New Tricks for an Old Dog: Visualizing Job Analysis Results

Article excerpt

For the past 30 years researchers and practitioners have relied on the same job analysis methods for their work. Most often, the projects begin with interviews of subject matter expert (SME) groups and conclude with task- or competency-based surveys. Regularly, the resulting product is a report consisting of 20 pages of text and 200 pages of tables. With the exception of two noteworthy advances, little has changed in job analysis since the 1970s.

The first advance has been competency-based job analysis. (1) There is some controversy, however, over how competencies and the traditionally used knowledge, skills, abilities and other characteristics differ. (2) The second important advance has been conducting Web-based surveys.

Even though there has been little change in job analysis, the tried-and-true methods have proven effective for characterizing jobs. Some would say, "If it's not broke, don't fix it." However, advances in data analysis and visualization techniques offer opportunities to incorporate new tools into job analysis and to build upon the success of past practices. Data analysis techniques that incorporate visualization offer an opportunity to better communicate job analysis results, thus making study results more influential (and probably improving the efficiency with which study results are communicated). It is time for job analysts to look outside their insular world and adopt technological advances and embrace information visualization and knowledge visualization techniques.

Information visualization and knowledge visualization are young interdisciplinary fields that draw heavily from cognitive science, visual perception, and computer science. Information visualization is the representation of selected features or elements of abstract and complex data. Whereas information visualization requires the use of computer-supported tools to analyze large amounts of data, knowledge visualization involves the transfer of knowledge among persons. (3) Both methods allow researchers to present data or information in nontraditional forms, using, for example, 2-D or 3-D color graphics or animation to show the structure of information. Information visualization and knowledge visualization also allow data users to navigate through the collected data and modify its presentation to explore, discover, and learn. Although the disciplines are in their relative infancy, information visualization and knowledge visualization each offer tools and methodologies that may be well suited to job analysis research.

Network analysis is another tool that can be useful for job analysis. This is a burgeoning field of study, and its methods have been applied to such diverse topics as the analysis of the U.S. power grid, the relationships among movie actors, and neurobiology. (4) Network analysis is important because job analysts often want to examine the relationships among a number of jobs (or a network of jobs) and network techniques emphasize the relational aspects of data.

Network analysis, when used in conjunction with visualization techniques, gives a job analyst a visually efficient way to present complex data to end users. However, behind the simplicity lie highly sophisticated tools that facilitate the elegant presentation of data. Thus, the complexity of job analysis data is made understandable to the end users.

A picture is worth a thousand words" may be a cliche, but it is absolutely true. The human eye and mind are particularly well suited to interpret images, forms, and patterns. Simply put, presenting data visually allows viewers to grasp the interrelationships of the data points without performing or understanding complex mathematics. (5) In other words, it allows data users to easily sort through and understand large amounts of data quickly. While the physical and engineering sciences have dealt with increasing data complexity by using visualization techniques, the behavioral sciences have been slower to adopt such tools. …