a given set of employees and, for instance, the software tools they use or their workflows.

As the title of the book suggests, this textbook focuses specifically on longitudinal analysis, not other types of analyses, such as cross-sectional studies.

Survival Analysis

In statistics, survival analysis examines the duration of time before an event or a set of events occur.

In the biological sciences, the death of an organism would be considered an event.

When viewed in the context of social sciences, however, the failure of a system would be considered an event.

Those performing survival analyses will define a specific set of variables, such as lifetime – the timeline of survival – and events such as failure.

While some of these variables are easy to define, others are not. At what point, for example, would it be determined that a business system or a mechanical system has failed?

Survival analysis is another topic covered in depth in this book.

Multilevel Models

A multilevel model is a statistical model that measures more than one parameter.

Multilevel models have become more popular as computing technology has become more advanced. And, as the title of the book suggests, this textbook covers these models in detail.

If a multilevel model is used to analyze employee performance, for example, such a model may track individual employees, as well as other variables, such as variables that affect the employee experience or the workplace.

Quantitative Analysis

Quantitative methods apply statistics and mathematics to the analysis of certain types of phenomena, such as human behavior, biological systems, financial systems, and so on.

A financial analyst, for instance, may use quantitative analysis to quantify the risk and potential returns of stocks.

A social scientist may use statistical methods to better understand and predict the behavior of a population.

This approach contrasts with qualitative analysis, which uses nonnumerical approaches to perform analyses.

How Statistical Methods Can Be Applied in the Workplace

Here are just a few examples of how statistical methods, such as those covered in this textbook, can be applied in business and in the workplace:

  • Quantitative analysis can be used for performance reviews. A straightforward way to apply quantitative methods in the workplace is to use them for employee performance reviews. The advantage to this approach is that quantitative methods are objective and based on data, rather than subjective and based on the perspectives of a small number of people. Today, some HR software includes features for quantitative performance analysis.
  • Understanding morale, job satisfaction, and employee engagement. One quantitative study of academic librarians looked at a number of variables that influenced the morale of librarians in their workplace. When focusing on variables that supervisors directly influenced, they discovered that variables such as feedback, work autonomy, transparency, and communication could predict morale, as well as turnover intention.
  • The impact of workplace culture on employee performance, mood, and health. Another quantitative study of organizational culture used statistical methods, such as multilevel regression analysis and a cultural framework, to understand the correlation between culture types and psychological distress, depression, emotional exhaustion, and well-being.

These examples demonstrate that statistics, such as those covered above, can be used in a variety of ways in the workplace.

Even if one is not an expert at statistics, with the right software, managers can leverage quantitative methods to gain insight into, improve, and grow their organization.

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