Preface |
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xiii | |
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1 | (21) |
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2 | (1) |
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3 | (7) |
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3 | (1) |
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3 | (1) |
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4 | (1) |
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5 | (1) |
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6 | (1) |
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7 | (1) |
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7 | (1) |
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8 | (2) |
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10 | (4) |
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11 | (1) |
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12 | (1) |
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13 | (1) |
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14 | (1) |
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Measurement Levels and Data Analysis |
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14 | (2) |
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Additional Measurement Classifications |
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16 | (1) |
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Discrete and Continuous Variables |
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16 | (1) |
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Dichotomous, Binary, and Dummy Variables |
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16 | (1) |
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Categories of Statistical Analyses |
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17 | (1) |
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Number of Variables in an Analysis |
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17 | (1) |
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Primary Purpose of the Analysis |
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17 | (1) |
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Analysis of Qualitative Data |
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18 | (1) |
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19 | (1) |
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20 | (2) |
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Frequency Distributions and Graphs |
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22 | (18) |
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23 | (4) |
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Absolute Frequency Distributions |
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24 | (1) |
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Cumulative Frequency Distributions |
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25 | (1) |
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Percentage Frequency Distributions |
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25 | (1) |
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Cumulative Percentage Frequency Distributions |
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26 | (1) |
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Grouped Frequency Distributions |
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27 | (1) |
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Using Frequency Distributions to Analyze Data |
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28 | (2) |
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Misrepresentation of Data |
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30 | (1) |
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31 | (6) |
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Bar Graphs and Line Diagrams |
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32 | (1) |
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32 | (2) |
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34 | (1) |
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35 | (1) |
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36 | (1) |
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A Common Mistake in Displaying Data |
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37 | (1) |
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38 | (1) |
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38 | (2) |
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Measures of Central Tendency and Variability |
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40 | (20) |
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Measures of Central Tendency |
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40 | (8) |
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41 | (1) |
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41 | (2) |
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43 | (3) |
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Which Measure of Central Tendency to Use? |
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46 | (2) |
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48 | (9) |
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49 | (1) |
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50 | (1) |
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51 | (1) |
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52 | (1) |
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52 | (4) |
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Reporting Measures of Variability |
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56 | (52) |
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Other Uses for Central Tendency and Variability |
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57 | (1) |
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58 | (1) |
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58 | (2) |
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60 | (19) |
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60 | (2) |
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62 | (1) |
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63 | (5) |
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Converting Raw Scores to Z Scores and Percentiles |
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68 | (7) |
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Practical Uses of z Scores |
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72 | (3) |
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Deriving Raw Scores from Percentiles |
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75 | (2) |
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77 | (1) |
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77 | (2) |
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The Basics of Hypothesis Testing |
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79 | (23) |
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80 | (4) |
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80 | (1) |
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81 | (2) |
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83 | (1) |
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Probability and Inference |
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84 | (1) |
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85 | (2) |
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85 | (1) |
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86 | (1) |
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More About Research Hypotheses |
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87 | (2) |
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The One-Tailed Research Hypothesis |
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88 | (1) |
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The Two-Tailed Research Hypothesis |
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88 | (1) |
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The ``No Relationship'' Research Hypothesis |
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88 | (1) |
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Testing the Null Hypothesis |
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89 | (2) |
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91 | (3) |
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92 | (1) |
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Rejection Levels (``Alpha'') |
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93 | (1) |
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Errors in Drawing Conclusions About Relationships |
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94 | (2) |
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95 | (1) |
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Statistically Significant Relationships and Meaningful Findings |
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96 | (3) |
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Assessing Strength of Relationships (Effect Size) |
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97 | (1) |
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Is the Relationship Surprising? |
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98 | (1) |
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Complex Interpretations of Statistically Significant Relationships |
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99 | (1) |
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99 | (1) |
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100 | (2) |
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Sampling Distributions and the Null Hypothesis Testing |
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102 | (17) |
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Sample Size and Sampling Error |
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103 | (1) |
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Sampling Distributions and Inference |
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104 | (3) |
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Comparing an Experimental Sample with Its Population |
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105 | (1) |
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Comparing a Non-Experimental Sample with Its Population |
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106 | (1) |
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Sampling Distribution of Means |
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107 | (8) |
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Samples Drawn from Normal Distributions |
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109 | (5) |
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Samples Drawn from Skewed Distributions |
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114 | (1) |
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115 | (3) |
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Constructing a 95 Percent Confidence Interval |
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116 | (1) |
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Constructing a 99 Percent Confidence Interval |
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116 | (2) |
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118 | (1) |
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118 | (1) |
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Selecting a Statistical Test |
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119 | (18) |
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The Importance of Selecting the Correct Test |
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119 | (2) |
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120 | (1) |
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121 | (7) |
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122 | (1) |
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Distribution of the Variables within the Population |
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123 | (1) |
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Level of Measurement of the Variables |
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123 | (1) |
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Desirable Amount of Statistical Power |
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124 | (4) |
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Robustness of Tests Being Considered |
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128 | (1) |
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Parametric and Nonparametric Tests |
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128 | (2) |
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130 | (1) |
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Deciding Which Test to Use |
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131 | (1) |
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132 | (1) |
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The Process of Hypothesis Testing |
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133 | (2) |
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135 | (1) |
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135 | (2) |
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137 | (28) |
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137 | (1) |
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138 | (3) |
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141 | (2) |
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143 | (1) |
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Interpreting Linear Correlations |
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143 | (7) |
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Understanding Correlation Coefficients |
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144 | (1) |
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145 | (2) |
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Correlation is Not Causation |
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147 | (1) |
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Using Correlation for Inference |
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148 | (2) |
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150 | (5) |
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Computation and Presentation |
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150 | (5) |
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Nonparametric Alternatives |
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155 | (2) |
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Spearman's Rho and Kendall's Tau |
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155 | (2) |
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Correlation with Three or More Variables |
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157 | (3) |
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157 | (1) |
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158 | (1) |
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159 | (1) |
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Other Multivariate Tests That Use Correlation |
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160 | (3) |
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161 | (1) |
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162 | (1) |
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163 | (1) |
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164 | (1) |
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165 | (25) |
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165 | (3) |
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What is Simple Linear Regression? |
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168 | (2) |
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Formulating a Research Question |
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169 | (1) |
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Limitations of Simple Linear Regression |
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170 | (1) |
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Computation of the Regression Equation |
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170 | (2) |
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More About the Regression Line |
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172 | (6) |
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The Least-squares Criterion |
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174 | (3) |
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Interchanging X and Y Variables |
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177 | (1) |
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178 | (3) |
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178 | (1) |
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178 | (1) |
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Using Regression in Social Work Practice |
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179 | (2) |
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Regression with Three or More Variables |
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181 | (4) |
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Other Types of Regression Analyses |
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185 | (3) |
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185 | (1) |
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186 | (2) |
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188 | (1) |
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189 | (1) |
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190 | (27) |
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The Chi-Square Test of Association |
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190 | (18) |
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192 | (2) |
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194 | (2) |
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196 | (1) |
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196 | (3) |
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199 | (1) |
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Interpreting the Results of a Chi-Square Analysis |
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200 | (1) |
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Meaningfulness and Sample Size |
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201 | (3) |
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Restrictions on the Use of Chi-Square |
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204 | (1) |
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An Alternative: Fisher's Exact Test |
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204 | (1) |
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Using Chi-Square in Social Work Practice |
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205 | (3) |
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Cross Tabulation with Three or More Variables |
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208 | (3) |
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Problems with Sizes of Expected Frequencies |
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210 | (1) |
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Effects of Introducing Additional Variables |
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210 | (1) |
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Special Applications of the Chi-Square Formula |
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211 | (4) |
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211 | (2) |
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213 | (2) |
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215 | (1) |
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216 | (1) |
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t Tests and Analysis of Variance |
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217 | (32) |
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218 | (1) |
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218 | (1) |
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219 | (7) |
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Determining If a Sample is Representative |
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221 | (1) |
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222 | (1) |
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223 | (1) |
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A Nonparametric Alternative: Chi-Square Goodness of Fit |
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223 | (3) |
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226 | (4) |
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Use with Two Connected (or Matched) Samples Measured Once |
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226 | (1) |
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Use with One Sample Measured Twice |
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227 | (1) |
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A Nonparametric Alternative: Wilcoxon Sign |
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228 | (2) |
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230 | (13) |
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Nonparametric Alternatives: U and K-s |
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239 | (3) |
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A Multivariate Alternative: T2 |
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242 | (1) |
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Simple Analysis of Variance (Simple Anova) |
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243 | (3) |
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245 | (1) |
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A Nonparametric Alternative: Kruskal-Wallis |
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246 | (1) |
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Multivariate Analysis of Variance |
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246 | (1) |
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247 | (1) |
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247 | (2) |
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Other Contributions of Statistics to Evidence-Based Practice |
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249 | (19) |
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250 | (3) |
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Answers Sought in Program Evaluations |
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253 | (1) |
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Needs Assessments and Formative Evaluations |
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253 | (1) |
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254 | (5) |
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Hypothesis Testing in Outcome Evaluations |
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254 | (3) |
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Statistical Analyses of Outcome Evaluation Data |
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257 | (2) |
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Answers Sought in Single-System Research |
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259 | (7) |
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Hypothesis Testing in Single-System Research |
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259 | (1) |
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Statistical Analyses of Single-System Data |
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260 | (1) |
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Using Familiar Statistical Tests |
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261 | (1) |
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262 | (4) |
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266 | (1) |
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267 | (1) |
Appendix A Beginning to Select a Statistical Test |
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268 | (2) |
Glossary |
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270 | (19) |
Index |
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289 | |