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PSY 420 Chapters 8 & 9 Book Notes

by: Samantha Bishop

PSY 420 Chapters 8 & 9 Book Notes PSY420

Marketplace > Lenoir-Rhyne University > Psychology > PSY420 > PSY 420 Chapters 8 9 Book Notes
Samantha Bishop

GPA 3.81

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About this Document

Covers Chapters 8 and 9 in the textbook: Validity
Psychological Assessment
Dr. Gordon Cappelletty
Class Notes
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This 5 page Class Notes was uploaded by Samantha Bishop on Tuesday September 27, 2016. The Class Notes belongs to PSY420 at Lenoir-Rhyne University taught by Dr. Gordon Cappelletty in Fall 2016. Since its upload, it has received 12 views. For similar materials see Psychological Assessment in Psychology at Lenoir-Rhyne University.


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Date Created: 09/27/16
PSY 420  Chapters 8 & 9 Book Notes Chapter 8: How Do We Gather Evidence of Validity Based on the Test­Criterion  Relationship? (p.209­236) Overview  Criterion: the measure of performance that we correlate with test scores  Criterion­related validity: when a test shows evidence of validity based on the  relationship between two variables Examples: SAT, GRE, GPA Methods for Providing Evidence of Validity p.211­217   The Predictive Method o Relationship between test scores and a future behavior Steps: 1. Large group of people take the test (the predictor) 2. Scores are held for a time interval 3. Researchers collect a measure of some behavior on the same people  (the criterion) 4. Correlate test scores with criterion scores 5. If there is a strong relationship then the test has demonstrated a  predictive evidence of validity o Validity coefficient: a statistic used to infer the strength of the evidence of validity  that the test scores might demonstrate in predicting job performance o Restriction of range: reduction in the range of scores that results when some  people are dropped from a validity study  The Concurrent Method o Test administration and criterion measurement happen at approximately the  same time  o Administering two measures, the test and a second measure of the attribute, to  the same group of people at as close to the same point in time as possible o Selecting a Criterion p.217­218  Objective criterion­ one that is observable and measureable  Subjective criterion­ based on a person’s judgment Does the Criterion Measure What it is supposed to Measure? p.218­220  Criterion contamination­ if the criterion measures more dimensions than those measured by the test Calculating and Evaluating Validity Coefficients p.220­223  Test of significance­ the process of determining what the probability is that a study would have yielded the observed results simply by chance  Coefficient of determination­ amount of shared variance by squaring the validity  r 2 coefficient to obtain    2 Ex: correlation(r) = 0.30 coefficient of determination ( r ) = 0.09  = 9% of variance in common  *larger validity coefficients represent stronger relationship* Using Validity Information to Make Predictions p.223­232  Linear Regression y =a+bx   y’= predicted score on the criterion a= intercept b= slope x= score the individual made on the predictor test  Slope or Weight (b) : expected change in y for every change in x  S y b=r S x r= correlation coefficient  x=¿  standard deviation of x S ¿ y=¿ S ¿  Standard deviation of y   Intercept: a place where the regression line crosses the y­axis a= ´ ­ b x   y  = mean of the distribution of y b= the slope x  = mean of the distribution of x  Multiple Regression: used when we have more than one set of test scores used for  predicting a set of criterion ' y =a+b x1+1 x 2b 2 3 3 … y’= predicted score on the criterion a= intercept b= slope x= predictor  Coefficient of Multiple Determination  o Through multiple regression analysis o Interpreted as the total proportion of variance that is accounted for by all the  predictors Ethical Issues p.233­234  Accessibility: the opportunity test takers have to demonstrate their standing on the  constructs the test is designed to measure  Universal Design: tests should be constructed from the outset in such a way that  accessibility is maximized for all individuals who may take the test in the future Chapter 9: How Do We Gather Evidence of Validity Based on a Test’s Relation to  Constructs? (p.237­264) Overview  Construct: attribute, trait, or characteristic that is not directly observable but can only be  inferred by looking at observable behaviors  Construct validity: based on sound psychological theory and measures what it is  supposed to measure What is a Construct?  Behaviors­ actions that are observable and measurable, concrete constructs  Construct Explication: 1. Identify the behaviors that relate to the construct 2. Identify other constructs that may be related to the construct being explained 3. Identify behaviors related to similar constructs and determine whether these  behaviors are related to the original construct  State­ temporary condition brought on by situational circumstances  Trait­ long­standing individual quality that has become an enduring part of a person’s  personality  Nomological network­ a method for defining a construct by illustrating its relation to as  many other constructs and behaviors as possible  Hypotheses­ educated guesses or predictions Gathering Evidence of Construct Validity p.243­250  Gather theoretical evidence o Establish nomological network o Propose experimental hypotheses  Gather psychometric evidence o Reliability o Convergent evidence o Discriminant evidence o Experimental interventions o Evidence based on content o Evidence based on relationship with criteria Factor Analysis p.250­262  Factors­ the underlying concepts or constructs that the tests or groups of test questions  are measuring  Factor analysis­ identifies whether the pattern of correlations among all the questions on  a test can be more simply explained by a smaller number of underlying constructs  Exploratory factor analysis­ researchers do not propose a formal hypothesis about the  factors that underlie a set of test scores, but instead use the procedure broadly to help  identify underlying components  Confirmatory factor analysis­ the researchers specify in advance what he believes the  factor structure of his data should look like and then statistically tests how well that  model actually fits the data 


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