IP 214 Study - Summary Industrial Psychology 224 PDF
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Uploaded by MilaBobo
Universiteit Stellenbosch
Mila Mohlathe
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Summary
This document is a summary of industrial psychology lectures. It covers two types of inferences based on test scores and how they are used to make decisions. Topics include predictor/criterion variables, validity, and calculating reliability.
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lOMoARcPSD|27073014 IP 214 Study - Summary Industrial psychology 224 Industrial psychology 224 (Universiteit Stellenbosch) Scan to open on Studocu Studocu is not sponsored or endorsed by any college or university Downloaded by Mila Mohlathe ([email protected]) lOMoARcPSD|27073014 Theory-Based Co...
lOMoARcPSD|27073014 IP 214 Study - Summary Industrial psychology 224 Industrial psychology 224 (Universiteit Stellenbosch) Scan to open on Studocu Studocu is not sponsored or endorsed by any college or university Downloaded by Mila Mohlathe ([email protected]) lOMoARcPSD|27073014 Theory-Based Content Two Types of inferences based on test scores 1. Inferences on an individual’s underlying constructs/attributes - Are the individual’s underlying characteristics accounted for? (measurement is done) 2. Inferences on an individual’s future behaviour based on their test score Directly linked to purpose of the test Does the test predict job performance? (analysing the measurements) Inferences on Attributes (Measurement) What do the test scores provide an indication of? Are you able to compare individuals to each other? Does the test measure what it is meant to Is the test valid? (does variations in test score relate to the hypothesized outcome – smart people have a higher IQ) Evidential basis Is there a relationship with other measures of the same factor of interest? Using Inferences to make decisions Making decisions about people based on their test score Can it be applied to a situation the determines future success? (Higher IQ = greater chance of passing a degree) Is the test valid? (does variations in test score relate to the hypothesized outcome – people with high IQ pass their degree) Predictor VS Criterion 1. Predictor = the test conducted 2. Criterion = the outcome as a result of the predictors used (A criterion is a measure that could be used to determine the accuracy of a decision, slide 8) Establishing Criterion-Related Validity Definitions Criterion-related validity = Extent to which inferences (derived from predictor measures) about criterion are justified (directly linked to test accuracy) Invalid test = ineffective & unfair decisions Downloaded by Mila Mohlathe ([email protected]) lOMoARcPSD|27073014 Establishment Correlate test scores with measures of success or outcomes of decisions Analysing a Correlation Mix to Determine the Best Predictor Phase 3, pg.5 (which test/predictor has the strongest correlation with first year averages?) Calculation-Based Content Interpreting confidence intervals obtained from SEM X ji ±( Z × [ S EM ] ) , where SEM =S [ X ji ] √ [ 1−r ttX ] ; S [ X ji ] =Std.dev of X Calculating reliability: Split-half Method even-numbered items uneven-numbered items S^2 = variance Downloaded by Mila Mohlathe ([email protected]) lOMoARcPSD|27073014 Convergence & Discrimination 1. Convergence = measures of constructs that should and are related to each other 2. Discrimination = measures of constructs that shouldn’t and aren’t related to each other. Self-esteem and Locus of control SHOULD NOT converge with each other (which is shown) but with its own constructs (which is shown) – therefore, they support both convergence and discrimination = CONSTRUCTS VALIDITY Utility Analysis Method where average job performance is known Average Increase per Person=Mean Performance of SELECTED group−Mean Perform Average MONETARY increase= Average increase per person׿ appointed applicants × years Costs=¿ APPLICANTS × cost per applicant ∈selection procedure Utility=Increase−Costs Method without average job performance . Average increase per person=Std. dev of job performance × standardized score o Total MONETARY increase=std. dev × standardized score × ¿ selected applicants × years Costs=¿ APPLICANTS × cost per applicant ∈selection procedure Utility=Increase−Costs Downloaded by Mila Mohlathe ([email protected]) lOMoARcPSD|27073014 Utility in Testing 1. In reality: not given Sy ∴ Sy = 0.40 x average salary (40% rule) 2. Not given Zys ∴ Zys = Zxs(m) x rxy Z ‾ y s =Z ‾ xs × r xy SR = given ∴ match with corresponding m(Zxs) Average increase per person=Sy ×m ×r xy Total MONETARY increase=Sy × m× r xy × ¿ appointed applicants × years Costs=¿ APPLICANTS × cost per applicant ∈selection procedure Utility=Increase−Costs Utility: Brogden-Cronbach-Gleser utility formula Return on investment = (n)(t)(r)(SDy)(m) – Cost of testing Regression Equation: Y’ = bx + m b = std.dev(y)/[std.dev(x) x rxy] m = b x mean(x) x mean(y) Y’ = [(rxy)(Sy/Sx)(X – Xmean)] + Ymean Downloaded by Mila Mohlathe ([email protected]) lOMoARcPSD|27073014 Calculating confidence with Standard Error of Estimate. =S 1−r 2 Sest y√ xy Confidence interval=Y ' ±(z × Sest ) Provides upper and lower bounds Success & Failure Ratios Chart Title Success 7 TRUE POSTIVES = righ琀昀ully accepted FALSE NEGATIVE = Falsely rejected TRUE NEGATIVE = rightfully rejected I II IV III FALSE POSITIVES = falsely accepted Failure 7 Reject (PREDICTED FAILURE) Accept (PREDICTED SUCCESS) Overall Accuracy Ratio = (II + IV)/(I + II + III + IV) Success Ratio = (II)/(II + III) Calculated/given Determined with SR & r (r) - given Downloaded by Mila Mohlathe ([email protected])