Vai al contenuto principale della pagina

Statistical power analysis with missing data : a structural equation modeling approach / / Adam Davey, Jyoti Savla



(Visualizza in formato marc)    (Visualizza in BIBFRAME)

Autore: Davey Adam Visualizza persona
Titolo: Statistical power analysis with missing data : a structural equation modeling approach / / Adam Davey, Jyoti Savla Visualizza cluster
Pubblicazione: New York : , : Routledge, , 2010
Descrizione fisica: 1 online resource (370 p.)
Disciplina: 001.422
519.5
Soggetto topico: Social sciences
Social sciences - Statistical methods
Social sciences - Mathematical models
Soggetto genere / forma: Electronic books.
Altri autori: SavlaJyoti  
Note generali: Description based upon print version of record.
Nota di bibliografia: Includes bibliographical references and index.
Nota di contenuto: Front 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
Vectors 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
Expectation 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
Further 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
Step 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
Step 7: Using the Results to Calculate Power or Required Sample Size
Sommario/riassunto: Statistical 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
Titolo autorizzato: Statistical power analysis with missing data  Visualizza cluster
ISBN: 1-135-26931-9
1-282-29455-5
9786612294556
0-203-86695-9
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910455417703321
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui