The hazards of dealing with response time outliers

The presence of outliers in response times can affect statistical analyses and lead to incorrect interpretation of the outcome of a study. Therefore, it is a widely accepted practice to try to minimize the effect of outliers by preprocessing the raw data. There exist numerous methods for handling outliers and researchers are free to choose among them. In this article, we use computer simulations to show that serious problems arise from this flexibility. Choosing between alternative ways for handling outliers can result in the inflation of p-values and the distortion of confidence intervals and measures of effect size. Using Bayesian parameter estimation and probability distributions with heavier tails eliminates the need to deal with response times outliers, but at the expense of opening another source of flexibility.

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty

This study explores how researchers' analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers' expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team's workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers' results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings.

Time Stand Still: Effects of Temporal Window Selection on Eye Tracking Analysis

The number of possible approaches to conducting and analyzing a research study—often referred to as researcher degrees of freedom—has been increasingly under scrutiny as a challenge to the reproducibility of experimental results. Here we focus on the specific instance of time window selection for time series data. As an example, we use data from a visual world eye tracking paradigm in which participants heard a word and were instructed to click on one of four pictures corresponding to the target (e.g., “Click on the hat”). We examined statistical models for a range of start times following the beginning of the carrier phrase, and for each start time a range of window lengths, resulting in 8281 unique time windows. For each time window we ran the same logistic linear mixed effects model, including effects of time, age, noise, and word frequency on an orthogonalized polynomial basis set. Comparing results across these time ranges shows substantial changes in both parameter estimates and p values, even within intuitively “reasonable” boundaries. In some cases varying the window selection in the range of 100–200 ms caused parameter estimates to change from positive to negative. Rather than rush to provide specific recommendations for time window selection (which differs across studies), we advocate for transparency regarding time window selection and awareness of the effects this choice may have on results. Preregistration and multiverse model exploration are two complementary strategies to help mitigate bias introduced by any particular time window choice.

Comparing dream to reality: an assessment of adherence of the first generation of preregistered studies

Preregistration is a method to increase research transparency by documenting research decisions on a public, third-party repository prior to any influence by data. It is becoming increasingly popular in all subfields of psychology and beyond. Adherence to the preregistration plan may not always be feasible and even is not necessarily desirable, but without disclosure of deviations, readers who do not carefully consult the preregistration plan might get the incorrect impression that the study was exactly conducted and reported as planned. In this paper, we have investigated adherence and disclosure of deviations for all articles published with the Preregistered badge in Psychological Science between February 2015 and November 2017 and shared our findings with the corresponding authors for feedback. Two out of 27 preregistered studies contained no deviations from the preregistration plan. In one study, all deviations were disclosed. Nine studies disclosed none of the deviations. We mainly observed (un)disclosed deviations from the plan regarding the reported sample size, exclusion criteria and statistical analysis. This closer look at preregistrations of the first generation reveals possible hurdles for reporting preregistered studies and provides input for future reporting guidelines. We discuss the results and possible explanations, and provide recommendations for preregistered research.

Examining the robustness of observational associations to model, measurement and sampling uncertainty with the vibration of effects framework

BACKGROUND

The results of studies on observational associations may vary depending on the study design and analysis choices as well as due to measurement error. It is important to understand the relative contribution of different factors towards generating variable results, including low sample sizes, researchers' flexibility in model choices, and measurement error in variables of interest and adjustment variables.

METHODS

We define sampling, model and measurement uncertainty, and extend the concept of vibration of effects in order to study these three types of uncertainty in a common framework. In a practical application, we examine these types of uncertainty in a Cox model using data from the National Health and Nutrition Examination Survey. In addition, we analyse the behaviour of sampling, model and measurement uncertainty for varying sample sizes in a simulation study.

RESULTS

All types of uncertainty are associated with a potentially large variability in effect estimates. Measurement error in the variable of interest attenuates the true effect in most cases, but can occasionally lead to overestimation. When we consider measurement error in both the variable of interest and adjustment variables, the vibration of effects are even less predictable as both systematic under- and over-estimation of the true effect can be observed. The results on simulated data show that measurement and model vibration remain non-negligible even for large sample sizes.

CONCLUSION

Sampling, model and measurement uncertainty can have important consequences for the stability of observational associations. We recommend systematically studying and reporting these types of uncertainty, and comparing them in a common framework.

Clarifying the relationship between body size and extinction risk in amphibians by complete mapping of model space

In vertebrates, large body size is often a key diagnostic feature of species threatened with extinction. However, in amphibians the link between body size and extinction risk is highly uncertain, with previous studies suggesting positive, negative, u-shaped, or no relationship. Part of the reason for this uncertainty is 'researcher degrees of freedom': the subjectivity and selectivity in choices associated with specifying and fitting models. Here, I clarify the size-threat association in amphibians using Specification Curve Analysis, an analytical approach from the social sciences that attempts to minimize this problem by complete mapping of model space. I find strong support for prevailing negative associations between body size and threat status, the opposite of patterns typical in other vertebrates. This pattern is largely explained by smaller species having smaller geographic ranges, but smaller amphibian species also appear to lack some of the life-history advantages (e.g. higher reproductive output) that are often assumed to 'protect' small species in other taxa. These results highlight the need for a renewed conservation focus on the smallest species of the world's most threatened class of vertebrates, as aquatic habitats become increasingly degraded by human activity.

The Multiverse of Methods: Extending the Multiverse Analysis to Address Data-Collection Decisions

When analyzing data, researchers may have multiple reasonable options for the many decisions they must make about the data-for example, how to code a variable or which participants to exclude. Therefore, there exists a multiverse of possible data sets. A classic multiverse analysis involves performing a given analysis on every potential data set in this multiverse to examine how each data decision affects the results. However, a limitation of the multiverse analysis is that it addresses only data cleaning and analytic decisions, yet researcher decisions that affect results also happen at the data-collection stage. I propose an adaptation of the multiverse method in which the multiverse of data sets is composed of real data sets from studies varying in data-collection methods of interest. I walk through an example analysis applying the approach to 19 studies on shooting decisions to demonstrate the usefulness of this approach and conclude with a further discussion of the limitations and applications of this method.

Replicator degrees of freedom allow publication of misleading failures to replicate

We show that commonly exercised flexibility at the experimental design and data analysis stages of replication testing makes it easy to publish false-negative replication results while maintaining the impression of methodological rigor. These findings have important implications for how the many ostensible nonreplications already in the literature should be interpreted and for how future replication tests should be conducted.

In recent years, the field of psychology has begun to conduct replication tests on a large scale. Here, we show that “replicator degrees of freedom” make it far too easy to obtain and publish false-negative replication results, even while appearing to adhere to strict methodological standards. Specifically, using data from an ongoing debate, we show that commonly exercised flexibility at the experimental design and data analysis stages of replication testing can make it appear that a finding was not replicated when, in fact, it was. The debate that we focus on is representative, on key dimensions, of a large number of other replication tests in psychology that have been published in recent years, suggesting that the lessons of this analysis may be far reaching. The problems with current practice in replication science that we uncover here are particularly worrisome because they are not adequately addressed by the field’s standard remedies, including preregistration. Implications for how the field could develop more effective methodological standards for replication are discussed.