Todd D. Little, Ph.D., is a Professor of Psychology, Director of the Quantitative Training Program, Director of the Undergraduate Social and Behavioral Sciences Methodology minor, and a member of the Developmental Training program at the University of Kansas.

• 1. Introduction ; Todd Little ; 2. The Philosophy of Quantitative Methods ; Brian D. Haig ; 3. Quantitative Methods and Ethics ; Ralph L. Rosnow and Robert Rosenthal ; 4. Special Populations ; Keith F. Widaman, Dawnte R. Early, and Rand D. Conger ; 5. Theory Construction, Model Building, and Model Selection ; James Jaccard ; 6. Teaching Quantitative Psychology ; Lisa L. Harlow ; 7. Modern Test Theory ; R. P. McDonald ; 8. The IRT Tradition and its Applications ; R.J. de Ayala ; 9. Survey Design and Measure Development ; Paul E. Spector ; 10. High Stakes Test Construction and Test Use ; Neal M. Kingston and Laura B. Kramer ; 11. Effect Size and Sample Size Planning ; Ken Kelley ; 12. Experimental Design for Causal Inference: Clinical Trials and Regression Discontinuity Designs ; Kelly Hallberg, Coady Wing, Vivian Wong, and Thomas D. Cook ; 13. Matching and Propensity Scores ; Peter M. Steiner and David Cook ; 14. Designs for and Analyses of Response Time Experiments ; Trisha Van Zandt and James T. Townsend ; 15. Observational Methods ; Jamie M. Ostrov and Emily J. Hart ; 16. A Primer of Epidemiologic Methods, Concepts, and Analysis with Examples and More Advanced Applications within Psychology ; David E. Bard, Joseph L. Rodgers, and Keith E. Muller ; 17. Program Evaluation: Principles, Procedures, and Practices ; Aurelio Jose Figueredo, Sally Gayle Olderbak, Gabriel Lee Schlomer, Rafael Antonio Garcia, and Pedro Sofio Abril Wolf ; 18. Overview of Statistical Estimation Methods ; Ke-Hai Yuan and Christof Schuster ; 19. Robust Statistical Estimation ; David M. Erceg-Hurn, Rand R. Wilcox, and Harvey J. Keselman ; 20. Bayesian Statistical Methods ; David Kaplan and Sarah Depaoli ; 21. Mathematical Modeling ; Daniel R. Cavagnaro, Jay I. Myung, and Mark A. Pitt ; 22. What Would Happen If...? Monte Carlo Analysis in Academic Research ; P. E. Johnson

Todd D. Little, Ph.D., is a Professor of Psychology, Director of the Quantitative Training Program, Director of the Undergraduate Social and Behavioral Sciences Methodology minor, and a member of the Developmental Training program at the University of Kansas.

• 1. Introduction ; Todd Little ; 2. The Philosophy of Quantitative Methods ; Brian D. Haig ; 3. Quantitative Methods and Ethics ; Ralph L. Rosnow and Robert Rosenthal ; 4. Special Populations ; Keith F. Widaman, Dawnte R. Early, and Rand D. Conger ; 5. Theory Construction, Model Building, and Model Selection ; James Jaccard ; 6. Teaching Quantitative Psychology ; Lisa L. Harlow ; 7. Modern Test Theory ; R. P. McDonald ; 8. The IRT Tradition and its Applications ; R.J. de Ayala ; 9. Survey Design and Measure Development ; Paul E. Spector ; 10. High Stakes Test Construction and Test Use ; Neal M. Kingston and Laura B. Kramer ; 11. Effect Size and Sample Size Planning ; Ken Kelley ; 12. Experimental Design for Causal Inference: Clinical Trials and Regression Discontinuity Designs ; Kelly Hallberg, Coady Wing, Vivian Wong, and Thomas D. Cook ; 13. Matching and Propensity Scores ; Peter M. Steiner and David Cook ; 14. Designs for and Analyses of Response Time Experiments ; Trisha Van Zandt and James T. Townsend ; 15. Observational Methods ; Jamie M. Ostrov and Emily J. Hart ; 16. A Primer of Epidemiologic Methods, Concepts, and Analysis with Examples and More Advanced Applications within Psychology ; David E. Bard, Joseph L. Rodgers, and Keith E. Muller ; 17. Program Evaluation: Principles, Procedures, and Practices ; Aurelio Jose Figueredo, Sally Gayle Olderbak, Gabriel Lee Schlomer, Rafael Antonio Garcia, and Pedro Sofio Abril Wolf ; 18. Overview of Statistical Estimation Methods ; Ke-Hai Yuan and Christof Schuster ; 19. Robust Statistical Estimation ; David M. Erceg-Hurn, Rand R. Wilcox, and Harvey J. Keselman ; 20. Bayesian Statistical Methods ; David Kaplan and Sarah Depaoli ; 21. Mathematical Modeling ; Daniel R. Cavagnaro, Jay I. Myung, and Mark A. Pitt ; 22. What Would Happen If...? Monte Carlo Analysis in Academic Research ; P. E. Johnson

Todd D. Little, Ph.D., is a Professor of Psychology, Director of the Quantitative Training Program, Director of the Undergraduate Social and Behavioral Sciences Methodology minor, and a member of the Developmental Training program at the University of Kansas.

• 1. Introduction ; Todd Little ; 2. The Philosophy of Quantitative Methods ; Brian D. Haig ; 3. Quantitative Methods and Ethics ; Ralph L. Rosnow and Robert Rosenthal ; 4. Special Populations ; Keith F. Widaman, Dawnte R. Early, and Rand D. Conger ; 5. Theory Construction, Model Building, and Model Selection ; James Jaccard ; 6. Teaching Quantitative Psychology ; Lisa L. Harlow ; 7. Modern Test Theory ; R. P. McDonald ; 8. The IRT Tradition and its Applications ; R.J. de Ayala ; 9. Survey Design and Measure Development ; Paul E. Spector ; 10. High Stakes Test Construction and Test Use ; Neal M. Kingston and Laura B. Kramer ; 11. Effect Size and Sample Size Planning ; Ken Kelley ; 12. Experimental Design for Causal Inference: Clinical Trials and Regression Discontinuity Designs ; Kelly Hallberg, Coady Wing, Vivian Wong, and Thomas D. Cook ; 13. Matching and Propensity Scores ; Peter M. Steiner and David Cook ; 14. Designs for and Analyses of Response Time Experiments ; Trisha Van Zandt and James T. Townsend ; 15. Observational Methods ; Jamie M. Ostrov and Emily J. Hart ; 16. A Primer of Epidemiologic Methods, Concepts, and Analysis with Examples and More Advanced Applications within Psychology ; David E. Bard, Joseph L. Rodgers, and Keith E. Muller ; 17. Program Evaluation: Principles, Procedures, and Practices ; Aurelio Jose Figueredo, Sally Gayle Olderbak, Gabriel Lee Schlomer, Rafael Antonio Garcia, and Pedro Sofio Abril Wolf ; 18. Overview of Statistical Estimation Methods ; Ke-Hai Yuan and Christof Schuster ; 19. Robust Statistical Estimation ; David M. Erceg-Hurn, Rand R. Wilcox, and Harvey J. Keselman ; 20. Bayesian Statistical Methods ; David Kaplan and Sarah Depaoli ; 21. Mathematical Modeling ; Daniel R. Cavagnaro, Jay I. Myung, and Mark A. Pitt ; 22. What Would Happen If...? Monte Carlo Analysis in Academic Research ; P. E. Johnson