The Army Selection and Classification Project has produced a comprehensive examination of job performance in 19 entry‐level Army jobs (Military Occupational Specialties) sampled from the existing population of entry‐level positions. Multiple methods of job analysis and criterion measurement were utilized in a subsample of nine jobs to generate over 200 performance indicators, which were then used to assess performance in a combined sample of 9,430 job incumbents. An iterative procedure involving a series of content analyses and principal components analyses was used to develop a basic array of up to 32 criterion scores for each job. This basic set of scores formed the starting point of an attempt to model the latent structure of performance in this population of jobs. After alternative models were proposed for the latent structure, the models were submitted to a goodness‐of‐fit test via LIS‐REL VI. After accounting for two components of method variance, a five‐factor solution was judged as the best fit. The implications of the results and the modeling procedure for future personnel research are discussed.

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With the 2014 publication of the 5th revision of the Standards for Educational and Psychological Testing, the cochairs of the Joint Committee for the revision process were asked to consider the role and importance of the Standards for the educational testing community, and in particular for members of the National Council on Measurement in Education. This article begins with an overview of the process used to create the 2014 revision of the Standards and a discussion of challenges faced by the Joint Committee in the revision process. The next section summarizes the major changes in the revised Standards. The final section considers uses of the revised Standards and is intended to stimulate discussion and debate on the role and importance of these revised Standards for the educational measurement community.

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The initial examination of validity generalization in the Army Selection and Classification Project used data from a concurrent validation sample of 4,039 job incumbents drawn from a representative sample of nine jobs. The available data consisted of 24 predictor scores and five job performance factor scores on each individual. The major objectives were to determine (a) the degree of validity generalization across the major components of performance, with the job held constant, and (b) the degree of validity generalization across jobs within each major performance factor. After reducing the predictor set by eliminating variables that added no information, a modified confirmatory analysis was used to test the hypotheses that one equation would fit the data from all performance components and that one equation would fit the data from all jobs, given a particular performance component. The major findings were that different predictor equations were needed for each of the five criterion factors. For generalization across jobs, within each criterion factor, one equation fit the data for four of the five performance components. Different prediction equations were required for the component that reflects proficiency on the technical tasks specific to each job.