Longitudinal Multivariate Psychology

Description

Contents......Page 3 Foreword......Page 6 Intro & Section Overview......Page 8 Section I: Extensions of Latent Change Score Models......Page 9 Section II: Measurement and Testing Issues in Longitudinal Modeling......Page 10 Section III: Novel Applications of Multivariate Longitudinal Methodology......Page 11 --- Extensions of Latent Change Score Models......Page 14 1 Methodological Issues & Extensions to Latent Difference Score Framework......Page 15 The Bivariate Dual Change Score (BDCS) Model......Page 17 IC Specifications in the BDCS......Page 19 Stochastic BDCS Model......Page 23 Empirical Illustration......Page 30 Discussion......Page 35 Limitations......Page 37 References......Page 38 Sample dynr Code for Fitting the Stochastic BDCS Models in the Illustrative Example.......Page 42 Development of Fluid Reasoning......Page 44 Models to Examine Changes in Fluid Reasoning across Measurement Occasions......Page 45 Models to Examine Dynamics of Fluid Reasoning and Underlying Developmental Mechanisms......Page 46 Data Description......Page 50 Change in Fluid Reasoning Across Measurement Occasions......Page 51 Developmental Changes in Fluid Reasoning......Page 56 Summary of Findings......Page 58 Methodological and Substantive Implications......Page 59 References......Page 60 Sample Code for Analyses......Page 62 3 Individually Varying Time Metrics in Latent Change Score Models......Page 67 Latent Change Score Models......Page 69 Structural Equation Modeling Framework......Page 70 Nonlinear Multilevel Modeling Framework......Page 72 JAGS......Page 74 Data......Page 76 SAS Programming......Page 77 NLMIXED Output......Page 78 JAGS......Page 79 Structural Equation Modeling Software......Page 80 Discussion......Page 81 References......Page 83 Introduction......Page 86 Methods......Page 89 Latent Constant Change Score Model......Page 90 Latent Quadratic Constant Change Score Model......Page 91 A Latent Change Score Model Representing Latent Basis Curve Model......Page 94 Bivariate Latent Quadratic Curve Model Based on Latent Change Score Approach......Page 98 Bivariate Quadratic Constant Latent Change Score Model with Coupling Effect......Page 100 Bivariate Quadratic Constant Latent Change Score Model with Self-Feedback and Coupling Effect......Page 104 Discussion......Page 107 References......Page 113 Regularized Estimation of Multivariate Latent Change Score Models......Page 115 Latent Change Score Framework......Page 116 Rationale......Page 119 Regularization......Page 120 Univariate LCS Regularization......Page 122 Time-Varying Effects Regularization......Page 124 Bivariate LCS Regularization......Page 126 Discussion......Page 127 References......Page 129 Setting the Stage......Page 132 The Breakthrough: RAM Algebra and RAM Path Analysis......Page 137 Some Personal History......Page 141 How Our Thinking About Modeling Has Changed......Page 143 Notes......Page 145 References......Page 146 --- Measurement & Testing Issues in Longitudinal Modeling......Page 149 Small Sample Corrections to Model Fit Criteria for Latent Change Score Models......Page 150 Methods......Page 157 Modeling Framework......Page 158 Number of Time Points......Page 161 Missing Data Corrections......Page 162 RMSEA......Page 163 Empirical Example......Page 164 Discussion......Page 165 References......Page 168 Introduction......Page 171 Latent Curve Modeling......Page 172 Specific vs. Generalized Variance Tests......Page 174 Unconstrained vs. Constrained Estimation and the Boundary Issue......Page 175 How Are the Tests Conducted in Practice?......Page 177 Type I Error Rates......Page 178 Statistical Power......Page 179 A Real Data Analysis Example......Page 181 Consequences of Over-Simplifying Covariance Structures in Linear Growth Curve Modeling......Page 184 Point Estimates of the Latent Factor Means or Fixed Effects......Page 185 Standard Error Estimates of the Latent Factor Mean Estimates......Page 187 Robust Standard Error Estimates......Page 188 General Discussion......Page 189 Note......Page 190 References......Page 191 Introduction......Page 194 A Univariate Latent Change Score Model......Page 196 A Bivariate Latent Change Score Model......Page 198 Statistical Power Analysis Based on Monte Carlo Simulation......Page 199 R Package......Page 202 Online Interface......Page 204 Example 9.1: Type I Error Rate Investigation for a Univariate LCSM......Page 205 Example 9.2: Power Analysis for a Univariate LCSM......Page 206 Example 9.3: Generate a Power Curve for Different Sample Sizes for a Univariate LCSM......Page 207 Example 9.4: Generate a Power Curve for Different Number of Occasions for a Univariate LCSM......Page 208 Example 9.5: Power Analysis for a Bivariate LCSM......Page 209 Discussion and Future Directions......Page 211 References......Page 213 10 Investigating the Performance of cart- & Random Forest-based Procedures for Dealing with Longitudinal Dropout in Small Sample Designs under mnar Missing Data......Page 217 Introduction to CART and Random Forests......Page 218 Using CART and Random Forests to Address Missing Data......Page 219 The Present Research......Page 221 Data Generation Model......Page 222 Factors Manipulated in the Simulation......Page 223 Analyses Conducted on Each Simulated Dataset......Page 226 Percent Bias......Page 228 Discussion......Page 238 Notes......Page 241 References......Page 242 Introduction......Page 245 Latent Variable Measurement, Indeterminacy, and Model Equivalence......Page 246 Problems with Longitudinal Measurement Modeling......Page 249 Factor of Curves Model......Page 250 Measurement Model of Derivatives......Page 252 Derivative Estimation......Page 253 Full Dimensionality......Page 254 Interpretable......Page 255 Multidimensional Measurement Modeling......Page 256 Redefining MMOD for Item-Level Data......Page 257 Defining Residual Dynamic Structure......Page 259 Discussion......Page 260 FOCUS, CUFFS, and Cross-Classified Data......Page 261 Notes......Page 262 References......Page 263 --- Novel Applications of Multivariate Longitudinal Methodology......Page 266 12 Role of Interval Measurement in Developmental Studies......Page 267 Validity......Page 268 Importance of Interval Scaling......Page 269 Mechanistic Modeling......Page 271 Rasch Measurement......Page 273 Need for Pragmatism......Page 274 Recommendations......Page 276 References......Page 278 Growth Modeling using the Differential Form: Translations from Study of Fish Growth......Page 281 Reformulating Growth Models in the Differential Form......Page 282 Translating Models from Ecology to Developmental Science......Page 285 A von Bertalanffy Growth Model for Fish Growth: The Integral Form......Page 286 Bioenergetic Determinants of Growth: The Differential Form......Page 289 Connecting the Differential and Integral Forms......Page 292 Example Extensions......Page 295 Future Directions......Page 298 Epilogue......Page 300 References......Page 301 Introduction......Page 305 Psychometric and Biometric Factor Models......Page 306 Latent Growth Modeling......Page 309 Conclusions......Page 314 References......Page 317 15 Making the Cut......Page 319 Research Can’t Answer Every Question......Page 321 Understand Your Audience......Page 322 You Need More and Better Data......Page 323 References......Page 325 Jack’s Successful Team-Approach in Working with Minority Researchers and Organizations......Page 327 Individualism......Page 328 Collectivism......Page 329 Trusting Relationships......Page 330 Training Approach......Page 331 Projects......Page 332 Methodologies and Statistical Analyses......Page 333 Strategically Planning for the Long Term......Page 337 References......Page 340 Index......Page 345