Sawilowsky / S. Real Data Analysis

1. Auflage 2007
ISBN: 978-1-60752-578-3
Verlag: Information Age Publishing
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 445 Seiten

Reihe: Quantitative Methods in Education and the Behavioral Sciences: Issues, Research, and Teaching

The invited authors of this edited volume have been prolific in the arena of Real Data Analysis (RDA) as it applies to the social and behavioral sciences, especially in the disciplines of education and psychology. Combined, this brain trust represents 3,247 articles in refereed journals, 127 books published, US $45.3 Million in extramural research funding, 34 teaching and 92 research awards, serve(d) as Editor/Assistant Editor/Editorial Board Member for 95 peer reviewed journals, and provide (d) ad hoc reviews for 362 journals. Their enormous footprint on real data analysis is showcased for professors, researchers, educators, administrators, and graduate students in the second text in the AERA/SIG ES Quantitative Methods series.

Autoren/Hrsg.

Sawilowsky, Shlomo S.

Sawilowsky,

S., Shlomo

Inhaltsverzeichnis

1;Cover;1
2;Contents;5
3;Preface;9
4;Part I - Foundations;23
4.1;Chapter 1. The Coevolution of Statistics and HZ;25
4.2;Chapter 2. Effective Sample Size: A Crucial Concept;43
4.3;Chapter 3. Advances in Missing Data Methods and Implications for Educational Research;53
4.4;Chapter 4. Methods for Simulating Real World Data for the Psycho-Educational Sciences;101
4.5;Chapter 5. How and Why I Use Real, Messy Data to Investigate Theory and Inform Decision Making;121
5;Part II - Statistical Methods;127
5.1;Chapter 6. Using E-mail Messages to Help Students Prepare for a Statistics Exam;129
5.2;Chapter 7. Randomization Tests: Statistical Tools for Assessing the Effects of Educational Interventions When Resources are Scarce;137
5.3;Chapter 8. A Skipped Multivariate Measure of Location: One- and Two-Sample Hypothesis Testing;147
5.4;Chapter 9. Robust Step-Down Tests for Multivariate Group Differences;161
5.5;Chapter 10. Dunn-Sidák Critical Values and p Values;171
5.6;Chapter 11. Controlling Experiment-Wise Type I Errors: Good Advice for Simultaneous and Sequential Hypothesis Testing;177
5.7;Chapter 12. Robustness and Power of Ordinal d for Paired Data;185
5.8;Chapter 13. Factorial ANOVA in SPSS: Fixed-, Random-, and Mixed-Effects Models;207
5.9;Chapter 14. ANOVA: Effect Sizes, Simulating Interaction vs. Main Effects, and a Modified ANOVA Table;213
5.10;Chapter 15. ANCOVA and Quasi-Experimental Design: The Legacy of Campbell and Stanley;235
6;Part III - Measurement;261
6.1;Chapter 16. Thinking About Item Response Theory from a Logistic Regression Perspective: A Focus on Polytomous Models;263
6.2;Chapter 17. Some Practical Uses of Item Response Time to Improve the Quality of Low-Stakes Achievement Test Data;293
6.3;Chapter 18. Using Moving Averages to Detect Exposed Test Items in Computer-Based Testing;299
6.4;Chapter 19. An Empirical Calibration of the Effects of Multiple Sources of Measurement Error on Reliability Estimates for Individual Differences Measures;309
6.5;Chapter 20. Latent Structure of Attitudes toward Abortion;315
7;Part IV - Data Analysis;321
7.1;Chapter 21. Hierarchical Linear Models and the Estimation of Students’ Mathematics Achievement;323
7.2;Chapter 22. Grade Inflation: An Examination at the Institutional Level;337
7.3;Chapter 23. Using Discrete-Time Survival Analysis to Study Gender Differences in Leaving Mathematics;347
7.4;Chapter 24. Nonparametric Procedures for Testing for Dropout Rates on University Courses With Application to an Italian Case Study;357
7.5;Chapter 25. Nonparametric Approaches for Multivariate Testing With Mixed Variables and for Ranking on Ordered Categorical Variables With an Application to the Evaluation of PhD Programs;377
7.6;Chapter 26. Randomized Replicated Single-case Experiments: Treatment of Pain-Related Fear by Graded Exposure In Vivo;409
7.7;Chapter 27. Whole Brain Correlations: Examining Similarity Across Conditions of Overall Patterns of Neural Activation in fMRI;419
7.8;28. Principal Component Analysis of Senate Voting Patterns;427
8;About the Authors;433