Book•
Designing Experiments and Analyzing Data: A Model Comparison Perspective, Third Edition
01 Dec 1999-
TL;DR: This book discusses conceptual Bases of Experimental Design and Analysis, model Comparisons for Between-Subjects Designs, and an Introduction to Multilevel Hierarchical Mixed Models: Nested Designs.
Abstract: Contents: Preface. Part I: Conceptual Bases of Experimental Design and Analysis. The Logic of Experimental Design. Introduction to the Fisher Tradition. Part II: Model Comparisons for Between-Subjects Designs. Introduction to Model Comparisons: One-Way Between-Subjects Designs. Individual Comparisons of Means. Testing Several Contrasts: The Multiple-Comparison Problem. Trend Analysis. Two-Way Between-Subjects Factorial Designs. Higher Order Between-Subjects Factorial Designs. Designs With Covariates: ANCOVA and Blocking. Designs With Random or Nested Factors. Part III: Model Comparisons for Designs Involving Within-Subjects Factors. One-Way Within-Subjects Designs: Univariate Approach. Higher-Order Designs With Within-Subjects Factors: Univariate Approach. One-Way Within-Subjects Designs: Multivariate Approach. Higher Order Designs With Within-Subjects Factors: Multivariate Approach. Part IV: Alternative Analysis Strategies. An Introduction to Multilevel Models for Within-Subjects Designs. An Introduction to Multilevel Hierarchical Mixed Models: Nested Designs. Appendices: Statistical Tables. Part 1. Linear Models: The Relation Between ANOVA and Regression. Part 2. A Brief Primer of Principles of Formulating and Comparing Models. Notes. Solutions to Selected Exercises. References.
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21 Mar 2002TL;DR: An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data is as discussed by the authors, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced.
Abstract: An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software
9,509 citations
Cites background from "Designing Experiments and Analyzing..."
...An extreme view, and not one to which we subscribe, might be to define a family as all the tests a researcher might do in a lifetime (see Maxwell & Delaney 1990 and Miller 1981 for discussion), and try to limit the Type I error rate over this family....
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...Maxwell & Delaney (1990) provide an overview from a behavioral sciences viewpoint and the first two chapters of Hilborn & Mangel (1997) emphasize alternatives to the Popperian approach in situations where experimental tests of hypotheses are simply not possible....
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Book•
01 Jun 2015TL;DR: A practical primer on how to calculate and report effect sizes for t-tests and ANOVA's such that effect sizes can be used in a-priori power analyses and meta-analyses and a detailed overview of the similarities and differences between within- and between-subjects designs is provided.
Abstract: Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists themselves, effect sizes are most useful because they facilitate cumulative science. Effect sizes can be used to determine the sample size for follow-up studies, or examining effects across studies. This article aims to provide a practical primer on how to calculate and report effect sizes for t-tests and ANOVA’s such that effect sizes can be used in a-priori power analyses and meta-analyses. Whereas many articles about effect sizes focus on between-subjects designs and address within-subjects designs only briefly, I provide a detailed overview of the similarities and differences between within- and between-subjects designs. I suggest that some research questions in experimental psychology examine inherently intra-individual effects, which makes effect sizes that incorporate the correlation between measures the best summary of the results. Finally, a supplementary spreadsheet is provided to make it as easy as possible for researchers to incorporate effect size calculations into their workflow.
5,374 citations
Cites background from "Designing Experiments and Analyzing..."
...…as a complementary resource for psychologists who want to learn more about effect sizes (for excellent books that discuss this topic in more detail, see Cohen, 1988; Maxwell and Delaney, 2004; Grissom and Kim, 2005; Thompson, 2006; Aberson, 2010; Ellis, 2010; Cumming, 2012; Murphy et al., 2012)....
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...Therefore, many researchers regard effect sizes in within-subjects designs as an overestimation of the “true” effect size (e.g., Dunlap et al., 1996; Olejnik and Algina, 2003; Maxwell and Delaney, 2004)....
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TL;DR: A nontechnical discussion is provided, emphasizing a substantive confound rarely articulated in textbooks and other general presentations, to complement the mathematical critiques already available.
Abstract: Despite numerous technical treatments in many venues, analysis of covariance (ANCOVA) remains a widely misused approach to dealing with substantive group differences on potential covariates, particularly in psychopathology research. Published articles reach unfounded conclusions, and some statistics texts neglect the issue. The problem with ANCOVA in such cases is reviewed. In many cases, there is no means of achieving the superficially appealing goal of "correcting" or "controlling for" real group differences on a potential covariate. In hopes of curtailing misuse of ANCOVA and promoting appropriate use, a nontechnical discussion is provided, emphasizing a substantive confound rarely articulated in textbooks and other general presentations, to complement the mathematical critiques already available. Some alternatives are discussed for contexts in which ANCOVA is inappropriate or questionable. In research comparing groups of participants, classical experimental design (Campbell & Stanley, 1963) relies, whenever possible, on random assignment of participants to groups. Observed differences between such groups, prior to experimental treatments, are due to chance rather than being meaningfully related to the
1,985 citations
TL;DR: The logic and statistical theory behind multilevel models are introduced, to illustrate how such models can be applied fruitfully in political science, and to call attention to some of the pitfalls in multileVEL analysis.
Abstract: data are becoming quite common in political science and provide numerous opportunities for theory testing and development. Unfortunately this type of data typically generates a number of statistical problems, of which clustering is particularly impor? tant. To exploit the opportunities of? fered by multilevel data, and to solve the statistical problems inherent in them, special statistical techniques are required. In this article, we focus on a technique that has become popular in educational statistics and sociology?multilevel analysis. In multilevel analysis, researchers build models that capture the layered structure of multilevel data, and determine how layers interact and impact a dependent variable of interest. Our objective in this article is to introduce the logic and statistical theory behind multilevel models, to illustrate how such models can be applied fruitfully in political science, and to call atten? tion to some of the pitfalls in multilevel analysis.
1,440 citations
TL;DR: In this article, the authors provide formulas for computing generalized eta and omega squared statistics, which provide estimates of effect size that are comparable across a variet yo f research designs, but do not consider the effect that design features of the study have on the size of these statistics.
Abstract: The editorial policies of several prominent educational and psychological journals require that researchers report some measure of effect size along with tests for statistical significance. In analysis of variance contexts, this requirement might be met by using eta squared or omega squared statistics. Current procedures for computing these measures of effect often do not consider the effect that design features of the study have on the size of these statistics. Because research-design features can have a large effect on the estimated proportion of explained variance, the use of partial eta or omega squared can be misleading. The present article provides formulas for computing generalized eta and omega squared statistics, which provide estimates of effect size that are comparable across a variet yo f research designs. It is often argued that researchers can enhance the presentation of their research findings by including an effect-size measure along with a test of statistical significance. An effect-size measure is a standardized index and estimates a parameter that is independent of sample size and quantifies the magnitude of the difference between populations or the relationship between explanatory and response variables. Two broad
1,281 citations
Cites methods from "Designing Experiments and Analyzing..."
...Textbooks presenting analysis of variance (ANOVA) models (eg, Keppel, 1991; Maxwell & Delaney, 2000; Stevens, 1999) and articles discussing and critiquing the use of effect-size measures (eg, Fern & Monroe, 1996; Richardson, 1996) have not addressed the issue....
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