Jonas Moss

Moss, Jonas & Grønneberg, Steffen (2023)

Partial Identification of Latent Correlations with Ordinal Data

Psychometrika Doi: 10.1007/s11336-022-09898-y - Full text in research archive

Moss, Jonas (2022)

Infinite diameter confidence sets in Hedges’ publication bias model

Journal of the Korean Statistical Society Doi: 10.1007/s42952-022-00169-1 - Full text in research archive

Meta-analysis, the statistical analysis of results from separate studies, is a fundamental building block of science. But the assumptions of classical meta-analysis models are not satisfied whenever publication bias is present, which causes inconsistent parameter estimates. Hedges’ selection function model takes publication bias into account, but estimating and inferring with this model is tough for some datasets. Using a generalized Gleser–Hwang theorem, we show there is no confidence set of guaranteed finite diameter for the parameters of Hedges’ selection model. This result provides a partial explanation for why inference with Hedges’ selection model is fraught with difficulties.

Moss, Jonas & De Bin, Riccardo (2021)

Modelling publication bias and p-hacking

Biometrics Doi: 10.1111/biom.13560 - Full text in research archive

Grønneberg, Steffen; Moss, Jonas & Foldnes, Njål (2020)

Partial identification of latent correlations with binary data

Psychometrika, 85, s. 1028- 1051. Doi: 10.1007/s11336-020-09737-y - Full text in research archive

The tetrachoric correlation is a popular measure of association for binary data and estimates the correlation of an underlying normal latent vector. However, when the underlying vector is not normal, the tetrachoric correlation will be different from the underlying correlation. Since assuming underlying normality is often done on pragmatic and not substantial grounds, the estimated tetrachoric correlation may therefore be quite different from the true underlying correlation that is modeled in structural equation modeling. This motivates studying the range of latent correlations that are compatible with given binary data, when the distribution of the latent vector is partly or completely unknown. We show that nothing can be said about the latent correlations unless we know more than what can be derived from the data. We identify an interval constituting all latent correlations compatible with observed data when the marginals of the latent variables are known. Also, we quantify how partial knowledge of the dependence structure of the latent variables affect the range of compatible latent correlations. Implications for tests of underlying normality are briefly discussed.

Moss, Jonas (2019)

univariateML: An R package for maximum likelihood estimation of univariate densities

Journal of Open Source Software (JOSS) Doi: 10.21105/joss.01863

Moss, Jonas (2018)

Seleksjon for signifikans: Hva er det og hva kan du gjøre med det

[Article in business/trade/industry journal]. Psykologisk tidsskrift, s. 46- 52.