In psychological science, the ongoing replicability crisis has sparked critical reflection on the field’s methodological practices and the integrity of its research outputs. While recent discourse often gravitates toward data transparency and analytical sophistication, emerging evidence underscores that the replicability of scientific findings is deeply rooted in the quality of research planning and design. Major initiatives addressing open science and rigorous data analysis have illuminated substantial advances; yet, several high-profile replication failures reveal persistent gaps when foundational decisions about research design are insufficiently robust. In landmark projects and meta-analyses, misalignments between hypotheses, procedures, and analyses have consistently undermined the credibility of findings, despite transparency in reporting or access to advanced analytical tools. These developments signal that, although improved analytic methods are essential, the logic, planning, and foresight embedded in initial design decisions ultimately dictate the integrity and validity of psychological research.
This Research Topic aims to place the spotlight back on design and planning as the bedrock of scientific reliability in psychology. We seek to clarify and elevate the role of the researcher as an agent of control, anticipation, and ethical responsibility, fundamentally accountable for navigating uncertainty and mitigating bias before data collection even begins. By examining how deliberate and methodologically sound planning can guard against threats to validity—such as attrition, confounding, or inadequate operationalization—this collection intends to inspire new standards for linking hypotheses, design, and analysis within a single transparent chain of decision-making. Specific objectives include identifying best practices in research design, proposing novel methodological frameworks for anticipating bias, and evaluating the ethical imperatives associated with planning and reporting in psychological science.
Recognizing that methodological choices made at the outset shape the trajectory and replicability of research, this Research Topic welcomes contributions that critically examine the principles and processes of research planning in psychology. Submissions may focus on methodological, theoretical, or applied perspectives, but should remain anchored in the centrality of design and researcher responsibility. To gather further insights in the boundaries of research planning, with respect for both conceptual innovation and practical application, we welcome articles addressing, but not limited to, the following themes:
- Methodological advances in experimental, non-experimental, and longitudinal research planning - Simulation studies examining how design decisions affect the validity and reliability of findings - Integration of bias control, treatment adherence, effect estimation, and handling of missing data into unified planning frameworks - Ethical considerations and researcher accountability in the planning and reporting of research procedures - The impact of open data practices and evolving analytic technologies on the primacy of research design
We invite a wide range of article types, including empirical studies, simulation reports, methodological tutorials, theoretical contributions, and ethical reflections.
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Case Report
Conceptual Analysis
Data Report
Editorial
FAIR² Data
FAIR² DATA Direct Submission
General Commentary
Hypothesis and Theory
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Brief Research Report
Case Report
Conceptual Analysis
Data Report
Editorial
FAIR² Data
FAIR² DATA Direct Submission
General Commentary
Hypothesis and Theory
Methods
Mini Review
Opinion
Original Research
Perspective
Registered Report
Review
Systematic Review
Technology and Code
Keywords: Replicability Crisis, Research Integrity, Sensitivity Analysis, Propensity Score, Sample Size, Monte Carlo Simulation Studies, Experimental Control, Meta-analysis, Machine Learning and Deep Learning techniques, Planning, Online Data, Validation
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.