1. Measures of Dispersion
Variance and Standard Deviation

2. Basic Assumptions about Distributions
We should be able to plot the number of times a specific value occurs on a graph using a line chart or histogram (interval/ratio data)
Some distributions will be normal or bell-shaped.
Some distributions will be bi-modal or will have data points distributed irregularly.
Some distributions will be skewed to the right or skewed to the left.
Theoretically, samples taken from one population, should over time, approximate a normal distribution.
We should have a normal distribution if we are to use inferential statistics.

3. Other reasons to use Measures of Dispersion
To see if variables taken from two or more samples are similar to one another.
To see if a variable taken from a sample is similar to the same variable taken from a population – in other words is our sample representative of people in the population at least on that one variable.

4. Variation in Two Samples1 2 3 4 5 7 9 6 10
Mo = 4
Mo, 3, 9
Mdn = 4
Mdn = 6
Mean = 4
Mean = 6

6. Sample 2

7. Normal Distributions are Bell-shaped and have the same number of measures on either side of the mean. Note: According to Montcalm & Royse only unimodal distributions can be normal distributions.

9. Normal Distributions 50% of all scores are on either side of the mean.
The distribution is symmetrical – same number of scores fall above and below the mean.
The mean is the midpoint of the distribution.
Mean = median = mode
The entire area under the bell-shaped curve = 100%.

10. A standard deviation is:
The degree to which each of the scores in a distribution vary from the mean. (x – mean)
Calculated by squaring the deviation of each score from the mean.
Based on first calculating a statistic called the variance.

11. Formulas are:
Variance = Sum of each deviation squared divided by (n -1) where n is the number of values in the distribution.
Standard Deviation = the square root of the sum of squares divided by (n – 1).

12. Using Sample 1 as an example
(1-4) = -3 9
 Mean = 4 2
(2-4) = -2 4 3
(3-4) = -1
(4-4) = 0
Variance
S.D.
28/(8-1)
Sq Root 4 5
(5-4) = 1 6
(6 - 4) = 2 7
(7 = 4) = 3
Total 28

13. Another variance/SD example1
-5.00
25.00
 Mean = 6 2
-4.00
16.00 4
-2.00
4.00 8
2.00 10
Variance = 90/(6-1)
SD = sq root of 18 11
5.00
18.00
4.24
Total
0.00
90.00

14. Other Important Terms in This Chapter
Mean squares – the average of squared deviations from the mean in a set of numbers. (Same as variance)
Interquartile range – points in a set of numbers that occur between 75% of the scores and 25% of the scores – that is, where the middle 50% of all scores lie (use cumulative percentages)
Box plot – gives graphic information about minimum, maximum, and quartile scores in a distribution.

15. Box Plot
Line in middle is median. Upper line is maximum; lower line is minimum. Red box is the interquartile range.

16. Interquartile Range Test Scores Frequency Percent Cumulative 100 3 25%
100% 90
75% 80
50% 70 1
8.3% 60 2
16.7%
Total 12
100.0%

17. This information is important to our discussion of normal distributions

18. Central Limit Theorem (we will discuss this in two weeks) specifies that:
50% of all scores in a normal distribution are on either side of the mean.
68.25% of all scores are one standard deviation from the mean.
95.44% of all scores are two standard deviations from the mean.
99.74% of all scores in a normal distribution are within 3 standard deviations of the mean.

19. Therefore, we will be able to
Predict what scores are contained within one, two, or three standard deviations from the mean in a normal distribution.
Compare the distribution of scores in samples.
Compare the distribution of scores from populations to samples.

20. To calculate measures of central tendency and dispersion in SPSS
Select descriptive statistics
Select descriptives
Select your variables
Select options (mean, sd, etc.)

21. SPSS output