distributions. what is a “distribution”? one distribution for a continuous variable. each youth...
TRANSCRIPT
DISTRIBUTIONS
What is a “distribution”?
One distribution for a continuous variable. Each youth homicide is a case. There is one variable: the number of youth homicide victims each month.
Two distributions, each for a single continuous variable: violent crimes and commitments to prison.
Each violent crime is a case. The variable is their number each year per 100,000 population
Each commitment to prison is a case. The variable is the number of commitments each year per 100,000 population
One distribution for TWO categorical variables:
Youth’s demeanor (two categories)
Officer disposition (four categories)
Each police encounter with a youth is a case.
An arrangement of cases in a sample or population according to their values or scores on one or more variables
(A case is a single unit that “contains” all the variables of interest)
Distributions can be depicted visually. How that is done depends on how many variables and their type (whether categorical or continuous).
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DEPICTING THE DISTRIBUTION OF CATEGORICAL VARIABLES
Depicting distribution of a categorical variable: the bar graphDistributions depict the
frequency (number of cases) at each value of a variable. Here there is one variable with two values: gender (M/F).
A case is a single unit that “contains” all the variables of interest.Here each student is a case
Frequency means the number of cases – students – at a single value of a variable. Frequencies are always on the Y axis
Values of the variable are always on the X axis
Distributions illustrate how cases cluster or spread out according to the value or score of the variable. Herethe proportions of men and women seem about equal.
n=15
n=17
Y -
axis
X - axis
Value or score of variable
How
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Bars are “made up of” cases. Here that means
students, arranged by the variable gender
N = 32
Using a table to display the distribution oftwo categorical variables
Val
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Number of cases at each value/score
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“cells”
Value or score of variable
DEPICTING THE DISTRIBUTION OF CONTINUOUS VARIABLES
Depicting the distribution of continuous variables: the histogram
X - axis
Value or score of variable
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Distributions depict the frequency (number of cases) at each value of a variable. Here there is one variable: age, measured on a scale of 20-33.
A case is a single unit that “contains” all the variables of interest.Here each student is a case
Frequency means the number of cases – students – at a single value of a variable. Frequencies are always on the Y axis
Values of the variable are always on the X axis
What is the area under the trend line “made up of”? Cases, meaning students (arranged by age)
Trend line
Y -
axis
Y -
axis
X - axis
Value or score of variable
How
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Sometimes, bar graphs are used forcontinuous variables
What are the bars “made of”? Cases, meaning homicides (arranged by the variable homicides per year)
Continuous variables: What “makes up”the areas under the trend lines?
Each violent crime is one “case”Variable: # crimes per 100,000 population each year
Each commitment to prison is one “case”Variable: # commitments to prison, per 100,000 population, each year
Value or score of variable
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Value or score of variable
How
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Trend line
Trend line
Trend line
Cases, that’s what!Each murdered youth is one “case”Variable: # youths murdered each month
CATEGORICAL VARIABLESSummarizing the distribution of
Summarizing the distribution of categorical variables using percentage
• Instead of using graphs or a lot of words, is there a single statistic that can convey what a distribution “looks like”?
• Percentage is a “statistic.” It’s a proportion with a denominator of 100.
• Percentages are used to summarize categorical data
– 70 percent of students are employed; 60 percent of parolees recidivate
• Since per cent means per 100, any decimal can be converted to a percentage by multiplying it by 100 (moving the decimal point two places to the right)
– .20 = .20 X 100 = 20 percent (twenty per hundred)
– .368 = .368 X 100 = 36.8 percent (thirty-six point eight per hundred)
• When converting, remember that there can be fractions of one percent
– .0020 = .0020 X 100 = .20 percent (two tenths of one percent)
• To obtain a percentage for a category, divide the number of cases in the category by the total number of cases in the sample
50,000 persons were asked whether crime is a serious problem: 32,700 said “yes.” What percentage said “yes”?
Using percentages tocompare datasets
• Percentages are “normalized” numbers (e.g., per 100), so they can be used tocompare datasets of different size
– Last year, 10,000 people were polled. Eight-thousand said crime is a seriousproblem
– This year 12,000 people were polled. Nine-thousand said crime is aserious problem.
Calculate the second percentage and compare it to the first
Class 1 Class 2
Draw two bar graphs, one for each class, depicting proportions for gender
Practical exercise
Calculating increases in percentage
2 times 3 timeslarger (2X) larger (3X)
200% 100% Original larger larger
Increases in percentage are computed off the base amount
Example: Jail with 120 prisoners. How many prisoners will there be...
…with a 100 percent increase?– 100 percent of the base amount, 120, is 120
(120 X 100/100)– 120 base + 120 increase = 240
(2 times the base amount) …with a 150 percent increase?
– 150 percent of 120 is 180 (120 X 150/100)– 120 base plus 180 increase = 300
(2½ times the base amount)
How many will there be with a 200 percent increase?
Percentage changes can mislead• Answer to preceding slide – prison with 120 prisoners
200 percent increase
200 percent of 120 is 240 (120 X 200/100)
120 base plus 240 = 360 (3 times the base amount)
• Percentages can make changes seem large when bases are small
Example: Increase from 1 to 3 convictions is 200 (two-hundred) percent
3-1 = 2
2/base = 2/1 = 2
2 X 100 = 200%
• Percentages can make changes seem small when bases are large
Example: Increase from 5,000 to 6,000 convictions is 20 (twenty) percent
6,000 - 5,000 = 1,0001,000/base = 1000/5,000 = .20 = 20%
CONTINUOUS VARIABLESSummarizing the distribution of
Four summary statistics forcontinuous variables
• Continuous variables – review– Can take on an infinite number of
values (e.g., age, height, weight, sentence length)
– Precise differences between cases– Equivalent differences: Distances
between 15-20 years same as 60-70 years
• Summary statistics for continuous variables– Mean: arithmetic average of scores– Median: midpoint of scores (half
higher, half lower)– Mode: most frequent score (or scores,
if tied)– Range: Difference between low and
high scores
3.5
1.3
Summarizing the distributionof continuous variables - the mean
• Arithmetic average of scores– Add up all the scores– Divide the result by the number of scores
• Example: Compare numbers of arrests for twenty police precincts during a certain shift
• Method: Use mean to summarize arrests at each precinct, then compare the means
Mean 3.0 Mean 3.5
arrests arrests
Variable: number of arrestsUnit of analysis: police precinctsCase: one precinct
Issue: Means are pulled in the directionof extreme scores, possibly misleadingthe comparison
Transforming categorical/ordinal variables into continuous variables, then using the mean
• Ordinal variables are categorical variables with an inherent order– Small, medium, large– Cooperative, uncooperative
• Can summarize in the ordinary way: proportions / percentages
• Can also transform them into continuous variables by assigning categories points on a scale, then calculating a mean
• Not always recommended because “distances” between points on scalemay not be equal, causing misleadingresults
• Is the distance between “Admonished” and “Informal” same as between “Informal and Citation”? “Citation” and “Arrest”?
Value
Severity of Disposition
Youths
Freq. %
4 Arrested 16 24
3Citationor officialreprimand 9 14
2Informalreprimand
16 24
1Admonished& released
25 38
Total (N) 66 100
Severity of disposition mean = 2.24(25 X 1) + (16 X 2) + (9 X 3) + (16 X 4) / 66
0, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 6
Exercise 1: 2, 3, 5, 5, 8, 12, 17, 19, 21
Exercise 2: 2, 3, 5, 5, 8, 12, 17, 19, 21, 21
Compute...
3 + 3 / 2 = 3
arrests
Summarizing the distributionof continuous variables - the median
• Median can be used withcontinuous or ordinal variables
• Median is a useful summarystatistic when there are extremescores, making the mean misleading
• In this example, which is identicalto the preceding page except forone outlier (16), the mean is 3.5 – .5 higher
• But the medians (3.0) are the same
• Score that occurs most often (with the greatest frequency)
• Here the mode is 3
• Modes are a useful summarystatistic when cases cluster at particular scores – aninteresting condition thatmight otherwise be overlooked
• Symmetrical distributions, like thisone, are called “normal” distributions. In suchdistributions the mean, mode and median arethe same. Near-normal distributions are common.
• There can be more than one mode (bi-modal, tri-modal, etc.). Identify the modes:
• Exercise 1: 2, 3, 5, 5, 8, 12, 17, 19, 21
• Exercise 2: 2, 3, 5, 5, 8, 12, 17, 19, 21, 21
arrests
Summarizing the distributionof continuous variables - the mode
• Answers to preceding side
Exercise 1: 2, 3, 5, 5, 8, 12, 17, 19, 21Mode = 5 (unimodal)
Exercise 2: 2, 3, 5, 5, 8, 12, 17, 19, 21, 21 Modes = 5, 21 (bimodal)
• Range: a simple way to convey the distribution of a continuous variable
–Depicts the lowest and highest scores in a distribution2, 3, 5, 5, 8, 12, 17, 19, 21 – range is “2 to 21”
–Range can also be defined as the difference between the scores(21-2 = 19). If so, minimum and maximum scores should also be given.
–Useful to cite range if there are outliers (extreme scores) that misleadingly distort the shape of the distribution
A final way to depict the distributionof continuous variables - the range
Practical exercise
• Calculate your class summary statistics for age and height – mean, median, mode and range
• Pictorially depict the distributions for age and height, placing the variables and frequencies on the correct axes
Case no.
Next week – Every week:Without fail – bring an approved calculator – the same one you will use for the exam.
It must be a basic calculator with a square root key. NOT a scientific or graphing calculator. NOT a cell phone, etc.
CaseNo.
Income
No. of arrests
Gender
1 15600 4 M
2 21380 3 F
3 17220 5 F
4 18765 2 M
5 23220 1 F
6 44500 0 M
7 34255 0 F
8 21620 0 F
9 14890 1 M
10 16650 2 F
11 44500 1 F
12 16730 3 M
13 23980 3 F
14 14005 0 F
15 21550 2 M
16 26780 4 M
17 18050 1 F
18 34500 1 M
19 33785 3 F
20 21450 2 F
HOMEWORK(link on weekly schedule)
1. Calculate all appropriate summary statistics for each distribution
2. Pictorially depict the distribution of arrests
3. Pictorially depict the distribution of gender