Common language effect size for correlations

Power to Detect What? Considerations for Planning and Evaluating Sample Size

ACADEMIC ABSTRACT

In the wake of the replication crisis, social and personality psychologists have increased attention to power analysis and the adequacy of sample sizes. In this article, we analyze current controversies in this area, including choosing effect sizes, why and whether power analyses should be conducted on already-collected data, how to mitigate the negative effects of sample size criteria on specific kinds of research, and which power criterion to use. For novel research questions, we advocate that researchers base sample sizes on effects that are likely to be cost-effective for other people to implement (in applied settings) or to study (in basic research settings), given the limitations of interest-based minimums or field-wide effect sizes. We discuss two alternatives to power analysis, precision analysis and sequential analysis, and end with recommendations for improving the practices of researchers, reviewers, and journal editors in social-personality psychology.

PUBLIC ABSTRACT

Recently, social-personality psychology has been criticized for basing some of its conclusions on studies with low numbers of participants. As a result, power analysis, a mathematical way to ensure that a study has enough participants to reliably "detect" a given size of psychological effect, has become popular. This article describes power analysis and discusses some controversies about it, including how researchers should derive assumptions about effect size, and how the requirements of power analysis can be applied without harming research on hard-to-reach and marginalized communities. For novel research questions, we advocate that researchers base sample sizes on effects that are likely to be cost-effective for other people to implement (in applied settings) or to study (in basic research settings). We discuss two alternatives to power analysis, precision analysis and sequential analysis, and end with recommendations for improving the practices of researchers, reviewers, and journal editors in social-personality psychology.

Evaluation of methods measuring medication adherence in patients with polypharmacy: a longitudinal and patient perspective

PURPOSE

To explore patients' willingness to have medication adherence measured using different methods and evaluate the feasibility and validity of their combination (i.e., pill counts, a medication diary and a questionnaire assessing adherence two months post-discharge).

METHODS

(1) A cross-sectional evaluation of the willingness of patients with polypharmacy to have their medication adherence measured post-discharge. (2) Medication adherence was monitored during two months using pill counts based on preserved medication packages and a diary in which patients registered their adherence-related problems. During a home visit, the Probabilistic Medication Adherence Scale (ProMAS) and a questionnaire on feasibility were administered.

RESULTS

A total of 144 participants completed the questionnaire at discharge. The majority was willing to communicate truthfully about their adherence (97%) and to share adherence-related information with healthcare providers (99%). More participants were willing to preserve medication packages (76%) than to complete a medication diary (67%) during two months. Most participants reported that preserving medication packages (91%), completing the diary (99%) and the ProMAS (99%) were no effort to them. According to the majority of participants (60%), pill counts most accurately reflected medication adherence, followed by the diary (39%) and ProMAS (1%). Medication adherence measured by pill counts correlated significantly with ProMAS scores, but not with the number of diary-reported problems. However, adherence measured by the medication diary and ProMAS correlated significantly.

CONCLUSION

Combining tools for measuring adherence seems feasible and can provide insight into the accordance of patients' actual medication use with their prescribed regimen, but also into problems contributing to non-adherence.

Combining Students’ Grades and Achievements on the National Assessment of knowledge: A Fuzzy Logic Approach

Although the idea of evaluating students’ mathematical knowledge with fuzzy logic is not new in the literature, few studies have explored the possibility of assessing students’ mathematical knowledge by combining teacher-assigned grades (i.e., school grades) with students’ achievements on standardized tests (e.g., national assessments). Thus, the present study aims to investigate the use of fuzzy logic to generate a novel assessment model, which combines teacher-assigned mathematics grades with students’ results on the Italian National Assessment of Mathematical Knowledge (INVALSI). We expanded the findings from previous works by considering a larger sample, which included more than 90,000 students attending grades 8, 10, and 13. The results showed that the tested model led to a lower assessment score compared to the traditional grading method based on teacher’s evaluation. Additionally, the use of fuzzy logic across the examined school levels yielded similar results, suggesting that the model is adequate among different educational levels.

Developing Machine Learning and Statistical Tools to Evaluate the Accessibility of Public Health Advice on Infectious Diseases among Vulnerable People

Background

From Ebola, Zika, to the latest COVID-19 pandemic, outbreaks of highly infectious diseases continue to reveal severe consequences of social and health inequalities. People from low socioeconomic and educational backgrounds as well as low health literacy tend to be affected by the uncertainty, complexity, volatility, and progressiveness of public health crises and emergencies. A key lesson that governments have taken from the ongoing coronavirus pandemic is the importance of developing and disseminating highly accessible, actionable, inclusive, coherent public health advice, which represent a critical tool to help people with diverse cultural, educational backgrounds and varying abilities to effectively implement health policies at the grassroots level.

Objective

We aimed to translate the best practices of accessible, inclusive public health advice (purposefully designed for people with low socioeconomic and educational background, health literacy levels, limited English proficiency, and cognitive/functional impairments) on COVID-19 from health authorities in English-speaking multicultural countries (USA, Australia, and UK) to adaptive tools for the evaluation of the accessibility of public health advice in other languages.

Methods

We developed an optimised Bayesian classifier to produce probabilistic prediction of the accessibility of official health advice among vulnerable people including migrants and foreigners living in China. We developed an adaptive statistical formula for the rapid evaluation of the accessibility of health advice among vulnerable people in China.

Results

Our study provides needed research tools to fill in a persistent gap in Chinese public health research on accessible, inclusive communication of infectious diseases' prevention and management. For the probabilistic prediction, using the optimised Bayesian machine learning classifier (GNB), the largest positive likelihood ratio (LR+) 16.685 (95% confidence interval: 4.35, 64.04) was identified when the probability threshold was set at 0.2 (sensitivity: 0.98; specificity: 0.94).

Conclusion

Effective communication of health risks through accessible, inclusive, actionable public advice represents a powerful tool to reduce health inequalities amidst health crises and emergencies. Our study translated the best-practice public health advice developed during the pandemic into intuitive machine learning classifiers for health authorities to develop evidence-based guidelines of accessible health advice. In addition, we developed adaptive statistical tools for frontline health professionals to assess accessibility of public health advice for people from non-English speaking backgrounds.