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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
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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)
Criterion Domain Objective data Productivity measures, absenteeism, tardiness, turnover, absenteeism Subjective data Performance ratings (e.g., supervisor, co-workers, self, subordinates, clients Contextual data Assisting others, loyalty, extra work/effort, volunteering, counterproductive behaviors
Criteria Dimensionality Static --- Individual performance varies by performance criteria Decision-making Communication
Criteria Dimensionality (cont.) Temporal --- Performance varies as a function of time; importance of when performance is assessed IQ 1st year Specific work methods, interests, personality, interpersonal relationships 2nd year
Criteria Dimensionality (cont.) Individual --- Employees excel at different aspects of job performance Role prescriptions, organizational impact Production Client support & satisfaction Employee # 1 Employee # 2
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
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)
Criteria Issues Relevance --- Generally considered the most important issue Objective data Subjective data r = .39 * Adequacy of production data for managerial personnel
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)
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
Sample Job Evaluation Process Selection of Job Tasks/Duties (Criteria) often via a job analysis Selection of Compensable Factors (e.g., Effort, Skill, Responsibility, Work Conditions) Ratings of Job Criteria on Compensable Factors Job Score (e.g., points) Compensation
Job Evaluation Example Ranking $ Total Score Job Tasks Compensable Factors Ratings (e.g., 1-5 scale) Task 1 Task 2 Task 3 Task 4 Task 5 110 100 98 88 76 75 67 65 56 43 1 2 3 4 5 6 7 8 9 10 Effort Skill Responsibility Work Conditions