| 1. |
- Kreiberg, David, 1971-
(författare)
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A covariance structure analysis approach to the errors-in-variables estimation problem
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- It is a well-known fact that standard regression techniques, when applied to errors-in-variables (EIV) models, lead to biased and inconsistent parameter estimation. The work presented in this thesis address the EIV estimation problem using covariance structure analysis (CSA). When performing CSA, the standard implementation of the minimum distance (MD) estimator is to apply computationally demanding nonlinear least squares (NLLS). This thesis provides a solution to this problem by proposing a computationally less demanding separable nonlinear least squares (SNLLS) implementation of the estimator.The thesis consists of four papers. The first paper presents a covariance matching (CM) approach for identifying the single-input single-output (SISO) EIV model. The outlined approach extends previous known results by deriving an asymptotic covariance matrix of the jointly estimated system parameters, noise variances and auxiliary parameters. The second paper introduces two formulations of the SISO EIV model using structural equation modeling (SEM). The two formulations allow for quick implementation using standard SEM-based software. The third paper propose a numerically more efficient implementation of the MD estimator for estimating confirmatory factor analysis (CFA) models. The implementation uses an SNLLS approach, which allows part of the parameter vector to be estimated using numerically efficient linear techniques. The fourth and final paper presents a CFA-EIV modeling approach that allows for colored output noise. The presentation extends previous work by including a detailed treatment of the theoretical aspects of the MD estimator. All four papers use simulation examples to illustrate the outlined procedures.
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| 2. |
- Vegelius, Johan, 1981-
(författare)
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Estimation of Nonlinear Latent Variable and Mixture Models
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In this thesis methods are developed for estimation of latent variable models. In particular nonlinear structural equation models are estimated in the presence of ordinal data and mixture models for count data. Paper I introduces an extended nonlinear structural model which allows for interactions between exogenous and endogenous latent variables in the presence of ordinal data. The adaptive Gauss-Hermite quadrature (AGHQ) and Laplace approximations are used to approximate intractable integrals.Paper II introduces a semiparametric approach for modeling a flexible nonlinear structural model in the presence of ordinal data. Intractable integrals are approximated by the AGHQ approximation.Paper III investigates and compares the error rates of three versions of the AGHQ approximation.Paper IV develops an extreme value and zero inflated regression model for modeling of count data which includes a proportion of excess zeroes and extreme values. This is a typical situation when modeling the number of fatalities in armed conflicts.
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| 3. |
- Andersson, Gustaf, et al.
(författare)
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Generalized Linear Factor Score Regression : A Comparison of Four Methods
- 2021
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Ingår i: Educational and Psychological Measurement. - : Sage Publications. - 0013-1644 .- 1552-3888. ; 81:4, s. 617-643
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Tidskriftsartikel (refereegranskat)abstract
- Factor score regression has recently received growing interest as an alternative for structural equation modeling. However, many applications are left without guidance because of the focus on normally distributed outcomes in the literature. We perform a simulation study to examine how a selection of factor scoring methods compare when estimating regression coefficients in generalized linear factor score regression. The current study evaluates the regression method and the correlation-preserving method as well as two sum score methods in ordinary, logistic, and Poisson factor score regression. Our results show that scoring method performance can differ notably across the considered regression models. In addition, the results indicate that the choice of scoring method can substantially influence research conclusions. The regression method generally performs the best in terms of coefficient and standard error bias, accuracy, and empirical Type I error rates. Moreover, the regression method and the correlation-preserving method mostly outperform the sum score methods.
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| 4. |
- Bali Swain, Ranjula, 1968-, et al.
(författare)
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Does Foreign Aid Improve Gender Performance In Recipient Countries?
- 2020
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Ingår i: Journal of International Development. - : Wiley. - 0954-1748 .- 1099-1328. ; 32:7, s. 1171-1193
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Tidskriftsartikel (refereegranskat)abstract
- An explicit goal of foreign aid is to promote female empowerment and gender equality in developing countries. We investigate if foreign aid achieves this intended goal by examining its impact on the gender performance of recipient countries at the country level. Employing structural equation models, our results suggest that aid alone, even when targeted to directly improve gender outcomes, is unlikely to shift systemic inequalities. Aid will need to bolster civil society efforts that challenge institutional structures and norms in order to impact gender outcomes at the country level.
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| 5. |
- Bali Swain, Ranjula, et al.
(författare)
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The impact of microfinance on factors empowering women : Differences in regional and delivery mechanisms in India’s SHG programme
- 2017
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Ingår i: Journal of Development Studies. - : Routledge. - 0022-0388 .- 1743-9140. ; 53:5, s. 684-699
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Tidskriftsartikel (refereegranskat)abstract
- We examine how the impact on women empowerment varies with respect to the location and type of group linkage of the respondent. Using household survey data from five states in India, we correct for selection bias to estimate a structural equation model. Our results reveal that in the southern states of India empowerment of women takes place through economic factors. For the other states, we find a significant correlation between women empowerment and autonomy in women’s decision-making and network, communication and political participation respectively. We do not however find any differential causal impact of different delivery methods (linkage models).
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| 6. |
- Jin, Shaobo, 1987-, et al.
(författare)
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A unified model-implied instrumental variable approach for structural equation modeling with mixed variables
- 2021
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Ingår i: Psychometrika. - : Springer Nature. - 0033-3123 .- 1860-0980. ; 86:2, s. 564-594
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Tidskriftsartikel (refereegranskat)abstract
- The model-implied instrumental variable (MIIV) estimator is an equation-by-equation estimator of structural equation models that is more robust to structural misspecifications than full information estimators. Previous studies have concentrated on endogenous variables that are all continuous (MIIV-2SLS) or all ordinal . We develop a unified MIIV approach that applies to a mixture of binary, ordinal, censored, or continuous endogenous observed variables. We include estimates of factor loadings, regression coefficients, variances, and covariances along with their asymptotic standard errors. In addition, we create new goodness of fit tests of the model and overidentification tests of single equations. Our simulation study shows that the proposed MIIV approach is more robust to structural misspecifications than diagonally weighted least squares (DWLS) and that both the goodness of fit model tests and the overidentification equations tests can detect structural misspecifications. We also find that the bias in asymptotic standard errors for the MIIV estimators of factor loadings and regression coefficients are often lower than the DWLS ones, though the differences are small in large samples. Our analysis shows that scaling indicators with low reliability can adversely affect the MIIV estimators. Also, using a small subset of MIIVs reduces small sample bias of coefficient estimates, but can lower the power of overidentification tests of equations.
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| 7. |
- Jin, Shaobo, 1987-, et al.
(författare)
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Robust nonlinear structural equation modeling with interaction between exogenous and endogenous latent variables
- 2021
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Ingår i: Structural Equation Modeling. - : Taylor & Francis Group. - 1070-5511 .- 1532-8007. ; 28:4, s. 547-556
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Tidskriftsartikel (refereegranskat)abstract
- A handful of studies have been devoted to nonlinear structural equation modeling (SEM) in the literature. However, they generally overlooked the interactions among exogenous and endogenous latent variables and the interactions among endogenous latent variables. In this study, we propose a maximum likelihood approach for a nonlinear SEM model that incorporates such overlooked interactions. We also propose a two-stage pseudo maximum likelihood approach under the assumption of a normal mixture, being computationally efficient and robust against distributional misspecification. The simulation study shows that both approaches accurately estimate the unknown parameters if the distribution is correctly specified. However, only the pseudo maximum likelihood approach is robust against distributional misspecification.
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| 8. |
- Persson, Inger, et al.
(författare)
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Confirming the Structure of the Survey of Attitudes Toward Statistics (SATS-36) by Swedish Students
- 2019
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Ingår i: Statistics Education Research Journal. - : International Association for Statistical Education. - 1570-1824. ; 18:1, s. 83-93
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Tidskriftsartikel (refereegranskat)abstract
- Research on students’ attitudes toward statistics has attracted many statistics instructors and statistics education researchers. In this study, we use confirmatory factor analysis to analyze data collected from an introductory statistics course using the Survey of Attitudes toward Statistics. Theresults suggest that the items and six factors are conceptually relevant, confirming the six-factor structure of the pretest version of SATS-36 on this sample of Swedish students, with a few suggested modifications of the original model structure. Two items are excluded from the Difficulty component, two items on the Affect component are allowed to correlate, and two items on the Cognitive competence component are also allowed to correlate.
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| 9. |
- Wang, Xiaoqin, Docent, 1963-, et al.
(författare)
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The statistical evidence missing from the Swedish decision-making of COVID-19 strategy during the early period : A longitudinal observational analysis
- 2022
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Ingår i: SSM - Population Health. - : Elsevier. - 2352-8273. ; 18
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Tidskriftsartikel (refereegranskat)abstract
- A controversy about the Swedish strategy of dealing with COVID-19 during the early period is how decision making was based on evidence, which refers to data and data analysis. During the earliest period of the pandemic, the Swedish decision-making was based on subjective perspective. However, when more data became available, the decision-making stood on mathematical and descriptive analyses. The mathematical analysis aimed to model the condition for herd immunity while the descriptive analysis compared different measures without adjustment of population differences and updating pandemic situations. Due to the dubious interpretations of these analyses, a mild measure was adopted in Sweden upon the arrival of the second wave, leading to a surge of poor public health outcomes compared to the other Nordic countries (Denmark, Norway, and Finland). In this article, using data available during the first wave, we conduct longitudinal analysis to investigate the consequence of the shred of evidence in the Swedish decision-making for the first wave, where the study period is between January 2020 and August 2020. The design is longitudinal observational study. The linear regressions based on the Poisson distribution and the binomial distribution are employed for the analysis. We found that the early Swedish measure had a long-term and significant effect on general mortality and COVID19 mortality and a certain mitigating effect on unemployment in Sweden during the first wave; here, the effect was measured by an increase of general deaths, COVID-19 deaths or unemployed persons under Swedish measure relative to the measures adopted by the other Nordic countries. These pieces of statistical evidence were not studied in the mathematical and descriptive analyses but could play an important role in the decision-making at the second wave. In conclusion, a timely longitudinal analysis should be part of the decision-making process for containing the current pandemic or a future one.
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