Preface |
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xv | |
Acknowledgments |
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xix | |
1 Introduction |
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1 | (12) |
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1 | (1) |
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2 | (1) |
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Importance of Statistics for the Social and Health Sciences and Medicine |
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3 | (1) |
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Historical Notes: Early Use of Statistics |
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4 | (2) |
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6 | (1) |
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Cases from Current Research |
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7 | (2) |
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9 | (1) |
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9 | (4) |
2 Descriptive Statistics: Central Tendency |
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13 | (42) |
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What is the Whole Truth? Research Applications (Spuriousness) |
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13 | (3) |
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Descriptive and Inferential Statistics |
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16 | (1) |
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The Nature of Data: Scales of Measurement |
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16 | (7) |
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Descriptive Statistics: Central Tendency |
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23 | (5) |
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Using SPSS® and Excel to Understand Central Tendency |
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28 | (7) |
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35 | (2) |
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Describing the Normal Distribution: Numerical Methods |
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37 | (4) |
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Descriptive Statistics: Using Graphical Methods |
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41 | (6) |
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47 | (2) |
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Data Lab and Examples (with Solutions) |
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49 | (2) |
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51 | (4) |
3 Descriptive Statistics: Variability |
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55 | (22) |
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55 | (1) |
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56 | (1) |
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Scores Based on Percentiles |
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57 | (1) |
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Using SPSS® and Excel to Identify Percentiles |
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57 | (3) |
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Standard Deviation and Variance |
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60 | (1) |
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Calculating the Variance and Standard Deviation |
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61 | (5) |
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Population SD and Inferential SD |
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66 | (1) |
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Obtaining SD from Excel and SPSS® |
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67 | (3) |
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70 | (1) |
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Data Lab and Examples (with Solutions) |
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71 | (2) |
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73 | (4) |
4 The Normal Distribution |
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77 | (28) |
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The Nature of the Normal Curve |
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77 | (2) |
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The Standard Normal Score: Z Score |
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79 | (1) |
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The Z Score Table of Values |
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80 | (1) |
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Navigating the Z Score Distribution |
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81 | (2) |
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83 | (1) |
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Creating Rules for Locating Z Scores |
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84 | (3) |
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87 | (3) |
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Working with Raw Score Distributions |
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90 | (1) |
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Using SPSS® to Create Z Scores and Percentiles |
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90 | (4) |
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Using Excel to Create Z Scores |
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94 | (3) |
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Using Excel and SPSS® for Distribution Descriptions |
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97 | (2) |
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99 | (1) |
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Data Lab and Examples (with Solutions) |
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99 | (2) |
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101 | (4) |
5 Probability And The Z Distribution |
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105 | (28) |
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The Nature of Probability |
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106 | (1) |
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106 | (3) |
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Combinations and Permutations |
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109 | (2) |
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Conditional Probability: Using Bayes' Theorem |
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111 | (1) |
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Z Score Distribution and Probability |
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112 | (5) |
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Using SPSS® and Excel to Transform Scores |
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117 | (2) |
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Using the Attributes of the Normal Curve to Calculate Probability |
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119 | (4) |
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123 | (3) |
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From Sample Values to Sample Distributions |
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126 | (1) |
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127 | (1) |
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Data Lab and Examples (with Solutions) |
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128 | (1) |
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129 | (4) |
6 Research Design And Inferential Statistics |
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133 | (32) |
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133 | (3) |
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136 | (4) |
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Non-Experimental or Post Facto Research Designs |
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140 | (3) |
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143 | (11) |
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154 | (1) |
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154 | (2) |
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156 | (1) |
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Practical Significance: Effect Size |
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156 | (1) |
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156 | (1) |
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Using SPSS® and Excel for the Z Test |
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157 | (1) |
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158 | (3) |
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Data Lab and Examples (with Solutions) |
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161 | (1) |
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162 | (3) |
7 The T Test For Single Samples |
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165 | (42) |
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166 | (1) |
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Z Versus T: Making Accommodations |
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166 | (1) |
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167 | (2) |
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169 | (4) |
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173 | (3) |
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The T Test: A Research Example |
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176 | (4) |
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Interpreting the Results of the T Test for a Single Mean |
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180 | (1) |
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181 | (1) |
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The Hypothesis Test for the Single Sample T Test |
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182 | (1) |
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Type I and Type II Errors |
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183 | (4) |
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187 | (1) |
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Effect Size for the Single Sample T Test |
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187 | (1) |
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Power, Effect Size, and Beta |
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188 | (1) |
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One- and Two-Tailed Tests |
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189 | (3) |
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Point and Interval Estimates |
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192 | (4) |
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Using SPSS® and Excel with the Single Sample T Test |
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196 | (5) |
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201 | (1) |
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Data Lab and Examples (with Solutions) |
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201 | (2) |
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203 | (4) |
8 Independent Sample T Test |
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207 | (48) |
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207 | (1) |
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208 | (1) |
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Experimental Designs and the Independent T Test |
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208 | (1) |
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209 | (1) |
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Between and Within Research Designs |
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210 | (1) |
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211 | (2) |
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Independent T Test: The Procedure |
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213 | (2) |
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Creating the Sampling Distribution of Differences |
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215 | (1) |
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The Nature of the Sampling Distribution of Differences |
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216 | (2) |
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Calculating the Estimated Standard Error of Difference with Equal Sample Size |
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218 | (1) |
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Using Unequal Sample Sizes |
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219 | (2) |
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221 | (1) |
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Independent T Test Example |
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222 | (1) |
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Hypothesis Test Elements for the Example |
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222 | (4) |
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Before-After Convention with the Independent T Test |
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226 | (1) |
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Confidence Intervals for the Independent T Test |
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227 | (1) |
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228 | (2) |
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The Assumptions for the Independent T Test |
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230 | (1) |
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SPSS® Explore for Checking the Normal Distribution Assumption |
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231 | (2) |
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Excel Procedures for Checking the Equal Variance Assumption |
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233 | (4) |
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SPSS® Procedure for Checking the Equal Variance Assumption |
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237 | (2) |
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Using SPSS® and Excel with the Independent T Test |
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239 | (1) |
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SPSS® Procedures for the Independent T Test |
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239 | (4) |
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Excel Procedures for the Independent T Test |
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243 | (2) |
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Effect Size for the Independent T Test Example |
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245 | (1) |
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245 | (1) |
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Nonparametric Statistics: The Mann-Whitney U Test |
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246 | (3) |
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249 | (1) |
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Data Lab and Examples (with Solutions) |
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249 | (2) |
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251 | (3) |
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Graphics in the Data Summary |
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254 | (1) |
9 Analysis Of Variance |
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255 | (42) |
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A Hypothetical Example of ANOVA |
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255 | (2) |
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257 | (1) |
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The Components of Variance |
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258 | (1) |
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259 | (1) |
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260 | (8) |
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268 | (1) |
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269 | (5) |
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274 | (1) |
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Additional Considerations with ANOVA |
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275 | (1) |
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The Hypothesis Test: Interpreting ANOVA Results |
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276 | (1) |
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276 | (6) |
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Using SPSS® and Excel with One-Way ANOVA |
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282 | (7) |
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289 | (1) |
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Non-Parametric ANOVA Tests: The Kruskal-Wallis Test |
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289 | (3) |
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292 | (1) |
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Data Lab and Examples (with Solutions) |
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293 | (1) |
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294 | (3) |
10 Factorial Anova |
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297 | (32) |
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297 | (1) |
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298 | (1) |
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299 | (1) |
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299 | (1) |
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299 | (1) |
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299 | (2) |
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301 | (1) |
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302 | (1) |
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Calculating Factorial ANOVA |
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303 | (3) |
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The Hypotheses Test: Interpreting Factorial ANOVA Results |
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306 | (2) |
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Effect Size for 2XANOVA: Partial 12 |
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308 | (1) |
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309 | (2) |
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Using SPSS® to Analyze 2XANOVA |
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311 | (8) |
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Summary Chart for 2XANOVA Procedures |
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319 | (1) |
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319 | (1) |
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Data Lab and Examples (with Solutions) |
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320 | (1) |
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320 | (9) |
11 Correlation |
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329 | (42) |
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The Nature of Correlation |
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330 | (1) |
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331 | (1) |
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Pearson's Correlation Coefficient |
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332 | (2) |
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Plotting the Correlation: The Scattergram |
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334 | (3) |
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Using SPSS® to Create Scattergrams |
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337 | (2) |
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Using Excel to Create Scattergrams |
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339 | (2) |
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341 | (1) |
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342 | (2) |
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344 | (1) |
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The Hypothesis Test for Pearson's r |
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345 | (2) |
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Effect Size: the Coefficient of Determination |
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347 | (2) |
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Diagnostics: Correlation Problems |
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349 | (3) |
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Correlation Using SPSS® and Excel |
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352 | (6) |
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Nonparametric Statistics: Spearman's Rank Order Correlation (r5) |
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358 | (5) |
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363 | (1) |
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Data Lab and Examples (with Solutions) |
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364 | (1) |
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365 | (6) |
12 Bivariate Regression |
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371 | (46) |
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372 | (2) |
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374 | (2) |
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376 | (3) |
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Effect Size of Regression |
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379 | (1) |
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The Z Score Formula for Regression |
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380 | (2) |
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Testing the Regression Hypotheses |
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382 | (1) |
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The Standard Error of Estimate |
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383 | (2) |
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385 | (1) |
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Explaining Variance Through Regression |
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386 | (3) |
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A Numerical Example of Partitioning the Variation |
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389 | (1) |
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Using Excel and SPSS® with Bivariate Regression |
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390 | (1) |
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The SPSS® Regression Output |
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390 | (6) |
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The Excel Regression Output |
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396 | (2) |
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Complete Example of Bivariate Linear Regression |
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398 | (1) |
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Assumptions of Bivariate Regression |
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398 | (6) |
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404 | (1) |
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404 | (1) |
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405 | (1) |
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The Regression Equation and Individual Predictor Test of Significance |
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405 | (1) |
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Advanced Regression Procedures |
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406 | (2) |
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Detecting Problems in Bivariate Linear Regression |
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408 | (1) |
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409 | (1) |
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Data Lab and Examples (with Solutions) |
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410 | (1) |
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411 | (6) |
13 Introduction To Multiple Linear Regression |
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417 | (38) |
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The Elements of Multiple Linear Regression |
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417 | (1) |
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Same Process as Bivariate Regression |
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418 | (1) |
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Some Differences between Bivariate Linear Regression and Multiple Linear Regression |
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419 | (1) |
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420 | (1) |
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Assumptions of Multiple Linear Regression |
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421 | (1) |
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Analyzing Residuals to Check MLR Assumptions |
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422 | (1) |
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Diagnostics for MLR: Cleaning and Checking Data |
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423 | (1) |
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424 | (4) |
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428 | (1) |
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429 | (1) |
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MLR Extended Example Data |
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430 | (1) |
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431 | (2) |
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Analyzing Residuals: Are Assumptions Met? |
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433 | (3) |
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Interpreting the SPSS® Findings for MLR |
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436 | (1) |
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Entering Predictors Together as a Block |
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437 | (5) |
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Entering Predictors Separately |
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442 | (5) |
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Additional Entry Methods for MLR Analyses |
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447 | (1) |
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448 | (1) |
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448 | (2) |
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Data Lab and Example (with Solution) |
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450 | (1) |
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450 | (5) |
14 Chi-Square And Contingency Table Analysis |
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455 | (34) |
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455 | (1) |
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The Chi-square Procedure and Research Design |
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456 | (1) |
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Chi-square Design One: Goodness of Fit |
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457 | (1) |
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A Hypothetical Example: Goodness of Fit |
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458 | (4) |
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Effect Size: Goodness of Fit |
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462 | (1) |
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Chi-square Design Two: The Test of Independence |
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463 | (1) |
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A Hypothetical Example: Test of Independence |
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464 | (4) |
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468 | (2) |
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Effect Size in 2 x 2 Tables: PHI |
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470 | (1) |
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Cramer's V: Effect Size for the Chi-square Test of Independence |
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471 | (1) |
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Repeated Measures Chi-square: Mcnemar Test |
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472 | (2) |
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Using SPSS® and Excel with Chi-square |
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474 | (1) |
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Using SPSS® for the Chi-square Test of Independence |
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475 | (6) |
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Using Excel for Chi-square Analyses |
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481 | (2) |
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483 | (1) |
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Data Lab and Examples (with Solutions) |
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483 | (1) |
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484 | (5) |
15 Repeated Measures Procedures: Tdep And ANOVAws |
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489 | (20) |
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Independent and Dependent Samples in Research Designs |
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490 | (1) |
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491 | (1) |
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The Dependent T Test Calculation: The "Long" Formula |
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491 | (1) |
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Example: The Long Formula |
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492 | (2) |
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The Dependent T Test Calculation: The "Difference" Formula |
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494 | (2) |
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496 | (1) |
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Conducting The Tdep Analysis Using SPSS® |
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496 | (2) |
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Conducting The Tdep Analysis Using Excel |
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498 | (1) |
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Within-Subject ANOVA (ANOVAWS) |
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498 | (1) |
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499 | (1) |
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500 | (1) |
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501 | (1) |
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Using SPSS® for Within-Subject Data |
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501 | (1) |
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502 | (2) |
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504 | (4) |
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508 | (1) |
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508 | (1) |
Appendices |
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509 | (22) |
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509 | (1) |
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510 | (3) |
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513 | (4) |
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Additional Management Functions |
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517 | (14) |
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531 | (14) |
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531 | (2) |
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533 | (8) |
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Using Statistical Functions |
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541 | (2) |
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Data Analysis. Procedures |
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543 | (1) |
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Missing Values and "0" Values in Excel Analyses |
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544 | (1) |
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Using Excel with "Real Data" |
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544 | (1) |
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Appendix C Statistical Tables |
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545 | (10) |
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Table C.1: Z-Score Table (Values Shown are Percentages - %) |
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545 | (2) |
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Table C.2: Exclusion Values for the T-Distribution |
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547 | (1) |
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Table C.3: Critical (Exclusion) Values for the Distribution of F |
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548 | (3) |
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Table C.4: Tukey's Range Test (Upper 5% Points) |
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551 | (1) |
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Table C.5: Critical (Exclusion) Values for Pearson's Correlation Coefficient, r |
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552 | (1) |
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Table C.6: Critical Values of the x2 (Chi-Square) Distribution |
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553 | (2) |
References |
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555 | (2) |
Index |
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557 | |