The exponentially weighted moving average (EWMA) procedure is a promising statistical process control technique for monitoring experience sampling method (ESM) data to detect imminent depressive episodes in real time, allowing for timely interventions. However, asking participants to complete multiple ESM questionnaires per day – relevant studies used a daily sample size of five or ten questionnaires – over extended periods is burdensome. We propose a variable sample size (VSS) EWMA approach in which the daily sample size depends on the extremity of participants’ ESM scores on the previous days. VSS EWMA allows us to keep the daily sample size and thus participant burden low when the individual is doing well. We evaluated VSS EWMA performance through a simulation study and an application to empirical ESM data. Our findings indicate that VSS EWMA improves the balance between detection speed and participant burden. Based on these results, we provide recommendations on how to tune VSS EWMA to different priorities and outline directions for future research.
Large language models (LLMs) are increasingly used to support qualitative research, yet clear methodological frameworks for integrating them into thematic analysis remain limited. A central challenge is how to scale qualitative coding with LLM assistance while preserving methodological rigor and reproducibility. This tutorial presents a structured, human-in-the-loop workflow for conducting large-scale thematic analysis with LLM support. Grounded in inductive and hybrid approaches to thematic analysis (Braun Clarke, 2006), the workflow proceeds in three phases. First, researchers conduct manual thematic analysis on a subset of data to establish an initial codebook and assess relevance. Second, the same subset is analyzed using an LLM to iteratively refine prompts and evaluate alignment between human and model outputs. Third, the validated approach is applied to the full corpus using a modular four-step pipeline: key-idea extraction, chunk-level thematic coding, theme consolidation, and clustering, with iterative human verification through random sampling of outputs. We demonstrate the workflow using 2,952 publicly available Reddit posts. Structured prompt design and JSON-formatted outputs enable traceability between themes and source data and support reproducible analysis. Prompt engineering is treated as an iterative analytic process rather than a one-time instruction. Under controlled conditions, human and LLM coding showed high agreement (Cohen’s κ = 0.91). All prompts, code, and reproducibility materials are openly available via an Open Science Framework repository. By providing detailed implementation guidance, embedded validation procedures, and safeguards against common failure modes such as hallucination and overgeneralization, this tutorial offers a practical and extensible framework for integrating LLMs into qualitative research across domains.
Bayesian multilevel models are increasingly used in psychological, educational, and behavioral research, yet applied guidance on specifying priors for scalar variance components remains limited. This tutorial provides a practical workflow for identifying target variance components, selecting benchmark values, calibrating priors on variance and standard-deviation scales, translating them into software-compatible prior specifications, evaluating their implications through prior predictive checks, posterior comparison, sensitivity analysis, and transparent reporting. We review several commonly used prior families, including gamma, inverse-gamma, half-t, and half-Cauchy distributions, and clarify how their parameterizations relate across variance, precision (the reciprocal of variance), and standard-deviation formulations. The tutorial focuses on helping researchers align prior choice with substantive knowledge, model parameterization, software constraints, and sensitivity goals. As a worked example, we use a two-level analysis of TIMSS 2019 data to show how benchmark information from a non-overlapping reference sample can be calibrated into alternative variance-prior specifications and then evaluated in a smaller worked example sample. The empirical illustration is used as a pedagogical example to demonstrate the workflow and its associated checking procedures. We emphasize practical safeguards, including prior-density inspection, prior predictive checking, posterior comparison, convergence assessment, sensitivity analysis, and transparent reporting of prior assumptions. The tutorial concludes by summarizing practical, literature-grounded considerations for documenting prior rationale, implementation decisions, and robustness checks in Bayesian multilevel applications.
Longitudinal studies are essential for understanding how behaviors, skills, and psychological states evolve over time. While online platforms have made study development easier, researchers still face major logistical challenges when managing multi-session studies, especially when coordinating large numbers of participants, session timing, reminders, and compensation. In this paper, we introduce GABLE (Google Apps script-Based Longitudinal Experimentation), a lightweight infrastructure built using Google Apps Script. GABLE automates participant scheduling, e-mail communication, session tracking, and compensation. We demonstrate its effectiveness through two real-world psychological studies involving more than 850 participants with 20 sessions, and discuss its benefits, limitations, and potential extensions. GABLE addresses many challenges of running large online longitudinal studies while limiting the need for custom infrastructure or manual intervention.
Most electroencephalography (EEG) research is conducted in White-majority Western countries by White researchers. EEG practices therefore often explicitly and implicitly exclude minoritised groups, leading to low diversity in EEG data samples that deepens biases in interpretation and clinical utility. Because EEG electrodes must contact the scalp, Black people and Muslim women who wear a hijab (or, Hijabis) are two systematically excluded groups. As a group of EEG researchers, Black people, and Hijabis, here we describe co-produced practical approaches to overcoming EEG participation barriers, from recruitment to data collection, and provide co-produced text, photographs, and videos designed to break down these barriers. These approaches provide further tools for the EEG community to tackle systemic exclusion and foster better, more representative neuroscience.
