LEADER 05668oam 2200733I 450 001 9910455417703321 005 20200520144314.0 010 $a1-135-26931-9 010 $a1-282-29455-5 010 $a9786612294556 010 $a0-203-86695-9 024 7 $a10.4324/9780203866955 035 $a(CKB)1000000000805900 035 $a(EBL)692353 035 $a(OCoLC)764572369 035 $a(SSID)ssj0000251128 035 $a(PQKBManifestationID)11204054 035 $a(PQKBTitleCode)TC0000251128 035 $a(PQKBWorkID)10247734 035 $a(PQKB)11774395 035 $a(MiAaPQ)EBC692353 035 $a(Au-PeEL)EBL692353 035 $a(CaPaEBR)ebr10545572 035 $a(CaONFJC)MIL229455 035 $a(OCoLC)449637649 035 $a(EXLCZ)991000000000805900 100 $a20180706d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical power analysis with missing data $ea structural equation modeling approach /$fAdam Davey, Jyoti Savla 210 1$aNew York :$cRoutledge,$d2010. 215 $a1 online resource (370 p.) 300 $aDescription based upon print version of record. 311 $a0-8058-6370-2 311 $a0-8058-6369-9 320 $aIncludes bibliographical references and index. 327 $aFront Cover; Statistical Power Analysis with Missing Data; Copyright Page; Contents; 1. Introduction; Overview and Aims; Statistical Power; Testing Hypotheses; Choosing an Alternative Hypothesis; Central and Noncentral Distributions; Factors Important for Power; Effect Sizes; Determining an Effect Size; Point Estimates and Confidence Intervals; Reasons to Estimate Statistical Power; Conclusions; Further Readings; Section I: Fundamentals; 2. The LISREL Model; Matrices and the LISREL Model; Latent and Manifest Variables; Regression Coefficient Matrices; Variance-Covariance Matrices 327 $aVectors of Means and InterceptsModel Parameters; Models and Matrices; Structure of a LISREL Program; Reading and Interpreting LISREL Output; Evaluating Model Fit; Measures of Population Discrepancy; Incremental Fit Indices; Absolute Fit Indices; Conclusions; Further Readings; 3. Missing Data: An Overview; Why Worry About Missing Data?; Types of Missing Data; Missing Completely at Random; Missing at Random; Missing Not at Random; Strategies for Dealing With Missing Data; Complete Case Methods; List-Wise Deletion; List-Wise Deletion With Weighting; Available Case Methods; Pair-Wise Deletion 327 $aExpectation Maximization AlgorithmFull Information Maximum Likelihood; Imputation Methods; Single Imputation; Multiple Imputation; Estimating Structural Equation Models With Incomplete Data; Conclusions; Further Readings; 4. Estimating Statistical Power With Complete Data; Statistical Power in Structural Equation Modeling; Power for Testing a Single Alternative Hypothesis; Tests of Exact, Close, and Not Close Fit; Tests of Exact, Close, and Not Close Fit Between Two Models; An Alternative Approach to Estimate Statistical Power; Estimating Required Sample Size for Given Power; Conclusions 327 $aFurther ReadingsSection II: Applications; 5. Effects of Selection on Means, Variances, and Covariances; Defining the Population Model; Defining the Selection Process; An Example of the Effects of Selection; Selecting Data Into More Than Two Groups; Conclusions; Further Readings; 6. Testing Covariances and Mean Differences With Missing Data; Step 1: Specifying the Population Model; Step 2: Specifying the Alternative Model; Step 3: Generate Data Structure Implied by the Population Model; Step 4: Decide on the Incomplete Data Model; Step 5: Apply the Incomplete Data Model to Population Data 327 $aStep 6: Estimate Population and Alternative Models With Missing DataStep 7: Using the Results to Estimate Power or Required Sample Size; Conclusions; Further Readings; 7. Testing Group Differences in Longitudinal Change; The Application; The Steps; Step 1: Selecting a Population Model; Step 2: Selecting an Alternative Model; Step 3: Generating Data According to the Population Model; Step 4: Selecting a Missing Data Model; Step 5: Applying the Missing Data Model to Population Data; Step 6: Estimating Population and Alternative Models With Incomplete Data 327 $aStep 7: Using the Results to Calculate Power or Required Sample Size 330 $aStatistical power analysis has revolutionized the ways in which we conduct and evaluate research. Similar developments in the statistical analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together under a common framework, in a way that is readily accessible to those with only an introductory familiarity with structural equation modeling. It answers many practical questions such as: How missing data affects the statistical power in a study How much power is likely with different 606 $aSocial sciences$vStatistics 606 $aSocial sciences$xStatistical methods 606 $aSocial sciences$xMathematical models 608 $aElectronic books. 615 0$aSocial sciences 615 0$aSocial sciences$xStatistical methods. 615 0$aSocial sciences$xMathematical models. 676 $a001.422 676 $a519.5 700 $aDavey$b Adam.$0607703 701 $aSavla$b Jyoti$0607704 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910455417703321 996 $aStatistical power analysis with missing data$91126356 997 $aUNINA