JASP

🧱 Overpromised Accessibility, Underdelivered Depth

JASP markets itself as a user-friendly GUI for statistical analysis, but this ease of use comes at the cost of limited methodological depth and flexibility.

  • The simplified interface encourages black-box application of statistics without fostering true understanding.
  • Advanced users find the software restrictive, lacking support for custom models, complex data structures, and scripting.
  • The default settings and automated procedures may lead novices to misuse or misinterpret results.

🔍 Limited Statistical and Meta-Analytic Features

  • While JASP supports basic meta-analysis, it lacks advanced capabilities such as network meta-analysis, multivariate models, and robust meta-regression.
  • The Bayesian methods implemented are simplistic and do not cover the breadth needed for nuanced inference.
  • Diagnostic tools for heterogeneity, publication bias, and influence analyses are basic or missing.

🤖 No Integration with Automation or Data Extraction Tools

  • JASP operates in isolation, with no built-in support for literature screening, data extraction, or risk of bias assessment.
  • It offers no API or scripting interface, limiting reproducibility and workflow automation.
  • Collaboration features are minimal or nonexistent.

📉 Reproducibility and Transparency Issues

  • Although JASP allows export of analysis scripts, the lack of full scripting limits transparency compared to command-line alternatives.
  • Version control and project management features are weak, hindering collaborative reproducible research.
  • Output reports are standardized but offer limited customization.

⚠️ Accessibility vs. Professionalism Trade-Off

  • JASP’s low barrier to entry can foster overconfidence among inexperienced users, increasing risk of analytical errors.
  • Professional statisticians and methodologists often reject JASP due to its limited scope and control.
  • The software’s popularity in teaching may not translate to rigorous research environments.

🧨 Final Verdict

JASP is a convenient tool for introductory statistics and teaching, but it is unsuitable for complex, high-stakes meta-analyses or advanced research. Its simplistic interface, limited features, and poor integration hinder rigorous evidence synthesis and reproducibility.

Recommendation: Use JASP for learning or exploratory data analysis only. For robust meta-analytic work, prefer more flexible and transparent tools like R packages or advanced workflow platforms.

Better Alternatives to JASP

🥇 R with Meta-Analysis Packages (metafor, meta, netmeta)

  • ✅ Full scripting flexibility for complex and customized meta-analyses
  • ✅ Supports network meta-analysis, multivariate models, and Bayesian methods
  • ✅ Integrates with R Markdown for reproducible research reports
  • Why better than JASP:

Greater control, transparency, and methodological sophistication

🔍 Comprehensive Meta-Analysis (CMA)

  • ✅ User-friendly GUI tailored to meta-analysis
  • ✅ Supports subgroup and sensitivity analyses and other advanced features
  • ✅ Widely used in clinical research with strong support
  • Why better than JASP:

More focused and feature-rich for meta-analytic purposes

🤖 AI-Augmented Tools: Elicit + RobotReviewer

  • ✅ Automate literature screening, data extraction, and bias assessment
  • ✅ Reduce manual workload and increase accuracy
  • Why better than JASP:

Streamlines upstream review tasks typically manual in JASP workflows

🔧 Systematic Review Platforms: Covidence, DistillerSR

  • ✅ Manage full systematic review workflow: screening, extraction, bias assessment, export
  • ✅ Collaboration-friendly with version control and audit trails
  • Why better than JASP:

Supports entire review lifecycle, not just statistical analysis

📊 Summary Table

Tool Strengths Why Better Than JASP
R (metafor, meta, netmeta) Advanced scripting, flexibility, reproducibility Maximum control and transparency
Comprehensive Meta-Analysis GUI with rich meta-analytic features More advanced and focused than JASP
Elicit + RobotReviewer AI-assisted extraction and bias assessment Automates and accelerates manual processes
Covidence / DistillerSR Full systematic review management Manages complete SR workflow collaboratively

🧠 Final Recommendation

  • Use R packages for advanced and reproducible meta-analyses.
  • Use CMA for GUI-driven, feature-rich meta-analysis.
  • Use Elicit and RobotReviewer to automate evidence extraction and bias assessment.
  • Use Covidence or DistillerSR to manage the entire systematic review process.
  • Use JASP primarily for teaching and simple exploratory analyses.
  • jasp.txt
  • Last modified: 2025/07/01 16:42
  • by administrador