Opaque prior distributions in Bayesian latent variable models

We review common situations in Bayesian latent variable models where the prior distribution that a researcher specifies does not match the prior distribution that the estimation method uses. These situations can arise from the positive definite requirement on correlation matrices, from the sign indeterminacy of factor loadings, and from order constraints on threshold parameters. The issue is especially problematic for reproducibility and for model checks that involve prior distributions, including prior predictive assessment and Bayes factors. In these cases, one might be assessing the wrong model, casting doubt on the relevance of the results. The most straightforward solution to these issues sometimes involves use of informative prior distributions. We explore other solutions and make recommendations for practice.Comment: 23 pages, 8 figure

A systematic review of Bayesian articles in psychology: The last 25 years

Although the statistical tools most often used by researchers in the field of psychology over the last 25 years are based on frequentist statistics, it is often claimed that the alternative Bayesian approach to statistics is gaining in popularity. In the current article, we investigated this claim by performing the very first systematic review of Bayesian psychological articles published between 1990 and 2015 (n = 1,579). We aim to provide a thorough presentation of the role Bayesian statistics plays in psychology. This historical assessment allows us to identify trends and see how Bayesian methods have been integrated into psychological research in the context of different statistical frameworks (e.g., hypothesis testing, cognitive models, IRT, SEM, etc.). We also describe take-home messages and provide “big-picture” recommendations to the field as Bayesian statistics becomes more popular. Our review indicated that Bayesian statistics is used in a variety of contexts across subfields of psychology and related disciplines. There are many different reasons why one might choose to use Bayes (e.g., the use of priors, estimating otherwise intractable models, modeling uncertainty, etc.). We found in this review that the use of Bayes has increased and broadened in the sense that this methodology can be used in a flexible manner to tackle many different forms of questions. We hope this presentation opens the door for a larger discussion regarding the current state of Bayesian statistics, as well as future trends.https://deepblue.lib.umich.edu/bitstream/2027.42/136925/1/A Systematic Review of Bayesian Articles in Psychology The Last 25 Years.pdfDescription of A Systematic Review of Bayesian Articles in Psychology The Last 25 Years.pdf : Main Articl

An Agenda for Open Science in Communication

In the last 10 years, many canonical findings in the social sciences appear unreliable. This so-called “replication crisis” has spurred calls for open science practices, which aim to increase the reproducibility, replicability, and generalizability of findings. Communication research is subject to many of the same challenges that have caused low replicability in other fields. As a result, we propose an agenda for adopting open science practices in Communication, which includes the following seven suggestions: (1) publish materials, data, and code; (2) preregister studies and submit registered reports; (3) conduct replications; (4) collaborate; (5) foster open science skills; (6) implement Transparency and Openness Promotion Guidelines; and (7) incentivize open science practices. Although in our agenda we focus mostly on quantitative research, we also reflect on open science practices relevant to qualitative research. We conclude by discussing potential objections and concerns associated with open science practices

Social mindfulness and prosociality vary across the globe

Humans are social animals, but not everyone will be mindful of others to the same extent. Individual differences have been found, but would social mindfulness also be shaped by one’s location in the world? Expecting cross-national differences to exist, we examined if and how social mindfulness differs across countries. At little to no material cost, social mindfulness typically entails small acts of attention or kindness. Even though fairly common, such low-cost cooperation has received little empirical attention. Measuring social mindfulness across 31 samples from industrialized countries and regions (n = 8,354), we found considerable variation. Among selected country-level variables, greater social mindfulness was most strongly associated with countries’ better general performance on environmental protection. Together, our findings contribute to the literature on prosociality by targeting the kind of everyday cooperation that is more focused on communicating benevolence than on providing material benefits

Advanced Methods for Detecting Specification Issues in Bayesian Structural Equation Modeling

This dissertation consists of two studies investigating model and prior specification issues in the context of Bayesian structural equation modeling (SEM). Two of the major advantages of Bayesian estimation for SEM are that complex models can more easily be estimated, and prior information can be directly included in the analysis. Two aspects of Bayesian estimation of SEM that are important for the applied researcher are model and prior specification assessment. In Study 1 of this dissertation, I examined the ability of several model fit and selection indices to detect model misspecification in two commonly used SEMs with data that are completely observed or that contain missing values. Simulation results showed that Bayesian approximate model fit indices may not be appropriate for model fit assessment of a single model. The posterior predictive p-value was more likely to detect model misspecification, although it was sensitive to sample size and the presence of missing values. Instead of focusing on a single model, researchers should aim to compare multiple models and focus on model selection, using Bayesian approximate fit indices, in addition to model fit assessment. Furthermore, informative priors that diverge from the population model worsened model fit even for a correctly specified model. Thus, researchers should examine whether there is disagreement between the priors and their observed data when using informative priors. This so-called prior-data disagreement was the focus of Study 2 of this dissertation. In this study, I examined three indices for detecting prior-data disagreement, the Data Agreement Criterion (DAC), Bayes Factor (BF), and prior-predictive p-value, and assessed their ability to detect diverging priors across 4 sample sizes and 49 prior specifications for the mean intercept and linear slope parameters of a latent growth model. Simulation results showed that while the DAC was easily implemented, it cannot assess interactions between priors placed on different parameters. Use of the BF becomes unfeasible as model complexity, sample size, or the number of prior specifications examined increase. Here, the prior-predictive p-value may offer an alternative, although it may not be appropriate for prior specifications that are partially or fully diffuse. Furthermore, all prior-data disagreement indices tended to be better at detecting disagreement with larger sample sizes, whereas the impact of disagreement is largest with small sample sizes. Other implications, suggestions for applied researchers, limitations, and future directions are also discussed

Here comes the bad news:Doctor robot taking over

To test in how far the Media Equation and Computers Are Social Actors (CASA) validly explain user responses to social robots, we manipulated how a bad health message was framed and the language that was used. In the wake of Experiment 2 of Burgers et al. (Patient Educ Couns 89(2):267–273, 2012. https://doi.org/10.1016/j.pec.2012.08.008), a human versus robot doctor delivered health messages framed positively or negatively, using affirmations or negations. In using frequentist (robots are different from humans) and Bayesian (robots are the same) analyses, we found that participants liked the robot doctor and the robot’s message better than the human’s. The robot also compelled more compliance to the medical treatment. For the level of expected quality of life, the human and robot doctor tied. The robot was not seen as affectively distant but rather involving, ethical, skilled, and people wanted to consult her again. Note that doctor robot was not a seriously looking physician but a little girl with the voice of a young woman. We conclude that both Media Equation and CASA need to be altered when it comes to robot communication. We argue that if certain negative qualities are filtered out (e.g., strong emotion expression), credibility will increase, which lowers affective distance to the messenger. Robots sometimes outperform humans on emotional tasks, which may relieve physicians from a most demanding duty of disclosing unfavorable information to a patient

Assessing Differences in How the CushingQoL Is Interpreted Across Countries: Comparing Patients From the U.S. and the Netherlands.

Background: Cultural factors influence how individuals define, evaluate, and approach their quality of life (QoL). The CushingQoL is a widely used disease-specific questionnaire to assess QoL in patients with Cushing's syndrome. However, there is no information about potential cross-country differences in the way patients interpret the items on the CushingQoL. Thus, the current study examined if the CushingQoL is interpreted in the same way across nationalities. Methods: Patients from the U.S. (n = 260) and the Netherlands (n = 103) were asked to fill out the CushingQoL and a short demographics survey. Measurement invariance testing was utilized to explore whether or not the patient samples from the U.S. and the Netherlands interpreted items on the CushingQoL in the same way. Results: A two-subscale scoring approach was used for the CushingQoL. Model fit was good for the U.S. sample (e.g., CFI = 0.983; TLI = 0.979), as well as the Dutch sample (e.g., CFI = 0.971; TLI = 0.964). Invariance testing revealed that three of the 12 items on the CushingQoL were interpreted differently across the groups. These items are all related to psychosocial issues (e.g., irritable mood and worrying about one's health). Items assessing physical aspects of QoL did not vary across the U.S. and Dutch samples. Conclusions: Interpreting results from the CushingQoL requires careful consideration of country of residence, as this appears to impact the interpretation of the questionnaire

Assessing Differences in How the CushingQoL Is Interpreted Across Countries: Comparing Patients From the U.S. and the Netherlands

Background: Cultural factors influence how individuals define, evaluate, and approach their quality of life (QoL). The CushingQoL is a widely used disease-specific questionnaire to assess QoL in patients with Cushing's syndrome. However, there is no information about potential cross-country differences in the way patients interpret the items on the CushingQoL. Thus, the current study examined if the CushingQoL is interpreted in the same way across nationalities.Methods: Patients from the U.S. (n = 260) and the Netherlands (n = 103) were asked to fill out the CushingQoL and a short demographics survey. Measurement invariance testing was utilized to explore whether or not the patient samples from the U.S. and the Netherlands interpreted items on the CushingQoL in the same way.Results: A two-subscale scoring approach was used for the CushingQoL. Model fit was good for the U.S. sample (e.g., CFI = 0.983; TLI = 0.979), as well as the Dutch sample (e.g., CFI = 0.971; TLI = 0.964). Invariance testing revealed that three of the 12 items on the CushingQoL were interpreted differently across the groups. These items are all related to psychosocial issues (e.g., irritable mood and worrying about one's health). Items assessing physical aspects of QoL did not vary across the U.S. and Dutch samples.Conclusions: Interpreting results from the CushingQoL requires careful consideration of country of residence, as this appears to impact the interpretation of the questionnaire