Observational cohort study exploring MediEmo smartphone application use, live birth, and in vitro fertilization treatment return rates.

Objective: To explore the associations between the use of the MediEmo smartphone application and in vitro fertilization (IVF) live birth and treatment return rates.

Design: A 3-year observational cohort study.

Subjects: Patients undergoing IVF were classified as users if they used the medication or emotion features of the MediEmo.

A pre-registered, multi-lab non-replication of the action-sentence compatibility effect (ACE).

The Action-sentence Compatibility Effect (ACE) is a well-known demonstration of the role of motor activity in the comprehension of language. Participants are asked to make sensibility judgments on sentences by producing movements toward the body or away from the body. The ACE is the finding that movements are faster when the direction of the movement (e.

Extraordinary claims, extraordinary evidence? A discussion.

Roberts (2020, Learning & Behavior, 48[2], 191-192) discussed research claiming honeybees can do arithmetic. Some readers of this research might regard such claims as unlikely. The present authors used this example as a basis for a debate on the criterion that ought to be used for publication of results or conclusions that could be viewed as unlikely by a significant number of readers, editors, or reviewers.

Beyond Statistical Ritual: Theory in Psychological Science.

More than 40 years ago, Paul Meehl (1978) published a seminal critique of the state of theorizing in psychological science. According to Meehl, the quality of theories had diminished in the preceding decades, resulting in statistical methods standing in for theoretical rigor. In this introduction to the special issue , we apply Meehl's account to contemporary psychological science.

What Are the Odds? Modern Relevance and Bayes Factor Solutions for MacAlister's Problem From the 1881 .

In 1881, Donald MacAlister posed a problem in the that remains relevant today. The problem centers on the statistical evidence for the effectiveness of a treatment based on a comparison between two proportions. A brief historical sketch is followed by a discussion of two default Bayesian solutions, one based on a one-sided test between independent rates, and one on a one-sided test between dependent rates.

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.

Bayesian inference for psychology. Part II: Example applications with JASP.

Bayesian hypothesis testing presents an attractive alternative to p value hypothesis testing. Part I of this series outlined several advantages of Bayesian hypothesis testing, including the ability to quantify evidence and the ability to monitor and update this evidence as data come in, without the need to know the intention with which the data were collected. Despite these and other practical advantages, Bayesian hypothesis tests are still reported relatively rarely.

Correction to 'A Bayesian bird's eye view of 'Replications of important results in social psychology'.

[This corrects the article DOI: 10.1098/rsos.160426.

A Bayesian bird's eye view of 'Replications of important results in social psychology'.

We applied three Bayesian methods to reanalyse the preregistered contributions to the special issue 'Replications of Important Results in Social Psychology' (Nosek & Lakens. 2014 Registered reports: a method to increase the credibility of published results. , 137-141.

Is There a Free Lunch in Inference?

The field of psychology, including cognitive science, is vexed by a crisis of confidence. Although the causes and solutions are varied, we focus here on a common logical problem in inference. The default mode of inference is significance testing, which has a free lunch property where researchers need not make detailed assumptions about the alternative to test the null hypothesis.

Default "Gunel and Dickey" Bayes factors for contingency tables.

The analysis of R×C contingency tables usually features a test for independence between row and column counts. Throughout the social sciences, the adequacy of the independence hypothesis is generally evaluated by the outcome of a classical p-value null-hypothesis significance test. Unfortunately, however, the classical p-value comes with a number of well-documented drawbacks.

Opportunity for verbalization does not improve visual change detection performance: A state-trace analysis.

Evidence suggests that there is a tendency to verbally recode visually-presented information, and that in some cases verbal recoding can boost memory performance. According to multi-component models of working memory, memory performance is increased because task-relevant information is simultaneously maintained in two codes. The possibility of dual encoding is problematic if the goal is to measure capacity for visual information exclusively.

Bayesian analysis of factorial designs.

This article provides a Bayes factor approach to multiway analysis of variance (ANOVA) that allows researchers to state graded evidence for effects or invariances as determined by the data. ANOVA is conceptualized as a hierarchical model where levels are clustered within factors. The development is comprehensive in that it includes Bayes factors for fixed and random effects and for within-subjects, between-subjects, and mixed designs.

Model comparison in ANOVA.

Analysis of variance (ANOVA), the workhorse analysis of experimental designs, consists of F-tests of main effects and interactions. Yet, testing, including traditional ANOVA, has been recently critiqued on a number of theoretical and practical grounds. In light of these critiques, model comparison and model selection serve as an attractive alternative.

The Peer Reviewers' Openness Initiative: incentivizing open research practices through peer review.

Openness is one of the central values of science. Open scientific practices such as sharing data, materials and analysis scripts alongside published articles have many benefits, including easier replication and extension studies, increased availability of data for theory-building and meta-analysis, and increased possibility of review and collaboration even after a paper has been published. Although modern information technology makes sharing easier than ever before, uptake of open practices had been slow.

Calibrated Bayes Factors Should Not Be Used: A Reply to Hoijtink, van Kooten, and Hulsker.

Hoijtink, Kooten, and Hulsker ( 2016 ) present a method for choosing the prior distribution for an analysis with Bayes factor that is based on controlling error rates, which they advocate as an alternative to our more subjective methods (Morey & Rouder, 2014 ; Rouder, Speckman, Sun, Morey, & Iverson, 2009 ; Wagenmakers, Wetzels, Borsboom, & van der Maas, 2011 ). We show that the method they advocate amounts to a simple significance test, and that the resulting Bayes factors are not interpretable. Additionally, their method fails in common circumstances, and has the potential to yield arbitrarily high Type II error rates.

Continued misinterpretation of confidence intervals: response to Miller and Ulrich.

Miller and Ulrich (2015) critique our claim (Hoekstra et al., Psychonomic Bulletin & Review, 21(5), 1157-1164, 2014), based on a survey given to researchers and students, of widespread misunderstanding of confidence intervals (CIs). They suggest that survey respondents may have interpreted the statements in the survey that we deemed incorrect in an idiosyncratic, but correct, way, thus calling into question the conclusion that the results indicate that respondents could not properly interpret CIs.

A Tutorial on Computing Bayes Factors for Single-Subject Designs.

When researchers are interested in the effect of certain interventions on certain individuals, single-subject studies are often performed. In their most simple form, such single-subject studies require that a subject is measured on relevant criterion variables several times before an intervention and several times during or after the intervention. Scores from the two phases are then compared in order to investigate the intervention effect.

The fallacy of placing confidence in confidence intervals.

Interval estimates - estimates of parameters that include an allowance for sampling uncertainty - have long been touted as a key component of statistical analyses. There are several kinds of interval estimates, but the most popular are confidence intervals (CIs): intervals that contain the true parameter value in some known proportion of repeated samples, on average. The width of confidence intervals is thought to index the precision of an estimate; CIs are thought to be a guide to which parameter values are plausible or reasonable; and the confidence coefficient of the interval (e.

Improving the analysis of routine outcome measurement data: what a Bayesian approach can do for you.

Since recent decades, clinicians offering interventions against mental problems must systematically collect data on how clients change over time. Since these data typically contain measurement error, statistical tests have been developed which should disentangle true changes from random error. These statistical tests can be subdivided into two types: classical tests and Bayesian tests.

A power fallacy.

The power fallacy refers to the misconception that what holds on average -across an ensemble of hypothetical experiments- also holds for each case individually. According to the fallacy, high-power experiments always yield more informative data than do low-power experiments. Here we expose the fallacy with concrete examples, demonstrating that a particular outcome from a high-power experiment can be completely uninformative, whereas a particular outcome from a low-power experiment can be highly informative.