Preface |
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xi | |
About the Authors |
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xviii | |
Chapter 1 An Introduction to Structural Equation Modeling |
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1 | (39) |
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1 | (1) |
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What Is Structural Equation Modeling? |
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2 | (4) |
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Considerations in Using Structural Equation Modeling |
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6 | (6) |
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6 | (1) |
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7 | (1) |
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8 | (2) |
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10 | (1) |
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10 | (2) |
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Principles of Structural Equation Modeling |
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12 | (3) |
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Path Models With Latent Variables |
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12 | (1) |
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Testing Theoretical Relationships |
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13 | (5) |
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14 | (1) |
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14 | (1) |
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PLS-SEM, CB-SEM, and Regressions Based on Sum Scores |
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15 | (3) |
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Considerations When Applying PLS-SEM |
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18 | (13) |
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Key Characteristics of the PLS-SEM Method |
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18 | (6) |
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24 | (6) |
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Minimum Sample Size Requirement |
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24 | (3) |
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27 | (1) |
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28 | (1) |
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28 | (1) |
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28 | (2) |
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30 | (1) |
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Guidelines for Choosing Between PLS-SEM and CB-SEM |
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31 | (1) |
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Organization of Remaining Chapters |
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32 | (2) |
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34 | (2) |
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36 | (1) |
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Critical Thinking Questions |
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36 | (1) |
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36 | (1) |
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37 | (3) |
Chapter 2 Specifying the Path Model and Examining Data |
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40 | (45) |
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41 | (1) |
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Stage 1: Specifying the Structural Model |
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41 | (9) |
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44 | (1) |
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45 | (2) |
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47 | (3) |
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Stage 2: Specifying the Measurement Models |
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50 | (11) |
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Reflective and Formative Measurement Models |
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51 | (6) |
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Single-Item Measures and Sum Scores |
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57 | (2) |
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59 | (2) |
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Stage 3: Data Collection and Examination |
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61 | (6) |
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62 | (2) |
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Suspicious Response Patterns |
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64 | (1) |
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64 | (1) |
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65 | (2) |
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Case Study Illustration-Specifying the PLS-SEM Model |
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67 | (12) |
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Application of Stage 1: Structural Model Specification |
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67 | (2) |
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Application of Stage 2: Measurement Model Specification |
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69 | (2) |
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Application of Stage 3: Data Collection and Examination |
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71 | (1) |
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Path Model Creation Using the SmartPLS Software |
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72 | (7) |
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79 | (2) |
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81 | (1) |
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Critical Thinking Questions |
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82 | (1) |
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82 | (1) |
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83 | (2) |
Chapter 3 Path Model Estimation |
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85 | (24) |
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85 | (1) |
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Stage 4: Model Estimation and the PLS-SEM Algorithm |
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86 | (11) |
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86 | (6) |
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92 | (2) |
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Algorithmic Options and Parameter Settings to Run the Algorithm |
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94 | (2) |
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96 | (1) |
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Case Study Illustration-PLS Path Model Estimation (Stage 4) |
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97 | (7) |
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97 | (2) |
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99 | (5) |
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104 | (1) |
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105 | (1) |
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Critical Thinking Questions |
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106 | (1) |
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106 | (1) |
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106 | (3) |
Chapter 4 Assessing PLS-SEM Results-Part I: Evaluation of the Reflective Measurement Models |
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109 | (31) |
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109 | (1) |
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Overview of Stage 5: Evaluation of Measurement Models |
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110 | (6) |
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Stage 5a: Assessing Results of Reflective Measurement Models |
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116 | (11) |
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Step 1: Indicator Reliability |
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117 | (1) |
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Step 2: Internal Consistency Reliability |
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118 | (2) |
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Step 3: Convergent Validity |
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120 | (1) |
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Step 4: Discriminant Validity |
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120 | (7) |
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Case Study Illustration-Evaluation of the Reflective Measurement Models (Stage 5a) |
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127 | (9) |
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Running the PLS-SEM Algorithm |
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127 | (1) |
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Reflective Measurement Model Evaluation |
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128 | (8) |
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136 | (1) |
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137 | (1) |
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Critical Thinking Questions |
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137 | (1) |
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137 | (1) |
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138 | (2) |
Chapter 5 Assessing PLS-SEM Results-Part II: Evaluation of the Formative Measurement Models |
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140 | (46) |
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140 | (1) |
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Stage 5b: Assessing Results of Formative Measurement Models |
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141 | (18) |
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Step 1: Assess Convergent Validity |
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143 | (2) |
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Step 2: Assess Formative Measurement Models for Collinearity Issues |
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145 | (3) |
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Step 3: Assess the Significance and Relevance of the Formative Indicators |
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148 | (4) |
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152 | (7) |
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152 | (4) |
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Bootstrap Confidence Intervals |
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156 | (3) |
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Case Study Illustration-Evaluation of the Formative Measurement Models (Stage 513) |
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159 | (23) |
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Extending the Simple Path Model |
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159 | (10) |
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Reflective Measurement Model Evaluation [ Recap) |
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169 | (2) |
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Formative Measurement Model Evaluation |
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171 | (11) |
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182 | (1) |
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183 | (1) |
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Critical Thinking Questions |
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183 | (1) |
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184 | (1) |
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184 | (2) |
Chapter 6 Assessing PLS-SEM Results-Part III: Evaluation of the Structural Model |
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186 | (42) |
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186 | (1) |
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Stage 6: Structural Model Results Evaluation |
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187 | (22) |
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Step 1: Assess the Structural Model for Collinearity |
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191 | (1) |
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Step 2: Assess the Significance and Relevance of the Structural Model Relationships |
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192 | (2) |
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Step 3: Assess the Model's Explanatory Power |
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194 | (2) |
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Step 4: Assess the Model's Predictive Power |
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196 | (9) |
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198 | (1) |
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199 | (1) |
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200 | (1) |
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201 | (3) |
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Treating Predictive Power Issues |
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204 | (1) |
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Step 5: Model Comparisons |
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205 | (4) |
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Case Study Illustration-Evaluation of the Structural Model [ Stage 6) |
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209 | (14) |
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223 | (2) |
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225 | (1) |
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Critical Thinking Questions |
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225 | (1) |
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225 | (1) |
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226 | (2) |
Chapter 7 Mediator and Moderator Analysis |
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228 | (43) |
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228 | (1) |
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229 | (14) |
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229 | (4) |
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Measurement and Structural Model Evaluation in Mediation Analysis |
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233 | (1) |
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Types of Mediating Effects |
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233 | (3) |
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Testing Mediating Effects |
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236 | (2) |
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238 | (2) |
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Case Study Illustration-Mediation |
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240 | (3) |
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243 | (17) |
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243 | (2) |
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Types of Moderator Variables |
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245 | (2) |
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Modeling Moderating Effects |
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247 | (2) |
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Creating the Interaction Term |
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249 | (37) |
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Product Indicator Approach |
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249 | (1) |
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250 | (1) |
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251 | (2) |
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Guidelines for Creating the Interaction Term |
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253 | (1) |
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253 | (1) |
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254 | (3) |
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Moderated Mediation and Mediated Moderation |
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257 | (3) |
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Case Study Illustration-Moderation |
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260 | (7) |
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267 | (1) |
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268 | (1) |
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Critical Thinking Questions |
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268 | (1) |
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269 | (1) |
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269 | (2) |
Chapter 8 Outlook on Advanced Methods |
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271 | (34) |
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271 | (2) |
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Importance-Performance Map Analysis |
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273 | (3) |
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Necessary Condition Analysis |
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276 | (1) |
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277 | (4) |
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Confirmatory Tetrad Analysis |
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281 | (4) |
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285 | (1) |
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Treating Observed and Unobserved Heterogeneity |
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286 | (8) |
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287 | (3) |
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Uncovering Unobserved Heterogeneity |
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290 | (4) |
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Measurement Model Invariance |
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294 | (2) |
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296 | (2) |
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298 | (2) |
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300 | (1) |
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Critical Thinking Questions |
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301 | (1) |
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301 | (1) |
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302 | (3) |
Glossary |
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305 | (22) |
References |
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327 | (25) |
Index |
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352 | |