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"Experimental psychology"
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Too good to be true: Publication bias in two prominent studies from experimental psychology
Empirical replication has long been considered the final arbiter of phenomena in science, but replication is undermined when there is evidence for publication bias. Evidence for publication bias in a set of experiments can be found when the observed number of rejections of the null hypothesis exceeds the expected number of rejections. Application of this test reveals evidence of publication bias in two prominent investigations from experimental psychology that have purported to reveal evidence of extrasensory perception and to indicate severe limitations of the scientific method. The presence of publication bias suggests that those investigations cannot be taken as proper scientific studies of such phenomena, because critical data are not available to the field. Publication bias could partly be avoided if experimental psychologists started using Bayesian data analysis techniques.
Journal Article
Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications
Bayesian parameter estimation and Bayesian hypothesis testing present attractive alternatives to classical inference using confidence intervals and
p
values. In part I of this series we outline ten prominent advantages of the Bayesian approach. Many of these advantages translate to concrete opportunities for pragmatic researchers. For instance, Bayesian hypothesis testing allows researchers to quantify evidence and monitor its progression as data come in, without needing to know the intention with which the data were collected. We end by countering several objections to Bayesian hypothesis testing. Part II of this series discusses JASP, a free and open source software program that makes it easy to conduct Bayesian estimation and testing for a range of popular statistical scenarios (Wagenmakers et al.
this issue
).
Journal Article
Ethical challenges in the behavioral and brain sciences : case studies and commentaries
\"In recent years, a growing number of scientific careers have been brought down by scientists' failure to satisfactorily confront ethical challenges. Scientists need to learn early on what constitutes acceptable ethical behavior in their professions. Ethical Principles for the Behavioral and Brain Sciences encourages readers to engage in discussions of the diverse ethical dilemmas encountered by behavioral and brain scientists. The goal is to allow scientists to reflect on ethical issues before potentially confronting them. Each chapter is authored by a prominent scientist in the field who describes a dilemma, how it was resolved, and what the scientist would do differently if confronted with the situation again. Featuring commentary throughout and a culmination of opinions and experiences shared by leaders in the field, the goal of this book is not to provide 'correct' answers to real-world ethical dilemmas. Instead, authors pose the dilemmas, discuss their experiences and viewpoints on them, and speculate on alternative reactions to the issues\"-- Provided by publisher.
Book
Understanding The New Statistics
This is the first book to introduce the new statistics - effect sizes, confidence intervals, and meta-analysis - in an accessible way. It is chock full of practical examples and tips on how to analyze and report research results using these techniques. The book is invaluable to readers interested in meeting the new APA Publication Manual guidelines by adopting the new statistics - which are more informative than null hypothesis significance testing, and becoming widely used in many disciplines.
Accompanying the book is the Exploratory Software for Confidence Intervals (ESCI) package, free software that runs under Excel and is accessible at www.thenewstatistics.com. The book's exercises use ESCI's simulations, which are highly visual and interactive, to engage users and encourage exploration. Working with the simulations strengthens understanding of key statistical ideas. There are also many examples, and detailed guidance to show readers how to analyze their own data using the new statistics, and practical strategies for interpreting the results. A particular strength of the book is its explanation of meta-analysis, using simple diagrams and examples. Understanding meta-analysis is increasingly important, even at undergraduate levels, because medicine, psychology and many other disciplines now use meta-analysis to assemble the evidence needed for evidence-based practice.
The book's pedagogical program, built on cognitive science principles, reinforces learning:
Boxes provide \"evidence-based\" advice on the most effective statistical techniques.
Numerous examples reinforce learning, and show that many disciplines are using the new statistics.
Graphs are tied in with ESCI to make important concepts vividly clear and memorable.
Opening overviews and end of chapter take-home messages summarize key points.
Exercises encourage exploration, deep understanding, and practical app
eBook
Standard errors and confidence intervals in within-subjects designs: Generalizing Loftus and Masson (1994) and avoiding the biases of alternative accounts
Repeated measures designs are common in experimental psychology. Because of the correlational structure in these designs, the calculation and interpretation of confidence intervals is nontrivial. One solution was provided by Loftus and Masson (
Psychonomic Bulletin & Review 1
:476–490,
1994
). This solution, although widely adopted, has the limitation of implying same-size confidence intervals for all factor levels, and therefore does not allow for the assessment of variance homogeneity assumptions (i.e., the circularity assumption, which is crucial for the repeated measures ANOVA). This limitation and the method’s perceived complexity have sometimes led scientists to use a simplified variant, based on a per-subject normalization of the data (Bakeman & McArthur,
Behavior Research Methods, Instruments, & Computers 28
:584–589,
1996
; Cousineau,
Tutorials in Quantitative Methods for Psychology 1
:42–45,
2005
; Morey,
Tutorials in Quantitative Methods for Psychology 4
:61–64,
2008
; Morrison & Weaver,
Behavior Research Methods, Instruments, & Computers 27
:52–56,
1995
). We show that this normalization method leads to biased results and is uninformative with regard to circularity. Instead, we provide a simple, intuitive generalization of the Loftus and Masson method that allows for assessment of the circularity assumption.
Journal Article
MorePower 6.0 for ANOVA with relational confidence intervals and Bayesian analysis
MorePower 6.0 is a flexible freeware statistical calculator that computes sample size, effect size, and power statistics for factorial ANOVA designs. It also calculates relational confidence intervals for ANOVA effects based on formulas from Jarmasz and Hollands (Canadian Journal of Experimental Psychology 63:124–138,
2009
), as well as Bayesian posterior probabilities for the null and alternative hypotheses based on formulas in Masson (Behavior Research Methods 43:679–690,
2011
). The program is unique in affording direct comparison of these three approaches to the interpretation of ANOVA tests. Its high numerical precision and ability to work with complex ANOVA designs could facilitate researchers’ attention to issues of statistical power, Bayesian analysis, and the use of confidence intervals for data interpretation. MorePower 6.0 is available at
https://wiki.usask.ca/pages/viewpageattachments.action?pageId=420413544
.
Journal Article