Causal Clustering for 1-Factor Measurement Models

Leave‐multiple‐out cross‐validation (LMO‐CV) is compared to leave‐one‐out cross‐validation (LOO‐CV) as objective function in variable selection for four real data sets. Two data sets stem from NIR spectroscopy and two from quantitative structure–activity relationships. In all four cases, LMO‐CV outperforms LOO‐CV with respect to prediction quality, model complexity (number of latent variables) and model size (number of variables). The number of objects left out in LMO‐CV has an important effect on the final results. It controls both the number of latent variables in the final model and the prediction quality. The results of variable selection need to be validated carefully with a validation step that is independent of the variable selection. This step needs to be done because the internal figures of merit (i.e. anything that is derived from the objective function value) do not correlate well with the external predictivity of the selected models. This is most obvious for LOO‐CV. LOO‐CV without further constraints always shows the best internal figures of merit and the worst prediction quality. Copyright © 2002 John Wiley & Sons, Ltd.

Using spatial information for evaluating the quality of prediction maps from hyperspectral images: A geostatistical approach

Structural Equation Model (SEM) is a statistical technique with simultaneous processing involves measurement errors, indicator variables, and latent variables. SEM is used to test hypotheses that state the relationships between latent variables when latent variables have been assessed through each of the indicator variables. Multiple Group SEM is a basic model analysis that uses more than one sample. This analysis aims to determine whether the components or models of measurement and structural models are invariant for the two sample groups. In this study, the data generated by some requirements. First, the data generated with sample size n = 250. The first generated data is homogeneous data where the measurement model is the same as the structural model in group 1 and group 2, while the second data is non-homogeneous data where the measurement model and the structural model in group 1 and group 2 is not the same. The data was analyzed using the help of the lavaan package available in R to obtain SEM estimation results and Goodness of Fit Model from some data that was formed. From the results of the merger of the two groups, it shows that the invariant of the two models with the largest df (63) which is Fit Mean model states the simplest model. However, the smallest df (48) with Fit.configural model states the most complex model.
 Keywords: SEM, Multiple Group, R Program

Linear models in social science, psychlology, economics, and epidemiology are often formullated in latent continuous variables, which can be measured only with error or through two or more observed indicators. Henice, the latent variables are connected with the observed variables by a measurement model. If the observed indicators are metric, the covariance matrix of the observed variables is analyzed by using covariance structure models. Covariance structure models have been extended by MuthMn (1984) to deal with indicators that are metric and ordered categorical rather than metric alone. By abandoning the analysis of covariance structures and returning to some of the original ideas of latent structure analysis, we can extend the class of variables that may serve as indicators for latent variables utsed in a simultaneous equation system to include variables on any level of measurement. This class comprises observed variables that may be metric, censored metric, counts, ordered, or unordered categorical. A limited marginal maximum likelihood approach is used to estimate the model. Numerical procedures necessary to implement such an estimation are discussed.

Latent variable (LV) models are increasingly being used in environmental epidemiology as a way to summarize multiple environmental exposures and thus minimize statistical concerns that arise in multiple regression. LV models may be especially useful when multivariate exposures are collected repeatedly over time. LV models can accommodate a variety of assumptions but, at the same time, present the user with many choices for model specification particularly in the case of exposure data collected repeatedly over time. For instance, the user could assume conditional independence of observed exposure biomarkers given the latent exposure and, in the case of longitudinal latent exposure variables, time invariance of the measurement model. Choosing which assumptions to relax is not always straightforward. We were motivated by a study of prenatal lead exposure and mental development, where assumptions of the measurement model for the time-changing longitudinal exposure have appreciable impact on (maximum-likelihood) inferences about the health effects of lead exposure. Although we were not particularly interested in characterizing the change of the LV itself, imposing a longitudinal LV structure on the repeated multivariate exposure measures could result in high efficiency gains for the exposure-disease association. We examine the biases of maximum likelihood estimators when assumptions about the measurement model for the longitudinal latent exposure variable are violated. We adapt existing instrumental variable estimators to the case of longitudinal exposures and propose them as an alternative to estimate the health effects of a time-changing latent predictor. We show that instrumental variable estimators remain unbiased for a wide range of data generating models and have advantages in terms of mean squared error. Copyright © 2017 John Wiley & Sons, Ltd.

Differential item functioning (DIF), referring to between-group variation in item characteristics above and beyond the group-level disparity in the latent variable of interest, has long been regarded as an important item-level diagnostic. The presence of DIF impairs the fit of the single-group item response model being used, and calls for either model modification or item deletion in practice, depending on the mode of analysis. Methods for testing DIF with continuous covariates, rather than categorical grouping variables, have been developed; however, they are restrictive in parametric forms, and thus are not sufficiently flexible to describe complex interaction among latent variables and covariates. In the current study, we formulate the probability of endorsing each test item as a general bivariate function of a unidimensional latent trait and a single covariate, which is then approximated by a two-dimensional smoothing spline. The accuracy and precision of the proposed procedure is evaluated via Monte Carlo simulations. If anchor items are available, we proposed an extended model that simultaneously estimates item characteristic functions (ICFs) for anchor items, ICFs conditional on the covariate for non-anchor items, and the latent variable density conditional on the covariate-all using regression splines. A permutation DIF test is developed, and its performance is compared to the conventional parametric approach in a simulation study. We also illustrate the proposed semiparametric DIF testing procedure with an empirical example.

The utilization of selection methods such as genetic algorithm (GA) aims to construct better partial least squares (PLS) and principal component regression (PCR) models than those established from the full-spectrum range. Determination of paracetamol (PAR), orphenadrine citrate (Or.cit), and caffeine (CAF) in the presence of PAR nephrotoxic impurity [p-aminophenol (PAP)]. GA was applied to select the optimum wavelengths used. A calibration set was prepared in which the three drugs, together with PAP, were modeled by multilevel multifactor design. This calibration set was used to build the PLS and PCR models, either with or without preprocessing the data using GA. Results were compared with and without preprocessing, and this revealed that GA can find an optimized combination of spectral wavelengths, yielding a lower root mean square error of prediction as well as a lower number of latent variables used. The results of the two models show that simultaneous determination of the aforementioned drugs can be performed in the concentration ranges of 20-60, 3-11, and 1-9 μg/mL for PAR, Or.cit, and CAF, respectively. The proposed models were applied for the determination of the three drugs in their pharmaceutical formulations, and the results were verified by the standard addition technique. GA can be useful as a wavelength selection tool before applying multivariate PLS and PCR methods. GA gives an improvement in the predictive ability of the models with lower RMSEP and less number of latent variables (LVs). The proposed PLS, PCR, GA-PLS, and GA-PCR spectrophotometric methods were able to determine paracetamol, orphenadrine citrate, and caffeine in the presence of p-aminophenol and severe spectral overlapping.

Service quality (SQ) and customer satisfaction as perceived by 1037 passengers on intercity train services in Bangladesh were examined using structural equation modeling to explain the overall SQ of intercity train service. This study presents two structural equation models to reveal the influence of the observed variables on the SQ of intercity train service in the context of a developing country. Out of the two models, the best one is selected based on statistical parameters and resemblance with real life expectations. The framework of the models is based on eighteen observed variables; among them five were considered as endogenous and rest of the thirteen as exogenous variables. The number of latent variables used in the models is one, which is confirmed by the factor analysis. Out of the eighteen observed variables, ‘Overall security’, ‘Female harassment’, ‘Fitness of compartment’ and ‘Waiting place condition’ are found to be the variables which predominantly influence SQ. Moreover, the effect of heterogeneity among users on the performance of the best model is investigated. All the study findings match the real life scenario of a developing country. Acknowledging limited resources of a developing country, the present study provides a guideline to the railway operator for making the intercity train service more attractive for both prospective and existing users.

ABSTRACTThis study presents the development process of a set of questionnaire items to establish a measurement model for the usability of shared workspace groupware systems, which is suggested as a usability scale called SWUS, the Shared Workspace Usability Scale. Manifest variables and latent variables are based on the various dimensions of teamwork collated through the literature. A structural model was built on the measurement model. Models were evaluated through PLS-SEM methods. Data acquired on candidate questionnaire items from 398 international respondents who are users of five different online collaborative word processors was used for the model analysis. Of 37 candidate manifest variables, 22 were retained, which were measuring seven latent constructs: “3C Mechanisms,” “Grounding,” “Team Integration,” “Communication,” “Shared Access,” “Awareness,” and “Usability.” The data provided empirical evidence for the structural model based on these latent variables. The responses of the participants were not sensitive to differences between users in terms of gender and native language but showed sensitivity to age, experience with the evaluated software, and different shared workspace groupware evaluated in the study. Our structural model attempts to integrate several frameworks and models of usability for CSCW environments and provides empirical evidence for its reliability and validity based on subjective responses from users of shared workspace groupware.

Linear structural equation models with latent variables are common in psychology, econometrics, sociology, and political science. Such models have two parts, a measurement model that specifies how the latent variables are measured, and a structural model which specifies the causal relations among the latent variables. In this paper I discuss search procedures for finding a ‘pure’ measurement model and for using this pure model to determine features of the structural model. The procedures are implemented as the Purify and MIMbuild modules of the TETRAD II program.KeywordsLatent VariableMeasurement ModelCausal StructureLatent Variable ModelMeasured IndicatorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

As part of efforts to promote sustainable mobility, many cities are currently experiencing the rapid expansion of their metro network. The consequent growth in ridership motivates a broad range of travel demand management (TDM) policies, both in terms of passenger flow control and dynamic pricing strategies. This work aims to reveal the impact of TDM on metro commuters’ behavioural loyalty using stated-preference data collected in Guangzhou, China. Commuters’ behavioural response to TDM strategies is investigated in terms of the possible shift in departure time and travel mode. A hybrid choice model framework is used to incorporate four latent variables of interest, i.e., service quality, overall impression, external attractiveness and switching cost, into the discrete choice model and thereby capture the relationships between the attitudinal factors and observed variables. The model estimation results indicate that the four latent variables all prove useful in interpreting commuters’ behavioural loyalty. Commuters’ perceived service quality and overall impression both show a positive effect on their willingness to continue travelling by metro and are thus instructive for ridership retention. External attractiveness is found to be significant only in the case of the tendency to shift to a private car. Switching costs reveal commuters’ emotional attachment to their already developed commuting habit. These insights into commuters’ behavioural change intention enable metro operators to enhance commuters’ loyalty to their service and develop more effective TDM strategies in future practice.

The number of latent variables (LVs) or the factor number is a key parameter in PLS modeling to obtain a correct prediction. Although lots of work have been done on this issue, it is still a difficult task to determine a suitable LV number in practical uses. A method named independent factor diagnostics (IFD) is proposed for investigation of the contribution of each LV to the predicted results on the basis of discussion about the determination of LV number in PLS modeling for near infrared (NIR) spectra of complex samples. The NIR spectra of three data sets of complex samples, including a public data set and two tobacco lamina ones, are investigated. It is shown that several high order LVs constitute main contributions to the predicted results, albeit the contribution of the low order LVs should not be neglected in the PLS models. Therefore, in practical uses of PLS for analysis of complex samples, it may be better to use a slightly large LV number for NIR spectral analysis of complex samples.

The high dimensionality of global gene expression profiles, where number of variables (genes) is very large compared to the number of observations (samples), presents challenges that affect generalizability and applicability of microarray analysis. Latent variable modeling offers a promising approach to deal with high-dimensional microarray data. The latent variable model is based on a few latent variables that capture most of the gene expression information. Here, we describe how to accomplish a reduction in dimension by a latent variable methodology, which can greatly reduce the number of features used to characterize microarray data. We propose a general latent variable framework for prediction of predefined classes of samples using gene expression profiles from microarray experiments. The framework consists of (i) selection of smaller number of genes that are most differentially expressed between samples, (ii) dimension reduction using hierarchical clustering, where each cluster partition is identified as latent variable, (iii) discretization of gene expression matrix, (iv) fitting the Rasch item response model for genes in each cluster partition to estimate the expression of latent variable, and (v) construction of prediction model with latent variables as covariates to study the relationship between latent variables and phenotype. Two different microarray data sets are used to illustrate a general framework of the approach. We show that the predictive performance of our method is comparable to the current best approach based on an all-gene space. The method is general and can be applied to the other high-dimensional data problems.

The objective of the present study was to test for multigroup invariance in measurement models and structural models between job characteristics, psychosocial intervening variables, health outcomes and sickness absenteeism. Four types of occupation were represented in the study: blue-collar workers ( n = 241), white-collar workers ( n = 209), elderly-care workers ( n = 338) and child-care workers ( n = 336). A first-order, six-factor multigroup confirmatory factor analysis model (i.e. measurement model) composed of two perceived job characteristics ( job autonomy and skill discretion), appraised workload, job satisfaction, stress-related ill-health and sickness absenteeism provided a good model fit. Invariance tests showed that the six-factor model fits well for all occupations. A partially recursive mediated multigroup structural model showed both similarities and differences across occupations as regards the relationships between independent latent variables ( job autonomy, skill discretion), intervening latent variables (appraised workload, job satisfaction) and dependent latent variables (stressrelated ill-health, sickness absenteeism). By comparing a generic model with occupation-specific models across occupations, this study showed that occupation-specific models were more plausible. The results indicate that it is important to examine different occupational contexts in detail to better understand how certain psychosocial factors at work influence strain in different occupations. Since job characteristics can potentially be amended, the findings have important implications for the differentiation of prevention and intervention in different occupations.

Structural equation modeling (SEM) consists of two parts, the measurement model and the structural model. In this paper, we develop a SEM model that the measurement model is linear and the structural model is nonlinear. Structural model explain about relationship between latent variables. Truncated spline is used for modeling the nonlinear relationship between the latent variables. Trought weighted least squares (WLS) method, It is found that estimation of parameter truncated spline in nonlinear SEM for two exogen and an endogen latent variables is resulted by optimizingminγ{‖Σ−12(ωη−Γξκγ)‖2}and it gave an estimatorγ^=(Γ′ξkΣ−1Γξk)−1Γ′ξkΣ−1ωηThe estimator influenced by the location and some knot points κ1 and κ2.