text:
@article{Sanchez-Meca2008,
abstract = {One of the main objectives in meta-analysis is to estimate the overall effect size by calculating a confidence interval (CI). The usual procedure consists of assuming a standard normal distribution and a sampling variance defined as the inverse of the sum of the estimated weights of the effect sizes. But this procedure does not take into account the uncertainty due to the fact that the heterogeneity variance (tau(2)) and the within-study variances have to be estimated, leading to CIs that are too narrow with the consequence that the actual coverage probability is smaller than the nominal confidence level. In this article, the performances of 3 alternatives to the standard CI procedure are examined under a random-effects model and 8 different tau(2) estimators to estimate the weights: the t distribution Cl, the weighted variance Cl (with an improved variance), and the quantile approximation method (recently proposed). The results of a Monte Carlo simulation showed that the weighted variance Cl outperformed the other methods regardless of the tau(2) estimator, the value of tau(2), the number of studies, and the sample size.},
address = {Washington},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {S{\'{a}}nchez-Meca, Julio and Mar{\'{i}}n-Mart{\'{i}}nez, Fulgencio and Sanchez-Meca, J and Marin-Martinez, F},
doi = {10.1037/1082-989X.13.1.31},
eprint = {arXiv:1011.1669v3},
isbn = {1082-989X},
issn = {1939-1463},
journal = {Psychological Methods},
keywords = {confidence intervals,difference,estimators,heterogeneity,heterogeneity variance,inference,meta-analysis,random-effects model,standardized mean difference,variance},
month = {oct},
number = {1},
pages = {31--48},
pmid = {18331152},
publisher = {Psychonomic Bulletin {\&} Review},
title = {{Confidence intervals for the overall effect size in random-effects meta-analysis.}},
url = {http://content.apa.org/journals/bul/92/2/490 http://doi.apa.org/getdoi.cfm?doi=10.1037/1082-989X.11.2.193 http://link.springer.com/10.3758/s13423-014-0601-x http://www.springerlink.com/index/10.3758/s13423-012-0322-y http://content.apa.org/books/10109-028},
volume = {13},
year = {2008}
}
@article{Smithson2001,
abstract = {The advantages that confidence intervals have over null-hypothesis significance testing have been presented on many occasions to researchers in psychology. This article provides a practical introduction to methods of constructing confidence intervals for multiple and partial R2 and related parameters in multiple regression models based on "noncentral" F and $\chi$2 distributions. Until recently, these techniques have not been widely available due to their neglect in popular statistical textbooks and software. These difficulties are addressed here via freely available SPSS scripts and software and illustrations of their use. The article concludes with discussions of implications for the interpretation of findings in terms of noncentral confidence intervals, alternative measures of effect size, the relationship between noncentral confidence intervals and power analysis, and the design of studies.},
author = {Smithson, Michael},
doi = {10.1177/00131640121971392},
isbn = {0013-1644},
issn = {0013-1644},
journal = {Educational and Psychological Measurement},
month = {aug},
number = {4},
pages = {605--632},
title = {{Correct confidence intervals for various regression effect sizes and parameters: The importance of noncentral distributions in computing intervals}},
volume = {61},
year = {2001}
}
@article{Cumming2014,
abstract = {We need to make substantial changes to how we conduct research. First, in response to heightened concern that our published research literature is incomplete and untrustworthy, we need new requirements to ensure research integrity. These include prespecification of studies whenever possible, avoidance of selection and other inappropriate data-analytic practices, complete reporting, and encouragement of replication. Second, in response to renewed recognition of the severe flaws of null-hypothesis significance testing (NHST), we need to shift from reliance on NHST to estimation and other preferred techniques. The new statistics refers to recommended practices, including estimation based on effect sizes, confidence intervals, and meta-analysis. The techniques are not new, but adopting them widely would be new for many researchers, as well as highly beneficial. This article explains why the new statistics are important and offers guidance for their use. It describes an eight-step new-statistics strategy for research with integrity, which starts with formulation of research questions in estimation terms, has no place for NHST, and is aimed at building a cumulative quantitative discipline.},
author = {Cumming, Geoff},
doi = {10.1177/0956797613504966},
isbn = {1467-9280 (Electronic)$\backslash$r0956-7976 (Linking)},
issn = {1467-9280},
journal = {Psychological Science},
keywords = {13,20,8,estimation,meta-analysis,received 7,replication,research integrity,research methods,revision accepted 8,statistical analysis,that most current,the new statistics,there is increasing concern},
month = {jan},
number = {1},
pages = {7--29},
pmid = {24220629},
title = {{The new statistics: Why and how}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/24220629{\%}5Cnhttp://pss.sagepub.com/lookup/doi/10.1177/0956797613504966 http://pss.sagepub.com/lookup/doi/10.1177/0956797613504966},
volume = {25},
year = {2014}
}
@article{Wetzels2011,
abstract = {Statistical inference in psychology has traditionally relied heavily on p-value significance testing. This approach to drawing conclusions from data, however, has been widely criticized, and two types of remedies have been advocated. The first proposal is to supplement p values with complementary measures of evidence, such as effect sizes. The second is to replace inference with Bayesian measures of evidence, such as the Bayes factor. The authors provide a practical comparison of p values, effect sizes, and default Bayes factors as measures of statistical evidence, using 855 recently published t tests in psychology. The comparison yields two main results. First, although p values and default Bayes factors almost always agree about what hypothesis is better supported by the data, the measures often disagree about the strength of this support; for 70{\%} of the data sets for which the p value falls between .01 and .05, the default Bayes factor indicates that the evidence is only anecdotal. Second, effect sizes can provide additional evidence to p values and default Bayes factors. The authors conclude that the Bayesian approach is comparatively prudent, preventing researchers from overestimating the evidence in favor of an effect.},
author = {Wetzels, Ruud and Matzke, Dora and Lee, Michael D. and Rouder, Jeffrey N. and Iverson, Geoffrey J. and Wagenmakers, Eric-Jan},
doi = {10.1177/1745691611406923},
issn = {1745-6916},
journal = {Perspectives on Psychological Science},
keywords = {Bayes factor,effect size,hypothesis testing,p value,t test},
month = {may},
number = {3},
pages = {291--298},
pmid = {26168519},
title = {{Statistical evidence in experimental psychology}},
url = {http://www.ncbi.nlm.nih.gov/pubmed/26168519 http://journals.sagepub.com/doi/10.1177/1745691611406923},
volume = {6},
year = {2011}
}
@article{Valentine2013,
abstract = {Null hypothesis significance testing is criticised for emphatically focusing on using the appropriate statistic for the data and an overwhelming concern with low p-values. Here, we present a new technique, Observation Oriented Modeling (OOM), as an alternative to traditional techniques in the social sciences. Ten experiments on judgements of associative memory (JAM) were analysed with OOM to show data analysis procedures and the consistency of JAM results across several types of experimental manipulations. In a typical JAM task, participants are asked to rate the frequency of word pairings, such as LOST-FOUND, and are then compared to actual normed associative frequencies to measure how accurately participants can judge word pairs. Three types of JAM tasks are outlined (traditional, paired, and instructional manipulations) to demonstrate how modelling complex hypotheses can be applied through OOM to this type of data that would be conventionally analysed with null hypothesis significance testing.$\backslash$nNull hypothesis significance testing is criticised for emphatically focusing on using the appropriate statistic for the data and an overwhelming concern with low p-values. Here, we present a new technique, Observation Oriented Modeling (OOM), as an alternative to traditional techniques in the social sciences. Ten experiments on judgements of associative memory (JAM) were analysed with OOM to show data analysis procedures and the consistency of JAM results across several types of experimental manipulations. In a typical JAM task, participants are asked to rate the frequency of word pairings, such as LOST-FOUND, and are then compared to actual normed associative frequencies to measure how accurately participants can judge word pairs. Three types of JAM tasks are outlined (traditional, paired, and instructional manipulations) to demonstrate how modelling complex hypotheses can be applied through OOM to this type of data that would be conventionally analysed with null hypothesis significance testing.},
author = {Valentine, Kathrene D. and Buchanan, Erin M.},
doi = {10.1080/20445911.2013.775120},
isbn = {10.1080/20445911.2013.775120},
issn = {2044-5911},
journal = {Journal of Cognitive Psychology},
keywords = {as a field of,association,before psychology was recognised,judgements,learning,memory,researchers used associative memory,science,statistics,structure,tasks to examine memory,techniques},
number = {4},
pages = {400--422},
title = {{JAM-boree: An application of observation oriented modelling to judgements of associative memory}},
url = {http://dx.doi.org/10.1080/20445911.2013.775120},
volume = {25},
year = {2013}
}
@article{Wagenmakers2007,
archivePrefix = {arXiv},
arxivId = {1105.1486},
author = {Wagenmakers, Eric-Jan},
doi = {10.3758/BF03194105},
eprint = {1105.1486},
isbn = {1069-9384},
issn = {1069-9384},
journal = {Psychonomic Bulletin {\&} Review},
month = {oct},
number = {5},
pages = {779--804},
pmid = {18087943},
title = {{A practical solution to the pervasive problems of p values}},
url = {http://www.springerlink.com/index/10.3758/BF03194105},
volume = {14},
year = {2007}
}
@article{Grice2012,
abstract = {Serious criticisms of psychology's research practices and data analysis methods date back to at least the mid-1900s after the Galtonian school of thought had thoroughly triumphed over the Wundtian school. In the wake of Bem's (2011) recent, highly publicized study on psi phenomena in a prestigious journal, psychologists are again raising serious questions about their dominant research script. These concerns are echoed in the current paper, and Observation Oriented Modeling (OOM) is presented as an alternative approach toward data conceptualization and analysis for the social and life sciences. This approach is rooted in philosophical realism and an attitude toward data analysis centered around causality and common sense. Three example studies and accompanying data analyses are presented and discussed to demonstrate a number of OOM's advantages over current researcher practices.},
author = {Grice, James W. and Barrett, Paul T. and Schlimgen, Liz A. and Abramson, Charles I.},
doi = {10.3390/bs2010001},
issn = {2076-328X},
journal = {Behavioral Sciences},
keywords = {Observation Oriented Modeling,integrated models,null hypothesis significance testing,research methods},
month = {jan},
number = {4},
pages = {1--22},
publisher = {Molecular Diversity Preservation International},
title = {{Toward a brighter future for psychology as an observation oriented science}},
url = {http://www.mdpi.com/2076-328X/2/1/1/},
volume = {2},
year = {2012}
}
@article{Grice2014,
abstract = {Observation Oriented Modeling is an alternative to traditional methods of data conceptualization and analysis that challenges researchers to develop integrated, explanatory models of patterns of observations. The focus of research is thus shifted away from aggregate statistics, such as means, variances, and correlations, and is instead directed toward assessing the accuracy of judgments based on the observations in hand. In this paper a number of example data sets will be used to demonstrate how Observation Oriented Modeling can be taught to undergraduate and graduate students. While the examples are drawn from psychology, the method of contrasting Observation Oriented Modeling with traditional methods of research design and statistical analysis can easily be adapted to examples from other sciences.},
author = {Grice, James W.},
doi = {10.2466/05.08.IT.3.3},
issn = {2165-2228},
journal = {Comprehensive Psychology},
month = {jan},
pages = {05.08.IT.3.3},
publisher = {SAGE PublicationsSage CA: Los Angeles, CA},
title = {{Observation Oriented Modeling: Preparing students for research in the 21st century}},
url = {http://cop.sagepub.com/lookup/doi/10.2466/05.08.IT.3.3},
volume = {3},
year = {2014}
}
@book{Grice2011,
abstract = {1st ed. Introduction -- Data at its core -- Rotating deep structures -- Modeling with deep structures -- Statistics and null hypothesis significance testing -- Modeling and inferential statistics -- Modeling and effect sizes -- Measurement and additive structures -- Cause and effect -- Coda.},
author = {Grice, James W.},
isbn = {9780123851956},
pages = {242},
publisher = {Elsevier/Academic Press},
title = {{Observation oriented modeling : analysis of cause in the behavioral sciences}},
year = {2011}
}
@book{Press2002,
address = {Hoboken, NJ, USA},
doi = {10.1002/9780470317105},
editor = {Press, S. James},
isbn = {9780470317105},
month = {nov},
publisher = {John Wiley {\&} Sons, Inc.},
series = {Wiley Series in Probability and Statistics},
title = {{Subjective and Objective Bayesian Statistics}},
url = {http://doi.wiley.com/10.1002/9780470317105},
year = {2002}
}
@article{DeLaplace1774,
abstract = {1. We can imagine thus the formation of a recurrent series: if $\phi$ expresses any function of x, and if we substitute successively in it, in place of x, 1, 2, 3, . . ., we will form a series of terms of which I designate by y x the one which corresponds to the number x; this put, if in this series each term is equal to any number of preceding terms, each multiplied by a function of x at will, the series is then recurrent. Such is the most general idea that is able to be formed from it, and it is under this point of view that I have considered them in a memoir previously presented to the Academy. 1 I suppose now that $\phi$ is a function of x and of n, and that we substitute successively in place of x and of n the numbers 1, 2, 3, . . .: we will form for each value of n a series in which I designate the term corresponding to the numbers x and n by n y x : now, if n y x is equal to any number of preceding terms taken in any number of these series and each multiplied by a function of x and of n, these series will be what I call suites r{\'{e}}curro-r{\'{e}}currentes; they differ from recurrent series in that their general term has two variable indices. As the consideration of these series has seemed very useful to me in the Theory of chances, and as they have not yet been examined by persons, that I know, I have belief that it would not be useless to develop them here to some extent.},
author = {{De Laplace}, P. S.},
journal = {M{\'{e}}m. Acad. R. Sci. Paris},
number = {8},
pages = {353--371},
title = {{M{\'{e}}moire sur les suites r{\'{e}}curro-r{\'{e}}currentes et sur leurs usages dans la th{\'{e}}orie des hasards}},
url = {http://cerebro.cs.xu.edu/math/Sources/Laplace/recurro{\_}recurrentes.pdf},
volume = {6},
year = {1774}
}
@book{Gelman2004,
abstract = {2nd ed. The second edition of Bayesian Data Analysis continues to emphasize practice over theory, clearly describing how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Its world-class authors provide detailed guidance on all aspects of Bayesian data analysis and include many examples of real statistical analyses, based on their own research. Part I: Fundamentals of Bayesian inference -- Background -- Single-parameter models -- Introduction to multiparameter models -- Large-sample inference and frequency properties of Bayesian inference -- Part II: Fundamentals of Bayesian data analysis -- Hierarchical models -- Model checking and improvement -- Modeling accounting for data collection -- Connections and challenges -- General advice -- Part III: Advanced computation -- Overview of computation -- Posterior simulation -- Approximations based on posterior modes -- Special topics in computation -- Part IV: Regression models -- Introduction to regression models -- Hierarchical linear models -- Generalized linear models -- Models for robust inference -- Part V: Specific models and problems -- Mixture models -- Multivariate models -- Nonlinear models -- Models for missing data -- Decision analysis -- Appendixes. Standard probability distributions -- Outline of proofs of asymptotic theorems -- Example of computation in R and Bugs.},
author = {Gelman, Andrew.},
isbn = {9781584883883},
pages = {668},
publisher = {Chapman {\&} Hall/CRC},
title = {{Bayesian data analysis}},
url = {https://www.crcpress.com/Bayesian-Data-Analysis-Second-Edition/Gelman-Carlin-Stern-Rubin/p/book/9781584883883},
year = {2004}
}
@article{Bellhouse2004,
abstract = {Abstract. Thomas Bayes, from whom Bayes theorem takes its name, was probably born in 1701, so the year 2001 marked the 300th anniversary of his birth. This biography was written to celebrate this anniversary. The current sketch of his life includes his family background and education, as well as his scientific and theological work. In contrast to some, but not all, biographies of Bayes, the current biography is an attempt to cover areas beyond Bayes' scientific work. When commenting on the writing of scientific biography, Pearson [(1978). The History of Statistics in the 17th and 18th Centuries. . . . Charles Griffin and Company, London] stated, “it is impossible to understand a man's work unless you understand something of his character and unless you understand something of his environment. And his environment means the state of affairs social and political of his own age.” The intention here is to follow this general approach to biography. There is very little primary source material on Bayes and his work. For example, only three of his letters and a notebook containing some sketches of his own work, almost all unpublished, as well as notes on the work of others are known to have survived. Neither the letters nor the notebook is dated, and only one of the letters can be dated accurately from internal evidence. This biography contains new information about Bayes. In particular, among the papers of the 2nd Earl Stanhope, letters and papers of Bayes have been uncovered that previously were not known to exist. The letters indirectly confirm the centrality of Stanhope in Bayes' election to the Royal Society. They also provide evidence that Bayes was part of a network of mathematicians initially centered on Stanhope. In addition, the letters shed light on Bayes' work in infinite series.},
author = {Bellhouse, D. R.},
doi = {10.1214/088342304000000189},
isbn = {08834237},
issn = {0883-4237},
journal = {Statistical Science},
month = {feb},
number = {1},
pages = {3--43},
publisher = {Institute of Mathematical Statistics},
title = {{The Reverend Thomas Bayes, FRS: A Biography to celebrate the tercentenary of his birth}},
url = {http://projecteuclid.org/euclid.ss/1089808271},
volume = {19},
year = {2004}
}
@unpublished{Haaf2017,
author = {Haaf, Julia and Rouder, Jeffrey N},
doi = {10.17605/OSF.IO/KTJNQ},
title = {{Developing constraint in bayesian mixed models}},
year = {2017}
}
@article{Sanchez-Meca2008a,
author = {Sanchez-Meca, J and Mar{\'{i}}n-Mart{\'{i}}nez, Fulgencio},
doi = {10.1037/1082-989x.13.1.31},
isbn = {1082-989X},
issn = {1082-989X},
journal = {Psychological Methods},
number = {1},
pages = {31--48},
pmid = {18331152},
title = {{Confidence intervals for the overall effect size in random-effects meta-analysis}},
volume = {13},
year = {2008}
}
@article{Klugkist2007,
author = {Klugkist, Irene and Hoijtink, Herbert},
doi = {10.1016/j.csda.2007.01.024},
issn = {01679473},
journal = {Computational Statistics {\&} Data Analysis},
month = {aug},
number = {12},
pages = {6367--6379},
title = {{The Bayes factor for inequality and about equality constrained models}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0167947307000606},
volume = {51},
year = {2007}
}
@article{Rosnow1989,
author = {Rosnow, Ralph L. and Rosenthal, Robert},
doi = {10.1037/0003-066X.44.10.1276},
issn = {1935-990X},
journal = {American Psychologist},
number = {10},
pages = {1276--1284},
title = {{Statistical procedures and the justification of knowledge in psychological science.}},
url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/0003-066X.44.10.1276},
volume = {44},
year = {1989}
}
@article{Fisher1932,
author = {Fisher, R. A.},
doi = {10.1017/S0305004100010094},
issn = {0305-0041},
journal = {Mathematical Proceedings of the Cambridge Philosophical Society},
month = {jul},
number = {03},
pages = {257},
title = {{Inverse probability and the use of likelihood}},
url = {http://www.journals.cambridge.org/abstract{\_}S0305004100010094},
volume = {28},
year = {1932}
}
@article{Lehmann1993,
author = {Lehmann, E. L.},
doi = {10.1080/01621459.1993.10476404},
issn = {0162-1459},
journal = {Journal of the American Statistical Association},
month = {dec},
number = {424},
pages = {1242--1249},
title = {{The Fisher, Neyman-Pearson theories of testing hypotheses: One theory or two?}},
url = {http://www.tandfonline.com/doi/abs/10.1080/01621459.1993.10476404},
volume = {88},
year = {1993}
}
@article{VantVeer2016,
author = {{Van't Veer}, Anna Elisabeth and Giner-Sorolla, Roger},
doi = {10.1016/j.jesp.2016.03.004},
issn = {00221031},
journal = {Journal of Experimental Social Psychology},
month = {nov},
pages = {2--12},
title = {{Pre-registration in social psychology—A discussion and suggested template}},
url = {http://linkinghub.elsevier.com/retrieve/pii/S0022103116301925},
volume = {67},
year = {2016}
}
@article{Nosek2014,
author = {Nosek, Brian A and Lakens, Dani{\"{e}}l},
doi = {10.1027/1864-9335/a000192},
issn = {1864-9335},
journal = {Social Psychology},
month = {may},
number = {3},
pages = {137--141},
publisher = {Hogrefe Publishing},
title = {{Registered reports}},
url = {http://econtent.hogrefe.com/doi/abs/10.1027/1864-9335/a000192},
volume = {45},
year = {2014}
}
@article{Finkel2015,
author = {Finkel, Eli J. and Eastwick, Paul W. and Reis, Harry T.},
doi = {10.1037/pspi0000007},
issn = {1939-1315},
journal = {Journal of Personality and Social Psychology},
number = {2},
pages = {275--297},
title = {{Best research practices in psychology: Illustrating epistemological and pragmatic considerations with the case of relationship science.}},
url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/pspi0000007},
volume = {108},
year = {2015}
}
@misc{Sauer2016,
author = {Sauer, Sebastian and Luebke, Karsten},
doi = {10.17605/OSF.IO/3J4XR},
month = {jan},
title = {{Observation Oriented Modeling revised from a statistical point of view}},
urldate = {2017-09-08},
year = {2016}
}
@misc{Buchanan2017,
author = {Buchanan, Erin M and Valentine, Kathrene D and Scofield, John E},
title = {{MOTE}},
url = {https://github.com/doomlab/MOTE},
year = {2017}
}
@misc{Morey2015c,
author = {Morey, Richard D},
title = {{On verbal categories for the interpretation of Bayes factors}},
url = {http://bayesfactor.blogspot.com/2015/01/on-verbal-categories-for-interpretation.html},
urldate = {2017-09-08},
year = {2015}
}
@misc{JASPTeam2017,
author = {{JASP Team}},
title = {{JASP}},
url = {https://jasp-stats.org/},
year = {2017}
}
@book{Kruschke2014,
abstract = {2E [edition]. What's in this book (Read this first!) -- Part I The basics: models, probability, Bayes' rule and r: Introduction: credibility, models, and parameters; The R programming language; What is this stuff called probability?; Bayes' rule -- Part II All the fundamentals applied to inferring a binomila probability: Inferring a binomial probability via exact mathematical analysis; Markov chain Monte Carlo; JAGS; Hierarchical models; Model comparison and hierarchical modeling; Null hypothesis significance testing; Bayesian approaches to testing a point ("Null") hypothesis; Goals, power, and sample size; Stan -- Part III The generalized linear model: Overview of the generalized linear model; Metric-predicted variable on one or two groups; Metric predicted variable with one metric predictor; Metric predicted variable with multiple metric predictors; Metric predicted variable with one nominal predictor; Metric predicted variable with multiple nominal predictors; Dichotomous predicted variable; Nominal predicted variable; Ordinal predicted variable; Count predicted variable; Tools in the trunk -- Bibliography -- Index.},
author = {Kruschke, John K},
edition = {2nd},
isbn = {9780124059160},
publisher = {Academic Press},
title = {{Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan}},
year = {2014}
}
@article{Lakens2017a,
abstract = {Scientists should be able to provide support for the absence of a meaningful effect. Currently, researchers often incorrectly conclude an effect is absent based a nonsignificant result. A widely recommended approach within a frequentist framework is to test for equivalence. In equivalence tests, such as the two one-sided tests (TOST) procedure discussed in this article, an upper and lower equivalence bound is specified based on the smallest effect size of interest. The TOST procedure can be used to statistically reject the presence of effects large enough to be considered worthwhile. This practical primer with accompanying spreadsheet and R package enables psychologists to easily perform equivalence tests (and power analyses) by setting equivalence bounds based on standardized effect sizes and provides recommendations to prespecify equivalence bounds. Extending your statistical tool kit with equivalence tests is an easy way to improve your statistical and theoretical inferences.},
author = {Lakens, Dani{\"{e}}l},
doi = {10.1177/1948550617697177},
issn = {1948-5506},
journal = {Social Psychological and Personality Science},
keywords = {equivalence testing,null hypothesis significance testing,power analysis,research methods},
month = {may},
number = {4},
pages = {355--362},
publisher = {SAGE PublicationsSage CA: Los Angeles, CA},
title = {{Equivalence tests}},
url = {http://journals.sagepub.com/doi/10.1177/1948550617697177},
volume = {8},
year = {2017}
}
@book{Dienes2008,
abstract = {Karl Popper and demarcation -- Kuhn and Lakatos : paradigms and programmes -- Neyman Pearson and hypothesis testing -- Bayes and the probability of hypotheses -- Fisher and the likelihood : the Royall Road to evidence.},
author = {Dienes, Zoltan},
isbn = {023054231X},
publisher = {Palgrave Macmillan},
title = {{Understanding psychology as a science: an introduction to scientific and statistical inference}},
year = {2008}
}
@book{AmericanPsychologicalAssociation2010,
abstract = {Sixth edition ODL{\_}FMG{\_}2017/2018. With millions of copies sold, the Publication Manual of the American Psychological Association is the style manual of choice for writers, editors, students, educators, and professionals in psychology, sociology, business, economics, nursing, social work, and justice administration, and other disciplines in which effective communication with words and data is fundamental. In addition to providing clear guidance on grammar, the mechanics of writing, and APA style, the Publication Manual offers an authoritative and easy-to-use reference and citation system and comprehensive coverage of the treatment of numbers, metrication, statistical and mathematical data, tables, and figures for use in writing, reports, or presentations. The new edition has been revised and updated to include: the latest guidelines and examples for referencing electronic and online sources, new and revised guidelines for submitting papers electronically, improved guidelines for avoiding plagiarism, simplified formatting guidelines for writers using up-to-date word-processing software, all new guidelines for presenting case studies, improved guidelines for the construction of tables, updates on copyright and permissions issues for writers, new reference examples for audiovisual media and patents, an expanded and improved index for quick and easy access. Writers, scholars, and professionals will also find: new guidelines on how to choose text, tables, or figures to present data, guidelines for writing cover letters for submitting articles for publication (plus a sample letter), expanded guidelines on the retention of raw data, new advice on establishing written agreements for the use of shared data, and, new information on the responsibilities of co-authors.--Publisher information. Writing for the behavioral and social sciences. Types of articles -- Ethical and legal standards in publishing -- Ensuring the accuracy of scientific knowledge -- Protecting the rights and welfare of research participants -- Protecting intellectual property rights -- Manuscript structure and content. Journal article reporting standards -- Manuscript elements -- Sample papers -- Writing clearly and concisely. Organization -- Writing style -- Reducing bias in language -- General guidelines for reducing bias -- Reducing bias by topic -- Grammar and usage -- The mechanics of style. Punctuation -- Spelling -- Capitalization -- Italics -- Abbreviations -- Numbers -- Metrication -- Statistical and mathematical copy -- Equations -- Displaying results. General guidance on tables and figures -- Tables -- Figures -- Presenting electrophysiological, radiological, and other biological data -- Crediting sources. When to cite -- Quoting and paraphrasing -- Citing references in text -- Reference list -- Reference components -- Reference examples. Types and variations -- Examples by type -- Appendix 7-1 : References to legal materials -- The publication process. Editorial process -- Author responsibilities -- Appendix. Journal article reporting standards (JARS), meta-analysis reporting standards (MARS), and flow of participants through each stage of an experiment or quasi-experiment.},
author = {{American Psychological Association}},
edition = {6th},
isbn = {9781433805592},
pages = {272},
publisher = {American Psychological Association},
title = {{Publication manual of the American Psychological Association}},
url = {http://www.apastyle.org/products/4200067.aspx},
year = {2010}
}
@book{Lehmann2011,
address = {New York, NY},
author = {Lehmann, E. L.},
publisher = {Springer},
title = {{Fisher, Neyman, and the creation of classical statistics}},
year = {2011}
}
@article{Carter2014,
abstract = {Few models of self-control have generated as much scientific interest as has the limited strength model. One of the entailments of this model, the depletion effect, is the expectation that acts of self- control will be less effective when they follow prior acts of self-control. Results from a previous meta-analysis concluded that the depletion effect is robust and medium in magnitude (d = 0.62). However, when we applied methods for estimating and correcting for small-study effects (such as publication bias) to the data from this previous meta-analysis effort, we found very strong signals of publication bias, along with an indication that the depletion effect is actually no different from zero. We conclude that until greater certainty about the size of the depletion effect can be established, circumspection about the existence of this phenomenon is warranted, and that rather than elaborating on the model, research efforts should focus on establishing whether the basic effect exists. We argue that the evidence for the depletion effect is a useful case study for illustrating the dangers of small- study effects as well as some of the possible tools for mitigating their influence in psychological science.},
author = {Carter, E. C. and McCullough, M. E.},
doi = {10.3389/fpsyg.2014.00823},
isbn = {1664-1078},
issn = {1664-1078},
journal = {Frontiers in Psychology},
keywords = {ego depletion,meta-analysis,publ,publication bias,self-control,self-regulation,small-study effects},
number = {July},
pages = {1--11},
pmid = {25126083},
title = {{Publication bias and the limited strength model of self-control: has the evidence for ego depletion been overestimated?}},
url = {http://www.frontiersin.org/Personality{\_}and{\_}Social{\_}Psychology/10.3389/fpsyg.2014.00823/abstract},
volume = {5},
year = {2014}
}
@incollection{Gigerenzer2004a,
address = {Thousand Oaks, CA},
author = {Gigerenzer, Gerd and Krauss, Stefan and Vitouch, Oliver},
booktitle = {The SAGE Handbook of Quantitative Methodology for the Social Sciences},
doi = {10.4135/9781412986311.n21},
isbn = {9780761923596},
issn = {0761923594},
pages = {392--409},
publisher = {SAGE Publications, Inc.},
title = {{The null ritual: What you always wanted to know about significance testing but were afraid to ask}},
url = {https://library.mpib-berlin.mpg.de/ft/gg/GG{\_}Null{\_}2004.pdf http://scholar.google.com/scholar?hl=en{\&}btnG=Search{\&}q=intitle:The+Null+Ritual+What+You+Always+Wanted+to+Know+About+Signifi+cance+Testing+but+Were+Afraid+to+Ask{\#}3{\%}5Cnhttp://scholar.google.com/schola},
year = {2004}
}
@article{Pericchi2016,
author = {Pericchi, Luis and Pereira, Carlos},
doi = {10.1214/14-BJPS257},
file = {::},
issn = {0103-0752},
journal = {Brazilian Journal of Probability and Statistics},
keywords = {Hypothesis testing,errors type I and type II,significance levels,weighting error probabilities},
month = {feb},
number = {1},
pages = {70--90},
publisher = {Brazilian Statistical Association},
title = {{Adaptative significance levels using optimal decision rules: Balancing by weighting the error probabilities}},
url = {http://projecteuclid.org/euclid.bjps/1453211803},
volume = {30},
year = {2016}
}
@article{Maxwell2015,
abstract = {Psychology has recently been viewed as facing a replica- tion crisis because efforts to replicate past study findings frequently do not show the same result. Often, the first study showed a statistically significant result but the rep- lication does not. Questions then arise about whether the first study results were false positives, and whether the replication study correctly indicates that there is truly no effect after all. This article suggests these so-called failures to replicate may not be failures at all, but rather are the result of low statistical power in single replication studies, and the result of failure to appreciate the need for multiple replications in order to have enough power to identify true effects. We provide examples of these power problems and suggest some solutions using Bayesian statistics and meta- analysis. Although the need for multiple replication studies may frustrate those who would prefer quick answers to psychology's alleged crisis, the large sample sizes typically needed to provide firm evidence will almost always require concerted efforts from multiple investigators. As a result, it remains to be seen how many of the recently claimed failures to replicate will be supported or instead may turn out to be artifacts of inadequate sample sizes and single study replications. Keywords:},
author = {Maxwell, Scott E and Lau, Michael Y and Howard, George S},
doi = {10.1037/a0039400},
isbn = {1935-990X (Electronic)$\backslash$r0003-066X (Linking)},
issn = {1935-990X},
journal = {American Psychologist},
keywords = {Bayesian methods,equivalence tests,false positive results,meta- analysis,statistical power},
number = {6},
pages = {487--498},
pmid = {26348332},
title = {{Is psychology suffering from a replication crisis? What does “failure to replicate” really mean?}},
url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/a0039400},
volume = {70},
year = {2015}
}
@article{Gilbert2016,
abstract = {A paper from the Open Science Collaboration (Research Articles, 28 August 2015, aac4716) attempting to replicate 100 published studies suggests that the reproducibility of psychological science is surprisingly low.We show that this article contains three statistical errors and provides no support for such a conclusion. Indeed, the data are consistent with the opposite conclusion, namely, that the reproducibility of psychological science is quite high.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Gilbert, Daniel T. and King, Gary and Pettigrew, Stephen and Wilson, Timothy D.},
doi = {10.1126/science.aad7243},
eprint = {arXiv:1011.1669v3},
isbn = {1095-9203 (Electronic)$\backslash$r0036-8075 (Linking)},
issn = {0036-8075},
journal = {Science},
number = {6277},
pages = {1037--b},
pmid = {26315443},
title = {{Comment on “Estimating the reproducibility of psychological science”}},
volume = {351},
year = {2016}
}
@article{Benjamin2017,
abstract = {We propose to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005.},
archivePrefix = {arXiv},
arxivId = {psyarxiv/mky9j},
author = {Benjamin, Daniel J. and Berger, James O. and Johannesson, Magnus and Nosek, Brian A and Wagenmakers, E.-J. and Berk, Richard and Bollen, Kenneth A. and Brembs, Bj{\"{o}}rn and Brown, Lawrence and Camerer, Colin and Cesarini, David and Chambers, Christopher D. and Clyde, Merlise and Cook, Thomas D. and {De Boeck}, Paul and Dienes, Zoltan and Dreber, Anna and Easwaran, Kenny and Efferson, Charles and Fehr, Ernst and Fidler, Fiona and Field, Andy P. and Forster, Malcolm and George, Edward I. and Gonzalez, Richard and Goodman, Steven and Green, Edwin and Green, Donald P. and Greenwald, Anthony G. and Hadfield, Jarrod D. and Hedges, Larry V. and Held, Leonhard and {Hua Ho}, Teck and Hoijtink, Herbert and Hruschka, Daniel J. and Imai, Kosuke and Imbens, Guido and Ioannidis, John P. A. and Jeon, Minjeong and Jones, James Holland and Kirchler, Michael and Laibson, David and List, John and Little, Roderick and Lupia, Arthur and Machery, Edouard and Maxwell, Scott E. and McCarthy, Michael and Moore, Don A. and Morgan, Stephen L. and Munaf{\'{o}}, Marcus and Nakagawa, Shinichi and Nyhan, Brendan and Parker, Timothy H. and Pericchi, Luis and Perugini, Marco and Rouder, Jeff and Rousseau, Judith and Savalei, Victoria and Sch{\"{o}}nbrodt, Felix D. and Sellke, Thomas and Sinclair, Betsy and Tingley, Dustin and {Van Zandt}, Trisha and Vazire, Simine and Watts, Duncan J. and Winship, Christopher and Wolpert, Robert L. and Xie, Yu and Young, Cristobal and Zinman, Jonathan and Johnson, Valen E.},
doi = {10.1038/s41562-017-0189-z},
eprint = {mky9j},
issn = {2397-3374},
journal = {Nature Human Behaviour},
month = {sep},
pages = {1--18},
primaryClass = {psyarxiv},
title = {{Redefine statistical significance}},
url = {http://www.nature.com/articles/s41562-017-0189-z},
year = {2017}
}
@article{Bakker2012,
abstract = {If science were a game, a dominant rule would probably be to collect results that are statistically significant. Several reviews of the psychological literature have shown that around 96{\%} of papers involving the use of null hypothesis significance testing report significant outcomes for their main results but that the typical studies are insufficiently powerful for such a track record. We explain this paradox by showing that the use of several small underpowered samples often represents a more efficient research strategy (in terms of finding p {\textless} .05) than does the use of one larger (more powerful) sample. Publication bias and the most efficient strategy lead to inflated effects and high rates of false positives, especially when researchers also resorted to questionable research practices, such as adding participants after intermediate testing. We provide simulations that highlight the severity of such biases in meta-analyses. We consider 13 meta-analyses covering 281 primary studies in various fields of psychology and find indications of biases and/or an excess of significant results in seven. These results highlight the need for sufficiently powerful replications and changes in journal policies.},
archivePrefix = {arXiv},
arxivId = {hep-ph/0006269v1},
author = {Bakker, Marjan and {Van Dijk}, Annette and Wicherts, Jelte M},
doi = {10.1177/1745691612459060},
eprint = {0006269v1},
isbn = {1745-6916$\backslash$n1745-6924},
issn = {1745-6916},
journal = {Perspectives on Psychological Science},
keywords = {1976,false positives,in many ways,individual players,involves rules,it,mahoney,not cheating,power,publication bias,replication,researchers,sample size,science resembles a game},
number = {6},
pages = {543--554},
pmid = {26168111},
primaryClass = {hep-ph},
title = {{The rules of the game called psychological science}},
url = {http://pps.sagepub.com/content/7/6/543.abstract{\%}5Cnhttp://pps.sagepub.com/content/7/6/543.full.pdf},
volume = {7},
year = {2012}
}
@article{Rouder2016b,
abstract = {Mycotoxins are small (MW approximately 700), toxic chemical products formed as secondary metabolites by a few fungal species that readily colonise crops and contaminate them with toxins in the field or after harvest. Ochratoxins and Aflatoxins are mycotoxins of major significance and hence there has been significant research on broad range of analytical and detection techniques that could be useful and practical. Due to the variety of structures of these toxins, it is impossible to use one standard technique for analysis and/or detection. Practical requirements for high-sensitivity analysis and the need for a specialist laboratory setting create challenges for routine analysis. Several existing analytical techniques, which offer flexible and broad-based methods of analysis and in some cases detection, have been discussed in this manuscript. There are a number of methods used, of which many are lab-based, but to our knowledge there seems to be no single technique that stands out above the rest, although analytical liquid chromatography, commonly linked with mass spectroscopy is likely to be popular. This review manuscript discusses (a) sample pre-treatment methods such as liquid-liquid extraction (LLE), supercritical fluid extraction (SFE), solid phase extraction (SPE), (b) separation methods such as (TLC), high performance liquid chromatography (HPLC), gas chromatography (GC), and capillary electrophoresis (CE) and (c) others such as ELISA. Further currents trends, advantages and disadvantages and future prospects of these methods have been discussed.},
archivePrefix = {arXiv},
arxivId = {arXiv:1011.1669v3},
author = {Rouder, Jeffrey N and Morey, Richard D and Verhagen, Josine and Province, Jordan M and Wagenmakers, Eric-Jan},
doi = {10.1111/tops.12214},
eprint = {arXiv:1011.1669v3},
isbn = {9780874216561},
issn = {17568765},
journal = {Topics in Cognitive Science},
keywords = {Inference,Philosophy of science,Replication crisis,Statistics},
number = {3},
pages = {520--547},
pmid = {15003161},
title = {{Is There a Free Lunch in Inference?}},
volume = {8},
year = {2016}
}
@article{Nosek2012,
abstract = {An academic scientist's professional success depends on publishing. Publishing norms emphasize novel, positive results. As such, disciplinary incentives encourage design, analysis, and reporting decisions that elicit positive results and ignore negative results. Prior reports demonstrate how these incentives inflate the rate of false effects in published science. When incentives favor novelty over replication, false results persist in the literature unchallenged, reducing efficiency in knowledge accumulation. Previous suggestions to address this problem are unlikely to be effective. For example, a journal of negative results publishes otherwise unpublishable reports. This enshrines the low status of the journal and its content. The persistence of false findings can be meliorated with strategies that make the fundamental but abstract accuracy motive—getting it right—competitive with the more tangible and concrete incentive—getting it published. This article develops strategies for improving scientific prac...},
archivePrefix = {arXiv},
arxivId = {1205.4251},
author = {Nosek, Brian A and Spies, Jeffrey R and Motyl, Matt},
doi = {10.1177/1745691612459058},
eprint = {1205.4251},
isbn = {1745-6916, 1745-6924},
issn = {1745-6916},
journal = {Perspectives on Psychological Science},
keywords = {false positives,incentives,inquiry from other methods,methodology,motivated reasoning,of,of acquiring knowledge is,replication,separates a scientific method,the chief thing which},
number = {6},
pages = {615--631},
pmid = {19565683},
title = {{Scientific utopia}},
url = {http://journals.sagepub.com/doi/10.1177/1745691612459058},
volume = {7},
year = {2012}
}
@article{Fanelli2012,
abstract = {Concerns that the growing competition for funding and citations might distort science are frequently discussed, but have not been verified directly. Of the hypothesized problems, perhaps the most worrying is a worsening of positive-outcome bias. A system that disfavours negative results not only distorts the scientific literature directly, but might also discourage high-risk projects and pressure scientists to fabricate and falsify their data. This study analysed over 4,600 papers published in all disciplines between 1990 and 2007, measuring the frequency of papers that, having declared to have ‘‘tested'' a hypothesis, reported a positive support for it. The overall frequency of positive supports has grown by over 22{\%} between 1990 and 2007, with significant differences between disciplines and countries. The increase was stronger in the social and some biomedical disciplines. The United States had published, over the years, significantly fewer positive results than Asian countries (and particularly Japan) but more than European countries (and in particular the United Kingdom). Methodological artefacts cannot explain away these patterns, which support the hypotheses that research is becoming less pioneering and/or that the objectivity with which results are produced and published is decreasing.},
author = {Fanelli, Daniele},
doi = {10.1007/s11192-011-0494-7},
isbn = {0138-9130},
issn = {01389130},
journal = {Scientometrics},
keywords = {Bias,Competition,Misconduct,Publication,Publish or perish,Research evaluation},
number = {3},
pages = {891--904},
title = {{Negative results are disappearing from most disciplines and countries}},
volume = {90},
year = {2012}
}
@unpublished{Lakens2017,
author = {Lakens, Dani{\"{e}}l and Adolfi, Federico G. and Albers, Casper J. and Anvari, Farid and Apps, Matthew A J and Argamon, Shlomo Engelson and van Assen, Marcel A.L.M. and Baguley, Thom and Becker, Raymond B. and Benning, Stephen D. and Bradford, Daniel E. and Buchanan, Erin M and Caldwell, Aaron R and Cal, Ben Van and Zwaan, Rolf A.},
doi = {10.17605/OSF.IO/9S3Y6},
title = {{Justify Your Alpha: A Response to “Redefine Statistical Significance”}},
url = {https://osf.io/by2kc},
year = {2017}
}
@article{Nelson2018,
author = {Nelson, Leif D and Simmons, Joseph P and Simonsohn, Uri},
doi = {10.1146/annurev-psych-122216-011836},
journal = {Annual Review of Psychology},
number = {September 2017},
pages = {1--41},
title = {{Psychology's Renaissance}},
year = {2018}
}
@article{Pashler2012,
abstract = {We discuss three arguments voiced by scientists who view the current outpouring of concern about replicability as overblown. The first idea is that the adoption of a low alpha level (e.g., 5{\%}) puts reasonable bounds on the rate at which errors can enter the published literature, making false-positive effects rare enough to be considered a minor issue. This, we point out, rests on statistical misunderstanding: The alpha level imposes no limit on the rate at which errors may arise in the literature (Ioannidis, 2005b). Second, some argue that whereas direct replication attempts are uncommon, conceptual replication attempts are common--providing an even better test of the validity of a phenomenon. We contend that performing conceptual rather than direct replication attempts interacts insidiously with publication bias, opening the door to literatures that appear to confirm the reality of phenomena that in fact do not exist. Finally, we discuss the argument that errors will eventually be pruned out of the literature if the field would just show a bit of patience. We contend that there are no plausible concrete scenarios to back up such forecasts and that what is needed is not patience, but rather systematic reforms in scientific practice.},
author = {Pashler, Harold and Harris, Christine R.},
doi = {10.1177/1745691612463401},
isbn = {1745-6916},
issn = {1745-6916},
journal = {Perspectives on Psychological Science},
keywords = {a,been around for a,crisis,has,in any enterprise that,like experimental psychol-,publication bias,replication,very long time,whenever people speak of},
number = {6},
pages = {531--536},
pmid = {26168109},
title = {{Is the replicability crisis overblown? Three arguments examined}},
url = {http://pps.sagepub.com/content/7/6/531{\%}5Cnhttp://pps.sagepub.com/content/7/6/531.short},
volume = {7},
year = {2012}
}
@misc{Morey2015b,
author = {Morey, Richard D and Rouder, Jeffrey N},
title = {{BayesFactor: Computation of Bayes Factors for common designs}},
url = {https://cran.r-project.org/package=BayesFactor},
year = {2015}
}
@unpublished{Scofield2017,
author = {Scofield, John E and Buchanan, Erin M},
title = {{Characteristics of Bias, Standard Deviation, and Root Mean Squared Error across Different Effect Sizes}},
url = {http://osf.io/ay4j2},
year = {2017}
}
@article{Carifio2008,
author = {Carifio, James and Perla, Rocco},
doi = {10.1111/j.1365-2923.2008.03172.x},
issn = {03080110},
journal = {Medical Education},
month = {dec},
number = {12},
pages = {1150--1152},
title = {{Resolving the 50-year debate around using and misusing Likert scales}},
url = {http://doi.wiley.com/10.1111/j.1365-2923.2008.03172.x},
volume = {42},
year = {2008}
}
@article{Albers2017,
author = {Albers, Casper J. and Lakens, Dani{\"{e}}l},
doi = {10.17605/OSF.IO/B7Z4Q},
journal = {PsyArxiv},
pages = {1--37},
title = {{When power analyses based on pilot data are biased: Inaccurate effect size estimators and follow-up bias}},
url = {http://osf.io/preprints/psyarxiv/b7z4q},
volume = {July 13},
year = {2017}
}
@article{Knapp1990,
author = {Knapp, Thomas R},
doi = {10.1097/00006199-199003000-00019},
issn = {0029-6562},
journal = {Nursing Research},
month = {mar},
number = {2},
pages = {121--123},
title = {{Treating ordinal scales as interval scales}},
url = {http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage{\&}an=00006199-199003000-00019},
volume = {39},
year = {1990}
}
@misc{Lawrence2017,
author = {Lawrence, Michael A},
title = {{ez: Easy analysis and visualization of factorial experiments}},
url = {http://cran.r-project.org/package=ez},
year = {2017}
}
@article{Matsumoto1998,
author = {Matsumoto, Makoto and Nishimura, Takuji},
doi = {10.1145/272991.272995},
issn = {10493301},
journal = {ACM Transactions on Modeling and Computer Simulation},
month = {jan},
number = {1},
pages = {3--30},
title = {{Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator}},
volume = {8},
year = {1998}
}
@article{Cohen1992a,
author = {Cohen, Jacob},
doi = {10.1037//0033-2909.112.1.155},
issn = {0033-2909},
journal = {Psychological Bulletin},
number = {1},
pages = {155--159},
title = {{A power primer.}},
url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/0033-2909.112.1.155},
volume = {112},
year = {1992}
}
@article{Bishop2015,
author = {Bishop, P A and Herron, R L},
journal = {International Journal of Exercise Science},
number = {3},
pages = {297--302},
title = {{Use and misuse of the Likert item responses and other ordinal measures}},
volume = {8},
year = {2015}
}
@misc{Genz2017,
author = {Genz, A and Bretz, F and Miwa, T and Mi, X and Leisch, F and Scheipl, F and Hothorn, T},
title = {{mvtnorm: Multivariate normal and t distributions}},
url = {http://cran.r-project.org/package=mvtnorm},
year = {2017}
}
@book{Pearson1905,
address = {London, UK},
author = {Pearson, K.},
publisher = {Dulau},
title = {{Mathematical contributions to the theory of evolution: XIV. On the general theory of skew correlations and nonlinear regression}},
year = {1905}
}
@article{Kelley1935,
author = {Kelley, T. L.},
doi = {10.1073/pnas.21.9.554},
journal = {Proceedings of the National Academy of Sciences},
pages = {554--559},
title = {{An unbiased correlation ratio measure}},
volume = {21},
year = {1935}
}
@book{Fan2001,
address = {Cary, NC},
author = {Fan, Xitao and Felsovalyi, A. and Sivo, S. A. and Keenan, S. C.},
publisher = {SAS Institute},
title = {{SAS for Monte Carlo studies: A guide for quantitative researchers}},
year = {2001}
}
@article{Robey1992,
author = {Robey, R. R. and Barcikowski, R. S.},
doi = {10.1111/j.2044-8317.1992.tb00993},
journal = {British Journal of Mathematical and Statistical Psychology},
number = {2},
pages = {283--288},
title = {{Type I error and the number of iterations in Monte Carlo studies of robustness}},
volume = {45},
year = {1992}
}
@book{Field2012,
address = {Thousand Oaks, CA},
author = {Field, A. and Miles, J. and Field, Z.},
publisher = {Sage},
title = {{Discovering statistics using R}},
year = {2012}
}
@article{Wilkinson1999,
author = {Wilkinson, Leland},
doi = {10.1037/0003-066X.54.8.594},
isbn = {0898620007},
issn = {1935-990X},
journal = {American Psychologist},
number = {8},
pages = {594--604},
pmid = {18793039},
title = {{Statistical methods in psychology journals: Guidelines and explanations.}},
url = {http://doi.apa.org/getdoi.cfm?doi=10.1037/0003-066X.54.8.594},
volume = {54},
year = {1999}
}
@article{Gigerenzer2004,
author = {Gigerenzer, Gerd},
doi = {10.1016/j.socec.2004.09.033},
journal = {The Journal of Socio-Economics},
number = {5},
pages = {587--606},
title = {{Mindless statistics}},
volume = {33},
year = {2004}
}
@article{Maxwell1981,
author = {Maxwell, Scott E and Camp, Cameron J. and Arvey, Richard D.},
doi = {10.1037/0021-9010.66.5.525},
file = {::},
issn = {0021-9010},
journal = {Journal of Applied Psychology},
keywords = {limitations {\&} recommendations for usage},
number = {5},
pages = {525--534},
publisher = {American Psychological Association},
title = {{Measures of strength of association: A comparative examination.}},
url = {http://content.apa.org/journals/apl/66/5/525},
volume = {66},
year = {1981}
}
@article{Raju1999,
author = {Raju, N. S. and Bilgic, R. and Edwards, J. E. and Fleer, P. F.},
doi = {10.1177/01466219922031220},
file = {::},
journal = {Applied Psychological Measurement},
month = {jun},
number = {2},
pages = {99--115},
publisher = {Sage PublicationsSage CA: Thousand Oaks, CA},
title = {{Accuracy of population validity and cross-validity estimation: An empirical comparison of formula-based, traditional empirical, and equal weights procedures}},
url = {http://apm.sagepub.com/cgi/doi/10.1177/01466219922031220},
volume = {23},
year = {1999}
}
@article{Olkin1958,
author = {Olkin, Ingram and Pratt, John W.},
doi = {10.1214/aoms/1177706717},
journal = {The Annals of Mathematical Statistics},
number = {1},
pages = {201--211},
title = {{Unbiased estimation of certain correlation coefficients}},
volume = {29},
year = {1958}
}
@book{Stevens1992,
address = {Hillsdale, NJ},
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