Approximated penalized maximum likelihood for exploratory factor analysis: an orthogonal case

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

Mediation modeling and analysis for high-throughput omics data

Abstract There is a strong need for powerful unified statistical methods for discovering underlying genetic architecture of complex traits with the assistance of omics information. In this paper, two methods aiming to detect novel association between the human genome and complex traits using intermediate omics data are developed based on statistical mediation modeling. We demonstrate theoretically that given proper mediators, the proposed statistical mediation models have better power than genome-wide association studies (GWAS) to detect associations missed in standard GWAS that ignore the mediators. For each of the modeling methods in this paper, an empirical example is given, where the association between a SNP and BMI missed by standard GWAS can be discovered by mediation analysis

A Livable City Study in China Using Structural Equation Models

Abstract The concept of livable city was put forward naturally and began to gain more attention when people care more about human needs during the economic development. In this paper, we define a livable city as an urban area takes the residents' demand as first priority. It has a pleasant ecological environment, a mature community with rich public resources such as culture, transportation and medical system, and being economically well developed. Our study first reviews the theory development and literature on the subject. Then we set up a structural equation model (SEM) to verify the theory based on early works and find the dimensions that are important to the development of livable city. Using the data from China City Yearbook, 2007, a SEM analysis was performed. The result showed that a well developing economic system has positive influence on a city's livability

Generalized Linear Factor Score Regression : A Comparison of Four Methods

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

Asymptotic Robustness Study Of The Polychoric Correlation Estimation

Asymptotic robustness against misspecification of the underlying distribution for the polychoric correlation estimation is studied. The asymptotic normality of the pseudo-maximum likelihood estimator is derived using the two-step estimation procedure. The t distribution assumption and the skew-normal distribution assumption are used as alternatives to the normal distribution assumption in a numerical study. The numerical results show that the underlying normal distribution can be substantially biased, even though skewness and kurtosis are not large. The skew-normal assumption generally produces a lower bias than the normal assumption. Thus, it is worth using a non-normal distributional assumption if the normal assumption is dubious

On the identification of the unrestricted Thurstonian model for ranking data

The identification issues of the unrestricted Thurstonian model for ranking data is the focus of the current paper. The Thurstonian framework has been proved very influential in modeling ranking data. Within this framework, the objects to be ranked are associated with a latent continuous variable, often interpreted as utility. The unrestricted Thurstonian model has a central role in the related theory development but due to the discrete and comparative nature of ranking data it faces more serious identification problems than the indeterminacy of the latent scale origin and unit. Most researchers resort to the study of the unrestricted model referring to the differences of object utilities but then the inference on object utilities becomes tricky. Maydeu-Olivares & Böckenholt (2005) suggest a strategy to overcome the identification problem of the unrestricted model referring to object utilities but this requires many extra identification constraints, additional to the ones needed for defining the scale origin and unit. In the current paper, we study the suggested identification approach to investigate its general applicability. Our findings indicate that the estimates obtained based on this approach can be seriously biased when the extra constraints deviate from the true values of the parameters. Besides, the effect of the constraints is not uniform on all estimated parameters.

Economic or Non-Economic Factors – What Empowers Women?

Microfinance programs like Self Help Group Bank linkage program (SHG), aim to empower women through provision of financial services. We investigate this further to determine whether it is the economic or the non-economic factors that have a greater impact on empowering women. Using household survey data on SHG from India, a general structural model is adopted where the latent women empowerment and its latent components (economic factors and financial confidence, managerial control, behavioural changes, education and networking, communication and political participation and awareness) are measured using observed indicators. The results show that for SHG members, economic factors, managerial control and behavioural changes are the most significant factors in empowering women.microfinance; impact; women empowerment

Economic or Non-Economic Factors – What Empowers Women?

Microfinance programs like Self Help Group Bank linkage program (SHG), aim to empower women through provision of financial services. We investigate this further to determine whether it is the economic or the non-economic factors that have a greater impact on empowering women. Using household survey data on SHG from India, a general structural model is adopted where the latent women empowerment and its latent components (economic factors and financial confidence, managerial control, behavioural changes, education and networking, communication and political participation and awareness) are measured using observed indicators. The results show that for SHG members, economic factors, managerial control and behavioural changes are the most significant factors in empowering women