Preface |
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xv | |
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1 | (34) |
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1 Introduction To Social And Marketing Research |
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3 | (32) |
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1.1 The Role of Information in Science |
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3 | (4) |
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4 | (2) |
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6 | (1) |
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1.1.3 Information and Power |
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7 | (1) |
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7 | (6) |
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1.2.1 Typical Applications of Marketing Research |
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10 | (2) |
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1.2.2 Some Misconceptions on MR |
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12 | (1) |
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1.3 Sources of Information |
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13 | (6) |
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13 | (1) |
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14 | (2) |
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1.3.3 Analyzing Secondary Information |
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16 | (2) |
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1.3.4 Database Management |
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18 | (1) |
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1.4 Types of Social and MR Studies |
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19 | (7) |
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1.4.1 Exploratory vs. Conclusive Research |
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19 | (5) |
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1.4.2 Qualitative and Quantitative Research |
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24 | (2) |
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1.5 History of Social and Market Research Methods |
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26 | (2) |
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28 | (4) |
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1.6.1 Decision Support Systems |
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29 | (1) |
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1.6.2 A More General Perspective on Information Systems |
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30 | (2) |
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1.7 Ethics in Social Research |
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32 | (2) |
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1.7.1 Ethics in MR: Scholarly and Professional Concern |
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33 | (1) |
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34 | (1) |
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Part II Qualitative Methods |
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35 | (116) |
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2 Qualitative Research Based On Direct Questioning |
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37 | (46) |
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2.1 What is Qualitative Research? |
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37 | (7) |
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38 | (2) |
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2.1.2 The Quantitative Fallacy |
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40 | (3) |
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43 | (1) |
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44 | (3) |
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47 | (9) |
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2.3.1 Conducting Depth Interviews |
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50 | (1) |
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51 | (5) |
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2.3.3 Online Depth Interviews |
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56 | (1) |
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2.4 Narrative Inquiry and Storytelling |
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56 | (2) |
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58 | (5) |
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2.5.1 Online Focus Groups |
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62 | (1) |
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63 | (1) |
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2.6 Technical Issues in IDIs and FGs |
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63 | (7) |
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2.6.1 Writing the Discussion Agenda (or Script of Questions) |
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64 | (2) |
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66 | (4) |
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2.7 Analysis of Transcripts and Written Texts |
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70 | (7) |
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2.7.1 Content Analysis and Thematic Analysis |
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70 | (4) |
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2.7.2 Linguistic and Discourse Analysis |
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74 | (1) |
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74 | (2) |
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2.7.4 Using Non-verbal Information |
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76 | (1) |
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77 | (1) |
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77 | (3) |
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80 | (3) |
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3 Indirect Questioning In Qualitative Research |
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83 | (20) |
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3.1 Handling Sensitive or Complex Issues |
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83 | (1) |
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84 | (14) |
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3.2.1 Association Techniques |
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85 | (3) |
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3.2.2 Construction Techniques |
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88 | (4) |
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3.2.3 Completion Techniques |
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92 | (4) |
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3.2.4 Ordering or Choice Techniques |
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96 | (1) |
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3.2.5 Expression Techniques |
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96 | (2) |
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98 | (3) |
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98 | (1) |
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99 | (2) |
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3.3.3 Other Qualitative Prediction Methods |
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101 | (1) |
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101 | (2) |
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103 | (48) |
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4.1 How Can We Use Observation? |
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103 | (2) |
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4.2 Direct Observation of Behavior |
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105 | (9) |
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4.2.1 Naturalistic Observation With a Relatively Passive or Mechanical Observer |
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105 | (4) |
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4.2.2 Active Observer Participation |
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109 | (5) |
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4.3 Indirect Observation (or Trace Analysis) of Behavior |
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114 | (1) |
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4.4 Observation of Digital Behavior |
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115 | (4) |
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4.5 Social Networks Analysis |
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119 | (13) |
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4.5.1 Background on Graph Theory |
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120 | (2) |
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4.5.2 Centrality of a Vertex |
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122 | (8) |
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4.5.3 Models of Network Formation: Random Graphs |
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130 | (2) |
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4.6 Psychophysiology (Biometry) |
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132 | (18) |
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4.6.1 Psychophysical Response |
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132 | (11) |
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143 | (5) |
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148 | (2) |
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150 | (1) |
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Part III Quantitative Data Analysis |
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151 | (418) |
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5 Uncertainty And Probability |
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153 | (52) |
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153 | (17) |
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5.1.1 Sets and Sigma Algebras |
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154 | (4) |
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5.1.2 Probability Measure |
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158 | (2) |
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5.1.3 Random Variables/Vectors and Integrals |
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160 | (8) |
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5.1.4 Conditional Probability |
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168 | (2) |
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5.2 Common Probability Distribution Families |
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170 | (15) |
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5.2.1 Univariate Discrete Families |
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171 | (2) |
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5.2.2 Univariate Continuous Families |
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173 | (3) |
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5.2.3 Multivariate Discrete Families |
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176 | (2) |
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5.2.4 Multivariate Continuous Families |
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178 | (2) |
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5.2.5 Continuous Distributions Derived From a Normal Distribution |
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180 | (5) |
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5.3 Convergence of Random Variables and Distributions |
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185 | (10) |
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5.3.1 Laws of Large Numbers |
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189 | (2) |
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5.3.2 Central Limit Theorems |
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191 | (4) |
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5.4 Random Number Generation and Monte Carlo |
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195 | (1) |
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196 | (7) |
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5.5.1 Asymptotic Theory for Stochastic Processes |
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200 | (2) |
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5.5.2 Random Elements on Metric Spaces |
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202 | (1) |
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203 | (2) |
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6 Statistical Analysis I: Parameters And Estimation |
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205 | (77) |
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205 | (2) |
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207 | (15) |
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6.2.1 Parameters and Point Estimation |
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208 | (4) |
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6.2.2 Finite-Sample Properties of Estimators |
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212 | (6) |
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6.2.3 Asymptotic Properties of Estimators |
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218 | (4) |
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6.3 Some General Families of Estimators |
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222 | (15) |
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222 | (7) |
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229 | (2) |
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6.3.3 Generalized Method of Moments |
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231 | (2) |
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6.3.4 Numerical Computation |
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233 | (2) |
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6.3.5 Other Related Estimators |
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235 | (2) |
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237 | (33) |
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238 | (2) |
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6.4.2 Maximum Likelihood Estimators |
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240 | (14) |
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6.4.3 Bayesian Estimators |
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254 | (7) |
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6.4.4 Other Parametric Model Estimators |
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261 | (2) |
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6.4.5 Selection of Parametric Models |
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263 | (5) |
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6.4.6 Data Heterogeneity and Mixtures |
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268 | (2) |
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6.5 Nonparametric Density Estimation |
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270 | (9) |
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272 | (2) |
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6.5.2 Kernel Density Estimators |
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274 | (4) |
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6.5.3 K-Nearest Neighbor Density Estimate |
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278 | (1) |
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279 | (1) |
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280 | (2) |
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7 Statistical Analysis II: Confidence Regions And Hypothesis Testing |
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282 | (68) |
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7.1 Statistical Inference |
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282 | (1) |
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7.2 Confidence Intervals and Regions |
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283 | (15) |
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7.2.1 Asymptotic Confidence Intervals |
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284 | (6) |
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7.2.2 Exact Confidence Intervals |
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290 | (2) |
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7.2.3 Confidence Regions for Multiple Parameters |
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292 | (4) |
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7.2.4 Bayesian Confidence Regions |
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296 | (1) |
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7.2.5 Warning on Misleading Interpretations |
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297 | (1) |
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298 | (33) |
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300 | (1) |
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7.3.2 Asymptotic Significance Tests for a Single Parameter |
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301 | (9) |
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7.3.3 Some Alternatives to Asymptotic Tests |
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310 | (3) |
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7.3.4 Tests for a Vector of Parameters |
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313 | (3) |
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7.3.5 Classical Econometric Tests |
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316 | (11) |
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7.3.6 Questionable Research Practices, Misleading Interpretations, and Controversies |
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327 | (4) |
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7.4 Multiple Testing Methods |
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331 | (8) |
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7.4.1 The Impact of Preliminary Testing on Estimation |
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334 | (5) |
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339 | (5) |
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339 | (2) |
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7.5.2 Fixed vs. Random Effects |
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341 | (2) |
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7.5.3 Two-Way and Multiway ANOVA |
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343 | (1) |
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344 | (5) |
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7.6.1 Goodness-of-Fit Tests |
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348 | (1) |
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349 | (1) |
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8 Regression Analysis I: General Linear Model |
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350 | (88) |
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8.1 What Does "Regression" Mean? |
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350 | (6) |
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8.1.1 Regression with Finite Variance |
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353 | (3) |
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356 | (6) |
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8.2.1 Best Linear Predictors |
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356 | (4) |
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8.2.2 Modeling Functional Data Discretely Sampled |
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360 | (2) |
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8.3 Estimating the General Linear Model |
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362 | (33) |
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8.3.1 Ordinary Least Squares Estimator |
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365 | (7) |
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8.3.2 Partitioned Regression and Frisch--Waugh--Lovell Theorem |
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372 | (3) |
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8.3.3 Finite Sample Properties of OLS |
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375 | (6) |
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8.3.4 Other Common Estimators Related to Least Squares |
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381 | (11) |
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8.3.5 Linear Dynamic Regression and Distributed Lag Models |
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392 | (3) |
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8.4 Asymptotic Theory for Least Squares |
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395 | (11) |
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395 | (3) |
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8.4.2 Asymptotic Normality of OLS |
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398 | (3) |
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8.4.3 Estimating the Asymptotic Covariance Matrix of OLS |
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401 | (3) |
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8.4.4 Bootstrap Approximation |
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404 | (2) |
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8.5 Inference in the General Linear Model |
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406 | (9) |
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8.5.1 Forecasting with Least Squares |
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406 | (2) |
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8.5.2 Testing Linear Hypothesis under Normality |
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408 | (4) |
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412 | (3) |
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8.6 Diagnosis in the General Linear Model |
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415 | (10) |
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8.6.1 Basic Residual Analysis |
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416 | (1) |
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8.6.2 Multicollinearity and Near-Collinearity |
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416 | (6) |
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422 | (1) |
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8.6.4 Inferences about Σ using Residuals |
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422 | (3) |
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8.7 Transformations to Achieve Linearity |
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425 | (3) |
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8.8 Dummy Regressors: Reconsidering Classical ANOVA |
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428 | (8) |
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8.8.1 One-Way ANOVA with Fixed Effects |
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429 | (3) |
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8.8.2 Analysis of Covariance and Panels |
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432 | (1) |
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8.8.3 Two-Way and Multiway ANOVA |
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433 | (1) |
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8.8.4 General Random Coefficients and Multi-level Modeling |
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434 | (2) |
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436 | (2) |
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9 Regression Analysis II: Flexible Methods And Machine Learning |
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438 | (56) |
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9.1 Variable Selection in Large Databases |
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438 | (10) |
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9.1.1 Classical Variable Selection |
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439 | (4) |
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9.1.2 Variable Screening Methods |
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443 | (1) |
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9.1.3 Regularization: Ridge, LASSO, and Related Procedures |
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444 | (4) |
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9.2 Nonlinear Parametric Regression Models |
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448 | (15) |
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9.2.1 Nonlinear Least Squares |
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451 | (2) |
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9.2.2 Regression Using Z-Estimators and GMM |
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453 | (1) |
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9.2.3 Maximum Likelihood and Bayes |
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454 | (3) |
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9.2.4 Models for Dependent Variables with Limited Support |
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457 | (6) |
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9.3 Recursive Estimation (Learning); Stochastic Approximation |
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463 | (4) |
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9.4 Nonparametric Regression Estimation |
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467 | (20) |
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9.4.1 Regressograms (Partitions) |
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470 | (3) |
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473 | (1) |
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9.4.3 K-Nearest Neighbor Estimators |
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474 | (1) |
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9.4.4 Projections: Sieves Estimators and Regularization Methods |
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474 | (6) |
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9.4.5 Recursive Estimators |
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480 | (1) |
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481 | (5) |
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486 | (1) |
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487 | (3) |
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9.6 General Test for Goodness-of-Fit |
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490 | (1) |
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491 | (3) |
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10 Multivariate Statistics And Econometrics |
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494 | (75) |
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10.1 Inferences for a Multivariate Normal |
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494 | (7) |
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10.1.1 Correlation and Partial Correlation |
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498 | (3) |
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10.2 Multi-equational Regression Models |
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501 | (15) |
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10.2.1 Linear SURE Models |
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502 | (7) |
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10.2.2 Dynamic SURE Models |
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509 | (4) |
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10.2.3 Nonlinear Multi-equational Regression |
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513 | (3) |
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10.3 Models with Endogeneity |
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516 | (8) |
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10.3.1 Proxies and Measurement Errors |
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517 | (1) |
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10.3.2 Instrumental Variables |
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518 | (5) |
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10.3.3 Control Function Method |
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523 | (1) |
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10.4 Econometric Structural Models: Linear Specifications |
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524 | (16) |
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528 | (6) |
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10.4.2 Estimation with Full Information |
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534 | (4) |
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10.4.3 Estimation with Limited Information |
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538 | (1) |
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10.4.4 Dynamic Linear Structural Models |
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539 | (1) |
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10.5 Econometric Nonlinear Structural Models |
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540 | (3) |
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10.6 Dimension Reduction Methods |
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543 | (14) |
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10.6.1 Principal Components |
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543 | (3) |
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10.6.2 Canonical Correlation (Canonical Variables) |
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546 | (2) |
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548 | (3) |
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10.6.4 Correspondence Analysis |
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551 | (1) |
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10.6.5 Multidimensional Scaling |
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552 | (2) |
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10.6.6 Dimension Reduction with Nonlinear Relationships |
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554 | (3) |
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10.7 Discriminant Analysis (Supervised Classification/Pattern Recognition) |
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557 | (5) |
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558 | (2) |
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10.7.2 Parametric Discriminant Analysis |
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560 | (1) |
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10.7.3 Nonparametric and Other Machine Learning Approaches |
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561 | (1) |
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10.8 Cluster Analysis (Unsupervised Classification) |
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562 | (4) |
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10.8.1 Worst-Case and K-Means Methods |
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563 | (1) |
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564 | (2) |
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566 | (3) |
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Part IV Quantitative Data Collection |
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569 | (250) |
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11 Quantitative Measurement |
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571 | (65) |
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11.1 Types of Quantitative Data |
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571 | (3) |
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11.1.1 Measurement Theory |
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572 | (2) |
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11.2 Measuring Attitudinal Magnitudes |
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574 | (3) |
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11.3 A Taxonomy of Measurement Scales |
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577 | (9) |
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11.3.1 Distortions Induced by the Scale |
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580 | (2) |
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11.3.2 Stevens's Classification of Scales |
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582 | (4) |
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11.4 Main Attitudinal Scales |
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586 | (36) |
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586 | (6) |
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592 | (25) |
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617 | (3) |
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620 | (2) |
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11.5 Multi-item Scales and Constructs |
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622 | (4) |
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626 | (8) |
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11.6.1 Classical Approach |
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628 | (3) |
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11.6.2 Rasch Model and Item Response Theory |
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631 | (3) |
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634 | (2) |
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636 | (57) |
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12.1 Key Concepts in Sampling |
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636 | (5) |
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636 | (2) |
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638 | (3) |
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12.2 Sample Representativeness and Biases |
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641 | (1) |
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12.3 Non-probabilistic Sampling |
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642 | (4) |
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642 | (3) |
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12.3.2 Recruiting Participants for Qualitative Research |
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645 | (1) |
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12.4 Probabilistic Sampling Methods |
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646 | (8) |
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646 | (8) |
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12.5 Statistical Inference Using Probabilistic Samples |
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654 | (24) |
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12.5.1 Simple Random Sampling |
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656 | (11) |
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12.5.2 Stratified Sampling |
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667 | (3) |
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12.5.3 General Procedures for Non-EPSEM Sampling |
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670 | (8) |
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12.6 Superpopulation Models |
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678 | (3) |
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12.7 Sample Size Selection |
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681 | (6) |
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12.7.1 Optimal Sizes in Stratified Sampling |
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684 | (3) |
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12.8 Nonresponse and Item-Nonresponse |
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687 | (3) |
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12.8.1 Item-Nonresponse and Imputation |
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689 | (1) |
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12.8.2 Dual and Multiple-Frame Sampling |
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690 | (1) |
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12.9 Combined Sampling and Other Shortcuts |
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690 | (1) |
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691 | (2) |
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13 Survey And Questionnaire Design |
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693 | (70) |
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693 | (4) |
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13.1.1 Survey Methodologies |
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693 | (4) |
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697 | (4) |
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13.2.1 Privacy: Anonymity, Confidentiality, and Disclosure |
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697 | (3) |
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13.2.2 Explicit Informed Consent |
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700 | (1) |
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13.3 Typical Survey Types |
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701 | (11) |
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13.3.1 Personal or Face-to-Face Surveys |
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701 | (3) |
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704 | (2) |
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706 | (1) |
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707 | (2) |
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13.3.5 Summary of Survey Modes and Response Rates |
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709 | (3) |
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712 | (35) |
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13.4.1 Priming with Accessible Information |
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713 | (1) |
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714 | (3) |
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13.4.3 How to Build a Good Questionnaire |
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717 | (15) |
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13.4.4 Questions for Sensitive Issues |
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732 | (9) |
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13.4.5 Complementary Elements |
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741 | (5) |
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13.4.6 Language Translations |
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746 | (1) |
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13.5 Sources of Survey Errors |
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747 | (7) |
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747 | (4) |
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13.5.2 Non-sampling Errors |
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751 | (3) |
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13.6 Data Processing and Analysis |
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754 | (6) |
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760 | (1) |
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761 | (2) |
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763 | (56) |
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14.1 What are Experiments? |
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763 | (3) |
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766 | (2) |
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14.3 Validity of Experimental Inferences |
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768 | (6) |
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769 | (3) |
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772 | (2) |
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14.4 Field vs. Lab Experiments |
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774 | (4) |
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14.4.1 Laboratory Experiments |
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774 | (1) |
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775 | (3) |
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778 | (1) |
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14.6 Typical Types of Experiment |
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778 | (4) |
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14.7 Analysis of Experimental Data |
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782 | (13) |
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14.7.1 Basic Inference Setup |
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783 | (4) |
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14.7.2 Multiple Treatment Experiments |
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787 | (1) |
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14.7.3 Factorial Analysis |
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|
788 | (2) |
|
14.7.4 Response Surface Models |
|
|
790 | (3) |
|
14.7.5 Difference in Differences and Dynamics |
|
|
793 | (2) |
|
|
795 | (5) |
|
|
797 | (1) |
|
14.8.2 Classical Procedures to Randomize |
|
|
798 | (2) |
|
14.9 Optimal Experimental Designs |
|
|
800 | (2) |
|
|
802 | (12) |
|
14.10.1 Natural Experiments |
|
|
803 | (1) |
|
14.10.2 Potentially Harmful Econometrics |
|
|
804 | (10) |
|
|
814 | (3) |
|
|
817 | (2) |
|
Part V Research Planning and Reporting |
|
|
819 | (28) |
|
15 Planning Social Research |
|
|
821 | (12) |
|
15.1 First Stage: Cost-Benefit Analysis |
|
|
821 | (6) |
|
15.1.1 The Value of Information |
|
|
822 | (5) |
|
15.2 Planning the Research Process |
|
|
827 | (4) |
|
15.2.1 Program Evaluation and Review Technique |
|
|
828 | (1) |
|
15.2.2 Fieldwork Planning |
|
|
829 | (2) |
|
|
831 | (1) |
|
|
832 | (1) |
|
16 Reporting Social And Market Research Studies |
|
|
833 | (13) |
|
16.1 Communicating the Findings |
|
|
833 | (1) |
|
|
834 | (9) |
|
16.2.1 Report Structure in a MR Report |
|
|
837 | (1) |
|
16.2.2 Plagiarism and References |
|
|
838 | (2) |
|
|
840 | (3) |
|
|
843 | (2) |
|
|
845 | (1) |
|
|
846 | (1) |
Index |
|
847 | |