Join Jeff Toister for an in-depth discussion in this video Introduction to data analysis, part of Instructional Design: Needs Analysis.
There are three major steps to conducting a training needs analysis, setting goals, gathering data, and analyzing data. It's that third step, data analysis, that allows you to answer important questions about the training program you are designing. Data analysis is often confused with the data presentation. So it's important to distinguish between the two. Data presentation is merely providing the results of a data-collection activity. For example, let's say your company surveys its customers and finds that it has an 80% customer satisfaction rating.
Sharing the results of the survey alone is a data presentation. The limitation is it doesn't give you any actionable insight. If you were asked to design a training program that could help improve the satisfaction rating to, say 85%, you'd have to guess at what employees really needed. That's where analysis comes in. Data analysis allows you to obtain a deeper understanding of the data so you can take action. For example, what if you are able to identify the biggest cause of customer dissatisfaction? Perhaps it was a specific procedure that confused employees.
Armed with this insight, you could design a training program that clarified the procedure that was most often responsible for unhappy customers. A thorough data analysis can help you answer the questions that most impact the success of your project. It can help you identify the root cause of gaps between existing and desired performance. It can help you identify the training participant's specific needs. You can use data analysis to determine if training will resolve the problem, or if other solutions are required. An analysis can help you establish objectives that will guide the development of the training program.
Finally, data analysis can help you find solutions to project constraints. A data analysis can also help you avoid making erroneous assumptions about your audience. We've been using an interviewing skills training program as an example throughout this course. The Midwest regional vice president, Stacey Jones, believes that giving supervisors in a region interviewing skills will help them make better hiring decisions. Her hope is that better hiring decisions will help decrease turnover among new employees from 30% down to 15%.
Without analyzing the data, we might assume that every supervisor has a high turnover rate. However, if we look at the distribution of supervisor turnover rates, a sightly different story emerges. This chart is called a histogram. It shows us that some supervisors have very high turnover rates, while other supervisors are already meeting the goal. The average is 30%, but turnover rates among individual supervisors vary quite widely. This data raises some important questions that we'll want to analyze.
Why do some supervisors have higher turnover rates than others? What are supervisors with low turnover rates doing differently than everyone else? What about supervisors with high turnover rates? What are they doing, or not doing. Analyzing data, can help us answer these questions. And, if we can learn the answers, we'll know how to design a training program, that achieves our sponsors' goals.
- Setting project objectives
- Identifying the target audience for training
- Selecting data sources
- Facilitating focus groups and interviews
- Designing effective surveys
- Identifying participant needs
- Defining learning outcomes
- Presenting results to project sponsors