1. COURSE: JUST 3900 INTRODUCTORY STATISTICS FOR CRIMINAL JUSTICE Instructor: Dr. John J. Kerbs, Associate Professor Joint Ph.D. in Social Work and Sociology Chapter 2: Frequency Distributions © 2013 - - DO NOT CITE, QUOTE, REPRODUCE, OR DISSEMINATE WITHOUT WRITTEN PERMISSION FROM THE AUTHOR: Dr. John J. Kerbs can be emailed for permission at kerbsj@ecu.edu

2. After collecting data, the first task for a researcher is to organize and simplify the data so that it is possible to get a general overview of the results. After collecting data, the first task for a researcher is to organize and simplify the data so that it is possible to get a general overview of the results. This is the goal of descriptive statistical techniques. This is the goal of descriptive statistical techniques. One method for simplifying and organizing data is to construct a frequency distribution. One method for simplifying and organizing data is to construct a frequency distribution. Frequency Distributions

3. A frequency distribution is an organized tabulation showing exactly how many individuals are located in each category on the scale of measurement. A frequency distribution presents an organized picture of the entire set of scores, and it shows where each individual is located relative to others in the distribution. A frequency distribution is an organized tabulation showing exactly how many individuals are located in each category on the scale of measurement. A frequency distribution presents an organized picture of the entire set of scores, and it shows where each individual is located relative to others in the distribution. Frequency Distributions (continued)

4. Frequency Distribution Tables A frequency distribution table consists of at least two columns - one listing categories on the scale of measurement (X) and another for frequency (f). A frequency distribution table consists of at least two columns - one listing categories on the scale of measurement (X) and another for frequency (f). In the X column, values are listed from the highest to lowest, without skipping any. In the X column, values are listed from the highest to lowest, without skipping any. For the frequency column, tallies are determined for each value (how often each X value occurs in the data set). These tallies are the frequencies for each X value. For the frequency column, tallies are determined for each value (how often each X value occurs in the data set). These tallies are the frequencies for each X value. The sum of the frequencies should equal N. The sum of the frequencies should equal N.

5. Frequency Distribution Tables (continued.) A third column can be used for the proportion (p) for each category: p = f/N. The sum of the p column should equal 1.00. A third column can be used for the proportion (p) for each category: p = f/N. The sum of the p column should equal 1.00. A fourth column can display the percentage of the distribution corresponding to each X value. The percentage is found by multiplying p by 100. The sum of the percentage column is 100%. A fourth column can display the percentage of the distribution corresponding to each X value. The percentage is found by multiplying p by 100. The sum of the percentage column is 100%.

6. Frequency Distribution Table: Example Xfp = f/N% = p(100) 511/10 = 0.1010% 422/10 = 0.2020% 333/10 = 0.3030% 233/10 = 0.3030% 111/10 = 0.1010% NOTE: Σf = N = 1 +2 +3 + 3+ 1 = 10

7. Regular Frequency Distribution When a frequency distribution table lists all of the individual categories (X values) it is called a regular frequency distribution. When a frequency distribution table lists all of the individual categories (X values) it is called a regular frequency distribution.

8. Grouped Frequency Distribution Sometimes, however, a set of scores covers a wide range of values. In these situations, a list of all the X values would be quite long - too long to be a “simple” presentation of the data. Sometimes, however, a set of scores covers a wide range of values. In these situations, a list of all the X values would be quite long - too long to be a “simple” presentation of the data. To remedy this situation, a grouped frequency distribution table is used. To remedy this situation, a grouped frequency distribution table is used.

9. Grouped Frequency Distribution (continued.) In a grouped table, the X column lists groups of scores, called class intervals, rather than individual values. In a grouped table, the X column lists groups of scores, called class intervals, rather than individual values. These intervals all have the same width, usually a simple number such as 2, 5, 10, and so on. These intervals all have the same width, usually a simple number such as 2, 5, 10, and so on. Each interval begins with a value that is a multiple of the interval width. The interval width is selected so that the table will have approximately ten intervals. Each interval begins with a value that is a multiple of the interval width. The interval width is selected so that the table will have approximately ten intervals.

10. Frequency Distribution Graphs In a frequency distribution graph, the score categories (X values) are listed on the X axis and the frequencies are listed on the Y axis. In a frequency distribution graph, the score categories (X values) are listed on the X axis and the frequencies are listed on the Y axis. When the score categories consist of numerical scores from an interval or ratio scale, the graph should be either a histogram or a polygon. When the score categories consist of numerical scores from an interval or ratio scale, the graph should be either a histogram or a polygon.

11. 4 Guidelines for Grouped Frequency Distribution Tables 1. Limit to around 10 class intervals 1. Limit to around 10 class intervals 2. Interval width should be a simple number (e.g., 2, 5, 10, 20, etc) 2. Interval width should be a simple number (e.g., 2, 5, 10, 20, etc) 3. The bottom interval should be a multiple of the width 3. The bottom interval should be a multiple of the width For example, if you use a width of 5 points, the intervals should start with 5, 10, 15, 20, and so on For example, if you use a width of 5 points, the intervals should start with 5, 10, 15, 20, and so on 4. All intervals should be the same width 4. All intervals should be the same width Intervals should cover the range of scores without gaps and overlap. Intervals should cover the range of scores without gaps and overlap.

12. Histograms In a histogram, a bar is centered above each score (or class interval) so that the height of the bar corresponds to the frequency and the width extends to the real limits, so that adjacent bars touch. In a histogram, a bar is centered above each score (or class interval) so that the height of the bar corresponds to the frequency and the width extends to the real limits, so that adjacent bars touch. Apparent Limits versus Real Limits: Apparent Limits versus Real Limits: Apparent limits for class intervals (e.g., 50-59) are represented by the lowest score (50) and the highest score (59) in any given interval (the upper and lower boundaries for the class interval) Apparent limits for class intervals (e.g., 50-59) are represented by the lowest score (50) and the highest score (59) in any given interval (the upper and lower boundaries for the class interval) Real Limits are defined by the boundaries of the lowest and highest scores Real Limits are defined by the boundaries of the lowest and highest scores Example: Class Interval of 50-59 for continuous variable Example: Class Interval of 50-59 for continuous variable Lowest Score = 49.5 to 50.5 Lowest Score = 49.5 to 50.5 Highest Score = 58.5 to 59.5 Highest Score = 58.5 to 59.5 Thus, real limits for 50-59 interval are 49.5 and 59.5 Thus, real limits for 50-59 interval are 49.5 and 59.5

13. Histogram Examples For discrete variables For grouped data Indicates omitted scores

14. Polygons In a polygon, a dot is centered above each score so that the height of the dot corresponds to the frequency. The dots are then connected by straight lines. An additional line is drawn at each end to bring the graph back to a zero frequency. In a polygon, a dot is centered above each score so that the height of the dot corresponds to the frequency. The dots are then connected by straight lines. An additional line is drawn at each end to bring the graph back to a zero frequency.

15. Polygon Examples Mid-point for group with grouped data

16. Bar Graphs When the score categories (X values) are measurements from a nominal or an ordinal scale, the graph should be a bar graph. When the score categories (X values) are measurements from a nominal or an ordinal scale, the graph should be a bar graph. A bar graph is just like a histogram except that gaps or spaces are left between adjacent bars. A bar graph is just like a histogram except that gaps or spaces are left between adjacent bars.

17. Bar Graph Example

18. Relative Frequency Many populations are so large that it is impossible to know the exact number of individuals (frequency) for any specific category. Many populations are so large that it is impossible to know the exact number of individuals (frequency) for any specific category. In these situations, population distributions can be shown using relative frequency instead of the absolute number of individuals for each category. In these situations, population distributions can be shown using relative frequency instead of the absolute number of individuals for each category.

19. Relative Frequency Example

20. Smooth Curve If the scores in the population are measured on an interval or ratio scale, it is customary to present the distribution as a smooth curve rather than a jagged histogram or polygon. If the scores in the population are measured on an interval or ratio scale, it is customary to present the distribution as a smooth curve rather than a jagged histogram or polygon. The smooth curve emphasizes the fact that the distribution is not showing the exact frequency for each category. The smooth curve emphasizes the fact that the distribution is not showing the exact frequency for each category.

21. Smooth Curve Example

22. Frequency Distribution Graphs Frequency distribution graphs are useful because they show the entire set of scores. Frequency distribution graphs are useful because they show the entire set of scores. At a glance, you can determine the highest score, the lowest score, and where the scores are centered. At a glance, you can determine the highest score, the lowest score, and where the scores are centered. The graph also shows whether the scores are clustered together or scattered over a wide range. The graph also shows whether the scores are clustered together or scattered over a wide range.

23. Three Characteristics of Distribution Graphs 1. Shape - - i.e., form of distribution 1. Shape - - i.e., form of distribution 2. Central Tendency - - i.e., where the center of the distribution is located 2. Central Tendency - - i.e., where the center of the distribution is located 3. Variability - - i.e., the spread of scores, either over a wide range or clustered together in a small range 3. Variability - - i.e., the spread of scores, either over a wide range or clustered together in a small range

24. Shape A graph shows the shape of the distribution. A graph shows the shape of the distribution. A distribution is symmetrical if the left side of the graph is (roughly) a mirror image of the right side. A distribution is symmetrical if the left side of the graph is (roughly) a mirror image of the right side. One example of a symmetrical distribution is the bell-shaped normal distribution. One example of a symmetrical distribution is the bell-shaped normal distribution. On the other hand, distributions are skewed when scores pile up on one side of the distribution, leaving a "tail" of a few extreme values on the other side. On the other hand, distributions are skewed when scores pile up on one side of the distribution, leaving a "tail" of a few extreme values on the other side.

25. Positively and Negatively Skewed Distributions In a positively skewed distribution, the scores tend to pile up on the left side of the distribution with the tail tapering off to the right. In a positively skewed distribution, the scores tend to pile up on the left side of the distribution with the tail tapering off to the right. In a negatively skewed distribution, the scores tend to pile up on the right side and the tail points to the left. In a negatively skewed distribution, the scores tend to pile up on the right side and the tail points to the left.

26. Symmetrical and Skewed Distributions: Examples Tail of Distribution

27. Percentiles, Percentile Ranks, and Interpolation The relative location of individual scores within a distribution can be described by percentiles and percentile ranks. The relative location of individual scores within a distribution can be described by percentiles and percentile ranks. The percentile rank for a particular X value is the percentage of individuals with scores equal to or less than that X value. The percentile rank for a particular X value is the percentage of individuals with scores equal to or less than that X value. When an X value is described by its rank, it is called a percentile. When an X value is described by its rank, it is called a percentile.

28. Percentiles, Percentile Ranks, and Interpolation (continued.) To find percentiles and percentile ranks, two new columns are placed in the frequency distribution table: One is for cumulative frequency (cf) and the other is for cumulative percentage (c%). To find percentiles and percentile ranks, two new columns are placed in the frequency distribution table: One is for cumulative frequency (cf) and the other is for cumulative percentage (c%). Each cumulative percentage identifies the percentile rank for the upper real limit of the corresponding score or class interval. Each cumulative percentage identifies the percentile rank for the upper real limit of the corresponding score or class interval.

29. Percentiles versus Percentile Ranks Xfcfc% 5120100% 451995% 381470% 24630% 12210% 2+4 +8 = 14(14/20)*100 = 70%

30. Interpolation When scores or percentages do not correspond to upper real limits or cumulative percentages, you must use interpolation to determine the corresponding ranks and percentiles. When scores or percentages do not correspond to upper real limits or cumulative percentages, you must use interpolation to determine the corresponding ranks and percentiles. Interpolation is a mathematical process based on the assumption that the scores and the percentages change in a regular, linear fashion as you move through an interval from one end to the other. Interpolation is a mathematical process based on the assumption that the scores and the percentages change in a regular, linear fashion as you move through an interval from one end to the other.

31. Interpolation Example

32. 4 Steps for Interpolation Step 1: Find the width of the interval on both scales Step 1: Find the width of the interval on both scales Step 2: Locate position of intermediate value in the interval based upon the respective fraction of the whole interval Step 2: Locate position of intermediate value in the interval based upon the respective fraction of the whole interval Fraction = Distance from top of interval / Width Fraction = Distance from top of interval / Width Step 3: Use same fraction to determine corresponding position on other scale. First, determine the distance from the top of the interval Step 3: Use same fraction to determine corresponding position on other scale. First, determine the distance from the top of the interval Distance = Fraction x Width Distance = Fraction x Width Step 4: Use the distance from the top to determine the position on the other scale Step 4: Use the distance from the top to determine the position on the other scale

33. Interpolation Example Notice that the 50 th percentile is between 10% and 60%, with associated upper real limits of 4.5 and 9.5, respectively. Notice that the 50 th percentile is between 10% and 60%, with associated upper real limits of 4.5 and 9.5, respectively. Xfcfc% 20-24220100% 15-1931890% 10-1431575% 5-9101260% 0-42210% Using the distribution of scores below, please locate the 50 th percentile.

34. Interpolation Example (continued) Scores (X)Percentages Top9.560% ???50% Intermediate Value Bottom4.510% Step 1: Find the width of the interval on both scales Step 1: Find the width of the interval on both scales 5 and 50 points, respectively 5 and 50 points, respectively Step 2: Locate position of intermediate value Step 2: Locate position of intermediate value 50% is located 10 points from top (10/50 = 1/5 of interval) 50% is located 10 points from top (10/50 = 1/5 of interval) Step 3: Use same fraction to determine corresponding position on other scale. First, determine the distance from the top of the interval Step 3: Use same fraction to determine corresponding position on other scale. First, determine the distance from the top of the interval Distance = Fraction x Width = (1/5) * (5 points) = 1 Point Distance = Fraction x Width = (1/5) * (5 points) = 1 Point Step 4: Use distance from top to determine the position on the other scale Step 4: Use distance from top to determine the position on the other scale 9.5 – 1 = 8.5 9.5 – 1 = 8.5 Thus, 50 th percentile for X is 8.5 Thus, 50 th percentile for X is 8.5

35. Stem-and-Leaf Displays A stem-and-leaf display provides an efficient method for obtaining and displaying a frequency distribution. A stem-and-leaf display provides an efficient method for obtaining and displaying a frequency distribution. Each score is divided into a stem consisting of the first digit or digits, and a leaf consisting of the final digit. Each score is divided into a stem consisting of the first digit or digits, and a leaf consisting of the final digit. Then, go through the list of scores, one at a time, and write the leaf for each score beside its stem. Then, go through the list of scores, one at a time, and write the leaf for each score beside its stem.

36. Stem-and-Leaf Displays (continued.) The resulting display provides an organized picture of the entire distribution. The number of leaves beside each stem corresponds to the frequency, and the individual leaves identify the individual scores. The resulting display provides an organized picture of the entire distribution. The number of leaves beside each stem corresponds to the frequency, and the individual leaves identify the individual scores.

37. Stem-and-Leave Displays: Example