Showing items with similar title or keywords.

• Comparison of three ordinal logistic regression methods for predicting person’s self-assessed health status with functional, haemodynamic covariates 

  Markkanen, Merri-Lotta (2023)

  Lääketieteen parissa perinteiset kyselytutkimukset ovat yhä suosittuja, jonka myötä myös järjestysasteikollisten muuttujien analyysia suoritetaan paljon. Modernin teknologian kehittyminen näkyy kuitenkin myös tällä ...

• Premature conclusions about the signal‐to‐noise ratio in structural equation modeling research : A commentary on Yuan and Fang (2023) 

  Schuberth, Florian; Schamberger, Tamara; Rönkkö, Mikko; Liu, Yide; Henseler, Jörg (John Wiley & Sons, 2023)

  In a recent article published in this journal, Yuan and Fang (British Journal of Mathematical and Statistical Psychology, 2023) suggest comparing structural equation modeling (SEM), also known as covariance-based SEM ...

• Too Small to Succeed : Small Samples and the p-Value Problem 

  Aguirre-Urreta, Miguel I.; Rönkkö, Mikko; McIntosh, Cameron N. (ACM, 2024)

  Determining an appropriate sample size is a critical planning decision in quantitative empirical research. In recent years, there has been a growing concern that researchers have excessively focused on statistical significance ...

• Intelligent solutions for real-life data-driven applications 

  Ivannikova, Elena (University of Jyväskylä, 2017)

  The subject of this thesis belongs to the topic of machine learning or, specifically, to the development of advanced methods for regression analysis, clustering, and anomaly detection. Industry is constantly seeking ...

• Eight Simple Guidelines for Improved Understanding of Transformations and Nonlinear Effects 

  Rönkkö, Mikko; Aalto, Eero; Tenhunen, Henni; Aguirre-Urreta, Miguel I. (SAGE Publications, 2022)

  Transforming variables before analysis or applying a transformation as a part of a generalized linear model are common practices in organizational research. Several methodological articles addressing the topic, either ...