- Shopping Bag ( 0 items )
Statistical methodology is often conceived by social scientists in a technical manner; they use it for support rather than for illumination. This two-volume set attempts to provide some partial remedy to the problems that have led to this state of affairs. Both traditional issues, such as analysis of variance and the general linear model, as well as more novel methods like exploratory data analysis, are included. The editors aim to provide an updated survey on different aspects of empirical research and data ...
Statistical methodology is often conceived by social scientists in a technical manner; they use it for support rather than for illumination. This two-volume set attempts to provide some partial remedy to the problems that have led to this state of affairs. Both traditional issues, such as analysis of variance and the general linear model, as well as more novel methods like exploratory data analysis, are included. The editors aim to provide an updated survey on different aspects of empirical research and data analysis, facilitate the understanding of the internal logic underlying different methods, and provide novel and broader perspectives beyond what is usually covered in traditional curricula.
Volume I: Methodological Issues. Contents: Preface. Part I: Models and Measurement. W.K. Estes, Mathematical Models in Psychology. N.A. Macmillan, Signal Detection Theory as Data Analysis Method and Psychological Decision Model. N. Cliff, What Is and Isn't Measurement. L.E. Jones, L.M. Koehly, Multidimensional Scaling. G. Shafer, Can the Various Meanings of Probability Be Reconciled? Part II: Methodological Issues. R.C. Serlin, D.K. Lapsley, Rational Appraisal of Psychological Research and the Good-Enough Principle. D. MacKay, The Theoretical Epistemology: A New Perspective on Some Long-Standing Methodological Issues in Psychology. G. Keren, Between- or Within-Subjects Design: A Methodological Dilemma. P.W. Holland, Which Comes First, Cause or Effect? N. Brenner-Golomb, R.A. Fisher's Philosophical Approach to Inductive Inference. Part III: Intuitive Statistics. G. Gigerenzer, The Superego, the Ego, and the Id in Statistical Reasoning. A. Tversky, D. Kahneman, Belief in the Law of Small Numbers. R.M. Dawes, D. Faust, P.E. Meehl, Statistical Prediction Versus Clinical Prediction: Improving What Works. M. Bar-Hillel, W.A. Wagenaar, The Perception of Randomness. P.J. Pashley, On Generating Random Sequences. Part IV: Hypothesis Testing, Power, and Effect Size. A.G. Greenwald, Consequences of Prejudice Against the Null Hypothesis. P. Pollard, How Significant Is "Significance"? M. Tatsuoka, Effect Size. D.W. Zimmerman, B.D. Zumbo, The Relative Power of Parametric and Nonparametric Statistical Methods. R. Rosenthal, Cumulating Evidence. Volume 2: Statistical Issues. Contents: Preface. Part I: Analysis of Variance and Multiple Regression. M. Tatsuoka, Elements of the General Linear Model. R. Zwick, Pairwise Comparison Procedures for One-Way Analysis of Variance Designs. C. Lewis, Analyzing Means From Repeated Measures Data. G. Keren, A Balanced Approach to Unbalanced Designs. N.M. Timm, MANOVA and MANCOVA: An Overview. J. Cohen, Set Correlation. Part II: Bayesian Statistics. R.L. Winkler, Bayesian Statistics: An Overview. C. Lewis, Bayesian Methods for the Analysis of Variance. Part III: Categorical Data and the Analysis of Frequencies. S.S. Brier, Analysis of Categorical Data. K.L. Delucchi, On the Use and Misuse of Chi-Square. B.S. Everitt, Some Aspects of the Analysis of Categorical Data. Part IV: Other Topics. A.F. Smith, D.A. Prentice, Exploratory Data Analysis. H. Wainer, D. Thissen, Graphical Data Analysis. R.M. Church, Uses of Computers in Psychological Research. G.R. Loftus, Computer Simulation: Some Remarks on Theory in Psychology. R.H. Rushe, J.M. Gottman, Essentials in the Design and Analysis of Time-Series Experiments.