Mikko Rönkkö
Biographic Data
| ID | 9661504 |
|---|---|
| NAME | Mikko Rönkkö |
| GIVEN NAMES | Mikko |
| FAMILY NAME | Rönkkö |
| SIGNATURE | RÖNKKÖ M |
| AFFILIATIONS | University of Jyväskylä |
| ORCID | 0000-0001-7988-7609 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Structural Parameters under Partial Least Squares and Covariance-Based Structural Equation Modeling
In their article, Yuan and Deng argue that a structural parameter under partial least squares structural equation modeling (PLS-SEM) is zero if and only if the same structural parameter is zero under covariance-based structural equation modeling (CB-SEM). Yuan and Deng then conclude that statistical tests on individual structural parameters assessing the null hypothesis of no effect can achieve the same purpose in CB-SEM and PLS-SEM. Our response…
An Updated Guideline for Assessing Discriminant Validity
Discriminant validity was originally presented as a set of empirical criteria that can be assessed from multitrait-multimethod (MTMM) matrices. Because datasets used by applied researchers rarely lend themselves to MTMM analysis, the need to assess discriminant validity in empirical research has led to the introduction of numerous techniques, some of which have been introduced in an ad hoc manner and without rigorous methodological support. We re…
On Ignoring the Random Effects Assumption in Multilevel Models
Entities such as individuals, teams, or organizations can vary systematically from one another. Researchers typically model such data using multilevel models, assuming that the random effects are uncorrelated with the regressors. Violating this testable assumption, which is often ignored, creates an endogeneity problem thus preventing causal interpretations. Focusing on two-level models, we explain how researchers can avoid this problem by includ…
No prominent works on this page.
On Ignoring the Random Effects Assumption in Multilevel Models
Entities such as individuals, teams, or organizations can vary systematically from one another. Researchers typically model such data using multilevel models, assuming that the random effects are uncorrelated with the regressors. Violating this testable assumption, which is often ignored, creates an endogeneity problem thus preventing causal interpretations. Focusing on two-level models, we explain how researchers can avoid this problem by includ…
An Updated Guideline for Assessing Discriminant Validity
Discriminant validity was originally presented as a set of empirical criteria that can be assessed from multitrait-multimethod (MTMM) matrices. Because datasets used by applied researchers rarely lend themselves to MTMM analysis, the need to assess discriminant validity in empirical research has led to the introduction of numerous techniques, some of which have been introduced in an ad hoc manner and without rigorous methodological support. We re…
Structural Parameters under Partial Least Squares and Covariance-Based Structural Equation Modeling
In their article, Yuan and Deng argue that a structural parameter under partial least squares structural equation modeling (PLS-SEM) is zero if and only if the same structural parameter is zero under covariance-based structural equation modeling (CB-SEM). Yuan and Deng then conclude that statistical tests on individual structural parameters assessing the null hypothesis of no effect can achieve the same purpose in CB-SEM and PLS-SEM. Our response…
Computer Science (3 works) · Mathematics (3 works) · Statistics (3 works) · Advanced Statistical Modeling Techniques (2 works) · Data mining (2 works) · Econometrics (2 works) · Psychology (2 works) · Psychometric Methodologies and Testing (2 works) · Applied Mathematics (1 work) · Artificial Intelligence (1 work)