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State vs Trait Constructs. Criterion vs Norm referenced n Criterion reference = compares to established standard, well defined objectives n Norm referenced.

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Presentation on theme: "State vs Trait Constructs. Criterion vs Norm referenced n Criterion reference = compares to established standard, well defined objectives n Norm referenced."— Presentation transcript:

1 State vs Trait Constructs

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

3 Norms n Types of norms?????

4 Sampling n Random n Stratified n Purposive n Incidental/convenience

5 Correlations n NOT causal n relationship between variables n predictive

6 Scatterplot

7 Positive Correlation

8 Negative Correlation

9 No correlation

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

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

12 Curvilinear

13 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

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

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

16 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

17 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

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

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

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

21 Meta-analysis

22 Project questions n 4 - Does your test measure a state or a trait? u Explain. n 5 - What sort of norms would be appropriate to collect to standardize your measure? u Why did you select those norms? n 6 - Which correlation formula would you use when correlating the scores from your measure with another variable? u Why?

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