STEP 2: Study design

In an empirical research project, the study design encompasses conceptualizing and planning the methodology for data collection and analysis. Additionally, documenting the decision-making process throughout the research project is crucial for enhancing reproducibility, enabling other researchers to understand and replicate the study with greater ease. It is essential to maintain flexibility in this pipeline, allowing for adjustments as the project progresses. In this step and for most empirical research projects, approval by the local ethics committee or institutional review board should be applied for. Another important aspect of the study design step is determining the appropriate sample size, target population (e.g., neurotypical individuals or patients), as well as the sampling strategy (e.g., stratified or convenience sampling; Stratton, 2021). To ensure that the study has sufficient statistical power to detect meaningful differences or associations, justification of one’s sample size is helpful at this stage, e.g., via a power analysis (➜ GPower, ➜ Justification Shinyapp or ➜ g_ci_spm; Cohen, 1962; Jones et al., 2003; Kemal, 2020; see Table 1). Considering ethics in experimental design involves taking steps to protect the rights and welfare of participants, weighing costs and benefits while minimizing risks, and ensuring the privacy of participants and the confidentiality of their data. It also involves considering the impact of the findings on society and potential biases that may exist in the study. In essence, the study design step lays out the foundation for the entire project and provides a roadmap for all subsequent steps. Most importantly, it considers data collection, analysis, and interpretation of results (Steps 5-8). It is essential for the study design to be well-conceived, well-executed, and well-documented to ensure the quality, integrity, and generalizability of the findings of the research. Drawing on the experience from supervisor(s), mentor(s), and/or collaborator(s) is key in this step, as they might have specific expertise or experience with certain aspects of the planned project. In this step, the importance of ‘Big Team Science’ and sharing of knowledge and expertise becomes especially clear (Hall et al., 2018). ARIADNE can help kick-start this process by providing grounds for tool selection. In this step, criteria, tasks, and rules for (co-)authorship should be discussed already at an early stage of the project, and re-discussed over its course if changes arise (➜ CRediT statement; Brand et al., 2015; Tay, 2021; see Table 1 and Step 8). Finally, the decision for a suitable task programming environment should take into account whether the study will be lab-based or implemented online and whether the program is freely available (➜ Psychopy vs. ➜ Psychtoolbox in Matlab).