1. State vs Trait Constructs

2. Project question 4 n Does your test measure a state or a trait?

3. Criterion vs Norm referenced n Criterion reference = compares to established standard, well defined objectives n Norm referenced = compares each score to other scores, relative

4. Norms n Types of norms?????

5. Project question 5 n What sort of norms would be appropriate to collect to standardize your measure? n Why did you select those norms?

6. Sampling n Random n Stratified n Purposive n Incidental/convenience

7. Correlations n NOT causal n relationship between variables n predictive

8. Scatterplot

9. Positive Correlation

10. Negative Correlation

11. No correlation

12. Correlation values -1 to +1.56, -.45, -.09,.89, -.93

14. Appropriate Correlations 1 - data must be linear not curvilinear (determine by scatterplot)

15. Curvilinear

16. Appropriate Correlation to use 1 – linear data 2 - type of scale interval (or ratio) = Pearson r ordinal = Spearman rho 3 - number of subjects more than 30 = Pearson fewer than 30 = Spearman

17. Decision Tree Linear No = no corryes = corr Scale Scale ordinal = rho interval = r number number 30 = r 30 = r

18. Project question #6 n Which correlation formula would you use when correlating the scores from your measure with another variable? n Why would you use that formula?

19. Multiple correlations n Correlations between more than one variable done at the same time.

20. Multiple regression n Relationship between more variables n Uses specific predictor and criterion variables n Looks at relationships between predictors n Can factor out partial relationships

21. Multiple regression - example n Grad school grade performance = criterion (or outcome) n Predictor variables = undergrad GPA = undergrad GPA = GRE scores = GRE scores = Quality of statement of purpose = Quality of statement of purpose

22. Multiple regression data PredictorBeta (=r) significance (p) GPA.80.01 GRE.55.05 statement.20.20

23. Multiple regression – example 2 n Predictor variables = Metacognition, Locus of Control, Learning Style n Criterion variable = academic performance (grade)

24. Multiple regression data PredictorBeta (=r) significance (p) Meta..75.01 LofC.65.05 L.S..32.15