Preface |
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xiii | |
About the Editor |
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xv | |
About the Contributors |
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xvii | |
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1 | (146) |
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1 Fundamentals of Hierarchical Linear and Multilevel Modeling |
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3 | (24) |
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3 | (2) |
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Why Use Linear Mixed/Hierarchical Linear/Multilevel Modeling? |
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5 | (2) |
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Types of Linear Mixed Models |
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7 | (5) |
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Generalized Linear Mixed Models |
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12 | (6) |
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Repeated Measures, Longitudinal and Growth Models |
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18 | (2) |
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18 | (1) |
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Longitudinal and Growth Models |
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19 | (1) |
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20 | (1) |
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21 | (2) |
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23 | (4) |
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2 Preparing to Analyze Multilevel Data |
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27 | (28) |
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Testing if Linear Mixed Modeling Is Needed for One's Data |
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27 | (1) |
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28 | (5) |
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Converging on a Solution in Linear Mixed Modeling |
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33 | (3) |
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Meeting Other Assumptions of Linear Mixed Modeling |
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36 | (4) |
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Covariance Structure Types |
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40 | (4) |
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Selecting the Best Covariance Structure Assumption |
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44 | (1) |
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Comparing Model Goodness of Fit With Information Theory Measures |
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44 | (1) |
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Comparing Models With Likelihood Ratio Tests |
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45 | (2) |
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Effect Size in Linear Mixed Modeling |
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47 | (1) |
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48 | (7) |
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3 Introductory Guide to HLM With HLM 7 Software |
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55 | (42) |
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55 | (1) |
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56 | (5) |
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Input Method 1 Separate Files for Each Level |
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56 | (1) |
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Input Method 2 Using a Single Statistics Program Data File |
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57 | (1) |
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57 | (4) |
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61 | (6) |
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A Random Coefficients Regression Model in HLM 7 |
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67 | (5) |
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Homogenous and Heterogeneous Full Random Coefficients Models |
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72 | (9) |
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Three-Level Hierarchical Linear Models |
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81 | (11) |
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84 | (1) |
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85 | (2) |
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87 | (5) |
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92 | (3) |
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95 | (2) |
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4 Introductory Guide to HLM With SAS Software |
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97 | (24) |
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97 | (4) |
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Direct Data Entry Using VIEWTABLE |
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97 | (2) |
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Data Entry Using the SAS Import Wizard |
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99 | (1) |
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Data Entry Using SAS Commands |
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100 | (1) |
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The Null Model in SAS PROC MIXED |
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101 | (3) |
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A Random Coefficients Regression Model in SAS 9.2 |
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104 | (2) |
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A Full Random Coefficients Model |
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106 | (4) |
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Three-Level Hierarchical Linear Models |
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110 | (8) |
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111 | (1) |
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112 | (3) |
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115 | (3) |
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118 | (3) |
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5 Introductory Guide to HLM With SPSS Software |
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121 | (26) |
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121 | (7) |
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A Random Coefficients Regression Model in SPSS 19 |
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128 | (5) |
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A Full Random Coefficients Model |
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133 | (4) |
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Three-Level Hierarchical Linear Models |
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137 | (9) |
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137 | (2) |
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139 | (2) |
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141 | (5) |
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146 | (1) |
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PART II INTRODUCTORY AND INTERMEDIATE APPLICATIONS |
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147 | (206) |
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6 A Random Intercepts Model of Part-Time Employment and Standardized Testing Using SPSS |
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149 | (18) |
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The Null Linear Mixed Model |
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150 | (1) |
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Interclass Correlation Coefficient (ICC) |
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151 | (1) |
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One-Way ANCOVA With Random Effects |
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152 | (1) |
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152 | (1) |
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153 | (1) |
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153 | (3) |
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156 | (2) |
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Traditional Ordinary Least Squares (OLS) Approach |
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156 | (2) |
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Linear Mixed Model (LMM) Approach |
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158 | (4) |
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162 | (1) |
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163 | (4) |
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7 A Random Intercept Regression Model Using HLM: Cohort Analysis of a Mathematics Curriculum for Mathematically Promising Students |
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167 | (16) |
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169 | (2) |
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171 | (1) |
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171 | (4) |
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175 | (5) |
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180 | (1) |
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181 | (2) |
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8 Random Coefficients Modeling With HLM: Assessment Practices and the Achievement Gap in Schools |
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183 | (22) |
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185 | (2) |
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An Application of the RC Model: Assessment Practices and the Achievement Gap in Schools |
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187 | (1) |
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188 | (2) |
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190 | (1) |
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191 | (2) |
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193 | (6) |
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199 | (6) |
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199 | (1) |
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200 | (1) |
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201 | (4) |
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9 Emotional Reactivity to Daily Stressors Using a Random Coefficients Model With SAS PROC MIXED: A Repeated Measures Analysis |
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205 | (14) |
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206 | (1) |
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206 | (1) |
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207 | (1) |
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208 | (1) |
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208 | (1) |
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209 | (1) |
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Unconditional Model Output |
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210 | (2) |
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Interpretation of Unconditional Model Results |
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212 | (1) |
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Random Coefficients Regression Model |
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212 | (1) |
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Random Coefficients Regression Output |
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213 | (4) |
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Interpretation of Random Coefficients Regression Results |
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217 | (1) |
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217 | (2) |
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10 Hierarchical Linear Modeling of Growth Curve Trajectories Using HLM |
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219 | (30) |
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The Challenges Posed by Longitudinal Data |
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219 | (2) |
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The Hierarchical Modeling Approach to Longitudinal Data |
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221 | (3) |
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Application: Growth Trajectories of U.S. County Robbery Rates |
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224 | (19) |
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225 | (1) |
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Estimation of the Linear Hierarchical Model |
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226 | (6) |
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Modeling the Variability of the Level I Coefficients |
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232 | (4) |
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236 | (3) |
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Estimating a Model for Counts |
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239 | (4) |
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Assessment of the Methods |
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243 | (6) |
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11 A Piecewise Growth Model Using HLM 7 to Examine Change in Teaching Practices Following a Science Teacher Professional Development Intervention |
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249 | (24) |
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250 | (2) |
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252 | (2) |
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254 | (3) |
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254 | (1) |
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255 | (2) |
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257 | (5) |
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257 | (5) |
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School as a Level 2 Predictor |
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262 | (2) |
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Alternative Error Covariance Structures |
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264 | (5) |
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269 | (4) |
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269 | (1) |
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270 | (3) |
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12 Studying Reaction to Repeated Life Events With Discontinuous Change Models Using HLM |
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273 | (18) |
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276 | (1) |
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277 | (1) |
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277 | (6) |
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278 | (1) |
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279 | (4) |
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283 | (4) |
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287 | (4) |
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13 A Cross-Classified Multilevel Model for First-Year College Natural Science Performance Using SAS |
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291 | (20) |
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292 | (2) |
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293 | (1) |
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294 | (3) |
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297 | (4) |
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Evaluating Residual Variability Due to the Cross-Classified Levels |
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297 | (2) |
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Specifying a Covariance Structure |
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299 | (1) |
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Building the Student-Level Model |
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299 | (1) |
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Building the College- and High School---Level Models |
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300 | (1) |
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300 | (1) |
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301 | (5) |
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Evaluating Residual Variability Due to the Cross-Classified Levels |
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301 | (1) |
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Specifying a Covariance Structure |
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302 | (1) |
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Building the Student-Level Model |
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303 | (2) |
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305 | (1) |
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Evaluating Residual Variability in the Final Model |
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305 | (1) |
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306 | (5) |
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Interpreting Fixed Parameter Estimates |
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306 | (5) |
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14 Cross-Classified Multilevel Models Using Stata: How Important Are Schools and Neighborhoods for Students' Educational Attainment? |
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311 | (22) |
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312 | (3) |
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315 | (1) |
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316 | (3) |
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319 | (11) |
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330 | (3) |
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15 Predicting Future Events From Longitudinal Data With Multivariate Hierarchical Models and Bayes' Theorem Using SAS |
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333 | (20) |
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336 | (1) |
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337 | (7) |
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344 | (1) |
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344 | (6) |
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350 | (3) |
Author Index |
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353 | (4) |
Subject Index |
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357 | |