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Factors Driving Construction Material Price Volatility in Qatar's Construction Industry: An Investigation Using generalized structured component analysis (GSCA)

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BUILDINGS 2025年第9期15卷 1475-1475页

作者： Gunduz, Murat Naji, Khalid K. Al-Marri, Hadi Qatar Univ Dept Civil & Environm Engn POB 2713 Doha Qatar Qatar Univ Coll Engn Dept Engn Management POB 2713 Doha Qatar

Although earlier studies had connected material scarcity and price fluctuation to cost overruns and delays in Qatar's building projects, the root causes of this volatility had not been investigated. To close this crucial gap, this study looked into these drivers. The research analyzed the factors affecting construction material prices by addressing three key objectives. First, it identified and categorized factors based on a comprehensive literature review. Second, it examined the influence of these factors and categories on material prices using generalized structured component analysis (GSCA). Third, it proposed strategies for mitigating price volatility within the Qatari construction sector. Fourteen factors were identified and grouped into three constructs: Economic and Regulatory, Logistics, and Market. Data were collected through an online survey, producing 195 responses. The GSCA method was applied to assess the model, which met the goodness-of-fit, reliability, and validity criteria for component-based structural equation modeling. The findings showed that all three constructs positively affected material prices. The Economic and Regulatory construct exerted the most significant influence, with a t-value of 5.987, followed by the Logistics construct with a t-value of 2.292. The Market construct had the lowest impact, with a t-value of 0.099. The model's R-square value of 0.41 indicated that the three constructs accounted for 41% of the variation in construction material prices in Qatar.

关键词： construction material prices construction industry price volatility generalized structured component analysis

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generalized structured component analysis Accommodating Convex components: A Knowledge-Based Multivariate Method with Interpretable Composite Indexes

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PSYCHOMETRIKA 2024年第1期89卷 241-266页

作者： Cho, Gyeongcheol Hwang, Heungsun Ohio State Univ Columbus OH USA McGill Univ Montreal PQ Canada Ohio State Univ Dept Psychol 1827 Neil Ave Columbus OH 43210 USA

generalized structured component analysis (GSCA) is a multivariate method for examining theory-driven relationships between variables including components. GSCA can provide the deterministic component score for each individual once model parameters are estimated. As the traditional GSCA always standardizes all indicators and components, however, it could not utilize information on the indicators' scale in parameter estimation. Consequently, its component scores could just show the relative standing of each individual for a component, rather than the individual's absolute standing in terms of the original indicators' measurement scales. In the paper, we propose a new version of GSCA, named convex GSCA, which can produce a new type of unstandardized components, termed convex components, which can be intuitively interpreted in terms of the original indicators' scales. We investigate the empirical performance of the proposed method through the analyses of simulated and real data.

关键词： generalized structured component analysis convex component multivariate analysis composite index interpretability

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generalized structured component analysis with Latent Interactions

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PSYCHOMETRIKA 2010年第2期75卷 228-242页

作者： Hwang, Heungsun Ho, Moon-Ho Ringo Lee, Jonathan McGill Univ Montreal PQ H3A 2T5 Canada Nanyang Technol Univ Singapore 639798 Singapore

generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling. In practice, researchers may often be interested in examining the interaction effects of latent variables. However, GSCA has been geared only for the specification and testing of the main effects of variables. Thus, an extension of GSCA is proposed to effectively deal with various types of interactions among latent variables. In the proposed method, a latent interaction is defined as a product of interacting latent variables. As a result, this method does not require the construction of additional indicators for latent interactions. Moreover, it can easily accommodate both exogenous and endogenous latent interactions. An alternating least-squares algorithm is developed to minimize a single optimization criterion for parameter estimation. A Monte Carlo simulation study is conducted to investigate the parameter recovery capability of the proposed method. An application is also presented to demonstrate the empirical usefulness of the proposed method.

关键词： generalized structured component analysis latent interactions alternating least squares

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A Prediction-Oriented Specification Search Algorithm for generalized structured component analysis

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STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL 2022年第4期29卷 611-619页

作者： Cho, Gyeongcheol Hwang, Heungsun Sarstedt, Marko Ringle, Christian M. McGill Univ Montreal PQ Canada Ludwig Maximilians Univ Munchen Munich Germany Babes Bolyai Univ Cluj Napoca Romania Hamburg Univ Technol TUHH Hamburg Germany

generalized structured component analysis (GSCA) is used for specifying and testing the relationships between observed variables and components. GSCA can perform model selection by comparing theoretically established models. In practice, however, theories may not always completely and unambiguously specify the relationships between variables in the model. In such situations, a specification search strategy allows for exploring potential relationships between variables in a data-driven manner. A specification search based on prediction of unseen observations is attractive as it does not require the provision of theoretically plausible models. To date, GSCA has not been equipped with such a specification search strategy. Addressing this limitation, we propose a prediction-oriented specification search algorithm for GSCA, which reveals the best combination of predictors that minimizes each target variable's prediction error. We conduct a simulation study to examine the new algorithm's performance and apply it to real data to further investigate and demonstrate its practical usefulness.

关键词： Cross-validation generalized structured component analysis predictive modeling specification search

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Deep Learning generalized structured component analysis: An Interpretable Artificial Neural Network Model with Composite Indexes

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STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL 2023年第2期31卷 265-279页

作者： Cho, Gyeongcheol Hwang, Heungsun McGill Univ Montreal PQ Canada McGill Univ Dept Psychol 2001 McGill Coll Ave Montreal PQ H3A1G1 Canada

generalized structured component analysis (GSCA) is a multivariate method for specifying and examining interrelationships between observed variables and components. Despite its data-analytic flexibility honed over the decade, GSCA always defines every component as a linear function of observed variables, which can be less optimal when observed variables for a component are nonlinearly related, often reducing the component's predictive power. To address this issue, we combine deep learning and GSCA into a single framework to allow a component to be a nonlinear function of observed variables without specifying the exact functional form in advance. This new method, termed deep learning generalized structured component analysis (DL-GSCA), aims to maximize the predictive power of components while their directed or undirected network remains interpretable. Our real and simulated data analyses show that DL-GSCA produces components with greater predictive power than those from GSCA in the presence of nonlinear associations between observed variables per component.

关键词： Composite index deep learning generalized structured component analysis nonlinear component

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gscaLCA in R: Fitting Fuzzy Clustering analysis Incorporated with generalized structured component analysis

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Computer Modeling in Engineering & Sciences 2022年第9期132卷 801-822页

作者： Ji Hoon Ryoo Seohee Park Seongeun Kim Heungsun Hwang Department of Education College of Educational SciencesYonsei UniversitySeoul03722South Korea Psychometrics Department American Board of Internal MedicinePhiladelphia19016USA Department of Educational Research Methodology School of EducationUniversity of North Carolina at GreensboroGreensboro27412USA Department of Psychology McGill UniversityMontrealH3A 0G4Canada

Clustering analysis identifying unknown heterogenous subgroups of a population(or a sample)has become increasingly popular along with the popularity of machine learning *** there are many software packages running clustering analysis,there is a lack of packages conducting clustering analysis within a structural equation modeling *** package,gscaLCA which is implemented in the R statistical computing environment,was developed for conducting clustering analysis and has been extended to a latent variable *** specifically,by applying both fuzzy clustering(FC)algorithm and generalized structured component analysis(GSCA),the package gscaLCA computes membership prevalence and item response probabilities as posterior probabilities,which is applicable in mixture modeling such as latent class analysis in *** a hybrid model between data clustering in classifications and model-based mixture modeling approach,fuzzy clusterwise GSCA,denoted as gscaLCA,encompasses many advantages from both methods:(1)soft partitioning from FC and(2)efficiency in estimating model parameters with bootstrap method via resolution of global optimization problem from *** main function,gscaLCA,works for both binary and ordered categorical *** addition,gscaLCA can be used for latent class regression as *** of profiles of latent classes based on the posterior probabilities is also available in the package *** paper contributes to providing a methodological tool,gscaLCA that applied researchers such as social scientists and medical researchers can apply clustering analysis in their research.

关键词： Fuzzy clustering generalized structured component analysis gscaLCA latent class analysis

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Multilevel Dynamic generalized structured component analysis for Brain Connectivity analysis in Functional Neuroimaging Data

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PSYCHOMETRIKA 2016年第2期81卷 565-581页

作者： Jung, Kwanghee Takane, Yoshio Hwang, Heungsun Woodward, Todd S. Univ Texas Hlth Sci Ctr Houston Houston TX 77030 USA Univ Victoria Victoria BC V8W 2Y2 Canada McGill Univ Montreal PQ H3A 2T5 Canada Univ British Columbia Vancouver BC V5Z 1M9 Canada

We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.

关键词： generalized structured component analysis multilevel analysis structural equation modeling alternating least squares (ALS) algorithm brain connectivity analysis time series data functional neuroimaging multi-subject data

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Fuzzy clusterwise generalized structured component analysis

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PSYCHOMETRIKA 2007年第2期72卷 181-198页

作者： Hwang, Heungsun DeSarbo, Wayne S. Takane, Yoshio McGill Univ Dept Psychol Montreal PQ H3A 1B1 Canada Penn State Univ University Pk PA 16802 USA McGill Univ Montreal PQ H3A 2T5 Canada

generalized structured component analysis (GSCA) was recently introduced by Hwang and Takane (2004) as a component-based approach to path analysis with latent variables. The parameters of GSCA are estimated by pooling data across respondents under the implicit assumption that they all come from a single, homogenous group. However, as has been empirically demonstrated by various researchers across a number of areas of inquiry, such aggregate analyses can often mask the true structure in data when respondent heterogeneity is present. In this paper, GSCA is generalized to a fuzzy clustering framework so as to account for potential group-level respondent heterogeneity. An alternating least-squares procedure is developed and technically described for parameter estimation. A small-scale Monte Carlo study involving synthetic data is carried out to compare the performance between the proposed method and an extant approach. In addition, an empirical application concerning alcohol use among adolescents from US northwestern urban areas is presented to illustrate the usefulness of the proposed method. Finally, a number of directions for future research are provided.

关键词： generalized structured component analysis fuzzy clustering respondent heterogeneity alternating least squares latent curve models alcohol use

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Bayesian generalized structured component analysis

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BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY 2020年第2期73卷 347-373页

作者： Choi, Ji Yeh Hwang, Heungsun Natl Univ Singapore Dept Psychol Singapore 117570 Singapore McGill Univ Montreal PQ Canada

generalized structured component analysis (GSCA) is a component-based approach to structural equation modelling, which adopts components of observed variables as proxies for latent variables and examines directional relationships among latent and observed variables. GSCA has been extended to deal with a wider range of data types, including discrete, multilevel or intensive longitudinal data, as well as to accommodate a greater variety of complex analyses such as latent moderation analysis, the capturing of cluster-level heterogeneity, and regularized analysis. To date, however, there has been no attempt to generalize the scope of GSCA into the Bayesian framework. In this paper, a novel extension of GSCA, called BGSCA, is proposed that estimates parameters within the Bayesian framework. BGSCA can be more attractive than the original GSCA for various reasons. For example, it can infer the probability distributions of random parameters, account for error variances in the measurement model, provide additional fit measures for model assessment and comparison from the Bayesian perspectives, and incorporate external information on parameters, which may be obtainable from past research, expert opinions, subjective beliefs or knowledge on the parameters. We utilize a Markov chain Monte Carlo method, the Gibbs sampler, to update the posterior distributions for the parameters of BGSCA. We conduct a simulation study to evaluate the performance of BGSCA. We also apply BGSCA to real data to demonstrate its empirical usefulness.

关键词： Bayesian analysis component-based approach generalized structured component analysis Gibbs sampler structural equation modelling

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Functional generalized structured component analysis

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PSYCHOMETRIKA 2016年第4期81卷 940-968页

作者： Suk, Hye Won Hwang, Heungsun Arizona State Univ Tempe AZ 85287 USA McGill Univ Montreal PQ Canada

An extension of generalized structured component analysis (GSCA), called Functional GSCA, is proposed to analyze functional data that are considered to arise from an underlying smooth curve varying over time or other continua. GSCA has been geared for the analysis of multivariate data. Accordingly, it cannot deal with functional data that often involve different measurement occasions across participants and a large number of measurement occasions that exceed the number of participants. Functional GSCA addresses these issues by integrating GSCA with spline basis function expansions that represent infinite-dimensional curves onto a finite-dimensional space. For parameter estimation, functional GSCA minimizes a penalized least squares criterion by using an alternating penalized least squares estimation algorithm. The usefulness of functional GSCA is illustrated with gait data.

关键词： generalized structured component analysis functional data analysis basis function expansion splines penalized least squares alternating least squares

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