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Objective data

Criterion Domain. Objective data. Productivity measures, absenteeism, tardiness, turnover, absenteeism. Subjective data. Performance ratings (e.g., supervisor, co-workers, self, subordinates, clients. Contextual data.

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Objective data

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  1. Criterion Domain Objective data Productivity measures, absenteeism, tardiness, turnover, absenteeism Subjective data Performance ratings (e.g., supervisor, co-workers, self, subordinates, clients Contextual data OCBs (assisting others, loyalty, extra work/effort, volunteering), emotional labor, counterproductive behaviors (late arrivals, sabotage, gossiping)

  2. Objective Appraisal Data • 1) Production Data (e.g., sales volume, units produced) • When observation occurs (timing), and how data is collected • Fairness and relevancy issue • Potential limited variability • Limitations regarding supervisory personnel • 2) Personnel Data • Absenteeism (excused versus unexcused) • Tardiness • Accidents (fault issue)

  3. Use of Objective Data Dynamic Criteria (cont.) Years on job 1 2 3 4 5 6 7 8 • Best predictor of performance • Verbal Ability • Aptitude Test scores • Best predictor of performance • Specific work methods • Co-worker relations

  4. Criteria Dimensionality Static --- Individual performance varies by performance criteria Decision-making Communication

  5. Criteria Dimensionality (cont.) Individual --- Employees excel at different aspects of job performance Role prescriptions, organizational impact Production Client support & satisfaction Employee # 1 Employee # 2

  6. Criteria Challenges Criterion unreliability --- Intrinsic (individual variations in performance) Extrinsic (equipment functioning, weather, supply chain, geographic region, information access) Recommended to always combine data across time and situations

  7. Use of Objective Data Dynamic Criteria Productivity (Sales) by Year 2001 2002 2003 2004 2005 2006 2007 • Individual variation in performance is often great across time • More consistency is achieved by using an incentive system and when output is measured over a significant number of occurrences (and over a wide variety of measures)

  8. Criteria Challenges (cont.) Observation --- Variation due to methods used, who observes Performance Dimensions --- Uni-dimensional vs. multidimensional criteria (Over-reliance on supervisor ratings of performance; 879/1506)

  9. Criteria Issues Relevance --- Generally considered the most important issue Objective data Subjective data r = .39 * Adequacy of production data for managerial personnel

  10. Criteria Issues (cont.) Dimensionality --- Does the criteria differentiate between employees? Low variability (e.g., production line speed, process limitations) Contamination --- Error b) Biases (e.g., rating scales, group membership, knowledge of predictor scores, self-fulfilling prophecy)

  11. To Combine or Not to Combine Criteria? Global criteria Separate, multiple criteria A A C C 3.0 GPA Is there a single, underlying dimension that “allows” combining separate criteria? Purposes of the data (e.g., a) for personnel decisions or b) feedback, understanding psychological and behavioral processes

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