1. Scales of Measurement n Nominal classificationlabels mutually exclusive exhaustive different in kind, not degree

2. Scales of Measurement n Ordinal rank ordering numbers reflect “greater than” only intraindividual hierarchies NOT interindividual comparisons

3. Scales of Measurement n Interval equal units on scale scale is arbitrary no 0 point meaningful differences between scores

4. Scales of Measurement n Ratio true 0 can be determined

5. Contributions of each scale n Nominal u creates the group n Ordinal u creates rank (place) in group n Interval u relative place in group n Ratio u comparative relationship

6. Project Question 2 n Which scale is used for your measure? u Is it appropriate? u Are there alternate ways (scales) that could be used for your measure? If so how?

7. Graphing data n X Axis horizontalabscissa independent variable

8. n Y Axis verticalordinate dependent variable

9. Types of Graphs n Bar graph qualitative or quantitative data nominal or ordinal scales categories on x axis, frequencies on y discrete variables not continuous not joined

10. Bar Graph

11. Types of Graphs n Histogram quantitative data continuous (interval or ratio) scales

12. Histogram

13. Types of Graphs n Frequency polygon quantitative data continuous scales based on histogram data use midpoint of range for interval lines joined

14. Frequency Polygon

16. Project Question 3 n What sort of graphs would you use to display the data from your measure? n Why would you use that one?

17. Interpreting Scores

18. Measures of Central Tendency n Mean n Median n Mode

19. Measures of Variability n Range n Standard Deviation

20. Assumptions of Normal Distribution (Gaussian) n The underlying variable is continuous n The range of values is unbounded n The distribution is symmetrical n The distribution is unimodal n May be defined entirely by the mean and standard deviation

21. Normal Distribution

22. Effect of standard deviation

23. Terms of distributions n Kurtosis n Modal n Skewedness

24. Skewed distributions

25. Linear transformations n Expresses raw score in different units n takes into account more information n allows comparisons between tests

26. Linear transformations n Standard Deviations + or - 1 to 3 n z score 0 = mean, - 1 sd = -1 z, 1 sd = 1 z n T scores u removes negatives u removes fractions u 0 z = 50 T

27. Example T = (z x 10) + 50 If z = 1.3 T = (1.3 x 10) +50 = 63

28. Example T = (z x 10) + 50 If z = -1.9 T = (-1.9 x 10) +50 = 31

29. Linear Transformations

30. Examples of linear transformations