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Lecture

MEASUREMENT PSYCH 339

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Department
Psychology
Course
PSYCH 339
Professor
Richard Ennis
Semester
Summer

Description
MEASUREMENT "The whole of science is nothing more than the refinement of everyday thinking.“ Albert Einstein *Consensus – “they say UW students are the best in Canada” – everybody agrees with that *Authority – “ I’ve been doing this job for 40 years and I know that UW students are the best in Canada” *Introspection (Rationalization/Intuition) – My own hypothesis, my own thinking – “I want someone from Laurier, forget someone from UW” Measurement (Test) Theory *Measurement consists of quantifying the thing being measured and quantifying something other than the thing being measured *Observation = True score + error (X = T + e) *There are two kinds of error in measurement Good Error *Random error (re) *X = T + re *Variance in scores caused by random fluctuations, deviations, or “noise” (caused by little inconsistencies in the measurement) *Errors that are higher & lower (more & less) than True Score *Why is it a good error? It is random if we take enough measures, those random chance might reach zero and if it reaches zero, it will result to a true score (if there are enough HR specialists you will reach zero) – as longa s there’s continuous data *Good error because: *It is measurable, quantifiable (error variance) *It is something to be seen easily *As sample size increases, random error approaches zero *Higher and lower deviations cancel each other *The combination of random error and enough HR staff, you will get the perfect or the right measure Bad Error *Systematic bias (sb) *X = T + sb + re *Variance in scores caused by a systematic, constant error in all scores *All scores are either inflated or depressed in the same direction *Errors that are consistently higher or lower (more or less) than True Score Bad Error *Bad error because: *It is NOT measurable, quantifiable *As sample size increases, systematic error does NOT decrease *Deviations are all in the same direction *No matter how many HRs are asked to do the measurement, there will be no difference on the deviations *Disguised as True Score (e.g. you may say that it’s included in andrew’s height but not) X = (T + sb) + re x X = (Tx+ T x + re Discrimination Good Discrimination * Selection based on Bona-fide Occupational Requirements (BFOR’s) *Basing the discrimination that is related to the job *E.g. I’ll choose the other one because she met the requirements more than the other one Bad Discrimination * Selection bas
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