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Criterion Problem
The term criterion problem refers to a general problem in regression analysis, especially when used for selection, when the criterion measure that is easily obtainable is not a good approximation of the actual criterion of interest. In other words, the criterion problem refers to the problem that measures of the criterion performance behaviors in which the researcher or practitioner is interested in predicting are not readily available. For example, although specific sets of performance behaviors are desirable in academic and employment settings, often the easily obtainable measures are 1st-year grade point averages (GPAs) and supervisory ratings of job performance. The criterion problem is that those easily obtainable measures (GPA and supervisory performance ratings) are not good measures of important performance behaviors. The criterion is said to be deficient if important performance behaviors are not captured in a particular criterion measure. It can also be considered contaminated if the criterion measure also assesses things that are unrelated to the performance behaviors of interest. This entry explores the criterion problem in both academic and employment settings, describes how the criterion problem can be addressed, and examines the implications for selection research.
Criterion Problem in Academic Settings
In academic settings, there are two typical and readily available criterion variables: GPA and student retention (whether a student has remained with the university). Although each of these criteria is certainly important, most researchers agree that there is more to being a good student than simply having good grades and graduating from the university. This is the essence of the criterion problem in academic settings: the specific behaviors that persons on the admissions staff would like to predict are not captured well in the measurement of the readily available GPA and retention variables. The first step in solving the criterion problem, then, is identifying which types of behaviors are important.
Thomas Taber and Judith Hackman provided one of the earliest attempts to model the performance behaviors relating to effective performance in undergraduate college students. After surveying many university students, staff, and faculty members, they identified 17 areas of student performance and two broad categories: academic performance and nonacademic performance. The academic performance factors, such as cognitive proficiency, academic effort, and intellectual growth, are reasonably measured in the easily obtained overall GPA; these factors are also predicted fairly well by the traditional variables that predict GPA (e.g., standardized tests, prior GPA). The nonacademic performance factors, on the other hand, are not captured well in the measurement of GPA. These factors include ethical behavior, discrimination issues, and personal growth, and none of these are well predicted by traditional predictor variables.
In more recent research, Frederick Oswald and colleagues modeled undergraduate student performance for the purposes of scale construction. By examining university mission statements across a range of colleges to determine the student behaviors in which stakeholders are ultimately interested, they identified 12 performance factors that can be grouped into three broad categories: intellectual, interpersonal, and intrapersonal. Intellectual behaviors are best captured with GPA and include knowledge, interest in learning, and artistic appreciation. Interpersonal behaviors include leadership, interpersonal skills, social responsibility, and multicultural tolerance. Intrapersonal behaviors include health, ethics, perseverance, adaptability, and career orientation. However, GPA does not measure the interpersonal or intrapersonal behaviors particularly well. It is interesting to note that this research also showed that while traditional predictors (e.g., standardized tests, prior GPA) predict college GPA well, they do not predict the nonintellectual factors; noncognitive variables, such as personality and scales developed to assess these performance dimensions, are much better predictors of the nonintellectual factors.
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