Introduction |
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2 | (1) |
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PART I CONCEPTS, STANDARDS, AND TOOLS |
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7 | (1) |
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7 | (12) |
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7 | (2) |
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9 | (1) |
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Basic Data Analyzed in SEM |
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9 | (1) |
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10 | (4) |
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Pedagogy and SEM Families |
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14 | (1) |
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15 | (1) |
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16 | (2) |
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18 | (1) |
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18 | (1) |
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18 | (1) |
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2 Background Concepts and Self-Test |
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19 | (13) |
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Uneven Background Preparation |
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19 | (1) |
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Potential Obstacles to Learning about SEM |
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20 | (3) |
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23 | (1) |
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Measurement and Psychometrics |
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24 | (1) |
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25 | (1) |
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26 | (1) |
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27 | (1) |
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28 | (4) |
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32 | (14) |
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32 | (6) |
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38 | (1) |
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39 | (2) |
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41 | (3) |
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44 | (1) |
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44 | (2) |
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46 | (21) |
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46 | (2) |
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48 | (1) |
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49 | (5) |
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Classical (Obsolete) Methods for Incomplete Data |
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54 | (1) |
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Modern Methods for Incomplete Data |
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55 | (1) |
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Other Data Screening Issues |
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56 | (6) |
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62 | (1) |
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62 | (1) |
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63 | (1) |
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Appendix 4.A Steps of Multiple Imputation |
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64 | (3) |
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67 | (12) |
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Ease of Use, Not Suspension of Judgment |
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67 | (1) |
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Human-Computer Interaction |
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68 | (1) |
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68 | (2) |
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Commercial versus Free Computer Tools |
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70 | (1) |
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71 | (2) |
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Free SEM Software with Graphical User Interfaces |
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73 | (1) |
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Commercial SEM Computer Tools |
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73 | (3) |
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SEM Resources for Other Computing Environments |
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76 | (1) |
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76 | (3) |
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PART II SPECIFICATION, ESTIMATION, AND TESTING |
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6 Nonparametric Causal Models |
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79 | (21) |
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Graph Vocabulary and Symbolism |
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79 | (1) |
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Contracted Chains and Confounding |
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80 | (1) |
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81 | (1) |
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82 | (2) |
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Conditional Independencies and Other Types of Bias |
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84 | (4) |
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Principles for Covariate Selection |
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88 | (1) |
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D-Separation and Basis Sets |
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89 | (3) |
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Graphical Identification Criteria |
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92 | (4) |
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96 | (2) |
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98 | (1) |
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99 | (1) |
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99 | (1) |
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7 Parametric Causal Models |
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100 | (17) |
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100 | (3) |
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Diagrams for Contracted Chains and Assumptions |
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103 | (2) |
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Confounding in Parametric Models |
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105 | (1) |
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Models with Correlated Causes or Indirect Effects |
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106 | (3) |
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Recursive, Nonrecursive, and Partially Recursive Models |
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109 | (2) |
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111 | (1) |
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111 | (1) |
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112 | (1) |
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112 | (1) |
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Appendix 7.A Advanced Topics in Parametric Models |
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113 | (4) |
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8 Local Estimation and Piecewise SEM |
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117 | (14) |
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Rationale of Local Estimation |
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117 | (1) |
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118 | (2) |
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120 | (10) |
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130 | (1) |
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130 | (1) |
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130 | (1) |
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9 Global Estimation and Mean Structures |
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131 | (25) |
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Simultaneous Methods and Error Propagation |
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131 | (1) |
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Maximum Likelihood Estimation |
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132 | (3) |
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135 | (2) |
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137 | (1) |
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138 | (1) |
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FIML for Incomplete Data versus Multiple Imputation |
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138 | (2) |
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Alternative Estimators for Continuous Outcomes |
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140 | (1) |
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Fitting Models to Correlation Matrices |
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141 | (1) |
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Healthy Perspective on Estimators and Global Estimation |
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142 | (1) |
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142 | (5) |
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Introduction to Mean Structures |
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147 | (3) |
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Precis of Global Estimation |
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150 | (1) |
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151 | (1) |
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151 | (1) |
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151 | (2) |
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Appendix 9.A Types of Information Matrices and Computer Options |
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153 | (2) |
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Appendix 9.B Casewise ML Methods for Data Missing Not at Random |
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155 | (1) |
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10 Model Testing and Indexing |
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156 | (26) |
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156 | (1) |
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156 | (5) |
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Scaled Chi-Squares and Robust Standard Errors for Nonnormal Distributions |
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161 | (2) |
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163 | (3) |
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166 | (2) |
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168 | (1) |
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169 | (1) |
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Thresholds for Approximate Fit Indexes |
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170 | (2) |
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Recommended Approach to Fit Evaluation |
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172 | (1) |
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Global Fit Statistics for the Detailed Example |
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173 | (1) |
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174 | (3) |
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177 | (2) |
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179 | (1) |
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179 | (1) |
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Appendix 10.A Significance Testing Based on the RMSEA |
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180 | (2) |
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182 | (21) |
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182 | (1) |
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183 | (1) |
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Empirical versus Theoretical Respecification |
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184 | (1) |
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Chi-Square Difference Test |
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184 | (3) |
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Modification Indexes and Related Statistics |
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187 | (1) |
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Intelligent Automated Search Strategies |
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188 | (1) |
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Model Building for the Detailed Example |
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188 | (2) |
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Comparing Nonnested Models |
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190 | (4) |
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194 | (2) |
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Coping with Equivalent or Nearly Equivalent Models |
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196 | (2) |
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198 | (1) |
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199 | (1) |
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199 | (1) |
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Appendix 11.A Other Types of Model Relations and Tests |
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200 | (3) |
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203 | (14) |
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Issues in Multiple-Group SEM |
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204 | (1) |
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Detailed Example for a Path Model of Achievement and Delinquency |
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205 | (6) |
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Tests for Conditional Indirect Effects Over Groups |
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211 | (1) |
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212 | (1) |
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213 | (1) |
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213 | (4) |
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PART III MULTIPLE-INDICATOR APPROXIMATION OF CONCEPTS |
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13 Multiple-Indicator Measurement |
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217 | (12) |
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Concepts, Indicators, and Proxies |
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218 | (1) |
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Reflective Measurement and Effect Indicators |
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219 | (1) |
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Causal-Formative Measurement and Causal Indicators |
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220 | (1) |
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Composite Measurement and Composite Indicators |
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221 | (1) |
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222 | (1) |
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Considerations in Selecting a Measurement Model |
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223 | (1) |
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Cautions on Formative Measurement |
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224 | (1) |
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Alternative Measurement Models and Approaches |
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225 | (2) |
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227 | (1) |
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228 | (1) |
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14 Confirmatory Factor Analysis |
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229 | (34) |
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229 | (2) |
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Suggestions for Selecting Indicators |
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231 | (1) |
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232 | (2) |
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Other Methods for Scaling Factors |
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234 | (2) |
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Detailed Example for a Basic CFA Model of Cognitive Abilities |
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236 | (7) |
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Respecification of CFA Models |
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243 | (3) |
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246 | (3) |
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249 | (2) |
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Special Tests with Equality Constraints |
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251 | (1) |
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Models for Multitrait-Multimethod Data |
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252 | (3) |
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Second-Order and Bifactor Models with General Factors |
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255 | (3) |
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258 | (1) |
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259 | (1) |
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259 | (1) |
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Appendix 14.A Identification Rules for Correlated Errors or Multiple Loadings |
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260 | (3) |
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15 Structural Regression Models |
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263 | (21) |
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263 | (2) |
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265 | (3) |
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Other Modeling Strategies |
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268 | (1) |
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Detailed Example of Two-Step Modeling in a High-Risk Sample |
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269 | (5) |
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Partial SR Models with Single Indicators |
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274 | (4) |
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Example for a Partial SR Model |
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278 | (3) |
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281 | (2) |
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283 | (1) |
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283 | (1) |
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284 | (25) |
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Modern Composite Analysis in SEM |
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285 | (1) |
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285 | (3) |
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288 | (1) |
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289 | (5) |
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Alternative Composite Model |
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294 | (3) |
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Partial Least Squares Path Modeling Algorithm |
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297 | (3) |
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PLS-PM Analysis of the Composite Model |
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300 | (1) |
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Henseler-Ogasawara Specification and ML Analysis |
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301 | (3) |
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304 | (1) |
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305 | (1) |
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305 | (4) |
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PART IV ADVANCED TECHNIQUES |
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17 Analyses in Small Samples |
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309 | (10) |
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Suggestions for Analyzing Common Factor Models |
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309 | (2) |
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Analysis of a Common Factor Model in a Small Sample |
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311 | (4) |
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Controlling Measurement Error in Manifest-Variable Path Models |
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315 | (1) |
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Adjusted Test Statistics for Small Samples |
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316 | (1) |
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Bayesian Methods and Regularized SEM |
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317 | (1) |
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318 | (1) |
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318 | (1) |
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318 | (1) |
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18 Categorical Confirmatory Factor Analysis |
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319 | (12) |
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Basic Estimation Options for Categorical Data |
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319 | (1) |
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Overview of Continuous/Categorical Variable Methodology |
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320 | (1) |
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Latent Response Variables and Thresholds |
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321 | (1) |
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321 | (2) |
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Measurement Model and Diagram |
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323 | (1) |
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Methods to Scale Latent Response Variables |
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323 | (1) |
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Estimators, Adjusted Test Statistics, and Robust Standard Errors |
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324 | (1) |
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Models with Continuous and Ordinal Indicators |
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325 | (1) |
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Detailed Example for Items about Self-Rated Depression |
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325 | (2) |
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Other Estimation Options for Categorical CFA |
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327 | (2) |
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Item Response Theory and CFA |
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329 | (1) |
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329 | (1) |
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330 | (1) |
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330 | (1) |
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19 Nonrecursive Models with Causal Loops |
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331 | (18) |
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331 | (2) |
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Assumptions of Causal Loops |
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333 | (1) |
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Identification Requirements |
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333 | (3) |
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Respecification of Nonrecursive Models That Are Not Identified |
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336 | (1) |
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Order Condition and Rank Condition |
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337 | (1) |
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Detailed Example for a Nonrecursive Partial SR Model |
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338 | (6) |
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Blocked-Error R2 for Nonrecursive Models |
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344 | (1) |
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345 | (1) |
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345 | (1) |
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346 | (1) |
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Appendix 19.A Evaluation of the Rank Condition |
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347 | (2) |
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20 Enhanced Mediation Analysis |
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349 | (23) |
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Mediation Analysis in Cross-Sectional Designs |
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350 | (3) |
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Effect Sizes for Indirect Effects |
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353 | (3) |
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Cross-Lag Panel Designs for Mediation |
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356 | (2) |
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Conditional Process Analysis |
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358 | (2) |
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Causal Mediation Analysis Based on Nonparametric Models and Counterfactuals |
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360 | (8) |
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Reporting Standards for Mediation Studies |
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368 | (3) |
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371 | (1) |
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371 | (1) |
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371 | (1) |
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21 Latent Growth Curve Models |
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372 | (21) |
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Basic Latent Growth Models |
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372 | (2) |
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Data Set for Analyzing Basic Growth Models with No Covariates |
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374 | (5) |
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Example Analyses of Basic Growth Models |
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379 | (3) |
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Example for a Growth Predictor Model with Time-Invariant Covariates |
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382 | (3) |
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Practical Suggestions for Latent Growth Modeling |
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385 | (1) |
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Extensions of Latent Growth Models |
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385 | (4) |
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389 | (1) |
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390 | (1) |
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390 | (1) |
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Appendix 21.A Unequal Measurement Intervals and Options for Defining the Intercept |
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391 | (2) |
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22 Measurement Invariance |
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393 | (21) |
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395 | (3) |
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398 | (3) |
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Partial Measurement Invariance |
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401 | (1) |
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Detailed Example for a Two-Factor Model of Divergent Thinking |
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402 | (6) |
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Practical Suggestions for Measurement Invariance Testing |
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408 | (1) |
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Measurement Invariance Testing in Categorical CFA |
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409 | (1) |
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Other Statistical Approaches to Estimating Measurement Invariance |
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410 | (2) |
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412 | (1) |
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412 | (1) |
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413 | (1) |
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414 | (11) |
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414 | (1) |
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Bottom Lines and Statistical Beauty |
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414 | (1) |
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Mightily Distinguish Your Work (Be a Hero) |
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415 | (1) |
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416 | (1) |
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417 | (1) |
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418 | (1) |
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419 | (1) |
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419 | (1) |
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420 | (2) |
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422 | (1) |
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422 | (1) |
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423 | (1) |
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424 | (1) |
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424 | (1) |
Suggested Answers to Exercises |
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425 | (16) |
References |
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441 | (30) |
Author Index |
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471 | (8) |
Subject Index |
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479 | (15) |
About the Author |
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494 | |