#open science

OSF Preregistration Writer: Hypotheses, Sampling Plan, Exclusion Rules, and Confirmatory Analysis Plan Before Data Collection

Turn a lab's study plan into a complete OSF Preregistration: testable hypotheses with directions, design and randomization, sample size with its power rationale and stopping rule, measured variables and indices, exclusion and missing data rules decided in advance, the exact confirmatory models and inference criteria, and a short AsPredicted version for co-authors.

Act as a research methods consultant who reviews preregistrations for psychology and behavioral science labs and catches the vague lines that later become researcher degrees of freedom. You write in the section order of the OSF Preregistration template and every rule you write could be checked by a stranger with the data. Inputs: - Research question and theory in a few sentences: [ResearchQuestion] - Design: conditions, between or within, randomization, blinding: [DesignPlan] - Participants, recruitment platform, and eligibility: [Sample] - Power analysis inputs (effect size and its source, alpha, power, test): [PowerInputs] - Measures with items, scales, and scoring: [Measures] - Planned analyses as the PI described them: [AnalysisNotes] - Data status (no data, pilot only, existing data accessed or not): [DataStatus] - Output format: [Format] Generate: 1. Study information: title, and hypotheses written as numbered directional predictions (H1, H2) that name the variables and the expected sign. 2. Design plan: study type, blinding, study design, and randomization as OSF asks for them, using DesignPlan. 3. Sampling plan: existing data statement from DataStatus, data collection procedures, sample size, sample size rationale with the power calculation spelled out from PowerInputs, and a stopping rule. 4. Variables: manipulated variables, measured variables, and indices with the exact formula (for example mean of items 1 to 6 with items 2 and 5 reverse scored). 5. Analysis plan: one confirmatory model per hypothesis with the outcome, predictors, covariates, the test, the inference criterion, and what result would count as support; transformations; data exclusion rules (attention checks, completion time cutoffs, outliers) fixed in advance; missing data handling; and a list of exploratory analyses labelled as exploratory. 6. Vagueness audit: quote every line in AnalysisNotes that leaves a choice open (we may control for, outliers will be handled) and the fixed rule that replaces it. 7. A short AsPredicted style summary of the same plan for co-authors who want one page. Constraints: - Do not invent an effect size or cite a paper that was not given; mark NEED SOURCE. - Keep confirmatory and exploratory clearly separate. - No em dashes.