Principles of model specification in ANOVA designs

ANOVA—the workhorse of experimental psychology—seems well understood in that behavioral sciences have agreed-upon contrasts and reporting conventions. Yet, we argue this consensus hides considerable flaws in common ANOVA procedures, and these flaws become especially salient in the within-subject and mixed-model cases. The main thesis is that these flaws are in model specification. The specifications underlying common use are deficient from a substantive perspective, that is, they do not match reality in behavioral experiments. The problem, in particular, is that specifications rely on coincidental rather than robust statements about reality. We provide specifications that avoid making arguments based on coincidences, and note these Bayes factor model comparisons among these specifications are already convenient in the BayesFactor package. Finally, we argue that model specification necessarily and critically reflects substantive concerns, and, consequently, is ultimately the responsibility of substantive researchers. Source code for this project is at github/PerceptionAndCognitionLab/stat_aov2

Bayes Factors for Mixed Models: a Discussion

van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comparison for mixed effects models. Seven response articles offered their own perspective on the preferred setup for mixed model comparison, on the most appropriate specification of prior distributions, and on the desirability of default recommendations. This article presents a round-table discussion that aims to clarify outstanding issues, explore common ground, and outline practical considerations for any researcher wishing to conduct a Bayesian mixed effects model comparison

Detection of peptide-based nanoparticles in blood plasma by ELISA

Aims: The aim of the current study was to develop a method to detect peptide-linked nanoparticles in blood plasma. Materials & Methods: A convenient enzyme linked immunosorbent assay (ELISA) was developed for the detection of peptides functionalized with biotin and fluorescein groups. As a proof of principle, polymerized pentafluorophenyl methacrylate nanoparticles linked to biotin-carboxyfluorescein labeled peptides were intravenously injected in Wistar rats. Serial blood plasma samples were analyzed by ELISA and by liquid chromatography mass spectrometry (LC/MS) technology. Results: The ELISA based method for the detection of FITC labeled peptides had a detection limit of 1 ng/mL. We were able to accurately measure peptides bound to pentafluorophenyl meth-acrylate nanoparticles in blood plasma of rats, and similar results were obtained by LC/MS. Conclusions: We detected FITC-labeled peptides on pentafluorophenyl methacrylate nanoparticles after injection in vivo. This method can be extended to detect nanoparticles with different chemical compositions

Bayes factors for mixed models: A discussion

van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comparison for mixed effects models. Seven response articles offered their own perspective on the preferred setup for mixed model comparison, on the most appropriate specification of prior distributions, and on the desirability of default recommendations. This article presents a round-table discussion that aims to clarify outstanding issues, explore common ground, and outline practical considerations for any researcher wishing to conduct a Bayesian mixed effects model comparison

A many-analysts approach to the relation between religiosity and well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N=10,535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β=0.120). For the second research question, this was the case for 65% of the teams (median reported β=0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

A Many-analysts Approach to the Relation Between Religiosity and Well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N = 10, 535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β = 0.120). For the second research question, this was the case for 65% of the teams (median reported β = 0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

A many-analysts approach to the relation between religiosity and well-being

The relation between religiosity and well-being is one of the most researched topics in the psychology of religion, yet the directionality and robustness of the effect remains debated. Here, we adopted a many-analysts approach to assess the robustness of this relation based on a new cross-cultural dataset (N=10,535 participants from 24 countries). We recruited 120 analysis teams to investigate (1) whether religious people self-report higher well-being, and (2) whether the relation between religiosity and self-reported well-being depends on perceived cultural norms of religion (i.e., whether it is considered normal and desirable to be religious in a given country). In a two-stage procedure, the teams first created an analysis plan and then executed their planned analysis on the data. For the first research question, all but 3 teams reported positive effect sizes with credible/confidence intervals excluding zero (median reported β=0.120). For the second research question, this was the case for 65% of the teams (median reported β=0.039). While most teams applied (multilevel) linear regression models, there was considerable variability in the choice of items used to construct the independent variables, the dependent variable, and the included covariates

Mucoadhesive thiolated chitosans as platforms for oral controlled drug delivery: synthesis and in vitro evaluation

The aim of the present study was to evaluate the influence of the degree of modification and the polymer chain length on the mucoadhesive properties and the swelling behavior of thiolated chitosan derivatives obtained via a simple one-step reaction between the polymer and 2-iminothiolane. The conjugates differing in molecular mass of the polymer backbone and in the amount of immobilized thiol groups were compressed into tablets. They were investigated for their mucoadhesive properties on freshly excised porcine mucosa via tensile studies and the rotating cylinder method. Moreover, the swelling behavior of these tablets in aqueous solutions was studied by a simple gravimetric method. The obtained results demonstrated that the total work of adhesion of chitosan-TBA (=4-thio-butyl-amidine) conjugates can be improved by an increasing number of covalently attached thiol groups; a 100-fold increase compared to unmodified chitosan was observed for a medium molecular mass chitosan-TBA conjugate exhibiting 264 microM thiol groups per gram polymer. Also, the polymer chain length had an influence on the mucoadhesive properties of the polymer. The medium molecular mass polymer displayed a fourfold improved adhesion on the rotating cylinder compared to the derivative of low molecular mass. These results contribute to the development of new delivery systems exhibiting improved mucoadhesive properties

Meaningful Comparisons With Ordinal-Scale Items

Ordinal-scale items—say items that assess agreement with a proposition on an ordinal rating scale from strongly disagree to strongly agree—are exceedingly popular in psychological research. A common research question concerns the comparison of response distributions on ordinal-scale items across conditions. In this context, there is often a lingering question of whether metric-level descriptions of the results and parametric tests are appropriate. We consider a different problem, perhaps one that supersedes the parametric-vs-nonparametric issue: When is it appropriate to reduce the comparison of two (ordinal) distributions to the comparison of simple summary statistics (e.g., measures of location)? In this paper, we provide a Bayesian modeling approach to help researchers perform meaningful comparisons of two response distributions and draw appropriate inferences from ordinal-scale items. We develop four statistical models that represent possible relationships between two distributions: an unconstrained model representing a complex, non-ordinal relationship, a nonparametric stochastic-dominance model, a parametric shift model, and a null model representing equivalence in distribution. We show how these models can be compared in light of data with Bayes factors and illustrate their usefulness with two real-world examples. We also provide a freely available web applet for researchers who wish to adopt the approach