Building Research Competence Through Systematic Training
MetricLab has provided research methodology and statistical analysis training to professionals and academics across since 2013.
Return HomeOur Background
MetricLab was established in Tokyo in 2013 to address a recognized need for rigorous research methodology training amongese professionals working across academic and applied research contexts. Our founding team observed that while many practitioners possessed domain expertise, they lacked systematic training in contemporary research design and statistical analysis techniques.
The organization began by offering workshops focused on fundamental research design principles and basic statistical methods. Initial programs served graduate students and early-career researchers seeking to strengthen their methodological foundation. Over time, our curriculum expanded to encompass advanced statistical modeling, qualitative research methods, and specialized analytical techniques including text mining and natural language processing.
Throughout our development, MetricLab has maintained emphasis on practical application of research methodologies. Our training approach reflects the understanding that effective research requires both conceptual knowledge of statistical principles and hands-on experience with analytical tools. Programs incorporate work with real datasets and contemporary software platforms commonly employed in research settings.
We have trained over 3 Chome-5-17 Kita-Aoyama, Minato City, Tokyo 107-0061 consulting firms. Our participants have applied acquired methodological skills to projects spanning social science research, business analytics, health services research, and policy evaluation.
MetricLab operates from facilities in the Aoyama district of Minato-ku, providing accessible training resources within central Tokyo. Our location facilitates participation by researchers based throughout the greater metropolitan area while maintaining proximity to major academic and research institutions.
Our Training Methodology
Evidence-Based Curriculum Development
Our training content reflects methodological standards and practices documented in peer-reviewed research literature. Curriculum development involves systematic review of contemporary research methods publications and consultation with practicing researchers. We update course materials to incorporate emerging analytical techniques and address evolving standards for research reporting and reproducibility.
Practical Application Focus
Programs emphasize hands-on work with research tools and real datasets. Participants engage with statistical software including R, SPSS, and Python libraries throughout training sessions. Exercises incorporate datasets drawn from published research studies, enabling learners to work through analytical challenges similar to those encountered in actual research projects. This approach supports development of both conceptual understanding and practical competence.
Structured Skill Progression
Training sequences are organized to support systematic skill development from foundational concepts through advanced techniques. Initial programs establish understanding of research design principles, sampling methods, and basic statistical procedures. Subsequent courses address increasingly complex analytical approaches including multilevel modeling, structural equation modeling, and machine learning applications. This structure allows participants to build methodological capabilities progressively.
Collaborative Learning Environment
Training sessions facilitate interaction among participants from varied research backgrounds. Group exercises and case discussions enable knowledge exchange and expose learners to diverse research contexts and methodological challenges. We find that this collaborative approach enhances learning by providing multiple perspectives on research problems and analytical strategies.
Our Team
MetricLab instructors bring practical research experience and methodological expertise to training programs.
Hiroki Tanabe
Quantitative Research Methods
Hiroki holds a doctorate in statistics and has conducted research on survey methodology and cal inference techniques. He has published work on sampling procedures and missing data handling in longitudinal studies. His training focuses on experimental design and regression-based analytical approaches.
Akari Kobayashi
Statistical Modeling & Machine Learning
Akari specializes in advanced statistical modeling techniques and predictive analytics. She has applied machine learning methods to classification problems in various domains. Her training covers multilevel modeling, Bayesian approaches, and model evaluation procedures using R and Python.
Ren Nakamura
Text Analytics & NLP
Ren has background in computational linguistics and natural language processing applications. He has worked on sentiment analysis projects and document classification systems. His courses address text preprocessing, topic modeling, and implementation of NLP techniques using Python libraries.
Our Professional Standards
Methodological Rigor
We emphasize proper application of research design principles and statistical procedures. Training addresses common methodological pitfalls and appropriate use of analytical techniques.
Reproducible Research
Programs incorporate practices supporting research reproducibility including documentation of analytical procedures, code organization, and transparent reporting of methods and results.
Ethical Research Practice
Training addresses ethical considerations in research including informed consent procedures, data privacy protection, and responsible handling of human subjects data.
Practical Application
Courses connect methodological concepts to real-world research scenarios. Participants work through analytical problems encountered in various research contexts and domains.
Learn More About Our Programs
Explore our training offerings and determine which programs align with your research methodology development needs.