A central theme in the field of survey statistics is estimating population-level quantities through data coming from potentially non-representative samples of the population. Multilevel regression and poststratification (MRP), a model-based approach, is gaining traction against the traditional weighted approach for survey estimates. MRP estimates are susceptible to bias if there is an underlying structure that the methodology does not capture. This work aims to provide a new framework for specifying structured prior distributions that lead to bias reduction in MRP estimates. We use simulation studies to explore the benefit of these prior distributions and demonstrate their efficacy on non-representative US survey data. We show that structured prior distributions offer absolute bias reduction and variance reduction for posterior MRP estimates in a large variety of data regimes.
Understanding and measuring sentence acceptability is of fundamental importance for linguists, but although many measures for doing so have been developed, relatively little is known about some of their psychometric properties. In this paper we evaluate within-and between-participant test-retest reliability on a wide range of measures of sentence acceptability. Doing so allows us to estimate how much of the variability within each measure is due to factors including participantlevel individual differences, sample size, response styles, and item effects. The measures examined include Likert scales, two versions of forced-choice judgments, magnitude estimation, and a novel measure based on Thurstonian approaches in psychophysics. We reproduce previous findings of high between-participant reliability within and across measures, and extend these results to a generally high reliability within individual items and individual people. Our results indicate that Likert scales and the Thurstonian approach produce the most stable and reliable acceptability measures and do so with smaller sample sizes than the other measures. Moreover, their agreement with each other suggests that the limitation of a discrete Likert scale does not impose a significant degree of structure on the resulting acceptability judgments.
Cognitive modelling shares many features with statistical modelling, making it seem trivial to borrow from the practices of robust Bayesian statistics to protect the practice of robust cognitive modelling. We take one aspect of statistical workflow-prior predictive checks-and explore how they might be applied to a cognitive modelling task. We find that it is not only the likelihood that is needed to interpret the priors, we also need to incorporate experiment information as well. This suggests that while cognitive modelling might borrow from statistical practices, especially workflow, care must be taken to make the necessary adaptions.Cognitive modelling aims to create generative, phenomenological models of cognitive processes. In contrast, statistical tools are mostly designed to disentangle a signal from measurement error in the data. This leads to some tension between the tasks statistical tools are designed for and the types of questions cognitive models attempt to answer. In this paper we highlight that it is important to remain aware of the difference in purpose when trying to apply modern (or, for that matter, classical) statistical techniques to cognitive modelling. We focus on Bayesian methods, but we believe that the general warning should hold more generally.We highlight this by exploring how an increasingly popular concept from statistical modelling-viewing modelling as a holistic workflow rather than a set of discrete and independent activities-can be applied to cognitive modelling practices. This idea, as outlined
Psychology research often focuses on interactions, and this has deep implications for inference from nonrepresentative samples. For the goal of estimating average treatment effects, we propose to fit a model allowing treatment to interact with background variables and then average over the distribution of these variables in the population. This can be seen as an extension of multilevel regression and poststratification (MRP), a method used in political science and other areas of survey research, where researchers wish to generalize from a sparse and possibly nonrepresentative sample to the general population. In this article, we discuss areas where this method can be used in the psychological sciences. We use our method to estimate the norming distribution for the Big Five Personality Scale using open source data. We argue that large open data sources like this and other collaborative data sources can potentially be combined with MRP to help resolve current challenges of generalizability and replication in psychology.
Trans women are a key, yet under-researched, population in the HIV epidemic. However, there remains a paucity of data on the health and wellbeing of trans women at risk of, or living with, HIV in the United Kingdom. This article provides a narrative review of key empirical research into HIV among trans women. In an effort to explore individual and social factors in relation to HIV in this population, we outline key tenets of identity process theory from social psychology and the concept of structural violence from medical anthropology. We focus on published studies around the following themes: (1) epidemiological data, (2) syndemic factors (3) barriers to social support, (4) HIV and gender transitioning, and (5) access to and engagement with health care. We identify lacunae and thus call for United Kingdom-based research in the following areas: (1) the prevalence and incidence of HIV in trans women, (2) the impact of syndemic factors on HIV risk and acquisition in trans women, (3) the nature of social support for coping with syndemic factors, (4) the interface of gender transitioning and HIV, and (5) barriers to accessing HIV prevention and care services. There is great scope (and urgency) for research into HIV among trans women, especially in the United Kingdom, to reduce incidence in this group, to enhance engagement in HIV care across the care continuum, and to improve the health and wellbeing of those living with HIV. A tentative model for HIV prevention and care is presented in this article.
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