Njål Foldnes
Biographic Data
| ID | 6528507 |
|---|---|
| NAME | Njål Foldnes |
| GIVEN NAMES | Njål |
| FAMILY NAME | Foldnes |
| SIGNATURE | FOLDNES N |
| AFFILIATIONS | University of Stavanger |
| ORCID | 0000-0001-6889-6067 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 3 |
| AUTHOR COUNT | 7 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 1 |
Test Statistics for Confirmatory Factor Analysis Under Non-Normality and Large p/n Ratios
Muda and Yangxuan recommended a ridge-calibrated test statistic for controlling Type I error inflation in confirmatory factor analysis under non-normality. Although improving finite-sample inference is an important goal, their empirical evaluation is undermined by incorrectly implemented benchmark statistics, misattributed comparison methods, and a simulation design restricted to asymptotically robust conditions. Using corrected code, we reanalyz…
Improved Goodness of Fit Procedures for Structural Equation Models
We propose new ways of robustifying goodness-of-fit tests for structural equation modeling under non-normality. These test statistics have limit distributions characterized by eigenvalues whose estimates are highly unstable and biased in known directions. To take this into account, we design model-based trend predictions to approximate the population eigenvalues. We evaluate the new procedures in a large-scale simulation study with three confirma…
Development and deployment of an adaptive national elementary reading screening test
Increasingly over the past decade, there has been a demand of more thorough documentation of the quality of reading assessments. Yet, available documentation of high-quality measures are often restricted to general framework descriptions providing psychometric information as a token of test quality. In a modern view of validity, understanding what is being measured and how scores are calculated is a prerequisite for good interpretation and use of…
School entry detection of struggling readers using gameplay data and machine learning
Introduction Current methods for reading difficulty risk detection at school entry remain error-prone. We present a novel approach utilizing machine learning analysis of data from GraphoGame, a fun and pedagogical literacy app. Methods The app was played in class daily for 10 min by 1,676 Norwegian first graders, over a 5-week period during the first months of schooling, generating rich process data. Models were trained on the process data combin…
Non-normal Data Simulation using Piecewise Linear Transforms
We present PLSIM, a new method for generating nonnormal data with a pre-specified covariance matrix that is based on coordinate-wise piecewise linear transformations of standard normal variables. In our presentation, the piecewise linear transforms are chosen to match pre-specified skewness and kurtosis values for each marginal distribution. We demonstrate the flexibility of the new method, and an implementation using R software is provided
How can we determine students’ motivation for reading before formal instruction? Results from a self-beliefs and interest scale validation
A Simple Simulation Technique for Nonnormal Data with Prespecified Skewness, Kurtosis, and Covariance Matrix
We present and investigate a simple way to generate nonnormal data using linear combinations of independent generator (IG) variables. The simulated data have prespecified univariate skewness and kurtosis and a given covariance matrix. In contrast to the widely used Vale-Maurelli (VM) transform, the obtained data are shown to have a non-Gaussian copula. We analytically obtain asymptotic robustness conditions for the IG distribution. We show empiri…
A Simple Simulation Technique for Nonnormal Data with Prespecified Skewness, Kurtosis, and Covariance Matrix
We present and investigate a simple way to generate nonnormal data using linear combinations of independent generator (IG) variables. The simulated data have prespecified univariate skewness and kurtosis and a given covariance matrix. In contrast to the widely used Vale-Maurelli (VM) transform, the obtained data are shown to have a non-Gaussian copula. We analytically obtain asymptotic robustness conditions for the IG distribution. We show empiri…
How can we determine students’ motivation for reading before formal instruction? Results from a self-beliefs and interest scale validation
Non-normal Data Simulation using Piecewise Linear Transforms
We present PLSIM, a new method for generating nonnormal data with a pre-specified covariance matrix that is based on coordinate-wise piecewise linear transformations of standard normal variables. In our presentation, the piecewise linear transforms are chosen to match pre-specified skewness and kurtosis values for each marginal distribution. We demonstrate the flexibility of the new method, and an implementation using R software is provided
Development and deployment of an adaptive national elementary reading screening test
Increasingly over the past decade, there has been a demand of more thorough documentation of the quality of reading assessments. Yet, available documentation of high-quality measures are often restricted to general framework descriptions providing psychometric information as a token of test quality. In a modern view of validity, understanding what is being measured and how scores are calculated is a prerequisite for good interpretation and use of…
School entry detection of struggling readers using gameplay data and machine learning
Introduction Current methods for reading difficulty risk detection at school entry remain error-prone. We present a novel approach utilizing machine learning analysis of data from GraphoGame, a fun and pedagogical literacy app. Methods The app was played in class daily for 10 min by 1,676 Norwegian first graders, over a 5-week period during the first months of schooling, generating rich process data. Models were trained on the process data combin…
Improved Goodness of Fit Procedures for Structural Equation Models
We propose new ways of robustifying goodness-of-fit tests for structural equation modeling under non-normality. These test statistics have limit distributions characterized by eigenvalues whose estimates are highly unstable and biased in known directions. To take this into account, we design model-based trend predictions to approximate the population eigenvalues. We evaluate the new procedures in a large-scale simulation study with three confirma…
Test Statistics for Confirmatory Factor Analysis Under Non-Normality and Large p/n Ratios
Muda and Yangxuan recommended a ridge-calibrated test statistic for controlling Type I error inflation in confirmatory factor analysis under non-normality. Although improving finite-sample inference is an important goal, their empirical evaluation is undermined by incorrectly implemented benchmark statistics, misattributed comparison methods, and a simulation design restricted to asymptotically robust conditions. Using corrected code, we reanalyz…
Computer Science (4 works) · Applied Mathematics (3 works) · Mathematics (3 works) · Reading and Literacy Development (3 works) · Statistics (3 works) · Artificial Intelligence (2 works) · Covariance (2 works) · Covariance matrix (2 works) · Econometrics (2 works) · Kurtosis (2 works)