Purpose: The ongoing replication crisis within and beyond psychology has revealed the numerous ways in which flexibility in the research process can affect study outcomes. In speech research, examples of these “researcher degrees of freedom” include the particular syllables, words, or sentences presented; the talkers who produce the stimuli and the instructions given to them; the population tested; whether and how stimuli are matched on amplitude; the type of masking noise used and its presentation level; and many others. In this research note, we argue that even seemingly minor methodological choices have the potential to affect study outcomes. To that end, we present a reanalysis of six existing data sets on spoken word identification in noise to assess how differences in talkers, stimulus processing, masking type, and listeners affect identification accuracy. Conclusions: Our reanalysis revealed relatively low correlations among word identification rates across studies. The data suggest that some of the seemingly innocuous methodological details that differ across studies—details that cannot possibly be reported in text given the idiosyncrasies inherent to speech—introduce unknown variability that may affect replicability of our findings. We therefore argue that publicly sharing stimuli is a crucial step toward improved replicability in speech research.
In the last decade, psychology and other sciences have implemented numerous reforms to improve the robustness of our research, many of which are based on increasing transparency throughout the research process. Among these reforms is the practice of preregistration, in which researchers create a time- stamped and uneditable document before data collection that describes the methods of the study, how the data will be analyzed, the sample size, and many other decisions. The current article highlights the benefits of preregistration with a focus on the specific issues that speech, language, and hearing researchers are likely to encounter, and additionally provides a tutorial for writing preregistrations. Conclusions: Although rates of preregistration have increased dramatically in recent years, the practice is still relatively uncommon in research on speech, language, and hearing. Low rates of adoption may be driven by a lack of under- standing of the benefits of preregistration (either generally or for our discipline in particular) or uncertainty about how to proceed if it becomes necessary to deviate from the preregistered plan. Alternatively, researchers may see the ben- efits of preregistration but not know where to start, and gathering this informa- tion from a wide variety of sources is arduous and time consuming. This tutorial addresses each of these potential roadblocks to preregistration and equips readers with tools to facilitate writing preregistrations for research on speech, language, and hearing.
This is part 2 of a 2 of a series of videos on mixed effects modeling. The intended audience is graduate student or anyone with some regression background but no background in mixed modeling.
This is part 1 of a 2 of a series of videos on mixed effects modeling. The intended audience is graduate student or anyone with some regression background but no background in mixed modeling.
This Tutorial serves as both an approachable theoretical introduction to mixed-effects modeling and a practical introduction to how to implement mixed-effects models in R. The intended audience is researchers who have some basic statistical knowledge, but little or no experience implementing mixed-effects models in R using their own data. In an attempt to increase the accessibility of this Tutorial, I deliberately avoid using mathematical terminology beyond what a student would learn in a standard graduate-level statistics course, but I reference articles and textbooks that provide more detail for interested readers. This Tutorial includes snippets of R code throughout; the data and R script used to build the models described in the text are available via OSF at https://osf.io/v6qag/, so readers can follow along if they wish. The goal of this practical introduction is to provide researchers with the tools they need to begin implementing mixed-effects models in their own research.
In response to growing concern in psychology and other sciences about low rates of replicability of published findings (Open Science Collaboration, 2015), there has been a movement toward conducting open and transparent research (see Chambers, 2017). This has led to changes in statistical reporting guidelines in journals (Appelbaum et al., 2018), new professional societies (e.g., Society for the Improvement of Psychological Science), frameworks for posting materials, data, code, and manuscripts (e.g., Open Science Framework, PsyArXiv), initiatives for sharing data and collaborating (e.g., Psych Science Accelerator, Study Swap), and educational resources for teaching through replication (e.g., Collaborative Replications and Education Project). This “credibility revolution” (Vazire, 2018) provides many opportunities for researchers. However, given the recency of the changes and the rapid pace of advancements (see Houtkoop et al., 2018), it may be overwhelming for faculty to know whether and how to begin incorporating open science practices into research with undergraduates. In this paper, we will not attempt to catalog the entirety of the open science movement (see recommended resources below for more information), but will instead highlight why adopting open science practices may be particularly beneficial to conducting and publishing research with undergraduates. The first author is a faculty member at Carleton College (a small, undergraduate-only liberal arts college) and the second is a former undergraduate research assistant (URA) and lab manager in Dr. Strand’s lab, now pursuing a PhD at Washington University in St. Louis. We argue that open science practices have tremendous benefits for undergraduate students, both in creating publishable results and in preparing students to be critical consumers of science.