Preregistration and Registered Reports for Research Training Course

Course Introduction

Preregistration and registered reports are increasingly important approaches for strengthening research transparency, reducing questionable research practices, and improving the credibility of scientific evidence. This Preregistration and Registered Reports for Research Training Course provides participants with practical knowledge and structured methods for planning, documenting, registering, reviewing, and communicating research before results are known.

The course examines how preregistration can create a clear record of research questions, hypotheses, methods, variables, sampling strategies, and planned analyses before researchers inspect or interpret study results. Participants learn how advance documentation can distinguish confirmatory research from exploratory work while creating greater transparency around methodological decisions and subsequent deviations.

Participants also explore registered reports as a research publication model in which study proposals and methodological plans can undergo peer review before data collection or outcome analysis. The course explains the stages of registered reports, including proposal development, methodological review, in-principle acceptance, study implementation, transparent reporting, and final publication.

The programme addresses practical challenges associated with preregistration and registered reports, including changing research conditions, unexpected methodological constraints, secondary analyses, exploratory discoveries, amendments, sensitive research data, qualitative and mixed-methods studies, and research designs that evolve during implementation. Participants learn how to document justified changes without undermining transparency.

Special attention is given to integrating preregistration and registered reports with open science, research data management, reproducible workflows, statistical analysis plans, research protocols, version control, evidence provenance, and transparent reporting. Participants examine how these practices can support clearer distinctions between planned analyses and post hoc discoveries.

Emerging issues such as computational preregistration, adaptive research designs, machine learning studies, AI-assisted research, replication studies, multisite research, living protocols, and increasingly flexible research methodologies are incorporated into the course. By the end of the programme, participants will be able to develop practical preregistration and registered report workflows suited to their research environments and methodological requirements.

Duration

5 days

Who Should Attend

• Academic researchers developing quantitative, qualitative, mixed-methods, experimental, observational, or computational studies

• Principal investigators and research project leaders responsible for transparent study planning and methodological governance

• Doctoral candidates and postgraduate researchers preparing research proposals, protocols, and analysis plans

• Research methodologists supporting rigorous study design and transparent research practices

• Data analysts and statisticians developing prespecified analytical strategies for research projects

• Research data managers supporting documentation, data governance, metadata, and reproducible research workflows

• Research software engineers working with computational studies that require transparent and reproducible analytical procedures

• Research integrity and compliance professionals supporting responsible research planning and documentation

• Journal editors and peer reviewers interested in registered reports and transparent research publication models

• Research administrators developing institutional open science, preregistration, and research transparency policies

• Evidence synthesis professionals working with systematic reviews, protocols, and transparent evidence-generation processes

• Researchers conducting replication, validation, experimental, clinical, social science, behavioral, or interdisciplinary studies

• Librarians and scholarly communication professionals supporting open research infrastructure and research registration services

• Monitoring, evaluation, and evidence professionals seeking stronger approaches to prospective study planning and transparent reporting

Course Objectives

By the end of this course, participants will be able to:

• Explain the principles, purposes, benefits, limitations, and appropriate applications of preregistration and registered reports across different research methodologies and disciplines.

• Design comprehensive preregistration documents that clearly specify research questions, hypotheses, study designs, variables, sampling procedures, outcomes, and planned analytical approaches.

• Develop detailed statistical and analytical plans that distinguish confirmatory analyses from exploratory analyses while documenting assumptions, decision rules, exclusions, and model specifications.

• Apply registered report workflows from initial study proposal and methodological review through in-principle acceptance, research implementation, results reporting, and final publication.

• Identify common threats to transparent research planning, including selective reporting, outcome switching, undisclosed analytical flexibility, post hoc hypotheses, and undocumented methodological changes.

• Manage legitimate changes to preregistered research plans by documenting amendments, deviations, unexpected circumstances, methodological adaptations, and their implications for interpretation.

• Integrate preregistration with research protocols, data management plans, version control, reproducible analytical workflows, open science practices, and evidence provenance frameworks.

• Adapt preregistration and registered report approaches to qualitative, mixed-methods, computational, adaptive, exploratory, machine learning, and other research designs where methodologies may evolve.

• Evaluate preregistration and registered report documents using practical criteria for methodological completeness, clarity, feasibility, transparency, reproducibility, and alignment between research questions and planned analyses.

• Develop institutional and project-level implementation strategies that make preregistration and registered reports practical, sustainable, and appropriately integrated into research governance and publication processes.

Comprehensive Course Outline

Module 1: Foundations of Preregistration and Registered Reports

• Defining preregistration, registered reports, research protocols, analysis plans, confirmatory research, and exploratory research within transparent research practice

• Understanding how prospective research documentation can reduce ambiguity around hypotheses, methods, outcomes, analytical decisions, and planned research procedures

• Examining the relationship between preregistration, research transparency, reproducibility, replication, research integrity, and credible evidence generation

• Comparing conventional publication workflows with preregistration and registered report models and identifying how each approach structures research planning and reporting

Module 2: Designing High-Quality Preregistration Plans

• Structuring preregistration documents to clearly capture research questions, hypotheses, theoretical assumptions, study designs, variables, outcomes, and methodological procedures

• Defining study populations, sampling strategies, sample sizes, recruitment procedures, eligibility criteria, exclusion rules, and stopping conditions before research implementation

• Documenting primary and secondary outcomes, operational definitions, measurement instruments, timing of assessments, and decision rules with sufficient methodological precision

• Identifying ambiguous statements and underspecified research plans that could create uncertainty when researchers later implement or analyze the study

Module 3: Statistical and Analytical Preregistration

• Developing detailed statistical analysis plans covering models, variables, transformations, missing data procedures, covariates, interactions, subgroup analyses, and statistical decision criteria

• Prespecifying data cleaning, quality control, outlier handling, exclusion procedures, assumption checks, and analytical contingencies before examining study outcomes

• Distinguishing confirmatory hypothesis tests from exploratory analyses and documenting how unexpected findings will be investigated without misrepresenting them as prespecified

• Managing analytical flexibility through transparent decision rules, version-controlled analysis plans, documented amendments, and reproducible computational workflows

Module 4: Registered Reports and Peer Review

• Understanding the structure of registered reports from initial proposal development and methodological review through in-principle acceptance and subsequent research completion

• Preparing registered report submissions that communicate research rationale, hypotheses, methods, analysis plans, feasibility considerations, and anticipated methodological challenges

• Examining how peer review before results are known can focus attention on research questions, methodological rigor, analytical validity, and study design

• Managing reviewer feedback, methodological revisions, protocol amendments, and editorial requirements while preserving a transparent record of research decisions

Module 5: Implementing Preregistered Research

• Translating preregistered protocols and analysis plans into practical research workflows while maintaining alignment between planned procedures and actual study implementation

• Documenting deviations caused by recruitment challenges, unavailable data, technical limitations, unexpected events, methodological constraints, or changes in research circumstances

• Managing amendments and protocol changes using versioned documentation that distinguishes original plans, approved modifications, implementation decisions, and post hoc adaptations

• Establishing governance procedures for determining when changes require formal amendment documentation, additional review, or explicit disclosure in research outputs

Module 6: Preregistration Across Different Research Methodologies

• Adapting preregistration principles for qualitative, mixed-methods, ethnographic, observational, longitudinal, experimental, computational, and interdisciplinary research designs

• Addressing methodological flexibility in qualitative and adaptive research while maintaining transparent records of research questions, sampling decisions, analytical strategies, and interpretive changes

• Developing appropriate preregistration strategies for systematic reviews, evidence syntheses, secondary data studies, replications, and large-scale collaborative research

• Evaluating when strict prespecification may be inappropriate and designing transparent alternatives that document evolving methodologies without restricting legitimate discovery

Module 7: Reproducibility, Data Management, and Open Research Integration

• Connecting preregistered research plans with data management plans, metadata standards, research repositories, version control, reproducible code, and computational research environments

• Establishing links between registered protocols, datasets, analytical scripts, research outputs, persistent identifiers, and supporting documentation for stronger evidence traceability

• Applying privacy, confidentiality, intellectual property, ethical, and security controls when preregistered research involves sensitive or restricted research information

• Developing reproducible workflows that allow independent researchers to understand how preregistered plans were translated into data processing, analysis, visualization, and reporting activities

Module 8: Transparency, Deviations, and Exploratory Research

• Managing deviations from preregistered plans transparently by distinguishing methodological amendments, unforeseen circumstances, exploratory analyses, and genuine implementation changes

• Reporting differences between planned and completed analyses without obscuring important methodological changes or creating misleading impressions about research intentions

• Preserving the value of exploratory research by clearly communicating post hoc discoveries, new hypotheses, additional analyses, and unexpected patterns separately from confirmatory findings

• Developing transparent reporting structures that allow readers to understand what was planned before data analysis and what emerged during subsequent research investigation

Module 9: Emerging Issues in Preregistration and Registered Reports

• Examining preregistration challenges associated with artificial intelligence, machine learning, automated analysis, generative AI, complex computational models, and changing software environments

• Exploring adaptive trials, sequential research designs, living protocols, real-time data collection, multisite studies, and other research environments where procedures may legitimately evolve

• Developing approaches for preregistering computational workflows, model architectures, training procedures, evaluation criteria, datasets, software dependencies, and machine learning decision rules

• Assessing emerging publication practices, open peer review models, research infrastructure, automated documentation, and digital registration systems supporting more transparent research planning

Module 10: Institutionalizing Preregistration and Registered Reports

• Developing institutional policies and practical guidance for incorporating preregistration and registered reports into research governance, ethics processes, funding requirements, and project management

• Establishing researcher support systems including templates, training programmes, methodological consultation, repository guidance, statistical planning assistance, and registered report resources

• Creating organizational workflows that connect preregistration with research data management, reproducibility checks, open science practices, publication planning, and research quality assurance

• Developing implementation roadmaps that encourage sustainable adoption while accounting for disciplinary differences, researcher incentives, institutional capabilities, and methodological diversity

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.