What is the basic purpose of a test for personnel selection?

1. What is the basic purpose of a test for personnel selection?

2. Positively Skewed Distribution Negatively Skewed Distribution 40 45 55 60 70 75 80 90 100 40 45 55 60 70 75 80 90 100 Test Scores Test Scores

3. Normal Curve -4 -3 -2 -1Mean +1 +2 +3 +4 Central Tendency a) Mode (most frequent score) b) Mean (average score; [EX/N]) c) Median (midpoint of scores) Variability (Spread in scores) a) Range (lowest to highest score) b) Standard Deviation c) Variance

4. Relationships Among Different Types of Test Scores in a Normal Distribution Number of Cases 2.14% 0.13% 0.13% 2.14% 13.59% 34.13% 34.13% 13.59% -4 -3 -2 -1 Mean +1 +2 +3 +4 Test Score Z score T score CEEB score Deviation IQ (SD = 15) Stanine Percentile -4 -3 -2 -1 0 +1 +2 +3 +4 10 20 30 40 50 60 70 80 90 200 300 400 500 600 700 800 55 70 85 100 115 130 145 4% 7% 12% 17% 20% 17% 12% 7% 4% 1 2 3 4 5 6 7 8 9 1 5 10 20 30 40 50 60 70 80 90 95 100

5. Standard Score Example Z = Raw Score – Mean/Standard deviation

6. ~ I-O Research ~ Measurement • Limit collection of categorical data Age in Years: _______ Income: ____________ Age 0 - 18 19 – 25 26 – 35 36 – 45 46 – 55 56 – 65 85 & Above Income 0 ------ 10,000 10,001 – 25,000 25,001 – 35,000 35,001 – 50,000 50,001 – 75,000 75,001 – 100,000 100,000 & Above

7. ~ I-O Research ~ Measurement (cont.) • Limit collection of dichotomous data Yes _____ No _____ _____ _____ _____ _____ _____ 1 2 3 4 5 Highly Highly Disagree Agree

8. ~ I-O Research ~ Measurement (cont.) • Restrict possibility of missing data Scale Questions 1. 2. 3. 4. 5. Missing Computed score for scale or subscales containing questions #5 and #48 will also be missing Missing 48 49 50

9. Absolute versus Relative (Comparative) Assessments • Absolute: “How many hours of TV did you watch last year? • “Is this drink sweet?” or “How sweet is this drink?” • Relative: Did you watch TV more hours than you spent reading the local paper? • “Which of these five drinks is the sweetest?” • Generally, it is easier for people to make relative vs. absolute judgments (more • accuracy and consistency exists) • People rarely make absolute assessments in everyday activities (most choices are • basically comparative) • Limitation with relative assessments and the instances when absolute judgments are vital ---

10. Scales of Measurement • 1)Nominal -- Indicates categories, classification (e.g., gender, race, yes/no) • Stats: N of cases (e.g., chi-square), mode • Ordinal -- Indicates relative position; greater than, less than (e.g., rank ordering percentiles) • Stats: Median, percentiles, order statistics, non-parametric analyses • 3)Interval -- Indicates an absolute judgment on an attribute (equal intervals) • No absolute zero point (a score of 80 is not twice as high as a score of 40) • Stats: Mean, variance, correlation • 4)Ratio -- Possesses an absolute zero point (e.g., number of units produced) • All numerical operations can be performed (add, subtract, multiply, divide) Does not indicate how much of an attribute one possesses (e.g., all may be low or all may be high) Does not indicate how far apart the people are with respect to the attribute 1st 2nd 3rd

11. ~ I-O Research ~ • Interesting fact: Substantial amount of I-O studies are non-experimental (about 50%) • Overall Point: • Best for research to be driven by theories and problem-solving approaches not by methodology/statistics • Much research efforts in I-O focus on rather trivial questions that can be studied with “fancy” techniques • Bulk of research has limited applied significance

12. ~ I-O Research Trends ~ Some Recent Articles in the Journal of Applied Psychology • Safety in work vehicles: A multilevel study linking safety values and individual • predictors to work-related driving crashes. • Beyond change management: A multilevel investigation of contextual and • personal influences on employees' commitment to change. • The development of collective efficacy in teams: A multilevel and longitudinal • perspective. Multi-level analysis (or hierarchical linear modeling; HLM). Allows for the assessment of variance in outcome variables to be investigated at multiple, hierarchical levels. Related analyses include structural equation modeling and latent class modeling Geographic location (region, country) Work team (or job category) Employee Study Variables

13. ~ I-O Research Trends ~ Some Recent Articles in the Journal of Applied Psychology (cont.) • Predicting workplace aggression: A meta-analysis. • The good, the bad, and the unknown about telecommuting: Meta-analysisof psychological mediators and individual consequences. Meta-analysis: Statistical approach that allows the combination of results from multiple independent studies on a given topic. It allows a better estimate of the true “effect size,” giving more “weight” to larger studies.

14. ~ I-O Research Trends ~ Some Recent Articles in the Journal of Applied Psychology (cont.) • Abusive supervision and workplace deviance and the moderating effects of negative reciprocity beliefs. • Emotional exhaustion and job performance: The mediating role of motivation. Moderating variable (or 3rd variable): A variable that affects the strength and/or direction of the relationship between two variables. Mediating variable: Variable that accounts for (explains) the relationship between two variables Job enrichment strategies Job Satisfaction Age (as moderator) (The relationship may be stronger for older individuals) Job enrichment strategies Job Satisfaction Growth need strength (as mediator) (When growth need strength is considered the relationship between job enrichment and satisfaction goes away)

15. ~ I-O Research ~ Data Analysis Factor Analysis --- Usage: Approximately 10% of papers published in Journal of Applied Psychology employ factor analysis (Structural Equation Modeling (SEM) ✖ Avoid: Varimax rotation Principle components analysis Automatically keep factors with eigenvalues greater than 1.0 Use: Iterative principle factors (least squares, or maximum likelihood) Oblique rotation (no assumption of factor independence) ✔

16. ~ I-O Research (cont.) ~ Suggestions • More use of “archival” data (many are of high quality with large sample sizes; e.g., government statistics on unemployment rates) • Longitudinal studies (assessment of change over time) • 3) Report confidence intervals and effect sizes in addition to significance levels (e.g., p < .01)

17. 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, emotional labor, volunteering, counterproductive behaviors (CWBs; tardiness, sabotage, gossiping)

18. 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)

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

20. 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

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

22. 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)

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

24. 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)

25. 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