Applied quantitative analysis in education and the social sciences
- New York : Routledge/Taylor & Francis Group, 2013.
- Physical description
- x, 376 pages : illustrations ; 26 cm.
QA278.2 .A67 2013
- Unknown QA278.2 .A67 2013
- Includes bibliographical references and index.
- Part I: Individual Level Analysis 1. Extending Conditional Means Modeling: An Introduction to Quantile Regression, Yaacov Petscher, Jessica A.R. Logan, and Chengfu Zhou 2. Using Dominance Analysis to Estimate Predictor: Importance in Multiple Regression, Razia Azen 3. I am ROC Curves (and so can you)!, Christopher Schatschneider Part II: Multilevel Analysis 4. Multilevel Modeling: Practical Examples to Illustrate a Special Case of SEM, Lee Branum-Martin 5. Linear and Quadratic Growth Models for Continuous and Dichotomous Outcomes, Ann A. O'Connell, Jessica A. R. Logan, Jill Pentimonti, and D. Betsy McCoach PART III: Item Level Analysis 6. Exploratory and Confirmatory Factor Analysis, Rex Kline 7. Factor Analysis with Categorical Indicators: Demonstrationof Item Response Theory, R.J. de Ayala Part IV: Covariance Structure Analysis 8. Introduction to Structural Equation Modeling, Richard Lomax 9. Latent Growth Curve Modeling using Structural Equation Modeling, Ryan Bowles and Janelle J. Montroy 10. Latent Class/Profile Analysis, Karen Samuelsen and Katherine Raczynski 11. n-level Structural Equation Modeling, Paras Mehta.
- (source: Nielsen Book Data)
- Publisher's Summary
- To say that complex data analyses are ubiquitous in the education and social sciences might be an understatement. Funding agencies and peer-review journals alike require that researchers use the most appropriate models and methods for explaining phenomena. Univariate and multivariate data structures often require the application of more rigorous methods than basic correlational or analysis of variance models. Additionally, though a vast set of resources may exist on how to run analysis, difficulties may be encountered when explicit direction is not provided as to how one should run a model and interpret results. The mission of this book is to expose the reader to advanced quantitative methods as it pertains to individual level analysis, multilevel analysis, item-level analysis, and covariance structure analysis. Each chapter is self-contained and follows a common format so that readers can run the analysis and correctly interpret the output for reporting.
(source: Nielsen Book Data)
- Included Work
- Petscher, Yaacov M. Extending conditional means modeling.
- Publication date
- edited by Yaacov Petscher, Florida State University, Florida Center for Reading Research, Christopher Schatschneider, Florida State University, Florida Center for Reading Research, Donald L. Compton, Vanderbilt University.