11.2 Preregistration of studies
10 min read•august 21, 2024
is a crucial practice in scientific research, enhancing and reducing bias. By outlining research plans before data collection or analysis, it promotes rigorous methodology and improves the credibility of findings.
This approach addresses issues like p-hacking and publication bias, while fostering open science principles. Preregistration platforms, timing considerations, and field-specific adaptations all play key roles in its effective implementation.
Definition of preregistration
- Preregistration forms a crucial component of reproducible and collaborative statistical data science by outlining research plans before data collection or analysis
- This practice enhances transparency in scientific research and reduces potential bias in data interpretation
- Preregistration aligns with open science principles, promoting rigorous methodology and enhancing the credibility of research findings
Purpose and benefits
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- Reduces researcher degrees of freedom limiting potential for p-hacking or HARKing (Hypothesizing After Results are Known)
- Enhances credibility of research findings by clearly distinguishing between confirmatory and exploratory analyses
- Improves study design by forcing researchers to think critically about methodology before data collection
- Facilitates easier detection of questionable research practices, promoting scientific integrity
Historical context
- Emerged in response to the replication crisis in various scientific fields (psychology, medicine)
- Gained prominence in the early 2010s as part of the broader open science movement
- Inspired by clinical trial registration practices established in the medical field
- Adoption accelerated with the development of dedicated preregistration platforms ()
Components of preregistration
- Preregistration in reproducible and collaborative statistical data science involves documenting key aspects of a study before its execution
- This practice ensures transparency and reduces potential biases in data analysis and interpretation
- Comprehensive preregistration includes several essential components that outline the entire research process
Research questions and hypotheses
- Clear statement of primary research questions guiding the study
- Specific, testable hypotheses derived from research questions
- Rationale for each hypothesis based on existing literature or theoretical frameworks
- Distinction between confirmatory and exploratory hypotheses
- Operationalization of key variables involved in the hypotheses
Study design
- Detailed description of the overall research design (experimental, observational, longitudinal)
- Specification of independent and dependent variables
- Explanation of control variables and their justification
- Randomization procedures for experimental studies
- Blinding methods to reduce bias (single-blind, double-blind)
Data collection methods
- Comprehensive outline of data collection procedures
- Description of measurement instruments or tools (surveys, equipment)
- Specification of data sources for secondary data analyses
- Sampling strategy and participant recruitment methods
- Timeline for data collection phases
Sample size and power
- Justification for the chosen sample size
- Power analysis calculations to determine adequate sample size
- Effect size estimates used in power calculations
- Consideration of potential attrition or missing data
- Stopping rules for data collection if applicable
Analysis plan
- Detailed description of statistical methods to be used
- Specification of primary outcome measures and their operationalization
- Outline of planned statistical tests for each hypothesis
- Procedures for handling missing data or outliers
- Criteria for excluding data points or participants from analysis
Preregistration platforms
- Preregistration platforms in reproducible and collaborative statistical data science provide structured environments for documenting research plans
- These platforms enhance transparency and accessibility of preregistered studies
- Different platforms cater to various research domains and offer specific features to support the preregistration process
Open Science Framework
- Versatile platform supporting preregistration across multiple scientific disciplines
- Offers customizable preregistration templates for different study types
- Provides version control and collaboration features for research teams
- Allows embargo periods for private preregistrations before public release
- Integrates with other open science tools (data repositories, project management)
AsPredicted
- Streamlined with a focus on simplicity and ease of use
- Offers a standardized nine-question format for preregistration
- Generates a unique, time-stamped PDF of the preregistration
- Allows anonymous preregistrations to reduce potential reviewer bias
- Supports both public and private preregistrations with optional embargoes
Clinicaltrials.gov
- Specialized platform for registering clinical trials and interventional studies
- Mandated by law for certain types of clinical research in the United States
- Provides a comprehensive structure for detailing study protocols and procedures
- Includes fields for participant eligibility criteria and outcome measures
- Offers a unique identifier (NCT number) for each registered trial
Timing of preregistration
- Timing of preregistration plays a crucial role in reproducible and collaborative statistical data science
- Proper timing ensures the integrity of the research process and maximizes the benefits of preregistration
- Different stages of research may require different approaches to preregistration
Before data collection
- Ideal timing for preregistration in most cases
- Ensures complete separation between study design and data-driven decisions
- Allows for peer review and feedback on methodology before resources are invested
- Prevents unintentional bias in study design based on preliminary results
- Enhances credibility by demonstrating commitment to predetermined analysis plans
After pilot studies
- Appropriate when initial data informs the main study design
- Requires clear distinction between pilot data and main study data
- Allows refinement of hypotheses and methods based on preliminary findings
- Necessitates transparency about the existence and influence of pilot data
- May include separate preregistrations for pilot and main studies
Types of preregistration
- Different types of preregistration exist to accommodate various research approaches in reproducible and collaborative statistical data science
- These types reflect the diverse nature of scientific inquiry and the need for flexibility in research practices
- Understanding different preregistration types helps researchers choose the most appropriate format for their studies
Confirmatory vs exploratory research
- Confirmatory research tests pre-specified hypotheses with predefined analysis plans
- Requires detailed preregistration of hypotheses and analytical approaches
- Enhances the credibility of findings by reducing researcher degrees of freedom
- Exploratory research investigates patterns or relationships without firm prior hypotheses
- May use more flexible preregistration formats
- Emphasizes transparency about the exploratory nature of analyses
- Hybrid approaches combine confirmatory and exploratory elements
- Clearly distinguish between pre-planned and post-hoc analyses
- May involve separate sections in preregistration for confirmatory and exploratory aspects
Registered reports
- Two-stage peer review process integrating preregistration with publication
- Initial stage involves review of introduction, methods, and analysis plan before data collection
- Accepted stage 1 submissions receive in-principle acceptance for publication
- Second stage reviews the full paper, focusing on adherence to preregistered plans
- Reduces publication bias by committing to publish regardless of results
- Encourages thorough planning and methodological rigor
Challenges in preregistration
- Preregistration in reproducible and collaborative statistical data science presents several challenges that researchers must navigate
- Addressing these challenges is crucial for maximizing the benefits of preregistration while maintaining research flexibility
- Understanding common difficulties helps researchers prepare for potential issues in the preregistration process
Flexibility in analysis
- Balancing predetermined analysis plans with the need for adaptive approaches
- Handling unexpected data characteristics that may require alternative analyses
- Developing strategies for transparently reporting deviations from preregistered plans
- Incorporating planned exploratory analyses without compromising confirmatory results
- Managing the tension between rigidity and necessary analytical flexibility
Unexpected issues during research
- Dealing with unforeseen complications in data collection or participant recruitment
- Adapting to changes in available resources or research team composition
- Handling technical issues or equipment failures that affect data quality
- Responding to new relevant literature published during the study period
- Navigating ethical concerns that emerge after preregistration
Balancing detail vs brevity
- Determining the appropriate level of specificity in preregistration documents
- Providing sufficient detail for reproducibility without overwhelming readers
- Ensuring clarity of preregistered plans while avoiding excessive length
- Striking a balance between comprehensive coverage and focused relevance
- Developing strategies for efficiently communicating complex methodological details
Preregistration in different fields
- Preregistration practices vary across different fields of study in reproducible and collaborative statistical data science
- Adapting preregistration to field-specific norms and requirements enhances its effectiveness
- Understanding disciplinary differences in preregistration helps researchers tailor their approach
Psychology
- Widespread adoption of preregistration in response to the replication crisis
- Focus on experimental designs and human subject research
- Emphasis on specifying hypotheses and statistical analyses in detail
- Use of platforms like OSF and AsPredicted for preregistration
- Growing trend towards in psychological journals
Medicine
- Long-standing tradition of clinical trial registration (Clinicaltrials.gov)
- Emphasis on patient safety and ethical considerations in preregistration
- Detailed specification of primary and secondary outcome measures
- Inclusion of data safety monitoring plans in preregistrations
- Integration with regulatory requirements and good clinical practice guidelines
Social sciences
- Increasing adoption of preregistration across various social science disciplines
- Adaptation of preregistration practices for observational and field studies
- Focus on transparency in data collection methods and variable operationalization
- Growing use of preregistration in qualitative and mixed-methods research
- Development of field-specific preregistration templates (political science, economics)
Critiques of preregistration
- Preregistration in reproducible and collaborative statistical data science has faced various criticisms and challenges
- Understanding these critiques helps researchers address potential limitations and improve preregistration practices
- Ongoing debates about preregistration contribute to the evolution of open science practices
Limitations and drawbacks
- Potential stifling of scientific creativity and serendipitous discoveries
- Risk of oversimplifying complex research processes
- Challenges in preregistering studies with evolving methodologies
- Increased administrative burden on researchers and institutions
- Difficulty in preregistering interdisciplinary or highly innovative research
Responses to criticisms
- Development of flexible preregistration formats to accommodate diverse research types
- Emphasis on distinguishing between confirmatory and exploratory analyses
- Promotion of preregistration as a tool for transparency rather than restriction
- Creation of guidelines for handling deviations from preregistered plans
- Ongoing refinement of preregistration practices based on researcher feedback
Impact on research quality
- Preregistration significantly influences the quality of research in reproducible and collaborative statistical data science
- This practice addresses several key issues that have historically compromised scientific integrity
- Understanding the impact of preregistration helps researchers appreciate its value in improving scientific rigor
Reduction of p-hacking
- Limits opportunities for selective reporting of significant results
- Decreases the likelihood of data dredging or fishing expeditions
- Encourages researchers to focus on meaningful effect sizes rather than p-values
- Promotes more honest reporting of null or unexpected findings
- Enhances the credibility of reported statistical analyses
Increased transparency
- Provides a clear record of initial research plans and hypotheses
- Allows readers to distinguish between planned and post-hoc analyses
- Facilitates easier detection of questionable research practices
- Encourages open sharing of materials, data, and analysis scripts
- Enhances the reproducibility of research findings by other scientists
Replication crisis mitigation
- Addresses key factors contributing to low replication rates in various fields
- Reduces the prevalence of false-positive findings in published literature
- Encourages more rigorous study designs and power analyses
- Promotes a culture of openness and critical evaluation in scientific communities
- Facilitates meta-analyses by providing access to unpublished or null results
Best practices for preregistration
- Implementing best practices for preregistration enhances its effectiveness in reproducible and collaborative statistical data science
- These practices help researchers maximize the benefits of preregistration while addressing potential challenges
- Adhering to best practices promotes transparency, rigor, and credibility in scientific research
Writing clear hypotheses
- Formulate specific, testable hypotheses based on existing literature
- Clearly distinguish between primary and secondary hypotheses
- Operationalize key variables and concepts within hypotheses
- Avoid vague or ambiguous language in hypothesis statements
- Specify the direction of expected effects when appropriate
Specifying analysis details
- Outline the complete data processing and analysis pipeline
- Define primary outcome measures and their calculation methods
- Specify statistical tests or models for each hypothesis
- Describe plans for handling missing data or outliers
- Include power analyses and sample size justifications
Handling deviations from plan
- Develop a strategy for transparently reporting any deviations
- Distinguish between minor adjustments and substantial changes
- Explain the rationale for any necessary deviations
- Document the timing and nature of changes to the original plan
- Consider preregistering amendments for significant modifications
Preregistration vs publication bias
- Preregistration addresses publication bias, a significant issue in reproducible and collaborative statistical data science
- This practice helps create a more complete and accurate representation of scientific findings
- Understanding the relationship between preregistration and publication bias is crucial for improving
File drawer problem
- Preregistration creates a public record of all initiated studies
- Reduces the likelihood of unpublished null or negative results
- Allows for tracking of studies that do not reach publication stage
- Facilitates meta-analyses by providing access to unpublished findings
- Encourages completion and reporting of preregistered studies
Negative results reporting
- Increases the likelihood of publishing studies with null or unexpected findings
- Reduces the pressure to find statistically significant results
- Encourages journals to accept well-designed studies regardless of outcome
- Promotes a more balanced representation of evidence in scientific literature
- Facilitates the identification of ineffective interventions or unsupported theories
Future of preregistration
- The future of preregistration in reproducible and collaborative statistical data science holds promising developments
- Emerging trends and integration with other open science practices are shaping the evolution of preregistration
- Understanding these future directions helps researchers prepare for upcoming changes in scientific practices
Emerging trends
- Increased adoption of preregistration across diverse scientific disciplines
- Development of machine-readable preregistration formats for automated checking
- Integration of preregistration with data management plans and open data practices
- Growing emphasis on preregistration education in research methods courses
- Exploration of blockchain technology for immutable preregistration records
Integration with open science
- Closer alignment of preregistration with other open science initiatives
- Development of comprehensive open science workflows incorporating preregistration
- Integration of preregistration platforms with data repositories and analysis tools
- Expansion of registered reports to cover a broader range of research outputs
- Creation of incentive structures that reward adherence to preregistered plans
Key Terms to Review (18)
American Psychological Association: The American Psychological Association (APA) is a professional organization representing psychologists in the United States, focusing on advancing psychological research, education, and practice. The APA is known for its publication guidelines, particularly the APA Style, which provides a standardized format for writing and citing research. This standardization is essential for ensuring clarity and consistency in scholarly communication, impacting how studies are preregistered and how research ethics are upheld in open science practices.
Center for Open Science: The Center for Open Science (COS) is a nonprofit organization dedicated to promoting openness, integrity, and reproducibility in research. COS develops tools and frameworks that help researchers share their findings, preregister studies, and improve collaboration across disciplines. By advocating for transparency in research practices, COS aims to enhance the credibility and impact of scientific work.
Collaborative Analysis: Collaborative analysis refers to the process of multiple researchers or analysts working together to interpret data, generate insights, and draw conclusions. This method leverages diverse perspectives and expertise, often leading to more robust findings than individual analysis. In the context of preregistration, collaborative analysis ensures that research questions and methodologies are well-defined before data collection, promoting transparency and reproducibility in scientific research.
COS Guidelines: COS Guidelines refer to the principles set by the Center for Open Science aimed at enhancing the transparency and reproducibility of research through preregistration. These guidelines encourage researchers to explicitly state their hypotheses, methods, and analysis plans before conducting their studies, promoting accountability and reducing biases that may arise from post hoc adjustments. By adhering to these guidelines, researchers can improve the credibility of their findings and facilitate replication efforts in the scientific community.
Data Sharing: Data sharing is the practice of making data available to others for use in research, analysis, or decision-making. This process promotes collaboration, enhances the reproducibility of research findings, and fosters greater transparency in scientific investigations.
Experimental Design: Experimental design is the process of planning an experiment to ensure that the results obtained are valid, reliable, and can effectively answer the research question. It involves selecting the appropriate methods, controls, and procedures for conducting the experiment while minimizing bias and maximizing the accuracy of the data collected. Good experimental design is crucial for reproducibility, allowing others to replicate the study and validate findings.
Hypothesis Testing: Hypothesis testing is a statistical method used to make decisions about the validity of a hypothesis based on sample data. It involves formulating a null hypothesis and an alternative hypothesis, then using data to determine whether there is enough evidence to reject the null hypothesis in favor of the alternative. This process connects deeply with data analysis techniques across programming languages and methodologies, as well as practices like preregistration and continuous testing.
Observational study: An observational study is a research method where the investigator observes and records behavior or outcomes without manipulating any variables or assigning treatments. This approach is often used to gather insights about real-world scenarios, helping researchers identify patterns, correlations, and potential causal relationships. By examining subjects in their natural settings, observational studies can provide valuable data while minimizing ethical concerns that arise from experimental manipulations.
Open Science Framework: The Open Science Framework (OSF) is a free and open-source web platform designed to support the entire research lifecycle by enabling researchers to collaborate, share their work, and make it accessible to the public. This platform emphasizes reproducibility, research transparency, and the sharing of data and methods, ensuring that scientific findings can be verified and built upon by others in the research community.
Preregistration: Preregistration is the process of formally documenting the research design, hypotheses, and analysis plan of a study before data collection begins. This practice helps to enhance transparency and accountability in research by making the research process more rigorous and reducing biases that can arise during data analysis. Preregistration plays a critical role in addressing concerns related to the replication crisis in science, as it allows for clearer comparisons between planned analyses and reported results.
Preregistration platform: A preregistration platform is an online tool or system where researchers can publicly document their study plans before data collection begins. This practice is designed to enhance transparency and accountability in research, allowing others to verify the intended methods, hypotheses, and analyses, thus reducing biases such as p-hacking and HARKing (Hypothesizing After the Results are Known). By using a preregistration platform, researchers can contribute to more reproducible and credible science.
Registered Reports: Registered reports are a type of scholarly article format where the research design and analysis plan are peer-reviewed and approved before data collection begins. This approach aims to enhance the credibility of scientific findings by minimizing biases and increasing transparency, making it an important response to issues related to replicability in research and the need for preregistration in scientific studies.
Replicability: Replicability refers to the ability to achieve consistent results using the same methods and data in scientific research. It emphasizes that experiments and analyses can be repeated with the same parameters, leading to similar conclusions, which is essential for establishing trust in research findings.
Research integrity: Research integrity refers to the adherence to ethical principles and professional standards in conducting and reporting research. It encompasses honesty, transparency, accountability, and responsible conduct throughout the research process, ensuring that findings are reliable and valid. Maintaining research integrity is crucial for building trust within the scientific community and ensuring the credibility of scientific work, which is vital in contexts like study preregistration, open science metrics, computational reproducibility, and economic research reproducibility.
Responsible conduct of research: Responsible conduct of research refers to the ethical and professional standards researchers are expected to follow throughout their work. It encompasses practices such as honesty in data collection, transparency in reporting results, and the responsible sharing of research findings. Adhering to these standards fosters trust in the research community and ensures the integrity of scientific inquiry.
Statistical Power: Statistical power is the probability that a statistical test will correctly reject a false null hypothesis, indicating that an effect or difference exists when it actually does. It is influenced by several factors including sample size, effect size, significance level, and the inherent variability in the data. High statistical power is crucial for ensuring that research findings are reliable and can be reproduced.
Top Guidelines: Top guidelines refer to the best practices and recommendations designed to enhance the transparency, integrity, and reproducibility of research. They emphasize the importance of clearly documenting research processes, ensuring accountability, and fostering collaboration among researchers. These guidelines help establish a framework for conducting ethical research that can be reliably reproduced and evaluated by others.
Transparency: Transparency refers to the practice of making research processes, data, and methodologies openly available and accessible to others. This openness fosters trust and allows others to validate, reproduce, or build upon the findings, which is crucial for advancing knowledge and ensuring scientific integrity.