Professor|Developer|Aviation Psychometrics & AI Architect
$ whoami
type `help` — this terminal actually works. · 24 commands available
01 — about
Muhsin Murat Yaşlıoğlu is a Professor at Istanbul University, School of Business, Department of Business Administration — a researcher in psychometrics and quantitative methods, and a full-stack developer working at the intersection of aviation safety systems and AI-driven architecture.
His academic work spans factor analysis and SEM methodology, organizational behavior, leadership, corporate reputation, and aviation human factors — with over 60 publications and 3,300+ citations. His most-cited paper on factor analysis and validity methods (2017) has guided researchers across the social sciences. His software work bridges that measurement rigor with production systems: manipulation-resistant market design for emissions trading, privacy-preserving safety management systems, pilot selection batteries, and cognitive architectures.
The portfolio below collects ten of those systems. Some are production platforms running real assessments; others are research artifacts and technical specifications. All of them are built to be read, used, and stress-tested.
02 — scholar works
// peer-reviewed articles, preprints, books, and book chapters
M. Murat Yaslioglu · Istanbul University
Istanbul University · 44 publications
researcher ids
awards
İstanbul Üniversitesi İşletme Fakültesi Dergisi, no.46, pp.75-87
Yaşlıoğlu, M. M.
Istanbul Management Journal, vol.88, pp.1-20
Yaşlıoğlu, M. M., Toplu Yaşlıoğlu, D.
IUP Journal of Business Strategy, vol.15, no.1, pp.42-55
Yaşlıoğlu, M. M., Erden, N. S.
Yönetim Bilimleri Dergisi, vol.11, no.22, pp.191-220
Yaşlıoğlu, M., Pekdemir, I., Toplu, D.
Procedia - Social and Behavioral Sciences, vol.99, pp.332-338
Yaşlıoğlu, M. M., Karagülle, A. Ö., Baran, M.
Procedia - Social and Behavioral Sciences, vol.99, pp.629-635
Yaşlıoğlu, M. M., Çalışkan, B. Ö. Ö., Şap, Ö.
Journal of Organizational Behavior Review, vol.6, no.1, pp.69-108
Ghaleb, M., Yaşlıoğlu, M. M.
Procedia - Social and Behavioral Sciences, vol.150, pp.726-734
Yaşlıoğlu, M. M., Şap, Ö., Toplu, D.
Ege Academic Review, vol.9, no.3, pp.969-976
Baran, M., Kanten, P., Kanten, S., Yaşlıoğlu, M. M.
The IUP Journal of Business Strategy, vol.11, no.2, pp.28-42
Toplu Yaşlıoğlu, D., Yaşlıoğlu, M. M., Erden, N. S.
The IUP Journal of Organizational Behavior, vol.12, no.2, pp.7-14
Erden, N. S., Toplu, D., Yaşlıoğlu, M.
International Journal of Managerial Studies and Research, vol.4, no.5, pp.54-67
Meral, Y., Yaşlıoğlu, M. M., Semercİöz, F.
Journal of Administrative Science, vol.14, no.28, pp.223-249
Yaşlıoğlu, M. M., Önder, E.
Istanbul University Journal of the School of Business, vol.42, no.2, pp.171-180
Yaşlıoğlu, M. M., Ülgen Aydınlık, A.
International Journal of Data Analysis Techniques and Strategies, vol.12, no.1, pp.1-29
Yaşlıoğlu, M. M.
Journal of Business Research- Turk, vol.12, no.4, pp.3383-3404
Erdağ, T., Yaşlıoğlu, M. M.
International Journal of Social Sciences, vol.5, no.2, pp.35-58
Erdağ, T., Yaşlıoğlu, M. M.
Journal of Aviation, vol.7, no.1, pp.17-28
Yaşlıoğlu, D. T., Akyurt, İ. Z., Yaşlıoğlu, M., Erdağ, T.
arXiv, vol.1, no.1, pp.1-20
Yaşlıoğlu, M. M.
arXiv, vol.2025, no.Dec, pp.1-20
Yaşlıoğlu, M. M.
arXiv, vol.2025, pp.1-20
Yaşlıoğlu, M. M.
Istanbul Business Research, vol.54, no.2, pp.200-213
Hamsho, M., Yaşlıoğlu, M. M.
Journal of Accounting and Organizational Change, 2025
Hameli, K., Yaşlıoğlu, M. M.
Beta, İstanbul
Yaşlıoğlu, M. M.
in: Leadership Perspectives on Effective Intergenerational Communication and Management, IGI-Global, pp.187-223
Ghaleb, M., Yaşlıoğlu, M. M.
in: Work Organization and Human Resource Management, Springer, London/Berlin, pp.27-42
Berber, A., Yaşlıoğlu, M. M.
in: Encyclopedia of Information Science and Technology, IGI-Global, pp.1259-1278
Yaşlıoğlu, M. M., Toplu Yaşlıoğlu, D.
02.5 — method notes
// short answers to the questions that keep coming back — factor analysis, fit indices, validity, method variance
Should I use exploratory or confirmatory factor analysis?
Use exploratory factor analysis when you do not know how many dimensions the construct has, and confirmatory factor analysis when theory or prior work already proposes a structure. They are not rival methods but consecutive stages: EFA reveals a structure, CFA tests it on an independent sample. Running EFA then CFA on the same data is not confirmation.
Which fit indices should I report in structural equation modelling?
Report at least one absolute index (χ²/df or SRMR), one comparative index (CFI or TLI) and one error-of-approximation index (RMSEA) together. Common thresholds: CFI and TLI ≥.95 good, ≥.90 acceptable; RMSEA and SRMR ≤.05 good, ≤.08 acceptable; χ²/df ≤3 good, ≤5 acceptable. These are conventions — sample size, model complexity and estimator all shift them.
How are validity and reliability reported, and how do they differ?
Reliability is the consistency of measurement; validity is whether the instrument measures what it claims to. Reliability is necessary for validity but nowhere near sufficient: an instrument that consistently measures the wrong thing will show high reliability. Report at least one internal consistency coefficient (Cronbach's α, or preferably McDonald's ω) together with construct validity evidence (AVE, CR, discriminant validity).
What is common method variance and how is it controlled?
Common method variance is spurious covariance produced when predictor and outcome are collected from the same source, in the same format, at the same time. The effective fix is at design stage: separate source, time, or format. After collection, Harman's single-factor test is weak evidence; unmeasured or measured latent method factor approaches are stronger.
What sample size is needed for factor analysis and SEM?
The 5–10 respondents per item rule is common but misleading; what matters is not headcount but the size of the loadings and the number of items per factor. With high loadings (≥.80) and 4+ items per factor, around 150 may suffice; with weak loadings and sparse factors, 300–500 may be needed. For SEM, 200 is the usual floor.
How is a mediation analysis run and reported?
Mediation is tested through the confidence interval of the indirect effect (a×b); Baron and Kenny's four steps and the Sobel test are no longer the standard. If a bias-corrected bootstrap confidence interval excludes zero, the indirect effect is significant. The 'full versus partial mediation' distinction is no longer recommended: a non-significant direct effect does not establish full mediation.
How do you adapt a scale developed in another language?
Adaptation requires at least three stages: linguistic equivalence through forward–back translation and expert review, comprehensibility through piloting, and re-demonstration of construct validity in the new sample. You cannot assume the original factor structure will reappear, and cross-cultural comparison is not permitted until measurement invariance has been tested.
02.7 — citation impact
// one dot per publication · height and size are citations, on a log scale
hover, tap, or tab to a dot for details
Each dot is a peer-reviewed publication with at least one citation, plotted across 2009–2024.
02.9 — measurement lab
// two working demos from the research — no data leaves your browser
The 60-second version of the alertness test used in aviation fatigue research (FRMS). Wait for green, then react.
Click, tap, or press Space as soon as the panel turns green.
Reference: rested adults average roughly 250–300 ms with near-zero lapses. Browser timing adds input and display latency, so this is a demonstration, not a clinical measurement.
03 — selected work
// hover a card to inspect · click to open the full brief