Selection is a personnel decision whereby an organization decides whether to hire individuals using each person’s score on a single assessment, such as a test or interview, or a single predicted performance score based on a composite of multiple assessments. Using this single score to assign each individual to one of multiple jobs or assignments is referred to as placement. An example of placement is when colleges assign new students to a particular level of math class based on a math test score. Classification refers to the situation in which each of a number of individuals is assigned to one of multiple jobs based on their scores on multiple assessments. Classification refers to a complex set of personnel decisions and requires more explanation.
A Conceptual Example
The idea of classification can be illustrated by an example. An organization has 50 openings in four entry-level jobs: Word processor has 10 openings, administrative assistant has 12 openings, accounting clerk has 8 openings, and receptionist has 20 openings. Sixty people apply for a job at this organization and each completes three employment tests: word processing, basic accounting, and interpersonal skills.
Generally, the goal of classification is to use each applicant’s predicted performance score for each job to fill all the openings and maximize the overall predicted performance across all four jobs. Linear computer programming approaches have been developed that make such assignments within the constraints of a given classification situation such as the number of jobs, openings or quotas for each job, and applicants. Note that in the example, 50 applicants would get assigned to one of the four jobs and 10 applicants would get assigned to not hired.
Using past scores on the three tests and measures of performance, formulas can be developed to estimate predicted performance for each applicant in each job. The tests differ in how well they predict performance in each job. For example, the basic accounting test is fairly predictive of performance in the accounting clerk job, but is less predictive of performance in the receptionist job. Additionally, the word processing test is very predictive of performance in the word processor job but is less predictive of performance in the receptionist job. This means that the equations for calculating predicted performance for each job give different weights to each test. For example, the equation for accounting clerk gives its largest weight to basic accounting test scores, whereas the receptionist equation gives its largest weight to interpersonal skill test scores and little weight to accounting test scores. Additionally, scores vary across applicants within each test and across tests within each individual. This means that each individual will have a different predicted performance score for each job.
One way to assign applicants to these jobs would be to calculate a single predicted performance score for each applicant, select all applicants who have scores above some cutoff, and randomly assign applicants to jobs within the constraints of the quotas. However, random assignment would not take advantage of the possibility that each selected applicant will not perform equally well on all available jobs. Classification takes advantage of this possibility. Classification efficiency can be viewed as the difference in overall predicted performance between this univariate (one score per applicant) strategy and the multivariate (one score per applicant per job) classification approach that uses a different equation to predict performance for each job.
A number of parameters influence the degree of classification efficiency. An important one is the extent to which predicted scores for each job are related to each other. The smaller the relationships among predicted scores across jobs, the greater the potential classification efficiency. That is, classification efficiency increases to the extent that multiple assessments capture differences in the individual characteristics that determine performance in each job.
Classification in the U.S. Military
With regard to most organizations and their personnel decisions, classification is much more of an idea than a practice. Although large organizations will apply classifications at a localized level, such as when staffing a new facility, most often an organization is considering a group of applicants who have applied for one particular job; that is, most personnel decisions are selection rather than classification. The armed services are a notable exception. Although their practice only approximates conceptual discussions of classification, the individual armed services (i.e., Army, Air Force, Navy, Marine Corps, and Coast Guard) constitute the best real-world example. On an annual basis, the services must select and assign a large number of inexperienced individuals to a large number of entry-level jobs. The situation requires use of classification principles.
Prospective armed service applicants complete a battery of tests. The tests an applicant completes are used to first determine whether the person qualifies for military service and second to assign the individual to one of many jobs. Qualification for military service is a selection decision. The methods the services use to narrow the range of jobs for selected individuals use ideas from classification.
The armed services hire approximately 180,000 new persons annually and need to fit them into roughly 800 entry-level jobs. Historically, the military was the first organization of any type to use large-scale testing for selection and job assignment, starting in about 1916. In 1976 a version of the current battery was put into use—the Armed Services Vocational Aptitude Battery (ASVAB). Although the ASVAB has gone through restructuring, renorming, and regular revision, it is the current official mental testing battery used by each service for entry and for job assignment on acceptance. The current ASVAB is a battery of nine operational tests:
- general science (GS),
- arithmetic reasoning (AR),
- word knowledge (WK),
- paragraph comprehension (PC),
- mathematics knowledge (MK),
- electronics information (EI),
- auto information (AI),
- shop information (SI), and
- mechanical comprehension (MC).
Selection and Assignment
Before individuals are assigned to a job, they must meet minimal criteria to join the armed services. One of these is a cut score on a composite of four ASVAB tests (WK, PC, AR, and MK) referred to as the Armed Forces Qualification Test (AFQT). Other criteria include age, education, passing a physical examination, and meeting background and moral character requirements.
AFQT is used only to determine overall service eligibility and is not used to determine whether someone is qualified to be trained in a specific job. Each individual service uses the tests somewhat differently to make job assignments. The rest of this discussion tracks examples of applications used by the U.S. Army. A significant contributor to the assignment decision in the Army is the individual’s score on each of nine scores of uniquely weighted composites of the ASVAB tests. Each entry-level job in the Army is associated with one of these aptitude area composites. The weights for each aptitude area were developed to predict training performance in Army jobs. For example, some entry-level Army jobs are assigned to the mechanical maintenance (MM) aptitude area. The weights for calculating the MM composite score emphasize the AI, SI, MC, and EI tests. Every Army job has a minimum cut score on its composite that an applicant must meet to be eligible for that job. There are many factors that determine to which job an applicant is assigned. Only one is whether the applicant’s aptitude area composite score satisfies the job’s minimum score. Other factors include current job openings, the Army’s priorities, when applicants choose to begin their term of service, and which job applicants prefer.
This job assignment process is only an approximation of the conceptual classification decision model described previously. First, the goal was not to assign applicants to jobs in a way that maximizes overall predicted performance but rather to assign applicants to jobs to
- meet minimum aptitude requirements for each job,
- fill current openings,
- satisfy applicant preferences, and
- meet other constraints.
Additionally, it is difficult to satisfy the pure version of the classification model when personnel decisions are made in real time rather than in large batches that allow classification efficiency advantages associated with optimizing assignments across a larger number of applicants. Although assignments made this way are not likely to achieve the level of classification efficiency that a model closer to the conceptual description of classification would produce, the Army application is still a substantial improvement over what would be realized by selection and unguided assignment.
The Future
Although the Army example presented is not classification in the strictest sense, it is a good large-scale approximation of classification and is frequently discussed in the literature. Nonetheless, the Army is working on potential improvements to its assignment system that would improve classification efficiency. The Army is currently considering adding applicants’ actual predicted score for each aptitude area to the decision process. That is, among other considerations, an applicant could choose or be assigned to a job for which the applicant’s predicted score is higher than others among those for which the applicant meets minimum qualifications. Another consideration is the possibility of using projections of the likely scores of applicants during a time period so that the assignment takes place in the context of a large batch of applicants rather than only those applying at that particular time. Finally, the Army is actively conducting research into potential additions to the ASVAB that could increase its classification efficiency. Measures of constructs in the areas of temperament, spatial and psychomotor aptitudes, and situational judgment are being examined.
References:
- Campbell, J. P. (1991). Modeling the performance prediction problem in industrial and organizational psychology. In M. D. Dunnette & L. M. Hough (Eds.), Handbook of industrial and organizational psychology (pp. 687-732). Palo Alto, CA: Consulting Psychologists Press.
- Rosse, R. L., Campbell, J. P., & Peterson, N. G. (2001). Personnel classification and differential job assignments: Estimating classification gains. In J. P. Campbell & D. J. Knapp (Eds.), Exploring the limits in personnel selection and classification (pp. 453-506). Mahwah, NJ: Lawrence Erlbaum.
- Waters, B. K. (1997). Army Alpha to CAT-ASVAB: Fourscore years of military personnel selection and classification testing. In R. F. Dillon (Ed.), Handbook on testing (pp. 187-203). Westport, CT: Greenwood Press.