The IBT is dedicated to creating open-source code and tools for academic research, prioritizing accessibility and straightforward documentation for fellow researchers.
The IBT is dedicated to creating open-source code and tools for academic research, prioritizing accessibility and straightforward documentation for fellow researchers.
Provides practical guidance for collecting, preprocessing, and analyzing GPS data to study human behavior and psychological patterns in real-world contexts.
| Müller, S. R., Bayer, J. B., Ross, M. Q., Mount, J., Stachl, C., Harari, G. M., Chang, Y.-J., & Le, H. T. K. (2022). Analyzing GPS data for psychological research: A tutorial. Advances in Methods and Practices in Psychological Science, 5(3), 25152459221082680. | Read |
Introduces core principles, workflows, and potential pitfalls of supervised machine learning tailored for psychological researchers.
| Pargent, F., Schoedel, R., & Stachl, C. (2023). Best practices in supervised machine learning: A tutorial for psychologists. Advances in Methods and Practices in Psychological Science, 6(3), 25152459231162559. | Read |
| Debelak, R., Koch, T. K., Aßenmacher, M., & Stachl, C. (2025). From Embeddings to Explainability: A Tutorial on Large-Language-Model-Based Text Analysis for Behavioral Scientists. Advances in Methods and Practices in Psychological Science, 8(3), 25152459251351285. | Read |
Outlines key considerations for designing, implementing, and evaluating digital mental health tools in clinical and research settings.
| Seiferth, C., Vogel, L., Aas, B. G., Brandhorst, I., Carlbring, P., Conzelmann, A., Esfandiari, N., Finkbeiner, M., Hollmann, K., Lautenbacher, H., Meinzinger, E., Newbold, A., Opitz, A., Renner, T. J., Sander, L. B., Santangelo, P. S., Schoedel, R., Schuller, B., Stachl, C., … Wolf, S. (2023). How to e‑mental health: A guideline for researchers and practitioners using digital technology in the context of mental health. Nature Mental Health, 1(8), 542–554. | Read |
Explains how to use individual-level data to analyze geographically aggregated patterns in psychological phenomena and regional effects.
| Ebert, T., Götz, F. M., Mewes, L., & Rentfrow, P. J. (2023). Spatial analysis for psychologists: How to use individual‑level data for research at the geographically aggregated level. Psychological Methods, 28(4), 1100–1121. | Read |
Demonstrates simple yet effective techniques for mapping psychological data and identifying spatial patterns using distance-weighted smoothing.
| Ebert, T., Mewes, L., Götz, F. M., & Brenner, T. (2022). Effective maps, easily done: Visualizing geo‑psychological differences using distance weights. Advances in Methods and Practices in Psychological Science, 5(3), 25152459221101816. | Read |
Presents theoretical foundations, methods for acoustic feature extraction, and practical applications of voice analytics in behavioral and business research.
Provides an overview of potential pitfalls when recording audio data with varying consumer-grade audio recording devices (such as a headset vs. lavalier microphone) and how to best adjust the hardware, software, and recording environment.
| Hildebrand, C. A., Efthymiou, F., Busquet i Segui, F., Hampton, W. H., Hoffmann, D. L., & Novak, P. T. (2020). Voice analytics in business research: Conceptual foundations, acoustic feature extraction, and applications. Journal of Business Research, 121, 364–374. |
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| Busquet, F., Efthymiou, F. & Hildebrand, C. Voice analytics in the wild: Validity and predictive accuracy of common audio-recording devices. Behav Res 56, 2114–2134 (2024). |