DESCRIPTIVE STATISTICS & DATA PRESENTATION*

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "DESCRIPTIVE STATISTICS & DATA PRESENTATION*"

Transcription

1 Level 1 Level 2 Level 3 Level evel 1 evel 2 evel 3 Level 4 DESCRIPTIVE STATISTICS & DATA PRESENTATION* Created for Psychology 41, Research Methods by Barbara Sommer, PhD Psychology Department Instructions: This is a practice review, based on Ch. 18 which must be read and understood first. Have a pencil and some scratch paper on hand as you go through this. When you see a statement or question in blue, try to answer or anticipate the information before you bring it onscreen. You can use the right-pointing arrow key on your keyboard to move forward and the left-pointing arrow to move back. At the top menu in Acrobat Reader, select Window > Show bookmarks. A menu bar will appear on the left and permit you to directly access a section - useful for review. * permission granted for educational use only, please credit the source.

2 Contents

3 Definitions & use Contents

4 Contents Definitions & use Selecting appropriate statistics

5 Contents Definitions & use Selecting appropriate statistics Specify variables

6 Contents Definitions & use Selecting appropriate statistics Specify variables Is outcome categorical or continuous?

7 Contents Definitions & use Selecting appropriate statistics Specify variables Is outcome categorical or continuous? Descriptive statistics for categorical outcomes

8 Contents Definitions & use Selecting appropriate statistics Specify variables Is outcome categorical or continuous? Descriptive statistics for categorical outcomes Percentages based on contingency tables

9 Contents Definitions & use Selecting appropriate statistics Specify variables Is outcome categorical or continuous? Descriptive statistics for categorical outcomes Percentages based on contingency tables Descriptive statistics for continuous outcomes

10 Contents Definitions & use Selecting appropriate statistics Specify variables Is outcome categorical or continuous? Descriptive statistics for categorical outcomes Percentages based on contingency tables Descriptive statistics for continuous outcomes Central tendency

11 Contents Definitions & use Selecting appropriate statistics Specify variables Is outcome categorical or continuous? Descriptive statistics for categorical outcomes Percentages based on contingency tables Descriptive statistics for continuous outcomes Central tendency Variability, dispersion

12 Contents Definitions & use Selecting appropriate statistics Specify variables Is outcome categorical or continuous? Descriptive statistics for categorical outcomes Percentages based on contingency tables Descriptive statistics for continuous outcomes Central tendency Variability, dispersion What is a standard deviation?

13 Contents Definitions & use Selecting appropriate statistics Specify variables Is outcome categorical or continuous? Descriptive statistics for categorical outcomes Percentages based on contingency tables Descriptive statistics for continuous outcomes Central tendency Variability, dispersion What is a standard deviation? Why bother with descriptive statistics?

14 Contents Definitions & use Selecting appropriate statistics Specify variables Is outcome categorical or continuous? Descriptive statistics for categorical outcomes Percentages based on contingency tables Descriptive statistics for continuous outcomes Central tendency Variability, dispersion What is a standard deviation? Why bother with descriptive statistics? To provide a quantitative description of a SAMPLE

15 DEFINITIONS

16 DEFINITIONS group of interest to researcher

17 DEFINITIONS group of interest to researcher POPULATION

18 DEFINITIONS group of interest to researcher POPULATION subset of a population

19 DEFINITIONS group of interest to researcher POPULATION subset of a population SAMPLE

20 DEFINITIONS group of interest to researcher POPULATION subset of a population SAMPLE quantitative characteristic of a population

21 DEFINITIONS group of interest to researcher POPULATION subset of a population SAMPLE quantitative characteristic of a population PARAMETER!

22 DEFINITIONS group of interest to researcher POPULATION subset of a population SAMPLE quantitative characteristic of a population PARAMETER! quantitative characteristic of a sample

23 DEFINITIONS group of interest to researcher POPULATION subset of a population SAMPLE quantitative characteristic of a population PARAMETER! quantitative characteristic of a sample STATISTIC

24 DEFINITIONS group of interest to researcher POPULATION subset of a population SAMPLE quantitative characteristic of a population PARAMETER! quantitative characteristic of a sample STATISTIC Commit these to memory right now.

25 Define: STATISTIC

26 Define: STATISTIC quantitative characteristic of a sample

27 Define: STATISTIC quantitative characteristic of a sample SAMPLE

28 Define: STATISTIC quantitative characteristic of a sample SAMPLE subset of a population

29 Define: STATISTIC quantitative characteristic of a sample SAMPLE subset of a population POPULATION

30 Define: STATISTIC quantitative characteristic of a sample SAMPLE subset of a population POPULATION group of interest to researcher

31 Define: STATISTIC quantitative characteristic of a sample SAMPLE subset of a population POPULATION group of interest to researcher PARAMETER!

32 Define: STATISTIC quantitative characteristic of a sample SAMPLE subset of a population POPULATION group of interest to researcher PARAMETER! quantitative characteristic of a population

33 Choosing the correct descriptive statistics - KEY question

34 Choosing the correct descriptive statistics - KEY question Is the OUTCOME (Dependent variable) categorical or continuous?

35 Choosing the correct descriptive statistics - KEY question Is the OUTCOME (Dependent variable) categorical or continuous? If the levels/values of the variable in question differ in quality or kind, then it is a? variable.

36 Choosing the correct descriptive statistics - KEY question Is the OUTCOME (Dependent variable) categorical or continuous? If the levels/values of the variable in question differ in quality or kind, then it is a? variable. Ans: CATEGORICAL

37 Choosing the correct descriptive statistics - KEY question Is the OUTCOME (Dependent variable) categorical or continuous? If the levels/values of the variable in question differ in quality or kind, then it is a? variable. Ans: CATEGORICAL Examples:

38 Choosing the correct descriptive statistics - KEY question Is the OUTCOME (Dependent variable) categorical or continuous? If the levels/values of the variable in question differ in quality or kind, then it is a? variable. Ans: CATEGORICAL Examples:

39 Choosing the correct descriptive statistics - KEY question Is the OUTCOME (Dependent variable) categorical or continuous? If the levels/values of the variable in question differ in quality or kind, then it is a? variable. Ans: CATEGORICAL Examples: or

40 Choosing the correct descriptive statistics - KEY question Is the OUTCOME (Dependent variable) categorical or continuous? If the levels/values of the variable in question differ in quality or kind, then it is a? variable. Ans: CATEGORICAL Examples: or YES

41 Choosing the correct descriptive statistics - KEY question Is the OUTCOME (Dependent variable) categorical or continuous? If the levels/values of the variable in question differ in quality or kind, then it is a? variable. Ans: CATEGORICAL Examples: or YES NO

42 Choosing the correct descriptive statistics - KEY question Is the OUTCOME (Dependent variable) categorical or continuous? If the levels/values of the variable in question differ in quality or kind, then it is a? variable. Ans: CATEGORICAL Examples: or YES NO DON T KNOW

43 CONTINUOUS VARIABLES are those whose levels or values vary.

44 CONTINUOUS VARIABLES are those whose levels or values vary. Ans: along a quantitative dimension

45 CONTINUOUS VARIABLES are those whose levels or values vary. Ans: along a quantitative dimension Examples:

46 CONTINUOUS VARIABLES are those whose levels or values vary. Ans: along a quantitative dimension Examples: Number of hours spent watching TV

47 CONTINUOUS VARIABLES are those whose levels or values vary. Ans: along a quantitative dimension Examples: Number of hours spent watching TV Enjoyment - on a scale of 1 to 10

48 EXAMPLE: the Reasoner Hypothesis: A woman crosses her legs within 30 seconds of sitting down.

49 EXAMPLE: the Reasoner Hypothesis: A woman crosses her legs within 30 seconds of sitting down. Examine hypothesis or question and specify variables

50 EXAMPLE: the Reasoner Hypothesis: A woman crosses her legs within 30 seconds of sitting down. Examine hypothesis or question and specify variables The hypothesis implies that men do not cross their legs within 30 seconds of sitting down.

51 EXAMPLE: the Reasoner Hypothesis: A woman crosses her legs within 30 seconds of sitting down. Examine hypothesis or question and specify variables The hypothesis implies that men do not cross their legs within 30 seconds of sitting down. The condition in question (the predictor or independent variable) is...

52 EXAMPLE: the Reasoner Hypothesis: A woman crosses her legs within 30 seconds of sitting down. Examine hypothesis or question and specify variables The hypothesis implies that men do not cross their legs within 30 seconds of sitting down. The condition in question (the predictor or independent variable) is... GENDER

53 EXAMPLE: the Reasoner Hypothesis: A woman crosses her legs within 30 seconds of sitting down. Examine hypothesis or question and specify variables The hypothesis implies that men do not cross their legs within 30 seconds of sitting down. The condition in question (the predictor or independent variable) is... GENDER Levels?

54 EXAMPLE: the Reasoner Hypothesis: A woman crosses her legs within 30 seconds of sitting down. Examine hypothesis or question and specify variables The hypothesis implies that men do not cross their legs within 30 seconds of sitting down. The condition in question (the predictor or independent variable) is... GENDER Levels? male female

55 EXAMPLE: the Reasoner Hypothesis: A woman crosses her legs within 30 seconds of sitting down. Examine hypothesis or question and specify variables The hypothesis implies that men do not cross their legs within 30 seconds of sitting down. The condition in question (the predictor or independent variable) is... GENDER Levels? male female Dependent Variable (outcome)?

56 EXAMPLE: the Reasoner Hypothesis: A woman crosses her legs within 30 seconds of sitting down. Examine hypothesis or question and specify variables The hypothesis implies that men do not cross their legs within 30 seconds of sitting down. The condition in question (the predictor or independent variable) is... GENDER Levels? male female Dependent Variable (outcome)? LEG CROSS

57 EXAMPLE: the Reasoner Hypothesis: A woman crosses her legs within 30 seconds of sitting down. Examine hypothesis or question and specify variables The hypothesis implies that men do not cross their legs within 30 seconds of sitting down. The condition in question (the predictor or independent variable) is... GENDER Levels? male female Dependent Variable (outcome)? LEG CROSS Levels?

58 EXAMPLE: the Reasoner Hypothesis: A woman crosses her legs within 30 seconds of sitting down. Examine hypothesis or question and specify variables The hypothesis implies that men do not cross their legs within 30 seconds of sitting down. The condition in question (the predictor or independent variable) is... GENDER Levels? male female Dependent Variable (outcome)? LEG CROSS Levels? Yes (within 30 seconds) No (> 30 seconds or not at all)

59 EXAMPLE: the Reasoner Hypothesis: A woman crosses her legs within 30 seconds of sitting down. Examine hypothesis or question and specify variables The hypothesis implies that men do not cross their legs within 30 seconds of sitting down. The condition in question (the predictor or independent variable) is... GENDER Levels? male female Dependent Variable (outcome)? LEG CROSS Levels? Yes (within 30 seconds) No (> 30 seconds or not at all) Note: these are operational definitions

60 Is the independent (predictor) variable categorical or continuous?

61 Is the independent (predictor) variable categorical or continuous? The levels of gender are male and female. These are discrete categories, therefore gender is categorical.

62 Is the independent (predictor) variable categorical or continuous? The levels of gender are male and female. These are discrete categories, therefore gender is categorical. Is the dependent variable (outcome) categorical or continuous?

63 Is the independent (predictor) variable categorical or continuous? The levels of gender are male and female. These are discrete categories, therefore gender is categorical. Is the dependent variable (outcome) categorical or continuous? The levels of leg cross are yes and no. These are discrete categories, therefore in this example, the dependent variable, leg cross, is categorical.

64 Is the independent (predictor) variable categorical or continuous? The levels of gender are male and female. These are discrete categories, therefore gender is categorical. Is the dependent variable (outcome) categorical or continuous? The levels of leg cross are yes and no. These are discrete categories, therefore in this example, the dependent variable, leg cross, is categorical. What if the outcome were measured in time (minutes and seconds) rather than YES vs. NO?

65 Is the independent (predictor) variable categorical or continuous? The levels of gender are male and female. These are discrete categories, therefore gender is categorical. Is the dependent variable (outcome) categorical or continuous? The levels of leg cross are yes and no. These are discrete categories, therefore in this example, the dependent variable, leg cross, is categorical. What if the outcome were measured in time (minutes and seconds) rather than YES vs. NO? In that case the dependent variable would be a continuous measure.

66 Your research assistant has collected observational data. Now it is up to you to make sense of it.

67 Your research assistant has collected observational data. Now it is up to you to make sense of it. The first step is to enter each COUNT in the appropriate cell on a CONTINGENCY TABLE. Here is an example:

68 Your research assistant has collected observational data. Now it is up to you to make sense of it. The first step is to enter each COUNT in the appropriate cell on a CONTINGENCY TABLE. Here is an example: cells

69 Your research assistant has collected observational data. Now it is up to you to make sense of it. The first step is to enter each COUNT in the appropriate cell on a CONTINGENCY TABLE. Here is an example: Put the independent variable (predictor) along the top, and the dependent variable (outcome) along the side. cells

70 Your research assistant has collected observational data. Now it is up to you to make sense of it. The first step is to enter each COUNT in the appropriate cell on a CONTINGENCY TABLE. Here is an example: Put the independent variable (predictor) along the top, and the dependent variable (outcome) along the side. GENDER female male cells

71 Your research assistant has collected observational data. Now it is up to you to make sense of it. The first step is to enter each COUNT in the appropriate cell on a CONTINGENCY TABLE. Here is an example: Put the independent variable (predictor) along the top, and the dependent variable (outcome) along the side. CROSS within 30 seconds Yes No GENDER female male cells

72 Your research assistant has collected observational data. Now it is up to you to make sense of it. The first step is to enter each COUNT in the appropriate cell on a CONTINGENCY TABLE. Here is an example: Put the independent variable (predictor) along the top, and the dependent variable (outcome) along the side. CROSS within 30 seconds Yes No GENDER female male 232 cells

73 Your research assistant has collected observational data. Now it is up to you to make sense of it. The first step is to enter each COUNT in the appropriate cell on a CONTINGENCY TABLE. Here is an example: Put the independent variable (predictor) along the top, and the dependent variable (outcome) along the side. CROSS within 30 seconds Yes No GENDER female male cells

74 Your research assistant has collected observational data. Now it is up to you to make sense of it. The first step is to enter each COUNT in the appropriate cell on a CONTINGENCY TABLE. Here is an example: Put the independent variable (predictor) along the top, and the dependent variable (outcome) along the side. CROSS within 30 seconds Yes No GENDER female male cells

75 Your research assistant has collected observational data. Now it is up to you to make sense of it. The first step is to enter each COUNT in the appropriate cell on a CONTINGENCY TABLE. Here is an example: Put the independent variable (predictor) along the top, and the dependent variable (outcome) along the side. CROSS within 30 seconds Yes No GENDER female male cells

76 Your research assistant has collected observational data. Now it is up to you to make sense of it. The first step is to enter each COUNT in the appropriate cell on a CONTINGENCY TABLE. Here is an example: Put the independent variable (predictor) along the top, and the dependent variable (outcome) along the side. CROSS within 30 seconds Yes No GENDER female male cells margins (totals)

77 Your research assistant has collected observational data. Now it is up to you to make sense of it. The first step is to enter each COUNT in the appropriate cell on a CONTINGENCY TABLE. Here is an example: Put the independent variable (predictor) along the top, and the dependent variable (outcome) along the side. CROSS within 30 seconds Yes No GENDER female male cells 361 margins (totals)

78 Your research assistant has collected observational data. Now it is up to you to make sense of it. The first step is to enter each COUNT in the appropriate cell on a CONTINGENCY TABLE. Here is an example: Put the independent variable (predictor) along the top, and the dependent variable (outcome) along the side. CROSS within 30 seconds Yes No GENDER female male cells margins (totals)

79 Your research assistant has collected observational data. Now it is up to you to make sense of it. The first step is to enter each COUNT in the appropriate cell on a CONTINGENCY TABLE. Here is an example: Put the independent variable (predictor) along the top, and the dependent variable (outcome) along the side. GENDER female male CROSS within 30 seconds Yes No cells margins (totals)

80 Your research assistant has collected observational data. Now it is up to you to make sense of it. The first step is to enter each COUNT in the appropriate cell on a CONTINGENCY TABLE. Here is an example: Put the independent variable (predictor) along the top, and the dependent variable (outcome) along the side. GENDER female male CROSS within 30 seconds Yes No cells margins (totals)

81 Your research assistant has collected observational data. Now it is up to you to make sense of it. The first step is to enter each COUNT in the appropriate cell on a CONTINGENCY TABLE. Here is an example: Put the independent variable (predictor) along the top, and the dependent variable (outcome) along the side. GENDER female male CROSS within 30 seconds Yes No cells margins (totals) N = 708 (check to see that the marginal totals add up correctly)

82 The General Case - contingency tables can have 2 or more columns and rows. Beyond 4 or 5 of either makes interpretation difficult.

83 The General Case - contingency tables can have 2 or more columns and rows. Beyond 4 or 5 of either makes interpretation difficult. On the table below, indicate a cell.

84 The General Case - contingency tables can have 2 or more columns and rows. Beyond 4 or 5 of either makes interpretation difficult. On the table below, indicate a cell. cell

85 The General Case - contingency tables can have 2 or more columns and rows. Beyond 4 or 5 of either makes interpretation difficult. On the table below, indicate a cell. And a margin. cell

86 The General Case - contingency tables can have 2 or more columns and rows. Beyond 4 or 5 of either makes interpretation difficult. On the table below, indicate a cell. And a margin. cell margins

87 The General Case - contingency tables can have 2 or more columns and rows. Beyond 4 or 5 of either makes interpretation difficult. On the table below, indicate a cell. And a margin. Where does the independent (or predictor) variable and its levels belong? cell margins

88 The General Case - contingency tables can have 2 or more columns and rows. Beyond 4 or 5 of either makes interpretation difficult. On the table below, indicate a cell. And a margin. Where does the independent (or predictor) variable and its levels belong? PREDICTOR Variable (INDEPENDENT Variable) Level 1 Level 2 cell margins

89 The General Case - contingency tables can have 2 or more columns and rows. Beyond 4 or 5 of either makes interpretation difficult. On the table below, indicate a cell. And a margin. Where does the independent (or predictor) variable and its levels belong? And the outcome (dependent variable)? PREDICTOR Variable (INDEPENDENT Variable) Level 1 Level 2 cell margins

90 The General Case - contingency tables can have 2 or more columns and rows. Beyond 4 or 5 of either makes interpretation difficult. On the table below, indicate a cell. And a margin. Where does the independent (or predictor) variable and its levels belong? And the outcome (dependent variable)? OUTCOME (Dependent variable) PREDICTOR Variable (INDEPENDENT Variable) Level 1 Level 2 Level 1 Level 2 cell margins Level 3

91 Returning to the Reasoner data N = 708

92 Returning to the Reasoner data N = 708 CROSS within 30 Yes seconds GENDER female male No

93 Returning to the Reasoner data N = 708 CROSS within 30 Yes seconds GENDER female male No The contingency table shows the raw data. You need to refine it a bit more for presentation.

94 A good way to summarize counts such as these is to transform them into?.

95 A good way to summarize counts such as these is to transform them into?. PERCENTAGES

96 A good way to summarize counts such as these is to transform them into?. PERCENTAGES Make a table showing the results using percentages (calculate from preceding slide).

97 A good way to summarize counts such as these is to transform them into?. PERCENTAGES Make a table showing the results using percentages (calculate from preceding slide). Table 1 Percentage of women and men who crossed their legs within 30 seconds of sitting down. Women Men (n = 361) (n = 347) 64.3% 27.7%

98 A good way to summarize counts such as these is to transform them into?. PERCENTAGES Make a table showing the results using percentages (calculate from preceding slide). Table 1 Percentage of women and men who crossed their legs within 30 seconds of sitting down. Women Men (n = 361) (n = 347) 64.3% 27.7% Note: The percentages are the descriptive statistics.

99 A good way to summarize counts such as these is to transform them into?. PERCENTAGES Make a table showing the results using percentages (calculate from preceding slide). Table 1 Percentage of women and men who crossed their legs within 30 seconds of sitting down. Women Men (n = 361) (n = 347) 64.3% 27.7% Note: The percentages are the descriptive statistics. A descriptive statistic is.

100 A good way to summarize counts such as these is to transform them into?. PERCENTAGES Make a table showing the results using percentages (calculate from preceding slide). Table 1 Percentage of women and men who crossed their legs within 30 seconds of sitting down. Women Men (n = 361) (n = 347) 64.3% 27.7% Note: The percentages are the descriptive statistics. A descriptive statistic is. Ans: A quantitative characteristic of a sample.

101 A narrative description can be used within report or talk. State the results in words.

102 A narrative description can be used within report or talk. State the results in words. Sixty-four percent of the women (n = 361) and 28 percent of the men (n = 347) crossed their legs within 30 seconds of sitting down.

103 Draw a graph of the results.

104 Draw a graph of the results. %

105 Draw a graph of the results. % Women GENDER

106 Draw a graph of the results. % Women Men GENDER

107 Draw a graph of the results. % Women Men GENDER Figure 1. Percent of women (n=361) and men (n=347) crossing within 30 seconds.

108 Draw a graph of the results. % Women Men GENDER Figure 1. Percent of women (n=361) and men (n=347) crossing within 30 seconds. Note: Using APA style, table titles are placed above the table, and figure titles are placed below the figure.

109 You would not use all three in a single report. Pick the one that best illustrates your findings.

110 You would not use all three in a single report. Pick the one that best illustrates your findings. Here is another example using additional data.

111 You would not use all three in a single report. Pick the one that best illustrates your findings. Here is another example using additional data. Table 2 Of those who crossed their legs, percentage showing each type of cross, by gender. Gender Type! Women! Men! (n=252)! (n=122) Knee-to-knee! 50.4! 9.0 Ankle-to-ankle! 17.1! 49.2 Ankle-to-knee! 10.3! 26.2 Cross-legged! 19.8! 13.9 Other! 2.4! 1.6

112 You would not use all three in a single report. Pick the one that best illustrates your findings. Here is another example using additional data. Table 2 Of those who crossed their legs, percentage showing each type of cross, by gender. Gender Type! Women! Men! (n=252)! (n=122) Knee-to-knee! 50.4! 9.0 Ankle-to-ankle! 17.1! 49.2 Ankle-to-knee! 10.3! 26.2 Cross-legged! 19.8! 13.9 Other! 2.4! 1.6 Note that the sample sizes (n) are provided so that the reader could reconstruct the actual numbers.

113 When outcome variable is CONTINUOUS,

114 When outcome variable is CONTINUOUS, for example, enjoyment - on a scale of 1 to 10, or score on an exam,

115 When outcome variable is CONTINUOUS, for example, enjoyment - on a scale of 1 to 10, or score on an exam, what two major aspects of the data need to be described [in addition to the sample size (N)]?

116 When outcome variable is CONTINUOUS, for example, enjoyment - on a scale of 1 to 10, or score on an exam, what two major aspects of the data need to be described [in addition to the sample size (N)]? Hint: One begins with central

117 When outcome variable is CONTINUOUS, for example, enjoyment - on a scale of 1 to 10, or score on an exam, what two major aspects of the data need to be described [in addition to the sample size (N)]? Hint: One begins with central Ans: central tendency and variability.

118 When outcome variable is CONTINUOUS, for example, enjoyment - on a scale of 1 to 10, or score on an exam, what two major aspects of the data need to be described [in addition to the sample size (N)]? Hint: One begins with central Ans: central tendency and variability. What are the three measures of central tendency?

119 When outcome variable is CONTINUOUS, for example, enjoyment - on a scale of 1 to 10, or score on an exam, what two major aspects of the data need to be described [in addition to the sample size (N)]? Hint: One begins with central Ans: central tendency and variability. What are the three measures of central tendency? Mean

120 When outcome variable is CONTINUOUS, for example, enjoyment - on a scale of 1 to 10, or score on an exam, what two major aspects of the data need to be described [in addition to the sample size (N)]? Hint: One begins with central Ans: central tendency and variability. What are the three measures of central tendency? Mean Median

121 When outcome variable is CONTINUOUS, for example, enjoyment - on a scale of 1 to 10, or score on an exam, what two major aspects of the data need to be described [in addition to the sample size (N)]? Hint: One begins with central Ans: central tendency and variability. What are the three measures of central tendency? Mean Median Mode

122 EXAMPLE: Scores on a 10-point quiz: Scores are 2, 10, 6, 2, 4, 8, 3

123 EXAMPLE: Scores on a 10-point quiz: Scores are 2, 10, 6, 2, 4, 8, 3 Put them in order with the proper label at the top.

124 EXAMPLE: Scores on a 10-point quiz: Scores are 2, 10, 6, 2, 4, 8, 3 Put them in order with the proper label at the top. X

125 EXAMPLE: Scores on a 10-point quiz: Scores are 2, 10, 6, 2, 4, 8, 3 Put them in order with the proper label at the top. X Calculate the following and also show the appropriate statistical symbol.

126 EXAMPLE: Scores on a 10-point quiz: Scores are 2, 10, 6, 2, 4, 8, 3 Put them in order with the proper label at the top. X Calculate the following and also show the appropriate statistical symbol. Sample size

127 EXAMPLE: Scores on a 10-point quiz: Scores are 2, 10, 6, 2, 4, 8, 3 Put them in order with the proper label at the top. X Calculate the following and also show the appropriate statistical symbol. Sample size N = 7

128 EXAMPLE: Scores on a 10-point quiz: Scores are 2, 10, 6, 2, 4, 8, 3 Put them in order with the proper label at the top. X Calculate the following and also show the appropriate statistical symbol. Sample size N = 7 Mode (no symbol)

129 EXAMPLE: Scores on a 10-point quiz: Scores are 2, 10, 6, 2, 4, 8, 3 Put them in order with the proper label at the top. X Calculate the following and also show the appropriate statistical symbol. Sample size N = 7 Mode (no symbol) = 2

130 EXAMPLE: Scores on a 10-point quiz: Scores are 2, 10, 6, 2, 4, 8, 3 Put them in order with the proper label at the top. X Calculate the following and also show the appropriate statistical symbol. Sample size N = 7 Mode (no symbol) = 2 Median

131 EXAMPLE: Scores on a 10-point quiz: Scores are 2, 10, 6, 2, 4, 8, 3 Put them in order with the proper label at the top. X Calculate the following and also show the appropriate statistical symbol. Sample size N = 7 Mode (no symbol) = 2 Median Mdn = 4

132 EXAMPLE: Scores on a 10-point quiz: Scores are 2, 10, 6, 2, 4, 8, 3 Put them in order with the proper label at the top. X Calculate the following and also show the appropriate statistical symbol. Sample size N = 7 Mode (no symbol) = 2 Median Mean Mdn = 4

133 EXAMPLE: Scores on a 10-point quiz: Scores are 2, 10, 6, 2, 4, 8, 3 Put them in order with the proper label at the top. X Calculate the following and also show the appropriate statistical symbol. Sample size N = 7 Mode (no symbol) = 2 Median Mdn = 4 Mean M or X = 5

134 Here is another set of scores. These have frequencies (f) listed -- meaning that there might be more than one of any given score. X! f 10! 1 9! 0 8! 1 7! 0 6! 1 5! 0 4! 1 3! 1 2! 2

135 Here is another set of scores. These have frequencies (f) listed -- meaning that there might be more than one of any given score. X! f 10! 1 9! 0 8! 1 7! 0 6! 1 5! 0 4! 1 3! 1 2! 2 Median?

136 Here is another set of scores. These have frequencies (f) listed -- meaning that there might be more than one of any given score. X! f 10! 1 9! 0 8! 1 7! 0 6! 1 5! 0 4! 1 3! 1 2! 2 Median? Mdn = 4

137 Here is another set of scores. These have frequencies (f) listed -- meaning that there might be more than one of any given score. X! f 10! 1 9! 0 8! 1 7! 0 6! 1 5! 0 4! 1 3! 1 2! 2 Median? Mdn = 4 If you didn t get it right, make a list of all of the individual scores.

138 Here is another set of scores. These have frequencies (f) listed -- meaning that there might be more than one of any given score. X! f 10! 1 9! 0 8! 1 7! 0 6! 1 5! 0 4! 1 3! 1 2! 2 Median? Mdn = 4 If you didn t get it right, make a list of all of the individual scores. X

139 Here is another set of scores. These have frequencies (f) listed -- meaning that there might be more than one of any given score. X! f 10! 1 9! 0 8! 1 7! 0 6! 1 5! 0 4! 1 3! 1 2! 2 Median? Mdn = 4 If you didn t get it right, make a list of all of the individual scores. X Find the middle score. That will be the median.

140 Here is another set of scores. These have frequencies (f) listed -- meaning that there might be more than one of any given score. X! f 10! 1 9! 0 8! 1 7! 0 6! 1 5! 0 4! 1 3! 1 2! 2 Median? Mdn = 4 If you didn t get it right, make a list of all of the individual scores. X Find the middle score. That will be the median.

141 Recap: We are reviewing necessary descriptive statistics for continuous variables. The first aspect of describing quantitative findings along a continuous variable is central tendency.

142 Recap: We are reviewing necessary descriptive statistics for continuous variables. The first aspect of describing quantitative findings along a continuous variable is central tendency. The other important aspect is

143 Recap: We are reviewing necessary descriptive statistics for continuous variables. The first aspect of describing quantitative findings along a continuous variable is central tendency. The other important aspect is VARIABILITY

144 Recap: We are reviewing necessary descriptive statistics for continuous variables. The first aspect of describing quantitative findings along a continuous variable is central tendency. The other important aspect is VARIABILITY Variability refers to the of the scores.

145 Recap: We are reviewing necessary descriptive statistics for continuous variables. The first aspect of describing quantitative findings along a continuous variable is central tendency. The other important aspect is VARIABILITY Variability refers to the of the scores. SPREAD or DISPERSION

146 Recap: We are reviewing necessary descriptive statistics for continuous variables. The first aspect of describing quantitative findings along a continuous variable is central tendency. The other important aspect is VARIABILITY Variability refers to the of the scores. SPREAD or DISPERSION Two indicators of variability are

147 Recap: We are reviewing necessary descriptive statistics for continuous variables. The first aspect of describing quantitative findings along a continuous variable is central tendency. The other important aspect is VARIABILITY Variability refers to the of the scores. SPREAD or DISPERSION Two indicators of variability are Range Standard Deviation (SD)

148 Recap: We are reviewing necessary descriptive statistics for continuous variables. The first aspect of describing quantitative findings along a continuous variable is central tendency. The other important aspect is VARIABILITY Variability refers to the of the scores. SPREAD or DISPERSION Two indicators of variability are Range Standard Deviation (SD) Calculate the range of these scores. X

149 Recap: We are reviewing necessary descriptive statistics for continuous variables. The first aspect of describing quantitative findings along a continuous variable is central tendency. The other important aspect is VARIABILITY Variability refers to the of the scores. SPREAD or DISPERSION Two indicators of variability are Range Standard Deviation (SD) Calculate the range of these scores. X Range = 10-2 = 8

150 What is so special about the standard deviation?

151 What is so special about the standard deviation? It shows how closely scores group around the mean.

152 What is so special about the standard deviation? It shows how closely scores group around the mean. As a set of scores become more spread out from the mean, the standard deviation (increases or decreases?)

153 What is so special about the standard deviation? It shows how closely scores group around the mean. As a set of scores become more spread out from the mean, the standard deviation (increases or decreases?) Increases. Larger SD = greater variability

154 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X

155 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M)

156 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5

157 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3

158 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1

159 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1

160 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2

161 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2-5 = -3

162 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2-5 = -3-5 = -3

163 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2-5 = -3-5 = -3 Note that each individual score is being subtracted from the mean. We can t do much with these numbers because if we add them, they will total 0. That is the nature of the mean. A solution is to square each difference score, and then add them.

164 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2-5 = -3-5 = -3 Note that each individual score is being subtracted from the mean. We can t do much with these numbers because if we add them, they will total 0. That is the nature of the mean. A solution is to square each difference score, and then add them. (X-M) 2 25

165 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2-5 = -3-5 = -3 Note that each individual score is being subtracted from the mean. We can t do much with these numbers because if we add them, they will total 0. That is the nature of the mean. A solution is to square each difference score, and then add them. (X-M)

166 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2-5 = -3-5 = -3 Note that each individual score is being subtracted from the mean. We can t do much with these numbers because if we add them, they will total 0. That is the nature of the mean. A solution is to square each difference score, and then add them. (X-M)

167 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2-5 = -3-5 = -3 Note that each individual score is being subtracted from the mean. We can t do much with these numbers because if we add them, they will total 0. That is the nature of the mean. A solution is to square each difference score, and then add them. (X-M)

168 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2-5 = -3-5 = -3 Note that each individual score is being subtracted from the mean. We can t do much with these numbers because if we add them, they will total 0. That is the nature of the mean. A solution is to square each difference score, and then add them. (X-M)

169 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2-5 = -3-5 = -3 Note that each individual score is being subtracted from the mean. We can t do much with these numbers because if we add them, they will total 0. That is the nature of the mean. A solution is to square each difference score, and then add them. (X-M)

170 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2-5 = -3-5 = -3 Note that each individual score is being subtracted from the mean. We can t do much with these numbers because if we add them, they will total 0. That is the nature of the mean. A solution is to square each difference score, and then add them. (X-M)

171 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2-5 = -3-5 = -3 Note that each individual score is being subtracted from the mean. We can t do much with these numbers because if we add them, they will total 0. That is the nature of the mean. A solution is to square each difference score, and then add them. (X-M)

172 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2-5 = -3-5 = -3 Note that each individual score is being subtracted from the mean. We can t do much with these numbers because if we add them, they will total 0. That is the nature of the mean. A solution is to square each difference score, and then add them. (X-M)

173 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2-5 = -3-5 = -3 Note that each individual score is being subtracted from the mean. We can t do much with these numbers because if we add them, they will total 0. That is the nature of the mean. A solution is to square each difference score, and then add them. (X-M) =

174 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2-5 = -3-5 = -3 Note that each individual score is being subtracted from the mean. We can t do much with these numbers because if we add them, they will total 0. That is the nature of the mean. A solution is to square each difference score, and then add them. (X-M) = average of the This is a sort of spread (squared).

175 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2-5 = -3-5 = -3 Note that each individual score is being subtracted from the mean. We can t do much with these numbers because if we add them, they will total 0. That is the nature of the mean. A solution is to square each difference score, and then add them. (X-M) = average of the This is a sort of spread (squared). Unsquare it (take square root) =

176 What is the Standard Deviation? Here is the long way to calculate it (so you can see where it comes from). Using the formula is easier. M = 5 X (X-M) - 5 = 5-5 = 3-5 = 1-5 = -1-5 = -2-5 = -3-5 = -3 Note that each individual score is being subtracted from the mean. We can t do much with these numbers because if we add them, they will total 0. That is the nature of the mean. A solution is to square each difference score, and then add them. (X-M) = average of the This is a sort of spread (squared). Unsquare it (take square root) = 2.88 = SD

177 Some Greek

178 Some Greek Σ =

179 Some Greek Σ = Sum of

180 Some Greek Σ = Sum of σ =

181 Some Greek Σ = Sum of σ = standard deviation for the population

182 Some Greek Σ = Sum of σ = standard deviation for the population Standard deviation can also be abbreviated as SD or StDev

183 Some Greek Σ = Sum of σ = standard deviation for the population Standard deviation can also be abbreviated as SD or StDev Formula for the standard deviation

184 Some Greek Σ = Sum of σ = standard deviation for the population Standard deviation can also be abbreviated as SD or StDev Formula for the standard deviation = ΣΧ 2 (ΣΧ) 2 Ν Ν 1

185 Some Greek Σ = Sum of σ = standard deviation for the population Standard deviation can also be abbreviated as SD or StDev Formula for the standard deviation = ΣΧ 2 (ΣΧ) 2 Ν Ν 1 Calculators often have 2 formulas for the Standard Deviation, one for the sample (shown as N or s) and the other for the population (shown as N-1 or σ). Use the population (N-1or σ) formula.

186 List three symbols or abbreviations for the standard deviation

187 List three symbols or abbreviations for the standard deviation σ SD StDev

188 List three symbols or abbreviations for the standard deviation σ SD StDev Describe each of the following arithmetic operations (i.e., how do you calculate these?).

189 List three symbols or abbreviations for the standard deviation σ SD StDev Describe each of the following arithmetic operations (i.e., how do you calculate these?). ΣX

190 List three symbols or abbreviations for the standard deviation σ SD StDev Describe each of the following arithmetic operations (i.e., how do you calculate these?). ΣX Add up the scores.

191 List three symbols or abbreviations for the standard deviation σ SD StDev Describe each of the following arithmetic operations (i.e., how do you calculate these?). ΣX Add up the scores. ΣΧ 2

192 List three symbols or abbreviations for the standard deviation σ SD StDev Describe each of the following arithmetic operations (i.e., how do you calculate these?). ΣX Add up the scores. ΣΧ 2 Square each score and then add them.

193 List three symbols or abbreviations for the standard deviation σ SD StDev Describe each of the following arithmetic operations (i.e., how do you calculate these?). ΣX Add up the scores. ΣΧ 2 Square each score and then add them. (ΣΧ) 2

194 List three symbols or abbreviations for the standard deviation σ SD StDev Describe each of the following arithmetic operations (i.e., how do you calculate these?). ΣX Add up the scores. ΣΧ 2 Square each score and then add them. (ΣΧ) 2 Add the scores and then square the total.

195 Fill-in the proper statistic.

196 Fill-in the proper statistic. When describing an outcome based on a continuous variable, always include.

197 Fill-in the proper statistic. When describing an outcome based on a continuous variable, always include. N (sample size)

198 Fill-in the proper statistic. When describing an outcome based on a continuous variable, always include. N (sample size) In most cases, also provide the and the.

199 Fill-in the proper statistic. When describing an outcome based on a continuous variable, always include. N (sample size) In most cases, also provide the and the. Mean... Standard Deviation

200 Fill-in the proper statistic. When describing an outcome based on a continuous variable, always include. N (sample size) In most cases, also provide the and the. Mean... Standard Deviation If the distribution of scores is skewed (see textbook), then use the and the.

201 Fill-in the proper statistic. When describing an outcome based on a continuous variable, always include. N (sample size) In most cases, also provide the and the. Mean... Standard Deviation If the distribution of scores is skewed (see textbook), then use the and the. Median... Range

202 Fill-in the proper statistic. When describing an outcome based on a continuous variable, always include. N (sample size) In most cases, also provide the and the. Mean... Standard Deviation If the distribution of scores is skewed (see textbook), then use the and the. Median... Range Only use the Standard Deviation when presenting the Mean (no SD with the Median or Mode).

203 Here are the students results for a 10-point quiz described earlier. N = 7 M = 5 SD = 2.88

204 Here are the students results for a 10-point quiz described earlier. N = 7 M = 5 SD = 2.88 How would you state these results in a research report?

205 Here are the students results for a 10-point quiz described earlier. N = 7 M = 5 SD = 2.88 How would you state these results in a research report? Seven students took a 10-point quiz (M=5, SD = 2.88). or The mean for the 10-point quiz was 5 (N=7, SD = 2.88)

206 REVIEW

207 REVIEW Examine outcome variable (dependent variable) and select the proper descriptive statistics.

208 REVIEW Examine outcome variable (dependent variable) and select the proper descriptive statistics. Outcome variable! Descriptive statistics

209 REVIEW Examine outcome variable (dependent variable) and select the proper descriptive statistics. Outcome variable! Descriptive statistics Categorical

210 REVIEW Examine outcome variable (dependent variable) and select the proper descriptive statistics. Outcome variable! Categorical Descriptive statistics Percentages based on numbers in contingency table

211 REVIEW Examine outcome variable (dependent variable) and select the proper descriptive statistics. Outcome variable! Categorical Descriptive statistics Percentages based on numbers in contingency table Continuous

212 REVIEW Examine outcome variable (dependent variable) and select the proper descriptive statistics. Outcome variable! Categorical Continuous Descriptive statistics Percentages based on numbers in contingency table Mean & Standard Deviation

213 REVIEW Examine outcome variable (dependent variable) and select the proper descriptive statistics. Outcome variable! Categorical Continuous Descriptive statistics Percentages based on numbers in contingency table Mean & Standard Deviation Always include

Chapter 15 Multiple Choice Questions (The answers are provided after the last question.)

Chapter 15 Multiple Choice Questions (The answers are provided after the last question.) Chapter 15 Multiple Choice Questions (The answers are provided after the last question.) 1. What is the median of the following set of scores? 18, 6, 12, 10, 14? a. 10 b. 14 c. 18 d. 12 2. Approximately

More information

Scientific Method. 2. Design Study. 1. Ask Question. Questionnaire. Descriptive Research Study. 6: Share Findings. 1: Ask Question.

Scientific Method. 2. Design Study. 1. Ask Question. Questionnaire. Descriptive Research Study. 6: Share Findings. 1: Ask Question. Descriptive Research Study Investigation of Positive and Negative Affect of UniJos PhD Students toward their PhD Research Project : Ask Question : Design Study Scientific Method 6: Share Findings. Reach

More information

Descriptive Statistics

Descriptive Statistics Y520 Robert S Michael Goal: Learn to calculate indicators and construct graphs that summarize and describe a large quantity of values. Using the textbook readings and other resources listed on the web

More information

Measures of Central Tendency and Variability: Summarizing your Data for Others

Measures of Central Tendency and Variability: Summarizing your Data for Others Measures of Central Tendency and Variability: Summarizing your Data for Others 1 I. Measures of Central Tendency: -Allow us to summarize an entire data set with a single value (the midpoint). 1. Mode :

More information

Data exploration with Microsoft Excel: analysing more than one variable

Data exploration with Microsoft Excel: analysing more than one variable Data exploration with Microsoft Excel: analysing more than one variable Contents 1 Introduction... 1 2 Comparing different groups or different variables... 2 3 Exploring the association between categorical

More information

Unit 4: Statistics Measures of Central Tendency & Measures of Dispersion

Unit 4: Statistics Measures of Central Tendency & Measures of Dispersion Unit 4: Statistics Measures of Central Tendency & Measures of Dispersion 1 Measures of Central Tendency a measure that tells us where the middle of a bunch of data lies most common are Mean, Median, and

More information

Why do we measure central tendency? Basic Concepts in Statistical Analysis

Why do we measure central tendency? Basic Concepts in Statistical Analysis Why do we measure central tendency? Basic Concepts in Statistical Analysis Chapter 4 Too many numbers Simplification of data Descriptive purposes What is central tendency? Measure of central tendency A

More information

Recitation, Week 3: Basic Descriptive Statistics and Measures of Central Tendency:

Recitation, Week 3: Basic Descriptive Statistics and Measures of Central Tendency: Recitation, Week 3: Basic Descriptive Statistics and Measures of Central Tendency: 1. What does Healey mean by data reduction? a. Data reduction involves using a few numbers to summarize the distribution

More information

MEASURES OF VARIATION

MEASURES OF VARIATION NORMAL DISTRIBTIONS MEASURES OF VARIATION In statistics, it is important to measure the spread of data. A simple way to measure spread is to find the range. But statisticians want to know if the data are

More information

1-2 Mean, Median, Mode, and Range

1-2 Mean, Median, Mode, and Range Learn to find the mean, median, mode, and range of a data set. mean median mode range outlier Vocabulary The mean is the sum of the data values divided by the number of data items. The median is the middle

More information

4. Descriptive Statistics: Measures of Variability and Central Tendency

4. Descriptive Statistics: Measures of Variability and Central Tendency 4. Descriptive Statistics: Measures of Variability and Central Tendency Objectives Calculate descriptive for continuous and categorical data Edit output tables Although measures of central tendency and

More information

Northumberland Knowledge

Northumberland Knowledge Northumberland Knowledge Know Guide How to Analyse Data - November 2012 - This page has been left blank 2 About this guide The Know Guides are a suite of documents that provide useful information about

More information

Frequency distributions, central tendency & variability. Displaying data

Frequency distributions, central tendency & variability. Displaying data Frequency distributions, central tendency & variability Displaying data Software SPSS Excel/Numbers/Google sheets Social Science Statistics website (socscistatistics.com) Creating and SPSS file Open the

More information

4.1 Exploratory Analysis: Once the data is collected and entered, the first question is: "What do the data look like?"

4.1 Exploratory Analysis: Once the data is collected and entered, the first question is: What do the data look like? Data Analysis Plan The appropriate methods of data analysis are determined by your data types and variables of interest, the actual distribution of the variables, and the number of cases. Different analyses

More information

Topic 9 ~ Measures of Spread

Topic 9 ~ Measures of Spread AP Statistics Topic 9 ~ Measures of Spread Activity 9 : Baseball Lineups The table to the right contains data on the ages of the two teams involved in game of the 200 National League Division Series. Is

More information

Data Analysis: Describing Data - Descriptive Statistics

Data Analysis: Describing Data - Descriptive Statistics WHAT IT IS Return to Table of ontents Descriptive statistics include the numbers, tables, charts, and graphs used to describe, organize, summarize, and present raw data. Descriptive statistics are most

More information

Dr. Peter Tröger Hasso Plattner Institute, University of Potsdam. Software Profiling Seminar, Statistics 101

Dr. Peter Tröger Hasso Plattner Institute, University of Potsdam. Software Profiling Seminar, Statistics 101 Dr. Peter Tröger Hasso Plattner Institute, University of Potsdam Software Profiling Seminar, 2013 Statistics 101 Descriptive Statistics Population Object Object Object Sample numerical description Object

More information

Data exploration with Microsoft Excel: univariate analysis

Data exploration with Microsoft Excel: univariate analysis Data exploration with Microsoft Excel: univariate analysis Contents 1 Introduction... 1 2 Exploring a variable s frequency distribution... 2 3 Calculating measures of central tendency... 16 4 Calculating

More information

Stats Review Chapters 3-4

Stats Review Chapters 3-4 Stats Review Chapters 3-4 Created by Teri Johnson Math Coordinator, Mary Stangler Center for Academic Success Examples are taken from Statistics 4 E by Michael Sullivan, III And the corresponding Test

More information

LEARNING OBJECTIVES SCALES OF MEASUREMENT: A REVIEW SCALES OF MEASUREMENT: A REVIEW DESCRIBING RESULTS DESCRIBING RESULTS 8/14/2016

LEARNING OBJECTIVES SCALES OF MEASUREMENT: A REVIEW SCALES OF MEASUREMENT: A REVIEW DESCRIBING RESULTS DESCRIBING RESULTS 8/14/2016 UNDERSTANDING RESEARCH RESULTS: DESCRIPTION AND CORRELATION LEARNING OBJECTIVES Contrast three ways of describing results: Comparing group percentages Correlating scores Comparing group means Describe

More information

The right edge of the box is the third quartile, Q 3, which is the median of the data values above the median. Maximum Median

The right edge of the box is the third quartile, Q 3, which is the median of the data values above the median. Maximum Median CONDENSED LESSON 2.1 Box Plots In this lesson you will create and interpret box plots for sets of data use the interquartile range (IQR) to identify potential outliers and graph them on a modified box

More information

TI-Inspire manual 1. Instructions. Ti-Inspire for statistics. General Introduction

TI-Inspire manual 1. Instructions. Ti-Inspire for statistics. General Introduction TI-Inspire manual 1 General Introduction Instructions Ti-Inspire for statistics TI-Inspire manual 2 TI-Inspire manual 3 Press the On, Off button to go to Home page TI-Inspire manual 4 Use the to navigate

More information

Describing Data. We find the position of the central observation using the formula: position number =

Describing Data. We find the position of the central observation using the formula: position number = HOSP 1207 (Business Stats) Learning Centre Describing Data This worksheet focuses on describing data through measuring its central tendency and variability. These measurements will give us an idea of what

More information

MBA 611 STATISTICS AND QUANTITATIVE METHODS

MBA 611 STATISTICS AND QUANTITATIVE METHODS MBA 611 STATISTICS AND QUANTITATIVE METHODS Part I. Review of Basic Statistics (Chapters 1-11) A. Introduction (Chapter 1) Uncertainty: Decisions are often based on incomplete information from uncertain

More information

When to use Excel. When NOT to use Excel 9/24/2014

When to use Excel. When NOT to use Excel 9/24/2014 Analyzing Quantitative Assessment Data with Excel October 2, 2014 Jeremy Penn, Ph.D. Director When to use Excel You want to quickly summarize or analyze your assessment data You want to create basic visual

More information

An introduction to using Microsoft Excel for quantitative data analysis

An introduction to using Microsoft Excel for quantitative data analysis Contents An introduction to using Microsoft Excel for quantitative data analysis 1 Introduction... 1 2 Why use Excel?... 2 3 Quantitative data analysis tools in Excel... 3 4 Entering your data... 6 5 Preparing

More information

STATISTICS FOR PSYCH MATH REVIEW GUIDE

STATISTICS FOR PSYCH MATH REVIEW GUIDE STATISTICS FOR PSYCH MATH REVIEW GUIDE ORDER OF OPERATIONS Although remembering the order of operations as BEDMAS may seem simple, it is definitely worth reviewing in a new context such as statistics formulae.

More information

A frequency distribution is a table used to describe a data set. A frequency table lists intervals or ranges of data values called data classes

A frequency distribution is a table used to describe a data set. A frequency table lists intervals or ranges of data values called data classes A frequency distribution is a table used to describe a data set. A frequency table lists intervals or ranges of data values called data classes together with the number of data values from the set that

More information

WHAT IS EXCEL, AND WHY DO WE USE IT?

WHAT IS EXCEL, AND WHY DO WE USE IT? WHAT IS EXCEL, AND WHY DO WE USE IT? Excel is a spreadsheet program that allows you to store, organize, and analyze information Descriptives: For continuous variables, displays mean, median, mode, minimum/maximum,

More information

Understanding Variability

Understanding Variability 3 Vive la Différence Understanding Variability Difficulty Scale (moderately easy, but not a cinch) How much Excel? (a ton) What you ll learn about in this chapter Why variability is valuable as a descriptive

More information

TIPS FOR DOING STATISTICS IN EXCEL

TIPS FOR DOING STATISTICS IN EXCEL TIPS FOR DOING STATISTICS IN EXCEL Before you begin, make sure that you have the DATA ANALYSIS pack running on your machine. It comes with Excel. Here s how to check if you have it, and what to do if you

More information

Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition

Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition Bowerman, O'Connell, Aitken Schermer, & Adcock, Business Statistics in Practice, Canadian edition Online Learning Centre Technology Step-by-Step - Excel Microsoft Excel is a spreadsheet software application

More information

Research Methods 1 Handouts, Graham Hole,COGS - version 1.0, September 2000: Page 1:

Research Methods 1 Handouts, Graham Hole,COGS - version 1.0, September 2000: Page 1: Research Methods 1 Handouts, Graham Hole,COGS - version 1.0, September 000: Page 1: DESCRIPTIVE STATISTICS - FREQUENCY DISTRIBUTIONS AND AVERAGES: Inferential and Descriptive Statistics: There are four

More information

Histogram. Graphs, and measures of central tendency and spread. Alternative: density (or relative frequency ) plot /13/2004

Histogram. Graphs, and measures of central tendency and spread. Alternative: density (or relative frequency ) plot /13/2004 Graphs, and measures of central tendency and spread 9.07 9/13/004 Histogram If discrete or categorical, bars don t touch. If continuous, can touch, should if there are lots of bins. Sum of bin heights

More information

Summarizing Scores with Measures of Central Tendency: The Mean, Median, and Mode

Summarizing Scores with Measures of Central Tendency: The Mean, Median, and Mode Summarizing Scores with Measures of Central Tendency: The Mean, Median, and Mode Outline of the Course III. Descriptive Statistics A. Measures of Central Tendency (Chapter 3) 1. Mean 2. Median 3. Mode

More information

IBM SPSS Statistics for Beginners for Windows

IBM SPSS Statistics for Beginners for Windows ISS, NEWCASTLE UNIVERSITY IBM SPSS Statistics for Beginners for Windows A Training Manual for Beginners Dr. S. T. Kometa A Training Manual for Beginners Contents 1 Aims and Objectives... 3 1.1 Learning

More information

CALCULATIONS & STATISTICS

CALCULATIONS & STATISTICS CALCULATIONS & STATISTICS CALCULATION OF SCORES Conversion of 1-5 scale to 0-100 scores When you look at your report, you will notice that the scores are reported on a 0-100 scale, even though respondents

More information

Chapter 1: Looking at Data Section 1.1: Displaying Distributions with Graphs

Chapter 1: Looking at Data Section 1.1: Displaying Distributions with Graphs Types of Variables Chapter 1: Looking at Data Section 1.1: Displaying Distributions with Graphs Quantitative (numerical)variables: take numerical values for which arithmetic operations make sense (addition/averaging)

More information

Scatter Plots with Error Bars

Scatter Plots with Error Bars Chapter 165 Scatter Plots with Error Bars Introduction The procedure extends the capability of the basic scatter plot by allowing you to plot the variability in Y and X corresponding to each point. Each

More information

Descriptive Statistics: Measures of Central Tendency and Crosstabulation. 789mct_dispersion_asmp.pdf

Descriptive Statistics: Measures of Central Tendency and Crosstabulation. 789mct_dispersion_asmp.pdf 789mct_dispersion_asmp.pdf Michael Hallstone, Ph.D. hallston@hawaii.edu Lectures 7-9: Measures of Central Tendency, Dispersion, and Assumptions Lecture 7: Descriptive Statistics: Measures of Central Tendency

More information

Joint Frequency Distributions. Lecture 6--Monday, Sept 27, 2010 Announcements. Lecture 6--Monday, Sept 27, 2010 Announcements 2 HOURS ONLY

Joint Frequency Distributions. Lecture 6--Monday, Sept 27, 2010 Announcements. Lecture 6--Monday, Sept 27, 2010 Announcements 2 HOURS ONLY Lecture 6--Monday, Sept 27, 2010 Announcements Join PRS depending on the side of the room you are sitting on! Right side (as you look at the screens) use: < x >, < y > Left side (as you look at the screens)

More information

Content DESCRIPTIVE STATISTICS. Data & Statistic. Statistics. Example: DATA VS. STATISTIC VS. STATISTICS

Content DESCRIPTIVE STATISTICS. Data & Statistic. Statistics. Example: DATA VS. STATISTIC VS. STATISTICS Content DESCRIPTIVE STATISTICS Dr Najib Majdi bin Yaacob MD, MPH, DrPH (Epidemiology) USM Unit of Biostatistics & Research Methodology School of Medical Sciences Universiti Sains Malaysia. Introduction

More information

Statistics. Measurement. Scales of Measurement 7/18/2012

Statistics. Measurement. Scales of Measurement 7/18/2012 Statistics Measurement Measurement is defined as a set of rules for assigning numbers to represent objects, traits, attributes, or behaviors A variableis something that varies (eye color), a constant does

More information

Section 2.4 Numerical Measures of Central Tendency

Section 2.4 Numerical Measures of Central Tendency Section 2.4 Numerical Measures of Central Tendency 2.4.1 Definitions Mean: The Mean of a quantitative dataset is the sum of the observations in the dataset divided by the number of observations in the

More information

Create Custom Tables in No Time

Create Custom Tables in No Time SPSS Custom Tables 17.0 Create Custom Tables in No Time Easily analyze and communicate your results with SPSS Custom Tables, an add-on module for the SPSS Statistics product line Share analytical results

More information

consider the number of math classes taken by math 150 students. how can we represent the results in one number?

consider the number of math classes taken by math 150 students. how can we represent the results in one number? ch 3: numerically summarizing data - center, spread, shape 3.1 measure of central tendency or, give me one number that represents all the data consider the number of math classes taken by math 150 students.

More information

TI-83 Plus Graphing Calculator Keystroke Guide

TI-83 Plus Graphing Calculator Keystroke Guide TI-83 Plus Graphing Calculator Keystroke Guide In your textbook you will notice that on some pages a key-shaped icon appears next to a brief description of a feature on your graphing calculator. In this

More information

Chapter 2: Exploring Data with Graphs and Numerical Summaries. Graphical Measures- Graphs are used to describe the shape of a data set.

Chapter 2: Exploring Data with Graphs and Numerical Summaries. Graphical Measures- Graphs are used to describe the shape of a data set. Page 1 of 16 Chapter 2: Exploring Data with Graphs and Numerical Summaries Graphical Measures- Graphs are used to describe the shape of a data set. Section 1: Types of Variables In general, variable can

More information

Chapter 2. Objectives. Tabulate Qualitative Data. Frequency Table. Descriptive Statistics: Organizing, Displaying and Summarizing Data.

Chapter 2. Objectives. Tabulate Qualitative Data. Frequency Table. Descriptive Statistics: Organizing, Displaying and Summarizing Data. Objectives Chapter Descriptive Statistics: Organizing, Displaying and Summarizing Data Student should be able to Organize data Tabulate data into frequency/relative frequency tables Display data graphically

More information

Descriptive Statistics

Descriptive Statistics Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize

More information

Nonparametric Tests. Chi-Square Test for Independence

Nonparametric Tests. Chi-Square Test for Independence DDBA 8438: Nonparametric Statistics: The Chi-Square Test Video Podcast Transcript JENNIFER ANN MORROW: Welcome to "Nonparametric Statistics: The Chi-Square Test." My name is Dr. Jennifer Ann Morrow. In

More information

Is it statistically significant? The chi-square test

Is it statistically significant? The chi-square test UAS Conference Series 2013/14 Is it statistically significant? The chi-square test Dr Gosia Turner Student Data Management and Analysis 14 September 2010 Page 1 Why chi-square? Tests whether two categorical

More information

CHAPTER 3 CENTRAL TENDENCY ANALYSES

CHAPTER 3 CENTRAL TENDENCY ANALYSES CHAPTER 3 CENTRAL TENDENCY ANALYSES The next concept in the sequential statistical steps approach is calculating measures of central tendency. Measures of central tendency represent some of the most simple

More information

MEASURES OF CENTRAL TENDENCY

MEASURES OF CENTRAL TENDENCY CHAPTER 5 MEASURES OF CENTRAL TENDENCY OBJECTIVES After completing this chapter, you should be able to define, discuss, and compute the most commonly encountered measures of central tendency the mean,

More information

Describe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo

Describe what is meant by a placebo Contrast the double-blind procedure with the single-blind procedure Review the structure for organizing a memo Readings: Ha and Ha Textbook - Chapters 1 8 Appendix D & E (online) Plous - Chapters 10, 11, 12 and 14 Chapter 10: The Representativeness Heuristic Chapter 11: The Availability Heuristic Chapter 12: Probability

More information

( ) ( ) Central Tendency. Central Tendency

( ) ( ) Central Tendency. Central Tendency 1 Central Tendency CENTRAL TENDENCY: A statistical measure that identifies a single score that is most typical or representative of the entire group Usually, a value that reflects the middle of the distribution

More information

Biostatistics: DESCRIPTIVE STATISTICS: 2, VARIABILITY

Biostatistics: DESCRIPTIVE STATISTICS: 2, VARIABILITY Biostatistics: DESCRIPTIVE STATISTICS: 2, VARIABILITY 1. Introduction Besides arriving at an appropriate expression of an average or consensus value for observations of a population, it is important to

More information

Chi Square Analysis. When do we use chi square?

Chi Square Analysis. When do we use chi square? Chi Square Analysis When do we use chi square? More often than not in psychological research, we find ourselves collecting scores from participants. These data are usually continuous measures, and might

More information

Descriptive Statistics and Measurement Scales

Descriptive Statistics and Measurement Scales Descriptive Statistics 1 Descriptive Statistics and Measurement Scales Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample

More information

Using SPSS, Chapter 2: Descriptive Statistics

Using SPSS, Chapter 2: Descriptive Statistics 1 Using SPSS, Chapter 2: Descriptive Statistics Chapters 2.1 & 2.2 Descriptive Statistics 2 Mean, Standard Deviation, Variance, Range, Minimum, Maximum 2 Mean, Median, Mode, Standard Deviation, Variance,

More information

Exploratory data analysis (Chapter 2) Fall 2011

Exploratory data analysis (Chapter 2) Fall 2011 Exploratory data analysis (Chapter 2) Fall 2011 Data Examples Example 1: Survey Data 1 Data collected from a Stat 371 class in Fall 2005 2 They answered questions about their: gender, major, year in school,

More information

Models for Discrete Variables

Models for Discrete Variables Probability Models for Discrete Variables Our study of probability begins much as any data analysis does: What is the distribution of the data? Histograms, boxplots, percentiles, means, standard deviations

More information

Creating A Grade Sheet With Microsoft Excel

Creating A Grade Sheet With Microsoft Excel Creating A Grade Sheet With Microsoft Excel Microsoft Excel serves as an excellent tool for tracking grades in your course. But its power is not limited to its ability to organize information in rows and

More information

What are Data? The Research Question (Randomised Controlled Trials (RCTs)) The Research Question (Non RCTs)

What are Data? The Research Question (Randomised Controlled Trials (RCTs)) The Research Question (Non RCTs) What are Data? Quantitative Data o Sets of measurements of objective descriptions of physical and behavioural events; susceptible to statistical analysis Qualitative data o Descriptive, views, actions

More information

Central Tendency - Computing and understanding averages. different in conception and calculation. They represent different notions of the center of a

Central Tendency - Computing and understanding averages. different in conception and calculation. They represent different notions of the center of a Central Tendency The objectives of this lesson are: Understanding measures of central tendency Computing the mean of a set of scores by hand Computing the median fir a set of scores by hand Determining/calculating

More information

Controlled Science Investigation NCATE Competency Assessment for SCE 4350 Indicator 8.1 and 8.2

Controlled Science Investigation NCATE Competency Assessment for SCE 4350 Indicator 8.1 and 8.2 Controlled Science Investigation NCATE Competency Assessment for SCE 4350 Indicator 8.1 and 8.2 The following guide provides steps for helping students learn how to 1) plan and conduct a simple investigation

More information

DDBA 8438: The t Test for Independent Samples Video Podcast Transcript

DDBA 8438: The t Test for Independent Samples Video Podcast Transcript DDBA 8438: The t Test for Independent Samples Video Podcast Transcript JENNIFER ANN MORROW: Welcome to The t Test for Independent Samples. My name is Dr. Jennifer Ann Morrow. In today's demonstration,

More information

Bivariate Descriptive Statistics: Unsing Spreadsheets to View and Summarize Data

Bivariate Descriptive Statistics: Unsing Spreadsheets to View and Summarize Data Connexions module: m47471 1 Bivariate Descriptive Statistics: Unsing Spreadsheets to View and Summarize Data Irene Mary Duranczyk Suzanne Loch Janet Stottlemyer This work is produced by The Connexions

More information

DESCRIPTIVE STATISTICS. The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses.

DESCRIPTIVE STATISTICS. The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses. DESCRIPTIVE STATISTICS The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses. DESCRIPTIVE VS. INFERENTIAL STATISTICS Descriptive To organize,

More information

TI-Inspire manual 1. I n str uctions. Ti-Inspire for statistics. General Introduction

TI-Inspire manual 1. I n str uctions. Ti-Inspire for statistics. General Introduction TI-Inspire manual 1 I n str uctions Ti-Inspire for statistics General Introduction TI-Inspire manual 2 General instructions Press the Home Button to go to home page Pages you will use the most #1 is a

More information

SPSS for Exploratory Data Analysis Data used in this guide: studentp.sav (http://people.ysu.edu/~gchang/stat/studentp.sav)

SPSS for Exploratory Data Analysis Data used in this guide: studentp.sav (http://people.ysu.edu/~gchang/stat/studentp.sav) Data used in this guide: studentp.sav (http://people.ysu.edu/~gchang/stat/studentp.sav) Organize and Display One Quantitative Variable (Descriptive Statistics, Boxplot & Histogram) 1. Move the mouse pointer

More information

14 Chi-Square CHAPTER Chapter Outline 14.1 THE GOODNESS-OF-FIT TEST 14.2 TEST OF INDEPENDENCE

14 Chi-Square CHAPTER Chapter Outline 14.1 THE GOODNESS-OF-FIT TEST 14.2 TEST OF INDEPENDENCE www.ck12.org CHAPTER 14 Chi-Square Chapter Outline 14.1 THE GOODNESS-OF-FIT TEST 14.2 TEST OF INDEPENDENCE 278 www.ck12.org Chapter 14. Chi-Square 14.1 The Goodness-of-Fit Test Learning Objectives Learn

More information

10-3 Measures of Central Tendency and Variation

10-3 Measures of Central Tendency and Variation 10-3 Measures of Central Tendency and Variation So far, we have discussed some graphical methods of data description. Now, we will investigate how statements of central tendency and variation can be used.

More information

CHI SQUARE DISTRIBUTION

CHI SQUARE DISTRIBUTION CI SQUARE DISTRIBUTION 1 Introduction to the Chi Square Test of Independence This test is used to analyse the relationship between two sets of discrete data. Contingency tables are used to examine the

More information

Minitab Guide. This packet contains: A Friendly Guide to Minitab. Minitab Step-By-Step

Minitab Guide. This packet contains: A Friendly Guide to Minitab. Minitab Step-By-Step Minitab Guide This packet contains: A Friendly Guide to Minitab An introduction to Minitab; including basic Minitab functions, how to create sets of data, and how to create and edit graphs of different

More information

TI-86 Graphing Calculator Keystroke Guide

TI-86 Graphing Calculator Keystroke Guide TI-86 Graphing Calculator Keystroke Guide In your textbook you will notice that on some pages a key-shaped icon appears next to a brief description of a feature on your graphing calculator. In this guide

More information

Sampling, frequency distribution, graphs, measures of central tendency, measures of dispersion

Sampling, frequency distribution, graphs, measures of central tendency, measures of dispersion Statistics Basics Sampling, frequency distribution, graphs, measures of central tendency, measures of dispersion Part 1: Sampling, Frequency Distributions, and Graphs The method of collecting, organizing,

More information

Contingency Tables and the Chi Square Statistic. Interpreting Computer Printouts and Constructing Tables

Contingency Tables and the Chi Square Statistic. Interpreting Computer Printouts and Constructing Tables Contingency Tables and the Chi Square Statistic Interpreting Computer Printouts and Constructing Tables Contingency Tables/Chi Square Statistics What are they? A contingency table is a table that shows

More information

Main Effects and Interactions

Main Effects and Interactions Main Effects & Interactions page 1 Main Effects and Interactions So far, we ve talked about studies in which there is just one independent variable, such as violence of television program. You might randomly

More information

F. Farrokhyar, MPhil, PhD, PDoc

F. Farrokhyar, MPhil, PhD, PDoc Learning objectives Descriptive Statistics F. Farrokhyar, MPhil, PhD, PDoc To recognize different types of variables To learn how to appropriately explore your data How to display data using graphs How

More information

Variance and Standard Deviation. Variance = ( X X mean ) 2. Symbols. Created 2007 By Michael Worthington Elizabeth City State University

Variance and Standard Deviation. Variance = ( X X mean ) 2. Symbols. Created 2007 By Michael Worthington Elizabeth City State University Variance and Standard Deviation Created 2 By Michael Worthington Elizabeth City State University Variance = ( mean ) 2 The mean ( average) is between the largest and the least observations Subtracting

More information

MAT 142 College Mathematics Module #3

MAT 142 College Mathematics Module #3 MAT 142 College Mathematics Module #3 Statistics Terri Miller Spring 2009 revised March 24, 2009 1.1. Basic Terms. 1. Population, Sample, and Data A population is the set of all objects under study, a

More information

Exercise 1.12 (Pg. 22-23)

Exercise 1.12 (Pg. 22-23) Individuals: The objects that are described by a set of data. They may be people, animals, things, etc. (Also referred to as Cases or Records) Variables: The characteristics recorded about each individual.

More information

Homework 3. Part 1. Name: Score: / null

Homework 3. Part 1. Name: Score: / null Name: Score: / Homework 3 Part 1 null 1 For the following sample of scores, the standard deviation is. Scores: 7, 2, 4, 6, 4, 7, 3, 7 Answer Key: 2 2 For any set of data, the sum of the deviation scores

More information

The Big 50 Revision Guidelines for S1

The Big 50 Revision Guidelines for S1 The Big 50 Revision Guidelines for S1 If you can understand all of these you ll do very well 1. Know what is meant by a statistical model and the Modelling cycle of continuous refinement 2. Understand

More information

Introduction to Statistics for Psychology. Quantitative Methods for Human Sciences

Introduction to Statistics for Psychology. Quantitative Methods for Human Sciences Introduction to Statistics for Psychology and Quantitative Methods for Human Sciences Jonathan Marchini Course Information There is website devoted to the course at http://www.stats.ox.ac.uk/ marchini/phs.html

More information

Variables and Data A variable contains data about anything we measure. For example; age or gender of the participants or their score on a test.

Variables and Data A variable contains data about anything we measure. For example; age or gender of the participants or their score on a test. The Analysis of Research Data The design of any project will determine what sort of statistical tests you should perform on your data and how successful the data analysis will be. For example if you decide

More information

IBM SPSS Statistics 20 Part 1: Descriptive Statistics

IBM SPSS Statistics 20 Part 1: Descriptive Statistics CALIFORNIA STATE UNIVERSITY, LOS ANGELES INFORMATION TECHNOLOGY SERVICES IBM SPSS Statistics 20 Part 1: Descriptive Statistics Summer 2013, Version 2.0 Table of Contents Introduction...2 Downloading the

More information

Variables. Exploratory Data Analysis

Variables. Exploratory Data Analysis Exploratory Data Analysis Exploratory Data Analysis involves both graphical displays of data and numerical summaries of data. A common situation is for a data set to be represented as a matrix. There is

More information

Statistics Review PSY379

Statistics Review PSY379 Statistics Review PSY379 Basic concepts Measurement scales Populations vs. samples Continuous vs. discrete variable Independent vs. dependent variable Descriptive vs. inferential stats Common analyses

More information

Technology Step-by-Step Using StatCrunch

Technology Step-by-Step Using StatCrunch Technology Step-by-Step Using StatCrunch Section 1.3 Simple Random Sampling 1. Select Data, highlight Simulate Data, then highlight Discrete Uniform. 2. Fill in the following window with the appropriate

More information

RIFIS Ad Hoc Reports

RIFIS Ad Hoc Reports RIFIS Ad Hoc Reports To retrieve the entire list of all Ad Hoc Reports, including the Base reports and any additional reports published to your Role, select Ad Hoc for the Type under Filter Report By and

More information

Analyzing and interpreting data Evaluation resources from Wilder Research

Analyzing and interpreting data Evaluation resources from Wilder Research Wilder Research Analyzing and interpreting data Evaluation resources from Wilder Research Once data are collected, the next step is to analyze the data. A plan for analyzing your data should be developed

More information

Measurement and Measurement Scales

Measurement and Measurement Scales Measurement and Measurement Scales Measurement is the foundation of any scientific investigation Everything we do begins with the measurement of whatever it is we want to study Definition: measurement

More information

Central Tendency. n Measures of Central Tendency: n Mean. n Median. n Mode

Central Tendency. n Measures of Central Tendency: n Mean. n Median. n Mode Central Tendency Central Tendency n A single summary score that best describes the central location of an entire distribution of scores. n Measures of Central Tendency: n Mean n The sum of all scores divided

More information

Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm

Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm

More information

Using CrunchIt (http://bcs.whfreeman.com/crunchit/bps4e) or StatCrunch (www.calvin.edu/go/statcrunch)

Using CrunchIt (http://bcs.whfreeman.com/crunchit/bps4e) or StatCrunch (www.calvin.edu/go/statcrunch) Using CrunchIt (http://bcs.whfreeman.com/crunchit/bps4e) or StatCrunch (www.calvin.edu/go/statcrunch) 1. In general, this package is far easier to use than many statistical packages. Every so often, however,

More information

Using Excel for descriptive statistics

Using Excel for descriptive statistics FACT SHEET Using Excel for descriptive statistics Introduction Biologists no longer routinely plot graphs by hand or rely on calculators to carry out difficult and tedious statistical calculations. These

More information

Church Growth Tools Overview

Church Growth Tools Overview Table of Contents Church Growth Tools Overview 3 Basic Church Growth Tool Steps 4 Accessing Church Growth Tools 5 Working with the Assignment Form 6 Setting up the Assignment Form 7 Filtering the Assignment

More information

Comments 2 For Discussion Sheet 2 and Worksheet 2 Frequency Distributions and Histograms

Comments 2 For Discussion Sheet 2 and Worksheet 2 Frequency Distributions and Histograms Comments 2 For Discussion Sheet 2 and Worksheet 2 Frequency Distributions and Histograms Discussion Sheet 2 We have studied graphs (charts) used to represent categorical data. We now want to look at a

More information