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xv | |
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xxiii | |
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xxv | |
Preface |
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xxix | |
Acknowledgments |
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xxxiii | |
Support materials for the book |
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xxxv | |
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1 | (20) |
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1 | (3) |
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4 | (3) |
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7 | (3) |
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1.4 Using an existing dataset |
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10 | (1) |
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1.5 An example of a short Stata session |
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11 | (7) |
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1.6 Video aids to learning Stata |
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18 | (1) |
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19 | (1) |
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19 | (2) |
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21 | (30) |
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21 | (3) |
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2.2 An example questionnaire |
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24 | (1) |
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2.3 Developing a coding system |
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25 | (4) |
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2.4 Entering data using the Data Editor |
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29 | (5) |
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33 | (1) |
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2.5 The Variables Manager |
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34 | (6) |
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2.6 The Data Editor (Browse) view |
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40 | (1) |
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41 | (2) |
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43 | (7) |
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50 | (1) |
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50 | (1) |
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3 Preparing data for analysis |
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51 | (26) |
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51 | (1) |
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52 | (5) |
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3.3 Creating value labels |
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57 | (3) |
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3.4 Reverse-code variables |
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60 | (5) |
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3.5 Creating and modifying variables |
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65 | (5) |
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70 | (4) |
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3.7 Saving some of your data |
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74 | (1) |
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75 | (1) |
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76 | (1) |
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4 Working with commands, do-files, and results |
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77 | (16) |
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77 | (1) |
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4.2 How Stata commands are constructed |
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78 | (5) |
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83 | (5) |
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4.4 Copying your results to a word processor |
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88 | (2) |
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4.5 Logging your command file |
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90 | (1) |
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91 | (1) |
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92 | (1) |
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5 Descriptive statistics and graphs for one variable |
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93 | (30) |
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5.1 Descriptive statistics and graphs |
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93 | (1) |
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5.2 Where is the center of a distribution? |
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94 | (4) |
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5.3 How dispersed is the distribution? |
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98 | (2) |
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5.4 Statistics and graphs---unordered categories |
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100 | (10) |
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5.5 Statistics and graphs---ordered categories and variables |
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110 | (2) |
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5.6 Statistics and graphs---quantitative variables |
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112 | (8) |
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120 | (1) |
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120 | (3) |
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6 Statistics and graphs for two categorical variables |
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123 | (28) |
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6.1 Relationship between categorical variables |
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123 | (1) |
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124 | (3) |
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127 | (5) |
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129 | (1) |
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129 | (3) |
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6.4 Percentages and measures of association |
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132 | (3) |
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6.5 Odds ratios when dependent variable has two categories |
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135 | (2) |
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6.6 Ordered categorical variables |
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137 | (3) |
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140 | (2) |
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6.8 Tables---linking categorical and quantitative variables |
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142 | (3) |
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6.9 Power analysis when using a chi-squared test of significance |
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145 | (3) |
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148 | (1) |
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148 | (3) |
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7 Tests for one or two means |
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151 | (42) |
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7.1 Introduction to tests for one or two means |
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151 | (3) |
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154 | (2) |
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156 | (1) |
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156 | (2) |
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7.5 One-sample test of a proportion |
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158 | (3) |
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7.6 Two-sample test of a proportion |
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161 | (4) |
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7.7 One-sample test of means |
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165 | (2) |
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7.8 Two-sample test of group means |
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167 | (10) |
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7.8.1 Testing for unequal variances |
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176 | (1) |
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7.9 Repeated-measures t test |
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177 | (2) |
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179 | (8) |
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7.11 Nonparametric alternatives |
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187 | (2) |
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7.11.1 Mann--Whitney two-sample rank-sum test |
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187 | (1) |
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7.11.2 Nonparametric alternative: Median test |
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188 | (1) |
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7.12 Video tutorial related to this chapter |
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189 | (1) |
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189 | (1) |
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190 | (3) |
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8 Bivariate correlation and regression |
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193 | (28) |
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8.1 Introduction to bivariate correlation and regression |
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193 | (1) |
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194 | (6) |
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8.3 Plotting the regression line |
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200 | (1) |
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8.4 An alternative to producing a scattergram, binscatter |
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201 | (4) |
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205 | (5) |
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210 | (5) |
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8.7 Spearman's rho: Rank-order correlation for ordinal data |
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215 | (1) |
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8.8 Power analysis with correlation |
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216 | (2) |
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218 | (1) |
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218 | (3) |
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221 | (52) |
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9.1 The logic of one-way analysis of variance |
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221 | (1) |
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222 | (9) |
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9.3 ANOVA example with nonexperimental data |
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231 | (3) |
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9.4 Power analysis for one-way ANOVA |
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234 | (2) |
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9.5 A nonparametric alternative to ANOVA |
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236 | (2) |
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9.6 Analysis of covariance |
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238 | (11) |
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249 | (6) |
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9.8 Repeated-measures design |
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255 | (5) |
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9.9 Intraclass correlation---measuring agreement |
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260 | (2) |
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9.10 Power analysis with ANOVA |
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262 | (8) |
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9.10.1 Power analysis for one-way ANOVA |
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263 | (2) |
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9.10.2 Power analysis for two-way ANOVA |
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265 | (2) |
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9.10.3 Power analysis for repeated-measures ANOVA |
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267 | (2) |
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9.10.4 Summary of power analysis for ANOVA |
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269 | (1) |
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270 | (1) |
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270 | (3) |
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273 | (64) |
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10.1 Introduction to multiple regression |
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273 | (1) |
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10.2 What is multiple regression? |
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274 | (1) |
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10.3 The basic multiple regression command |
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275 | (4) |
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10.4 Increment in R-squared: Semipartial correlations |
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279 | (2) |
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10.5 Is the dependent variable normally distributed? |
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281 | (3) |
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10.6 Are the residuals normally distributed? |
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284 | (6) |
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10.7 Regression diagnostic statistics |
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290 | (6) |
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10.7.1 Outliers and influential cases |
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290 | (2) |
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10.7.2 Influential observations: DFbeta |
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292 | (2) |
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10.7.3 Combinations of variables may cause problems |
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294 | (2) |
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296 | (2) |
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10.9 Categorical predictors and hierarchical regression |
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298 | (9) |
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10.10 A shortcut for working with a categorical variable |
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307 | (1) |
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10.11 Fundamentals of interaction |
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308 | (7) |
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10.12 Nonlinear relations |
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315 | (12) |
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10.12.1 Fitting a quadratic model |
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317 | (6) |
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10.12.2 Centering when using a quadratic term |
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323 | (2) |
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10.12.3 Do we need to add a quadratic component? |
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325 | (2) |
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10.13 Power analysis in multiple regression |
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327 | (5) |
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332 | (1) |
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333 | (4) |
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337 | (40) |
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11.1 Introduction to logistic regression |
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337 | (1) |
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338 | (4) |
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11.3 What is an odds ratio and a logit? |
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342 | (3) |
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344 | (1) |
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11.3.2 The logit transformation |
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344 | (1) |
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11.4 Data used in the rest of the chapter |
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345 | (2) |
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347 | (10) |
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357 | (4) |
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11.6.1 Testing individual coefficients |
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358 | (1) |
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11.6.2 Testing sets of coefficients |
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359 | (2) |
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11.7 Margins: More on interpreting results from logistic regression |
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361 | (8) |
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11.8 Nested logistic regressions |
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369 | (2) |
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11.9 Power analysis when doing logistic regression |
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371 | (3) |
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11.10 Next steps for using logistic regression and its extensions |
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374 | (1) |
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374 | (1) |
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375 | (2) |
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12 Measurement, reliability, and validity |
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377 | (34) |
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12.1 Overview of reliability and validity |
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377 | (1) |
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12.2 Constructing a scale |
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378 | (3) |
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12.2.1 Generating a mean score for each person |
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379 | (2) |
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381 | (8) |
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12.3.1 Stability and test-retest reliability |
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382 | (1) |
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383 | (1) |
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12.3.3 Split-half and alpha reliability---internal consistency |
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383 | (3) |
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12.3.4 Kuder--Richardson reliability for dichotomous items |
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386 | (1) |
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12.3.5 Rater agreement---kappa (κ) |
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387 | (2) |
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389 | (7) |
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390 | (1) |
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12.4.2 Criterion-related validity |
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391 | (1) |
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12.4.3 Construct validity |
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391 | (5) |
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396 | (4) |
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400 | (7) |
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12.6.1 Orthogonal rotation: Varimax |
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404 | (2) |
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12.6.2 Oblique rotation: Promax |
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406 | (1) |
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12.7 But we wanted one scale, not four scales |
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407 | (2) |
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12.7.1 Scoring our variable |
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408 | (1) |
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409 | (1) |
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410 | (1) |
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13 Structural equation and generalized structural equation modeling |
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411 | (30) |
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13.1 Linear regression using sem |
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412 | (9) |
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13.1.1 Using the sem command directly |
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413 | (1) |
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13.1.2 SEM and working with missing values |
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414 | (5) |
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13.1.3 Exploring missing values and auxiliary variables |
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419 | (2) |
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13.1.4 Getting auxiliary variables into your SEM command |
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421 | (1) |
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13.2 A quick way to draw a regression model |
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421 | (4) |
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13.3 The gsem command for logistic regression |
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425 | (7) |
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13.3.1 Fitting the model using the logit command |
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425 | (3) |
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13.3.2 Fitting the model using the gsem command |
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428 | (4) |
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13.4 Path analysis and mediation |
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432 | (5) |
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13.5 Conclusions and what is next for the sem command |
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437 | (2) |
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439 | (2) |
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14 Working with missing values---multiple imputation |
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441 | (22) |
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14.1 Working with missing values---multiple imputation |
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441 | (1) |
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14.2 What variables do we include when doing imputations? |
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442 | (2) |
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14.3 The nature of the problem |
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444 | (1) |
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14.4 Multiple imputation and its assumptions about the mechanism for missingness |
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445 | (2) |
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447 | (1) |
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448 | (12) |
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14.6.1 Preliminary analysis |
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449 | (3) |
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14.6.2 Setup and multiple-imputation stage |
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452 | (2) |
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14.6.3 The analysis stage |
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454 | (2) |
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14.6.4 For those who want an R2 and standardized (3s |
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456 | (2) |
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14.6.5 When impossible values are imputed |
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458 | (2) |
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460 | (1) |
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461 | (2) |
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15 An introduction to multilevel analysis |
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463 | (30) |
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15.1 Questions and data for groups of individuals |
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463 | (1) |
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15.2 Questions and data for a longitudinal multilevel application |
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464 | (1) |
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15.3 Fixed-effects regression models |
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465 | (1) |
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15.4 Random-effects regression models |
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466 | (2) |
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468 | (4) |
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15.5.1 Research questions |
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468 | (1) |
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15.5.2 Reshaping data to do multilevel analysis |
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469 | (3) |
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15.6 A quick visualization of our data |
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472 | (1) |
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15.7 Random-intercept model |
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473 | (10) |
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15.7.1 Random intercept---linear model |
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473 | (3) |
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15.7.2 Random-intercept model---quadratic term |
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476 | (4) |
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15.7.3 Treating time as a categorical variable |
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480 | (3) |
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15.8 Random-coefficients model |
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483 | (3) |
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15.9 Including a time-invariant covariate |
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486 | (5) |
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491 | (1) |
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492 | (1) |
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16 Item response theory (IRT) |
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493 | (38) |
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16.1 How are IRT measures of variables different from summated scales? |
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494 | (2) |
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16.2 Overview of three IRT models for dichotomous items |
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496 | (4) |
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16.2.1 The one-parameter logistic (1PL) model |
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496 | (2) |
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16.2.2 The two-parameter logistic (2PL) model |
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498 | (1) |
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16.2.3 The three-parameter logistic (3PL) model |
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499 | (1) |
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16.3 Fitting the 1PL model using Stata |
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500 | (8) |
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502 | (2) |
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16.3.2 How important is each of the items? |
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504 | (2) |
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16.3.3 An overall evaluation of our scale |
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506 | (1) |
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16.3.4 Estimating the latent score |
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507 | (1) |
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16.4 Fitting a 2PL IRT model |
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508 | (7) |
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16.4.1 Fitting the 2PL model |
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509 | (6) |
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16.5 The graded response model---IRT for Likert-type items |
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515 | (7) |
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515 | (2) |
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16.5.2 Fitting our graded response model |
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517 | (5) |
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16.5.3 Estimating a person's score |
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522 | (1) |
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16.6 Reliability of the fitted IRT model |
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522 | (3) |
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16.7 Using the Stata menu system |
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525 | (3) |
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528 | (1) |
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529 | (2) |
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531 | (10) |
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A.1 Introduction to the appendix |
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531 | (1) |
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531 | (8) |
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532 | (2) |
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534 | (2) |
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536 | (1) |
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537 | (1) |
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A.2.5 Learning from the postestimation methods |
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538 | (1) |
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539 | (2) |
Glossary of acronyms |
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541 | (2) |
Glossary of mathematical and statistical symbols |
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543 | (2) |
References |
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545 | (6) |
Author Index |
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551 | (2) |
Subject Index |
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553 | |