One Direction? A Tutorial for Circular Data Analysis Using R With Examples in Cognitive Psychology

Circular data is data that is measured on a circle in degrees or radians. It is fundamentally different from linear data due to its periodic nature (0° = 360°). Circular data arises in a large variety of research fields. Among others in ecology, the medical sciences, personality measurement, educational science, sociology, and political science circular data is collected. The most direct examples of circular data within the social sciences arise in cognitive and experimental psychology. However, despite numerous examples of circular data being collected in different areas of cognitive and experimental psychology, the knowledge of this type of data is not well-spread and literature in which these types of data are analyzed using methods for circular data is relatively scarce. This paper therefore aims to give a tutorial in working with and analyzing circular data to researchers in cognitive psychology and the social sciences in general. It will do so by focusing on data inspection, model fit, estimation and hypothesis testing for two specific models for circular data using packages from the statistical programming language R

Bayesian evidence synthesis as a flexible alternative to meta-analysis: A simulation study and empirical demonstration

Synthesizing results across multiple studies is a popular way to increase the robustness of scientific findings. The most well-known method for doing this is meta-analysis. However, because meta-analysis requires conceptually comparable effect sizes with the same statistical form, meta-analysis may not be possible when studies are highly diverse in terms of their research design, participant characteristics, or operationalization of key variables. In these situations, Bayesian evidence synthesis may constitute a flexible and feasible alternative, as this method combines studies at the hypothesis level rather than at the level of the effect size. This method therefore poses less constraints on the studies to be combined. In this study, we introduce Bayesian evidence synthesis and show through simulations when this method diverges from what would be expected in a meta-analysis to help researchers correctly interpret the synthesis results. As an empirical demonstration, we also apply Bayesian evidence synthesis to a published meta-analysis on statistical learning in people with and without developmental language disorder. We highlight the strengths and weaknesses of the proposed method and offer suggestions for future research

Evaluation of Bayesian Hui-Walter and logistic regression latent class models to estimate diagnostic test characteristics with simulated data

Estimation of the accuracy of diagnostic tests in the absence of a gold standard is an important research subject in epidemiology (Dohoo et al., 2009). One of the most used methods the last few decades is the Bayesian Hui-Walter (HW) latent class model (Hui and Walter, 1980). However, the classic HW models aggregate the observed individual test results to the population level, and as a result, potentially valuable information from the lower level(s) is not fully incorporated. An alternative approach is the Bayesian logistic regression (LR) latent class model that allows inclusion of individual level covariates (McInturff et al., 2004). In this study, we explored both classic HW and individual level LR latent class models using Bayesian methodology within a simulation context where true disease status and true test properties were predefined. Population prevalences and test characteristics that were realistic for paratuberculosis in cattle (Toft et al., 2005) were used for the simulation. Individual animals were generated to be clustered within herds in two regions. Two tests with binary outcomes were simulated with constant test characteristics across the two regions. On top of the prevalence properties and test characteristics, one animal level binary risk factor was added to the data. The main objective was to compare the performance of Bayesian HW and LR approaches in estimating test sensitivity and specificity in simulated datasets with different population characteristics. Results from various settings showed that LR models provided posterior estimates that were closer to the true values. The LR models that incorporated herd level clustering effects provided the most accurate estimates, in terms of being closest to the true values and having smaller estimation intervals. This work illustrates that individual level LR models are in many situations preferable over classic HW models for estimation of test characteristics in the absence of a gold standard

Determining fit: the role of matching procedures in prospective higher education students’ enrolment behaviour

In the Netherlands, the implementation of mandatory procedures in which prospective students do a final check on their initial higher education program choice (so-called matching procedures), were introduced to improve student-program fit. We argue that prospective students who lack feelings of fit with the program during these matching procedures are less likely to finalise their enrolment. Using data of 13 programs at four Dutch universities, the association between various matching procedures and finalising enrolment, and finalising enrolment before and after the implementation of matching were examined. Enrolment rates were lower in programs with more intensive matching procedures and higher in pre-matching cohorts than in matching cohorts, indicating the potential value of pre-enrolment fit checks. In conclusion, this study gives indications that it can be worthwhile to invest in guiding prospective students in their program choice by obliging them to test their fit with the program through intensive matching procedures

An explorative analysis of the association between teachers' use of effective teacher practices and teachers' interpersonal relations

In deze studie worden relaties verkend tussen het pedagogisch-didactisch handelen van leraren en hun interpersoonlijke stijl van communicatie, met als toekomstig doel om feedback en coaching van leraren te optimaliseren. Het pedagogisch-didactisch handelen en de interpersoonlijke communicatie zijn gemeten aan de hand van een leerlingenvragenlijst. De steekproef bestaat uit 474 leerlingen die de vragenlijst invulden over 20 leraren VO. De verkenning geeft weer dat grotere vaardigheid in pedagogisch-didactisch handelen samengaat met meer overeenkomst in leerlingpercepties van de interpersoonlijke communicatie van leraren. De resultaten bevestigen ook eerder onderzoek dat stelde dat leraren die meer activerende werkvormen gebruiken een meer “sturende” en “vriendelijke” stijl van interpersoonlijke communicatie hebben. Discussie kan zich richtten op de hoe de resultaten informerend zijn voor coaches

One Direction? : A Tutorial for Circular Data Analysis Using R With Examples in Cognitive Psychology

Circular data is data that is measured on a circle in degrees or radians. It is fundamentally different from linear data due to its periodic nature (0° = 360°). Circular data arises in a large variety of research fields. Among others in ecology, the medical sciences, personality measurement, educational science, sociology, and political science circular data is collected. The most direct examples of circular data within the social sciences arise in cognitive and experimental psychology. However, despite numerous examples of circular data being collected in different areas of cognitive and experimental psychology, the knowledge of this type of data is not well-spread and literature in which these types of data are analyzed using methods for circular data is relatively scarce. This paper therefore aims to give a tutorial in working with and analyzing circular data to researchers in cognitive psychology and the social sciences in general. It will do so by focusing on data inspection, model fit, estimation and hypothesis testing for two specific models for circular data using packages from the statistical programming language R

Supplementary files for 'Standard Errors, Priors, and Bridge Sampling: A Discussion of Liu et al.'

Data archive for discussion of Liu et al paper by Veen and Klugkist. See `PDF` and `HTML` files for the suppelementairy materials and data archive