ppt for the normal distribution Nominal ordinal

© 2006 McGraw-Hill Higher Education. All rights reserved.
Chapter 2
Describing and Presenting a
Distribution of Scores
Chapter 2

Chapter Objectives
After completing this chapter, you should be able to
1. Define all statistical terms that are presented.
2. Describe the four scales of measurement and provide
examples of each.
3. Describe a normal distribution and four curves for
distributions that are not normal.
4. Define the terms measures of central tendency and
measures of variability.
5. Define the three measures of central tendency, identify
the symbols used to represent them, describe their
characteristics, calculate them with ungrouped and
grouped data, and state how they can be used to
interpret data.

Chapter Objectives
6. Define the three measures of variability, identify
the symbols used to represent them, describe
their characteristics, calculate them with
ungrouped and grouped data, and state how
they can be used to interpret data.
7. Define percentile and percentile rank, identify
the symbols used to represent them, calculate
them with ungrouped and grouped date, and
state how they can be used to interpret data.
8. Define standard scores, calculate z-scores, and
interpret their meanings.

Statistical Terms
• data
• variable
• population
• sample
• random sample
• parameter
• Statistic
• descriptive statistics
• inferential statistics
• discrete data
• continuous data
• ungrouped data
• grouped data

Numbers
• Numbers mean different things in different
situations. Consider three answers that
appear to be identical but are not.
• “What number were you wearing in the
race?” “5”
• What place did you finish in ?” “5”
• How many minutes did it take you to
finish?” “5”

Number Scales
• Nominal Scale
• Ordinal Scale
• Interval
• Ratio

Nominal Scale: This scale refers to
a classificatory approach, i.e.,
categorizing observations. Distinct
characteristics must exist to
categorize: gender, race essentially
you can only be assigned one
group. KEY: to distinguish one from
another.

Ordinal Scale: This scale puts order into
categories. It only ranks categories by ability,
but there is no specific quantification
between categories. It is only placement,
e.g., judging a swimming race without a
stopwatch, i.e., there is no quantitiy to
determine the difference between ranks.
KEY: placement without quantification.

Interval Scale: This scale adds equal
intervals between observed categories. We
know that 75 points is halfway between
scores of 70 and 80 points on a scale. KEY:
how much was the difference between 1st
and 2nd place?

Ratio Scale: this scale has all the
qualities of an interval scale with
the added property of a true zero.
Not all qualities can be assigned to
a ratio scale. KEY: quality of
measurement must represent a
true zero.

Normal Distribution
• Most statistical methods are based on
assumption that a distribution of scores
is normal and that the distribution can be
graphically represented by the normal
curve (bell-shaped).
• Normal distribution is theoretical and is
based on the assumption that the
distribution contains an infinite number of
scores.

Characteristics of Normal Curve
• Bell-shaped curve
• Symmetrical distribution about vertical axis
of curve
• Greatest number of scores found in middle
of curve
• All measures of central tendency at
vertical axis

mean
median
mode

Different Curves
• leptokurtic - very homogeneous group
• platykurtic - very heterogeneous group
• bimodal - two high points
• skewed - scores clustered at one end;
positive or negative

Score Rank
1. List scores in descending order.
2. Number the scores; highest score is
number 1 and last score is the number of
the total number of scores.
3. Average rank of identical scores and
assign them the same rank (may
determine the midpoint and assign that
rank).

Table 2.2 Rank of Volleyball Knowledge Test Scores
Rank Score Rank Score
1 96 16 16 88
2 95 17 88
3 93 18 88
4 4.5 92 19 87
5 92 20 20 87
6 6.5 91 21 87
7 91 22 22.5 86
8 90 23 86
9 9 90 24 24.5 85
10 90 25 85
11 89 26 26.5 84
12 12 89 27 84
13 89 28 83
14 88 29 82
15 88 30 81

Measures of Central Tendency
• descriptive statistics
• describe the middle characteristics of
the data (distribution of scores); represent
scores in a distribution around which other
scores seem to center
• most widely used statistics
• mean, median, and mode

Mean
The arithmetic average of a distribution of scores; most
generally used measure of central tendency.
Characteristics
• Most sensitive of all measures of central tendency
• Most appropriate measure of central tendency to use for
ratio data (may be used on interval data)
• Considers all information about the data and is used to
perform other statistical calculations
• Influenced by extreme scores, especially if the
distribution is small

Symbols Used to Calculate Mean
X = the mean (called X-bar)
 = (Greek letter sigma) = “the sum of”
X = individual score
N = the total number of scores in distribution
Mean Formula X = X
N
Table 2.3: X = 2644 = 88.1
30

Median
Score that represents the exact middle of the
distribution; the fiftieth percentile; the score that
50% of the scores are above and 50% of the
scores are below.
Characteristics
• Not affected by extreme scores.
• A measure of position.
• Not used for additional statistical calculations.
• Represented by Mdn or P50.

Steps in Calculation of Median
1. Arrange the scores in ascending order.
2. Multiple N by .50.
3. If the number of scores is odd, P50 is the
middle score of the distribution.
4. If the number of scores is even, P50 is the
arithmetic average of the two middle scores of
the distribution.
Table 2.3: .50(30) = 15
Fifteenth and sixteenth scores are 88
P50 = 88

Mode
Score that occurs most frequently; may have
more than one mode.
Characteristics
Least used measure of central tendency.
Not used for additional statistics.
Not affected by extreme scores.
Table 2.3: Mode = 88

Which Measure of Central Tendency is
Best for Interpretation of Test Results?
• Mean, median, and mode are the same for a
normal distribution, but often will not have a
normal curve.
• The farther away from the mean and median the
mode is, the less normal the distribution.
• The mean and median are both useful
measures.
• In most testing, the mean is the most reliable
and useful measure of central tendency; it is
also used in many other statistical procedures.

Measures of Variability
• To provide a more meaningful interpretation of
data, you need to know how the scores spread.
• Variability - the spread, or scatter, of scores;
terms dispersion and deviation often used
• With the measures of variability, you can
determine the amount that the scores spread, or
deviate, from the measures of central tendency.
• Descriptive statistics; reported with measures of
central tendency

Range
Determined by subtracting the lowest score from
the highest score; represents on the extreme
scores.
Characteristics
1. Dependent on the two extreme scores.
2. Least useful measure of variability.
Formula: R = Hx - Lx
Table 2.3: R = 96 - 81 = 15

Quartile Deviation
Sometimes called semiquartile range; is the spread of
middle 50% of the scores around the median. Extreme
scores will not affect the quartile deviation.
Characteristics
1. Uses the 75th and 25th percentiles; difference between
these two percentiles is referred to as the interquartile
range.
2. Indicates the amount that needs to be added to, and
subtracted from, the median to include the middle
50% of the scores.
3. Usually not used in additional statistical calculations.

Quartile Deviation
Symbols
Q = quartile deviation
Q1 = 25th percentile or first quartile (P25) =
score in which 25% of scores are below
and 75% of scores are above
Q3 = 75th percentile or third quartile (P75) =
score in which 75% of scores are below
and 25% of scores are above

Steps for Calculation of Q3
1. Arrange scores in ascending order.
2. Multiply N by .75 to find 75% of the distribution.
3. Count up from the bottom score to the number
determined in step 2. Approximation and interpolation
may be required.
Steps for Calculation of Q1
1. Multiply N by .25 to find 25% of the distribution.
2. Count up from the bottom score to the number
determined in step 1.
To Calculate Q
Substitute values in formula: Q = Q3 - Q1
2

Quartiles
Q1 = 25%
Q2 = 50%
Q3 = 75%
Q4 = 100%
Q2 - Q1 = range of scores below median
Q3 - Q2 = range of scores above median

Table 2.3:
1. .75(30) = 22.5; twenty-second score = 90; twenty-third
score = 90; midway between two scores would be
same score
75% = 90
2. .25(30) = 7.5; seventh score = 85; eight score = 86;
midway between two scores = 85.5
3. Q = 90 - 85.5 = 4.5 = 2.25
2 2
Table 2.3:
88 + 2.25 = 90.25
88 - 2.25 = 85.75
Theoretically, middle 50% of scores fall between the
scores of 85.75 and 90.25.

Standard Deviation
• Most useful and sophisticated measure of variability.
• Describes the scatter of scores around the mean.
• Is a more stable measure of variability than the range or
quartile deviation because it depends on the weight of
each score in the distribution.
• Lowercase Greek letter sigma is used to indicate the
the standard deviation of a population; letter s is used to
indicate the standard deviation of a sample.
• Since you generally will be working with small samples,
the formula for determining the standard deviation will
include (N - 1) rather than N.

Characteristics of Standard
Deviation
1. Is the square root of the variance, which is the average
of the squared deviations from the mean. Population
variance is represented as F2 and the sample variance
is represented as s2.
2. Is applicable to interval and ratio data, includes all
scores, and is the most reliable measure of variability.
3. Is used with the mean. In a normal distribution, one
standard deviation added to the mean and one standard
deviation subtracted from the mean includes the middle
68.26% of the scores.

Characteristics of Standard Deviation
4. With most data, a relatively small standard deviation
indicates that the group being tested has little
variability (performed homogeneously). A relatively
large standard deviation indicates the group has much
variability (performed heterogeneously).
5. Is used to perform other statistical calculations.
Symbols used to determine the standard deviation:
s = standard deviation X = individual score
X = mean N = number of scores
= sum of
d = deviation score (X - X)

Calculation of Standard Deviation with X2
1. Arrange scores into a series.
2. Find X2.
3. Square each of the scores and add to determine the X2.
4. Insert the values into the formula
NX2 - (X)2
s = N(N- 1)
Table 2.3:
X = 2644 N = 30
X2 = 233,398 s = 3.6

Calculation of Standard Deviation with
d2
1. Arrange the scores into a series.
2. Calculate X.
3. Determine d and d2 for each score; calculate
d2.
4. Insert the values into the formula
d2
s = N - 1
Table 2.4:
X = 88.1 s = 3.6
d2 = 373.5
N = 30

Interpretation of Standard Deviation
in Tables 2.3 and 2.4
S = 3.6
X = 88.1
88.1 + 3.6 = 91.7
88.1 - 3.6 = 84.5
In a normal distribution, 68.26% of the scores would
fall between 84.5 and 91.7.

Relationship of Standard Deviation and
Normal Curve
Based on the probability of a normal distribution, there is
an exact relationship between the standard deviation and
the proportion of area and scores under the curve.
1. 68.26% of the scores will fall between +1.0 and -1.0
standard deviations.
2. 95.44% of the scores will fall between +2.00 and
-2.00 standard deviations.
3. 99.73% of the scores will fall between +3.0 and -3.00
standard deviations.
4. Generally, scores will not exceed +3.0 and -3.0
standard deviations from the mean.

Figure 2.4 Characteristics of normal curve.

60-sec Sit-up Test to Two
Fitness Classes
Class 1 Class 2
X = 32 X = 28
s = 2 s = 4
Figure 2.5 compares the spread of the two distributions.
Individual A in Class 1 completed 34 sit-ups and individual
B completed 34 sit-ups in Class 2. Both individuals have
the same score, but do not have the same relationship to
their respective means and standard deviations. Figure 2.6
compares the individual performances.

Calculation of Percentile Rank through Use of
Mean and Standard Deviation.
1. Calculate the deviation of the score from the mean.
d = (X - X)
2. Calculate the number of standard deviation units the
score is from the mean (z-scores).
No. of standard deviation units from the mean = d
s
3. Use table 2.5 to determine where the percentile rank
of the score is on the curve. If negative value found in
step 1, the percentile rank will always be less than 50.

Which Measure of Variability is Best for
Interpretation of Test Results?
1. Range is the least desirable.
2. The quartile deviation is more meaningful than the
range, but it considers only the middle 50% of
the scores.
3. The standard deviation considers every score, is the
most reliable, and is the most commonly used
measure of variability.

Percentiles and Percentile Ranks
Percentile - a point in a distribution of scores below
which a given percentage of scores fall.
Examples - 60th percentile and 40 percentile
Percentile rank - percentage of the total scores that
fall below a given score in a distribution; determined
by beginning with the raw scores and calculating the
percentile ranks for the scores.

Weakness of Percentiles
1. The relative distance between percentile scores are the
same, but the relative distances between the observed scores
are not.
2. Since percentile scores are based on the number of scores in a
distribution rather than the size of the score obtained, it is
sometimes more difficult to increase a percentile score at the
ends of the scale than in in the middle.
3. Average performers (in middle of distribution) need only a
small change in their raw scores to produce a large change in
their percentile scores.
4. Below average and above average performers (at ends of
distribution) need a large change in their raw scores to
produce even a small change in their percentile scores.

Analysis of Grouped Data
Frequency distribution – method for arranging the data in a
More convenient form.
Simple frequency distribution – all scores are listed in
Descending order and the number of times each individual
Scores occurs is indicated in a frequency column.
Table 2.6 shows a simple frequency distribution.
Sometimes more convenient to represent scores in a grouped
frequency distribution.

Tennis Serve Test Scores
88 83 75 81 56 82 86 62 87 79 93 58 61 61 75
73 94 48 79 72 81 85 52 73 62 80 73 84 63 61
67 63 75 73 67 72 73 72 77 73 85 82 70 57 58
54 79 68 54 70 77 81 68 83 65 77 90 52 75 62
84 69 56 68 69 63 70 91 70 80 65 70 88 72 63

Steps to Construct Frequency
Distribution
Step 1. Determine the range.
The highest score minus lowest score.
94 – 48 = 46
Step 2. Determine the number of class intervals.
Depends on the number of scores, the range of the
scores, and the purpose of organizing the frequency
table.
Generally it is best to have between 10 and 20 intervals.

Steps to Construct Frequency Distribution
Determine the size of the class interval (i).
Estimate if i can be found by dividing the range of scores
by the number of intervals wanted.
Example: if range of scores for a distribution = 54
54 = 3.6
15
Easier to work with whole numbers, so choice of 3 or 4 as
step size.

Distribution
When i smaller than 10, generally best to use numbers
2, 3, 5, 7, and 9. May use even numbers, but midpoint of
of odd numbers will be whole number.
May also determine number of class intervals by dividing
the range by estimate of appropriate i.
Example: 54 = 18 or 54 = 11
3 5

Distribution
Class intervals for tennis serve test:
46 = 3.06
15
With i = 3, there will be 16 intervals
See table 2.7.

Distribution
Step 3. Determine the limits of the bottom class interval.
Usually begin bottom interval with a number that is
multiple of the interval size. May begin interval
with lowest score or make the lowest score the
midpoint of the interval.
Step 4. Construct the table.
Remaining intervals are formed by increasing each
interval by the size of i.
Note difference in the “apparent limits” and “real limits” of
the intervals.

Distribution
Step 5. Tally the scores.
Step 6. Record the tallies under the column headed f and
sum the frequencies (f = N)

Measures of Central Tendency
Note other columns in table 2.7. These columns are used to
calculate the measures of central tendency and variability.
With the exception of the mode, the definitions,
characteristics, and uses for these measures are the same.

The Mode
Midpoint of the interval with the largest number of
frequencies.
Calculated by adding ½ of i to the real lower limit (LL) of
interval.
Mode in table 2.7 is
Mo = LL of interval + ½ (i)
= 71.5 + ½(3)
= 71.5 +1.5
Mo = 73

The Mean
AM = assumed mean; midpoint of interval you assume
the mean to be
fd = sum of f x d
Steps for calculation:
1. Label a column d. Place a 0 in the interval in which
you assume the mean is located.
2. Indicate the deviation of each interval from the
assumed mean by numbering consecutively above and
below the interval of the assumed mean.

The Mean
3. Label a column fd. Multiply f times d for each interval.
4. Calculate fd. Be aware that you are summing
positive and negative numbers.
5. Substitute the values in the formula
X = AM + i fd
N

The Mean
The calculation of the mean from the distribution in table
2.7 is
X = 73 + 3 -21
75
= 73 + 3(-.28)
= 73 - .84
X = 72.16

The Median
Symbols
LL = the real lower limit of interval containing the percentile
of interest
% = the percentile you wish to determine
cfb = the cumulative frequency in the interval below the
interval of interest
fw = the frequency of scores in the interval of interest

The Median
1. Label a column cf and determine the cumulative
frequency for each interval.
2. Multiply .50(N) and determine in which interval P50 is
located.
3. Identify cfb and fw.
P50 = LL + i %(N) – cfb
fw

The Median
The calculation of the median from the distribution in
table 2.7 is
.50(75) = 37.5
P50 = 71.5 + 3 37.5 – 34
10
= 71.5 + 3 3.5
10
P50 =71.5 + 3(.35) = 72.55

Measures of Variability
Calculation of the range was described previously.
Quartile deviation and standard deviation will be covered
now.

The Quartile Deviation
The calculation of Q from the distribution in table 2.7 is
Q3 Q1
.75(75) = 56.25 .25(75) = 18.75
Q3 = 80.5 + 3 56.25 – 56 Q1 = 62.5 + 3 18.75 – 16
7 6
= 80.5 + 3 .25 = 62.5 + 3 2.75
7 6
= 80.5 + .11 = 62.5 + 1.37
Q3 = 80.61 Q1 = 63.87

The Quartile Deviation
Q = Q3 – Q1
2
= 80.61 – 63.87
2
= 16.74
2
Q = 8.37

The Standard Deviation
New symbol
fd2 = sum of d x fd
1. Label a column fd2 and determine fd2 for each interval.
2. Calculate fd2.
s = i fd2 – fd 2
N N

The Standard Deviation
The calculation of s in the distribution in table 2.7 is
s = 3 973 - - 21 2
75 75
= 3 12.9733 – (.28)2
= 3 12.9733 - .0784
= 3 12.8949
s = 3 (3.59) = 10.77

Graphs
1. Enable individuals to interpret data without reading
raw data or tables.
2. Different types of graphs are used.
Examples - histogram (column), frequency polygon (line),
pie chart, area, scatter, and pyramid
3. Standard guidelines should be used when constructing
graphs.
See figures 2.7 and 2.8.

Standard Scores
Provide method for comparing unlike scores; can obtain
an average score, or total score for unlike scores.
z-score - represents the number of standard deviations a
raw score deviated from the mean
FORMULA
z = X - X
s

z = X - X
s
z = 88 - 72.2 = 15.8 z = 54 - 72.2 = -18.2
10.8 10.8 10.8 10.8
z = 1.46 z = -1.69
INTERPRETATION?
Table 2.7- Tennis Serve Scores
Scores of 88 and 54; X = 72.2; s = 10.8
z-Scores

z-Scores
• The z-scale has a mean of 0 and a standard
deviation of 1.
• Normally extends from –3 to +3 standard
deviations.
• All standard scored are based on the z-score.
• Since z-scores are expressed in small, involve
decimals, and may be positive or negative, many
testers do not use them.
Table 2.5 shows relationship of standard deviation
units and percentile rank.

T-Scores
T-scale
• Has a mean of 50.
• Has a standard deviation of 10.
• May extend from 0 to 100.
• Unlikely that any t-score will be beyond 20 or 80
(this range includes plus and minus 3 standard
deviations).
Formula
T-score = 50 + 10 (X - X) = 50 + 10z
s
Figure 2.9 shows the relationship of z-scores,
T-scores, and the normal curve.

Figure 2.9 z-scores and T-scores plotted on normal curve.

T-Scores
Table 2.7 - Tennis Serve Scores
Scores of 88 and 54; X = 72.2; s = 10.8
T88 = 50 + 10(1.46) T54 = 50 + 10 (-1.69)
= 50 + 14.6 = 50 + (-16.9)
= 64.6 = 65 = 33.1 = 33
(T-scores are reported as whole numbers)

T-Scores
T-scores may be used in same way as z-scores, but
usually preferred because:
• Only positive whole numbers are reported.
• Range from 0 to 100.
Sometime confusing because 60 or above is good
score.

T-Scores
May convert raw scores in a
distribution to T-scores
1. Number a column of T-scores from 20 to 80.
2. Place the mean of the distribution of the scores
opposite the T-score of 50.
3. Divide the standard deviation of the distribution by
ten. The standard deviation for the T-scale is 10, so
each T-score from 0 to 100 is one-tenth of the
standard deviation.

T-Scores
4. Add the value found in step 3 to the mean and each
subsequent number until you reach the T-score of 80.
5. Subtract the value found in step 3 from the mean and
each decreasing number until you reach the number 20.
6. Round off the scores to the nearest whole number.
*For some scores, lower scores are better (timed events).

Percentiles
• Are standard scores and may be used to compare scores
of different measurements.
• Change at different rates (remember comparison of low
and and high percentile scores with middle percentiles),
so they should not be used to determine one score for
several different tests.
• May prefer to use T-scale when converting raw scores
to standard scores.

ppt for the normal distribution Nominal ordinal

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