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- Andersson, Carolyn J., et al.
(författare)
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Stories of successful careers in psychometrics and what we can learn from them
- 2020
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Ingår i: Quantitative psychology. - New York : Springer. - 9783030434687 - 9783030434694 ; , s. 1-17
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Bokkapitel (refereegranskat)abstract
- This paper was inspired by the presentations and discussions from the panel "Successful Careers in Academia and Industry and What We Can Learn from Them”" that took place at the IMPS meeting in 2019. In this paper, we discuss what makes a career successful in academia and industry and we provide examples from the past to the present. We include education and career paths as well as highlights of achievements as researchers and teachers. The paper provides a brief historical context for the representation of women in psychometrics and an insight into strategies for success for publishing, for grant applications and promotion. The authors outline the importance of interdisciplinary work, the inclusive citation approaches, and visibility of research in academia and industry. The personal stories provide a platform for considering the needs for a supportive work environment for women and for work-life balance. The outcome of these discussions and reflections of the panel members are included in the paper.
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| 4. |
- Jin, Shaobo, 1987-, et al.
(författare)
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Approximated penalized maximum likelihood for exploratory factor analysis : An orthogonal case
- 2018
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Ingår i: Psychometrika. - : Springer Science and Business Media LLC. - 0033-3123 .- 1860-0980. ; 83:3, s. 628-649
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Tidskriftsartikel (refereegranskat)abstract
- The problem of penalized maximum likelihood (PML) for an exploratory factor analysis (EFA) model is studied in this paper. An EFA model is typically estimated using maximum likelihood and then the estimated loading matrix is rotated to obtain a sparse representation. Penalized maximum likelihood simultaneously fits the EFA model and produces a sparse loading matrix. To overcome some of the computational drawbacks of PML, an approximation to PML is proposed in this paper. It is further applied to an empirical dataset for illustration. A simulation study shows that the approximation naturally produces a sparse loading matrix and more accurately estimates the factor loadings and the covariance matrix, in the sense of having a lower mean squared error than factor rotations, under various conditions.
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| 5. |
- Katsikatsou, Myrsini, 1981-
(författare)
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Composite Likelihood Estimation for Latent Variable Models with Ordinal and Continuous, or Ranking Variables
- 2013
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The estimation of latent variable models with ordinal and continuous, or ranking variables is the research focus of this thesis. The existing estimation methods are discussed and a composite likelihood approach is developed. The main advantages of the new method are its low computational complexity which remains unchanged regardless of the model size, and that it yields an asymptotically unbiased, consistent, and normally distributed estimator.The thesis consists of four papers. The first one investigates the two main formulations of the unrestricted Thurstonian model for ranking data along with the corresponding identification constraints. It is found that the extra identifications constraints required in one of them lead to unreliable estimates unless the constraints coincide with the true values of the fixed parameters.In the second paper, a pairwise likelihood (PL) estimation is developed for factor analysis models with ordinal variables. The performance of PL is studied in terms of bias and mean squared error (MSE) and compared with that of the conventional estimation methods via a simulation study and through some real data examples. It is found that the PL estimates and standard errors have very small bias and MSE both decreasing with the sample size, and that the method is competitive to the conventional ones.The results of the first two papers lead to the next one where PL estimation is adjusted to the unrestricted Thurstonian ranking model. As before, the performance of the proposed approach is studied through a simulation study with respect to relative bias and relative MSE and in comparison with the conventional estimation methods. The conclusions are similar to those of the second paper.The last paper extends the PL estimation to the whole structural equation modeling framework where data may include both ordinal and continuous variables as well as covariates. The approach is demonstrated through an example run in R software. The code used has been incorporated in the R package lavaan (version 0.5-11).
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| 6. |
- Katsikatsou, Myrsini, et al.
(författare)
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Pairwise Likelihood Estimation for factor analysis models with ordinal data
- 2011
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Pairwise maximum likelihood (PML) estimation is developed for factor analysis models with ordinal data fitted both in an exploratory and confirmatory set-up, and its performance is studied and compared with full information maximum likelihood (FIML) and a three-stage limited information estimation method. More specifically, estimates and standard errors ob- tained from PML are compared with those obtained from FIML and those from robust un- weighted least squares (3S-RULS). All three methods provide very close estimates and stan- dard errors. However, the PML estimates and standard errors are on average slightly closer to FIML than the 3S-RULS are. The advantage of PML over FIML is mainly computational. The computational complexity of FIML increases with the number of factors or observed variables depending on the model formulation, while that of PML is affected by neither of them. Contrary to 3S-RULS, in PML, all model parameters are simultaneously estimated and therefore the final estimates reflect all the sampling variability. In the 3S-RULS method the standard errors of the parameter estimates in stage three do not incorporate the variability of the estimates obtained in step one. Furthermore, PML does not require the estimation of a weight matrix for computing correct standard errors. The performance of PML estimates and their estimated asymptotic standard errors are investigated through a simulation study where the effect of different models and sample sizes are studied. The bias and mean squared error of PML estimators and their standard errors are found to be small in all experimental conditions and decreasing with the sample size.
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| 7. |
- Katsikatsou, Myrsini, 1981-, et al.
(författare)
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Pairwise likelihood estimation for factor analysis models with ordinal data
- 2012
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Ingår i: Computational Statistics & Data Analysis. - : Elsevier BV. - 0167-9473 .- 1872-7352. ; 56:12, s. 4243-4258
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Tidskriftsartikel (refereegranskat)abstract
- A pairwise maximum likelihood (PML) estimation method is developed for factor analysis models with ordinal data and fitted both in an exploratory and confirmatory set-up. The performance of the method is studied via simulations and comparisons with full information maximum likelihood (FIML) and three-stage limited information estimation methods, namely the robust unweighted least squares (3S-RULS) and robust diagonally weighted least squares (3S-RDWLS). The advantage of PML over FIML is mainly computational. Unlike PML estimation, the computational complexity of FIML estimation increases either with the number of factors or with the number of observed variables depending on the model formulation. Contrary to 3S-RULS and 3S-RDWLS estimation, PML estimates of all model parameters are obtained simultaneously and the PML method does not require the estimation of a weight matrix for the computation of correct standard errors. The simulation study on the performance of PML estimates and estimated asymptotic standard errors investigates the effect of different model and sample sizes. The bias and mean squared error of PML estimates and their standard errors are found to be small in all experimental conditions and decreasing with increasing sample size. Moreover, the PML estimates and their standard errors are found to be very close to those of FIML.
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| 8. |
- Liu, Xinyi, et al.
(författare)
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Rotation to sparse loadings using Lp losses and related inference problems
- 2023
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Ingår i: Psychometrika. - : Springer. - 0033-3123 .- 1860-0980. ; 88, s. 527-553
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Tidskriftsartikel (refereegranskat)abstract
- Researchers have widely used exploratory factor analysis (EFA) to learn the latent structure underlying multivariate data. Rotation and regularised estimation are two classes of methods in EFA that they often use to find interpretable loading matrices. In this paper, we propose a new family of oblique rotations based on component-wise Lp loss functions (0<p≤1)(0<p≤1) that is closely related to an Lp regularised estimator. We develop model selection and post-selection inference procedures based on the proposed rotation method. When the true loading matrix is sparse, the proposed method tends to outperform traditional rotation and regularised estimation methods in terms of statistical accuracy and computational cost. Since the proposed loss functions are nonsmooth, we develop an iteratively reweighted gradient projection algorithm for solving the optimisation problem. We also develop theoretical results that establish the statistical consistency of the estimation, model selection, and post-selection inference. We evaluate the proposed method and compare it with regularised estimation and traditional rotation methods via simulation studies. We further illustrate it using an application to the Big Five personality assessment.
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