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

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Presentation on theme: "Scales of Measurement n Nominal classificationlabels mutually exclusive exhaustive different in kind, not degree."— Presentation transcript:

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 Graphing data n X Axis horizontalabscissa independent variable

7 n Y Axis verticalordinate dependent variable

8 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

9 Bar Graph

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

11 Histogram

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

13 Frequency Polygon

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15 Interpreting Scores

16 Measures of Central Tendency n Mean n Median n Mode

17 Measures of Variability n Range n Standard Deviation

18 Effect of standard deviation

19 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

20 Normal Distribution

21 Terms of distributions n Kurtosis n Modal n Skewedness

22 Skewed distributions

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

24 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

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

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

27 Linear Transformations

28 Examples of linear transformations

29 Chapter 3 - assignment n Which scale is used for your measure? u Is it appropriate? Are there alternates? n How will you graph the data from your measure?

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