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List of Figures, Tables and Boxes |
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ix | |
About the Authors |
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xvii | |
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1 Introducing Descriptive and Exploratory Statistics |
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1 | (22) |
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2 | (1) |
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2 | (2) |
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New to Statistical Analysis? This Book Is for You! |
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4 | (1) |
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So, What Are Descriptive Statistics? |
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4 | (5) |
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What No Statistical Testing? |
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9 | (1) |
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Sounds Like Inferential Statistics Are More Important |
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9 | (1) |
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Types of Descriptive Statistics |
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10 | (5) |
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Categorical Data: Frequency Distributions |
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10 | (3) |
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Continuous or Interval-Level Data: Measures of Central Tendency |
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13 | (1) |
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Continuous or Interval-Level Data: Measures of Dispersion |
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13 | (2) |
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15 | (1) |
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So, What Can I Do With Descriptive Statistics? |
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16 | (4) |
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Why Not Try Exploratory Data Analysis |
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20 | (3) |
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2 Finding Data to Describe |
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23 | (32) |
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24 | (1) |
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24 | (3) |
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24 | (2) |
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26 | (1) |
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27 | (3) |
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What Do We Mean by Research Data? |
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27 | (1) |
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28 | (2) |
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What's All This About `Open Data'? |
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30 | (2) |
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32 | (5) |
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A Little Exploration First |
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32 | (1) |
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33 | (1) |
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Accessing and Downloading Data |
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34 | (1) |
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Are You Sure About This Data? |
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35 | (1) |
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36 | (1) |
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37 | (11) |
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The General Social Survey |
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37 | (5) |
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The National Survey of Sexual Attitudes and Lifestyles 2010-2012 (Natsal-3) |
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42 | (6) |
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Got Data: Let's Look Inside! |
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48 | (1) |
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49 | (1) |
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Shit In, Shit Out: And Other Key Principles of Data Management |
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49 | (1) |
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50 | (3) |
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53 | (2) |
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3 Measure Everything -- Learn Something -- Answer Nothing: An Exploration Into variables and Types of Measurement |
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55 | (26) |
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56 | (1) |
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Measurement as a Taken for Granted |
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57 | (3) |
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Measuring the Social World |
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60 | (4) |
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Controversial and Contested Measurements |
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64 | (1) |
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Units of Measurement: Variables |
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64 | (8) |
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Classification - Gender: Men and Women? |
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65 | (1) |
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Counting Gender: How Many Men and Women? |
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66 | (4) |
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Measurement: Different Experiences of Men and Women? |
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70 | (1) |
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Explaining: Different Experiences of Men and Women? |
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71 | (1) |
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72 | (2) |
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74 | (1) |
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74 | (1) |
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74 | (1) |
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74 | (3) |
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74 | (1) |
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75 | (2) |
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77 | (4) |
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4 I Am Not a Number, I Am a Categorical Variable |
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81 | (50) |
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82 | (3) |
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Percentages: A Story of Parts and Wholes |
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85 | (2) |
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Categorical Data and Percentages |
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87 | (8) |
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Working With Valid Percent and Percent: Working With Missing Data |
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95 | (4) |
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Presenting Percentages, Don't Forget then |
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97 | (1) |
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What's This Cumulative Percent Column All About? |
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98 | (1) |
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To Merge or Not to Merge Responses |
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98 | (1) |
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Writing Up Results: Producing Descriptive Summaries |
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99 | (2) |
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100 | (1) |
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101 | (1) |
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What to Report With Missing Data? |
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101 | (2) |
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Strengths of Using the Percent |
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103 | (2) |
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Working With Contingency Tables |
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105 | (2) |
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How to Guides for IBM SPSS and MS Excel |
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107 | (1) |
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How to Guide for IBM SPSS |
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107 | (8) |
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Carrying Out Univariate Analysis |
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108 | (1) |
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109 | (1) |
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Two's Company: Carrying Out Bivariate Analysis Using IBM SPSS |
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110 | (3) |
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Bivariate Analysis: Including Percentages |
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113 | (2) |
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How to Guide for MS Excel |
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115 | (12) |
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Using MS Word to Make Frequency Tables |
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117 | (7) |
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Two's Company in MS Excel |
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124 | (3) |
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127 | (4) |
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5 I Like Being Average, I Am an Interval Variable |
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131 | (28) |
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132 | (1) |
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Measures of Central Tendency and Dispersion |
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132 | (1) |
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Averages in Everyday Life: Feedback Scores |
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133 | (1) |
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The Importance of Averages: Exploring Income and Poverty |
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134 | (2) |
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Rich Man, Poor Man, Beggar Man, Thief: The Problem When Measuring Average Incomes |
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136 | (1) |
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What's Wrong With Being Average: Income Inequalities and the Problem With Outliers |
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137 | (4) |
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Measures of Dispersion: Adding More Context to the Data |
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141 | (2) |
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143 | (2) |
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Standard Deviation: Horrible Name, but Really Useful |
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143 | (1) |
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Home on the Range? Using the Range Rule for a `Quick' Standard Deviation |
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144 | (1) |
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Average UK Income: But Which Average? |
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145 | (2) |
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Bivariate Analysis Using MCT |
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147 | (1) |
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When Averages Are Just Plain Silly |
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148 | (1) |
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I Am Above Average Actually!' |
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149 | (1) |
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Writing Up the Results: The End Is Nigh |
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149 | (2) |
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`How to' Calculate MCT and MoD Using IBM SPSS |
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151 | (4) |
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`How to' Calculate MCT and MoD Using MS Excel |
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155 | (1) |
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155 | (4) |
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159 | (40) |
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Does a Picture Tell a Thousand Words? |
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160 | (1) |
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Does It Really Matter How I Present My Data? |
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160 | (4) |
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164 | (25) |
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164 | (5) |
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169 | (5) |
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174 | (2) |
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The One With That Sounds Like Instagram |
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176 | (6) |
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The One With the Whiskers |
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182 | (3) |
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The One With All the Dots |
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185 | (4) |
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189 | (1) |
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189 | (2) |
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191 | (6) |
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197 | (2) |
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7 The Story Waiting to Be Told |
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199 | (22) |
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200 | (1) |
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201 | (1) |
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202 | (1) |
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203 | (2) |
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Measurement Still Matters |
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205 | (1) |
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`Let's Talk About Sex Baby' |
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205 | (2) |
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The Importance of Context When Exploring Data |
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207 | (2) |
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Beyond the Double Standard: Telling Stories of Sexual Difference |
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209 | (1) |
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Men Estimate -- Women Count: A Gender Difference to Recollecting |
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210 | (1) |
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Trigger Warning! Statistics in the Media |
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210 | (3) |
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The Transient Nature of the News Media |
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213 | (1) |
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Butter Is Good/Bad/Good/Bad/WTF for You! |
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213 | (3) |
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The Fine Line Between Mistakes and Misleading |
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216 | (1) |
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The Devil's in the Detail! |
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217 | (1) |
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217 | (4) |
Glossary |
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221 | (6) |
References |
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227 | (6) |
Index |
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233 | |