Dissertations / Theses on the topic 'Dynamic structural equation models'

As organizações estão expostas a uma quantidade e variabilidade cada vez mais crescente de informações. A capacidade de se antecipar às tendências e se adequar ao ambiente passa a ser, além de fonte de vantagem competitiva, fator necessário para a sobrevivência. Nessa realidade, organizações frequentemente apresentam dificuldades de leitura de seu ambiente e adaptação ao meio. Acredita-se na necessidade de desenvolvimento de uma capacidade interna da organização para que o monitoramento do ambiente se estabeleça, bem como análise de oportunidades, planejamento de ações de melhoria e reconfiguração da organização. Este estudo buscou verificar a relação da Inteligência - enquanto processo de monitoramento do ambiente - como uma Capacidades Dinâmica, e de como esta pode contribuir com a geração de vantagem competitiva. Criou-se um modelo de pesquisa, utilizando-se dos modelos de Rios (2010) e Teece (2014), relacionando os conceitos de Inteligência e Capacidade Dinâmicas, e estas com a Vantagem Competitiva. Por meio de um questionário, realizou-se uma Pesquisa Survey, onde coletaram-se respostas de funcionários e/ou sócio de empresas brasileiras, independente de porte ou segmento. Para análise, utilizou-se da Modelagem de Equações Estruturais, e foi possível demonstrar que a Inteligência influencia positivamente nas Capacidades Dinâmicas do sub-grupo Transforming, na Estratégia e na Vantagem Competitiva. Dessa forma o modelo desenvolvido, tendo apresentado boa confiabilidade e aderência, pode também ser validado.<br>Organizations are exposed to an increasing amount and variability of information. The ability to anticipate trends and adapt to the environment becomes, besides a source of competitive advantage, a necessary factor for survival. In this reality, organizations frequently present difficulties in reading their environment and adapting to them. We believe in the need to develop an internal capacity of the organization for the monitoring of the environment to be established, as well as analysis of opportunities, planning of actions of improvement and reconfiguration of the organization. This study sought to verify the relationship of Intelligence - as a process of monitoring the environment - as a Dynamic Capabilities, and how this can contribute to the generation of competitive advantage. A research model was created, using the models of Rios (2010) and Teece (2014), relating the concepts of Dynamic Intelligence and Capacity, and these with the Competitive Advantage. By means of a questionnaire, a Survey Research was conducted, where responses were collected from employees and / or partners of Brazilian companies, regardless of size or segment. For the analysis, it was used the Modeling of Structural Equations, and it was possible to demonstrate that the Intelligence influences positively in the Dynamic Capacities of the Transforming subgroup, in the Strategy and the Competitive Advantage. In this way the developed model, having presented good reliability and adhesion, can also be validated.

Causal models have been used in different areas of knowledge in order to comprehend the causal associations between variables. Over the past decades, the amount of studies using these models have been growing a lot, especially those related to biological systems where studying and learning causal relationships among traits are essential for predicting the consequences of interventions in such system. Graph analysis (GA) and structural equation modeling (SEM) are tools used to explore such associations. While GA allows searching causal structures that express qualitatively how variables are causally connected, fitting SEM with a known causal structure allows to infer the magnitude of causal effects. Also SEM can be viewed as multiple regression models in which response variables can be explanatory variables for others. In quantitative genetics studies, SEM aimed to study the direct and indirect genetic effects associated to individuals through information related to them, beyond the observed characteristics, such as the kinship relations. In those studies typically the assumptions of linear relationships among traits are made. However, in some scenarios, nonlinear relationships can be observed, which make unsuitable the mentioned assumptions. To overcome this limitation, this paper proposes to use a mixed effects polynomial structural equation model, second or superior degree, to model those nonlinear relationships. Two studies were developed, a simulation and an application to real data. The first study involved simulation of 50 data sets, with a fully recursive causal structure involving three characteristics in which linear and nonlinear causal relations between them were allowed. The second study involved the analysis of traits related to dairy cows of the Holstein breed. Phenotypic relationships between traits were calving difficulty, gestation length and also the proportion of perionatal death. We compare the model of multiple traits and polynomials structural equations models, under different polynomials degrees in order to assess the benefits of the SEM polynomial of second or higher degree. For some situations the inappropriate assumption of linearity results in poor predictions of the direct, indirect and total of the genetic variances and covariance, either overestimating, underestimating, or even assign opposite signs to covariances. Therefore, we conclude that the inclusion of a polynomial degree increases the SEM expressive power.<br>Modelos causais têm sido muitos utilizados em estudos em diferentes áreas de conhecimento, a fim de compreender as associações ou relações causais entre variáveis. Durante as últimas décadas, o uso desses modelos têm crescido muito, especialmente estudos relacionados à sistemas biológicos, uma vez que compreender as relações entre características são essenciais para prever quais são as consequências de intervenções em tais sistemas. Análise do grafo (AG) e os modelos de equações estruturais (MEE) são utilizados como ferramentas para explorar essas relações. Enquanto AG nos permite buscar por estruturas causais, que representam qualitativamente como as variáveis são causalmente conectadas, ajustando o MEE com uma estrutura causal conhecida nos permite inferir a magnitude dos efeitos causais. Os MEE também podem ser vistos como modelos de regressão múltipla em que uma variável resposta pode ser vista como explanatória para uma outra característica. Estudos utilizando MEE em genética quantitativa visam estudar os efeitos genéticos diretos e indiretos associados aos indivíduos por meio de informações realcionadas aos indivíduas, além das característcas observadas, como por exemplo o parentesco entre eles. Neste contexto, é tipicamente adotada a suposição que as características observadas são relacionadas linearmente. No entanto, para alguns cenários, relações não lineares são observadas, o que torna as suposições mencionadas inadequadas. Para superar essa limitação, este trabalho propõe o uso de modelos de equações estruturais de efeitos polinomiais mistos, de segundo grau ou seperior, para modelar relações não lineares. Neste trabalho foram desenvolvidos dois estudos, um de simulação e uma aplicação a dados reais. O primeiro estudo envolveu a simulação de 50 conjuntos de dados, com uma estrutura causal completamente recursiva, envolvendo 3 características, em que foram permitidas relações causais lineares e não lineares entre as mesmas. O segundo estudo envolveu a análise de características relacionadas ao gado leiteiro da raça Holandesa, foram utilizadas relações entre os seguintes fenótipos: dificuldade de parto, duração da gestação e a proporção de morte perionatal. Nós comparamos o modelo misto de múltiplas características com os modelos de equações estruturais polinomiais, com diferentes graus polinomiais, a fim de verificar os benefícios do MEE polinomial de segundo grau ou superior. Para algumas situações a suposição inapropriada de linearidade resulta em previsões pobres das variâncias e covariâncias genéticas diretas, indiretas e totais, seja por superestimar, subestimar, ou mesmo atribuir sinais opostos as covariâncias. Portanto, verificamos que a inclusão de um grau de polinômio aumenta o poder de expressão do MEE.

In structural equation models with latent variables, maximum likelihood (ML) estimation is currently the most prevailing estimation method. However, the ML method fails to provide accurate solutions in a number of situations including those involving small sample sizes, nonnormality, and model misspecification. To over come these difficulties, regularized extensions of two-stage least squares estimation are proposed that incorporate a ridge type of regularization in the estimation of parameters. Two simulation studies and two empirical applications demonstrate that the proposed method is a promising alternative to both the maximum likelihood and non-regularized two-stage least squares estimation methods. An optimal value of the regularization parameter is found by the K-fold cross validation technique. A nonparametric bootstrap method is used to evaluate the stability of solutions. A goodness-of-fit measure is used for assessing the overall fit.<br>Dans les modèles d'équations structurales avec des variables latentes, l'estimation demaximum devraisemblance est la méthode d'estimation la plus utilisée. Par contre, la méthode de maximum devraisemblance souvent ne réussit pas á fournir des solutions exactes, par exemple lorsque les échantillons sont petits, les données ne sont pas normale, ou lorsque le modèle est mal specifié. L'estimation des moindres carrés á deux-phases est asymptotiquement sans distribution et robuste contre mauvaises spécifications, mais elle manque de robustesse quand les chantillons sont petits. Afin de surmonter les trois difficultés mentionnés ci-dessus et d'obtenir une estimation plus exacte, des extensions régularisées des moindres carrés á deux phases sont proposé á qui incorporent directement un type de régularisation dans les modèles d'équations structurales avec des variables latentes. Deux études de simulation et deux applications empiriques démontrent que la méthode propose est une alternative prometteuse aux méthodes de maximum vraisemblance et de l'estimation des moindres carrés á deux-phases. Un paramètre de régularisation valeur optimale a été trouvé par la technique de validation croisé d'ordre K. Une méthode non-paramétrique Bootstrap est utilisée afin d'évaluer la stabilité des solutions. Une mesure d'adéquation est utilisée pour estimer l'adéquation globale.

Fit indices and fit measures commonly used to determine the accuracy and desirability of structural equation models are expected to be insensitive to nonlinearity in the data. This includes measures as ubiquitous as the CFI, TLI, RMSEA, SRMR, AIC, and BIC. Despite this, some software will report these measures when certain models are used. Consequently, some researchers may be led to use these fit measures without realizing the impropriety of the act. Alternative fit measures have been proposed, but these measures require further testing. As part of this thesis, a large simulation study was carried out to investigate alternative fit measures and to confirm whether the traditional measures are practically blind to nonlinearity in the data. The results of the simulation provide conclusive evidence that fit statistics and fit indices based on the chi-square distribution or the residual covariance matrix are entirely insensitive to nonlinearity. The posterior predictive p-value was also insensitive to nonlinearity. Only fit measures based on the structural residuals (i.e., HFI and R-squared) showed any sensitivity to nonlinearity. Of these, the R-squared was the only reliable measure of nonlinear model misspecification. This thesis shows that an effective strategy for determining whether a nonlinear model is preferable to a linear one involves using the R-squared to compare models that have been fit to the same data. An R-squared that is much larger for the nonlinear model than the linear model suggests that the linear model may be less desirable than the nonlinear model. The proposed method is intended to be supplementary to substantive theory. It is argued that any dependence on fit indices or fit statistics that places these measures on a higher pedestal than substantive theory will invariably lead to blindness on the part of the researcher. In other words, unwavering adherence to goodness-of-fit measures limits the researcher<'>s vision to what the measures themselves can detect.

Modelling of uncertainty increases trust in analysis tools by providing predictions with confidence levels, produces more robust designs, and reduces design cycle time/cost by reducing the amount of experimental verification and validation that is required. However, uncertainty-based methods are more complex and computationally expensive than their deterministic counterparts, the characterisation of uncertainties is a non-trivial task, and the industry feels comfortable with the traditional design methods. In this work the three most popular uncertainty propagation methods (Monte Carlo simulation, perturbation, and fuzzy) are extensively benchmarked in structural dynamics applications. The main focus of the benchmark is accuracy, simplicity, and scalability. Some general guidelines for choosing the adequate uncertainty propagation method for an application are given. Since direct measurement is often prohibitively costly or even impossible, a novel method to characterise uncertainty sources from indirect measurements is presented. This method can accurately estimate the probability distribution of uncertain parameters by maximising the likelihood of the measurements. The likelihood is estimated using efficient variations of the Monte Carlo simulation and perturbation methods, which shift the computational burden to the outside of the optimisation loop, achieving a substantial time-saving without compromising accuracy. The approach was verified experimentally in several applications with promising results. A novel probabilistic procedure for robust design is proposed. It is based on reweighting of the Monte Carlo samples to avoid the numerical inefficiencies of resampling for every candidate design. Although not globally convergent, the proposed method is able to quickly estimate with high accuracy the optimum design. The method is applied to a numerical example, and the obtained designs are verified with regular Monte Carlo. The main focus of this work was on structural dynamics, but care was taken to make the approach general enough to allow other kinds of structural and non- structural analyses.

Latent variable models (LVMs) are widely used in many scientific fields due to the ubiquitousness and feasibility of latent variables. Conventional LVMs, however, have limitations because they model relationships between covariates and latent variables or among latent variables with a parametric fashion. A more flexible model framework is therefore needed, especially without prior knowledge of sensible parametric forms. This thesis proposes a new non-parametric LVM for the need. We define a model structure with particular features, including a multi-layered structure constituting of non-parametric Gaussian Processes regression and parametric factor analysis. The connections to existing popular LVMs approaches, such as structural equation models and latent curve models, are also discussed. The model structure is subsequently extended for observed binary responses and longitudinal application. It follows that model identifiability is examined through parameter constraints and algebraic manipulations. The proposed model, despite convenient applicability, has a computational burden for analysing large data sets due to the computation of the inverse of a large covariance matrix. To address the issue, a sparse approximation method using a small number of M selected inputs (inducing inputs) is adopted. The associated computational cost can be reduced to O(M²NQ²) (or O(M²NT²)) where N and Q are the numbers of data points and latent variables (or time points T), respectively. Inference within this framework requires a series of algorithmic developments in a Bayesian paradigm. The algorithms, using Markov Chain Monte Carlo sampling-based methods and Expectation Maximisation optimisation methods with stochastic variant, are presented. A hybrid estimation procedure with two-step implementations is proposed as well, which can further reduce computational cost. Furthermore, a greedy selection scheme for inducing inputs is provided for better model predictive performance. Empirical studies of the modelling framework are conducted for various experiments. Interest lies in inference, including parameter estimation and realization of distribution of latent variables; and assessments and comparisons of predictive performance with two baseline techniques. Discussion and suggestions for improvement are provided based on results.

The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed on September 15, 2009). Thesis advisor: Dr. Stanislav Kolenikov. Includes bibliographical references.

This thesis which consists of four papers is concerned with estimation methods in factor analysis and structural equation models. New estimation methods are proposed and investigated. In paper I an approximation of the penalized maximum likelihood (ML) is introduced to fit an exploratory factor analysis model. Approximated penalized ML continuously and efficiently shrinks the factor loadings towards zero. It naturally factorizes a covariance matrix or a correlation matrix. It is also applicable to an orthogonal or an oblique structure. Paper II, a simulation study, investigates the properties of approximated penalized ML with an orthogonal factor model. Different combinations of penalty terms and tuning parameter selection methods are examined. Differences in factorizing a covariance matrix and factorizing a correlation matrix are also explored. It is shown that the approximated penalized ML frequently improves the traditional estimation-rotation procedure. In Paper III we focus on pseudo ML for multi-group data. Data from different groups are pooled and normal theory is used to fit the model. It is shown that pseudo ML produces consistent estimators of factor loadings and that it is numerically easier than multi-group ML. In addition, normal theory is not applicable to estimate standard errors. A sandwich-type estimator of standard errors is derived. Paper IV examines properties of the recently proposed polychoric instrumental variable (PIV) estimators for ordinal data through a simulation study. PIV is compared with conventional estimation methods (unweighted least squares and diagonally weighted least squares). PIV produces accurate estimates of factor loadings and factor covariances in the correctly specified confirmatory factor analysis model and accurate estimates of loadings and coefficient matrices in the correctly specified structure equation model. If the model is misspecified, robustness of PIV depends on model complexity, underlying distribution, and instrumental variables.

Matrix population models are discrete in both time and state-space, where a matrix with density-dependent entries is used to project a population vector of a stage-structured population from one time to the next. Such models are useful for modeling populations with discrete categorizations (e.g. developmental cycles, communities of multiple species, differing sizes, etc.). We present a general matrix model of two interacting populations where one (the resident) has a stable cycle, and we analyze when the other population (the invader) can successfully invade. Specifically, we study the local bifurcations of coexistence cycles as the resident cycle destabilizes, where a cycle of length 1 corresponds to an equilibrium. We make no assumptions on the types of interactions between the populations or on the population structure of the resident; we consider when the invader's projection matrix is primitive or imprimitive and 2x2. The simplest biological scenarios for such structures are an iteroparous invader and a two-stage semelparous invader. When the invader has a primitive projection matrix, coexistence cycles (of the same period as the resident cycle) bifurcate from the resident-cycle. When the invader has an imprimitive two-stage projection matrix, two types of coexistence cycles bifurcate from the resident-cycle: cycles of the same period and cycles of double the period. In both the primitive and imprimitive cases, we provide diagnostic quantities to determine the direction of bifurcation and the stability of the bifurcating cycles. Because we only perform a local stability analysis, the only successful invasion provided by our results is through stable coexistence cycles. As we show in some simple examples, however, the invader may persist when the coexistence cycles are unstable through competitive exclusion where the branch of bifurcating cycles connects to a branch of invader attractors and creates a multi-attractor scenario known as a strong Allee effect.

A new approach to structural equation modeling based on so-called extended redundancy analysis (ERA) is proposed. In ERA, latent variables are obtained as exact linear combinations of observed variables, and model parameters are estimated by consistently minimizing a single criterion. As a result, the method can avoid limitations of covariance structure analysis (e.g., stringent distributional assumptions, improper solutions, and factor score indeterminacy) in addition to those of partial least squares (e.g., the lack of a global optimization procedure). The method is simple yet versatile enough to fit more complex models; e.g., those with higher-order latent variables and direct effects of observed variables. It can also fit a model to more than one sample simultaneously. Other relevant topics are also discussed, including data transformations, missing data, metric matrices, robust estimation, and efficient estimation. Examples are given to illustrate the proposed method.

The classical approach to testing for structural change employs retrospective tests using a historical data set of a given length. Here we consider a wide array of fluctuation-type tests in a monitoring situation - given a history period for which a regression relationship is known to be stable, we test whether incoming data are consistent with the previously established relationship. Procedures based on estimates of the regression coefficients are extended in three directions: we introduce (a) procedures based on OLS residuals, (b) rescaled statistics and (c) alternative asymptotic boundaries. Compared to the existing tests our extensions offer better power against certain alternatives, improved size in finite samples for dynamic models and ease of computation respectively. We apply our methods to two data sets, German M1 money demand and U.S. labor productivity.<br>Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

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.

In last decades researchers have focused on the analysis of SEM models with nonlinear effects involving exogenous variables, i.e. which are not linearly dependent on other variables. The main problems studied are the estimation process, the choice of the indicators for nonlinear terms, when the variables are unobserved and the possibility of distinguishing interaction models from curvilinear models, while the causal analysis is not taken into account. In this thesis I introduce nonlinear terms involving endogenous variables in SEM model with 2 mediators. I focus my attention on the interaction and curvilinear effects as its particular case. This analysis is made both with observed variables and with unobserved or latent variables. To address causal analysis, I propose two different approaches and I compare them using simulated data with different sample sizes and different covariances between the 2 mediators. I find that my model with Pearl's (2012) causal theory and exogenous interaction, i.e. which does not depend linearly on other variables, is preferable for its simplicity and because it requires a smaller sample size. Pearl's theory can be applied to very general models and for this reason it has problems when the mediators are correlated given the mediated variable. Then I propose a modified formula to apply this theory. This approach has good performances both for interaction models and for curvilinear models and I propose a procedure to recognize the true model. Finally from a managerial perspective using the exogenous interaction model with Pearl's modified causal theory proposed by me, I show that, in a customer satisfaction context, positive emotions and negative emotions influence "jointly" future behavior. As emotions are in turn influenced by the design of the restaurant, managers can use it to enhance customers' loyalty both directly and indirectly by jointly inducing more positive emotions and less negative ones. This way a model with interacting mediators may help to better understand customers' behavior<br>Negli ultimi decenni i ricercatori hanno focalizzato la loro attenzione sull'analisi di modelli SEM con effetti non lineari che coinvolgono variabili esogene, ossia che non sono linearmente dipendenti da altre variabili. I principali problemi studiati sono il processo di stima, la scelta degli indicatori per i termini non lineari quando le variabili sono non osservate e la possibilità di distinguere i modelli con interazione dai modelli curvilinei, non prendendo in considerazione l'analisi causale. Introduco in questa tesi i termini non lineari che coinvolgono variabili endogene nel modello SEM con 2 mediatori. Focalizzo la mia attenzione sull'interazione e, come suo caso particolare, sugli effetti curvilinei. Questa analisi viene effettuata sia con le variabili osservate sia con le variabili non osservate o latenti. Per esaminare l'analisi causale, propongo due approcci diversi e li confronto utilizzando i dati simulati con differenti dimensioni del campione e con diverse covarianze tra i 2 mediatori. Ho trovato che il modello con la teoria causale di Pearl (2012) e con l'interazione esogena, cioè che non dipende linearmente da altre variabili, è preferibile per la sua semplicità richiedendo un campione di dimensioni più piccole. La teoria di Pearl può essere applicata a modelli molto generali e quindi presenta problemi quando i mediatori sono correlati data la variabile mediata.Per applicare questa teoria propongo una formula da me modificata. Propongo una procedura per riconoscere il vero modello dando questo approccio buoni risultati sia per modelli con interazione sia per modelli curvilinei. Infine dal punto di vista gestionale, utilizzando il modello con l'interazione esogena e con la teoria causale modificata di Pearl, dimostro che, in un contesto di soddisfazione del cliente, le emozioni positive e le emozioni negative influenzano "congiuntamente" il comportamento futuro. Essendo le emozioni a loro volta influenzate dal design del ristorante, i manager possono utilizzarlo per migliorare la fidelizzazione dei clienti sia direttamente che indirettamente e indurre congiuntamente più emozioni positive e meno quelle negative. In questo modo un modello con i mediatori che interagiscono può aiutare a comprendere meglio il comportamento dei clienti

Proper mathematical models of structures are beneficial for designers and analysts. The accuracy of the results is essential. Therefore, verification and/or correction of the models is vital. This can be done by utilizing experimental results or other analytical solutions. There are different methods of generating the accurate mathematical models. These methods range from completely analytically derived models, completely experimentally derived models, to a combination of the two. These model generation procedures are called System Identification. Today a popular method is to create an analytical model as accurately as possible and then improve this model using experimental results. This thesis provides a review of System Identification methods as applied to vibrating structures. One simple method and three more complex methods, chosen from current engineering literature, are implemented on the computer. These methods offer the capability to correct a discrete (for example, finite element based) model through the use of experimental measurements. The validity of the methods is checked on a two degree of freedom problem, an eight degree of freedom example frequently used in the literature, and with experimentally derived vibration results of a free-free beam.<br>Master of Science

La tesi di dottorato si propone di aprire una riflessione su come misurare le dinamiche di cambiamento dei processi psicologici in ottica di complessità, presentando un’applicazione al processo di creazione di significato (meaning-making). Dopo la formulazione di una nuova definizione concettuale integrata del processo di meaning-making a seguito di una revisione sistematica (primo capitolo), si è sviluppata una nuova misura self-report del significato della vita (SMILE; situational meaning in life evaluation), validata in un campione rappresentativo nazionale e in un campione di giovani adulti emergenti (secondo capitolo). Il terzo capitolo ha visto la conduzione di due studi con l’utilizzo rispettivamente dei modelli dinamici di equazioni strutturali (DSEM) e l’approccio della Network Psychometric Analysis per indagare le dinamiche di cambiamento del processo di meaning-making nella vita quotidiana di giovani adulti che, durante la pandemia di Covid-19, hanno partecipato a due raccolte giornaliere (measurement burst design). In questi studi è stato indagato il ruolo di fattori individuali (condizione transitiva in amore e lavoro), situazionali (eventi positivi e negativi) e contestuali (pandemia) come attivatori del processo di meaning-making. Le evidenze raccolte mostrano come sia importante indagare i processi psicologici tenendo conto sia del cambiamento intra-soggetto nel tempo, sia delle differenze tra gli individui.<br>This doctoral thesis aims to open a reflection on how to measure dynamics of change of psychological processes by presenting an application of the complexity framework to the meaning-making process. The first chapter fronts the challenge of how to conceptualize the meaning-making process, by conducting a systematic review of the literature that led toward the formulation of a new integrated conceptual definition of meaning-making. The second chapter presents the development of a new self-report measure of meaning in life (SMILE; situational meaning in life evaluation) that has been validated in a national representative sample and in a sample of emerging and young adults. The third chapter deals with the challenge of how to investigate the dynamics of change of the meaning-making process in the daily life by applying two state-of-the-art data analysis approaches, the Dynamic Structural Equation Models (DSEM) and the Multilevel Network Psychometric approach. Data from emerging and young adults were collected with a measurement burst design made of two daily diary studies during the COVID-19 pandemic. The role of individual factors (transitive condition in love and work), situational factors (positive vs negative events), and contextual factors (pandemic) as activators of the meaning-making process has also been investigated.

In this thesis we study the existence and stability of positive equilibrium of a general model for the dynamics of several interacting, age-structured population. We begin with the formulation and proof of a global existence theorem for the initial value problem. The proof of this theorem is used to develop an algorithm and a FORTRAN code for the numerical solution of initial value problems for the single species case. This computer program is used to study prototype models for the dynamics of a population whose fertility and mortality rates exhibit an "Allee effect". This is done from a bifurcation theoretic point of view, using the inherent net reproductive rate as a bifurcating parameter. An unstable "left" bifurcation is found. Multi-equilibria and various kinds of oscillations are studied as a function of r, the fertility window, and the nature of the density dependence.

This study evaluated the relative fit of both Finn's (1989) Participation-Identification and Wehlage, Rutter, Smith, Lesko and Fernandez's (1989) School Membership models of high school completion to a sample of 4,597 eighth graders taken from the National Educational Longitudinal Study of 1988 , (NELS:88), utilizing structural equation modeling techniques. This study found support for the importance of educational engagement as a factor in understanding academic achievement. The Participation-Identification model was particularly well fitting when applied to the sample of high school completers, dropouts (both overall and White dropouts) and African-American students. This study also confirmed the contribution of school environmental factors (i.e., size, diversity of economic and ethnic status among students) and family resources (i.e., availability of learning resources in the home and parent educational level ) to students' educational engagement. Based on these findings, school social workers will need to be more attentive to utilizing macro-level interventions (i.e., community organization, interagency coordination) to achieve the organizational restructuring needed to address future challenges. The support found for the Participation-Identification model supports a shift in school social workers' attention from reactive attempts to improve the affective-interpersonal lives of students to proactive attention to their academic lives. The model concentrates school social work practices on the central mission of schools, which is educational engagement. School social workers guided by this model would be encouraged to seek changes in school policies and organization that would facilitate educational engagement.

This study was an examination of the effect of type of correlation matrix on the robustness of LISREL maximum likelihood and unweighted least squares structural parameter estimates for models with categorical manifest variables. Two types of correlation matrices were analyzed; one containing Pearson product-moment correlations and one containing tetrachoric, polyserial, and product-moment correlations as appropriate. Using continuous variables generated according to the equations defining the population model, three cases were considered by dichotomizing some of the variables with varying degrees of skewness. When Pearson product-moment correlations were used to estimate associations involving dichotomous variables, the structural parameter estimates were biased when skewness was present in the dichotomous variables. Moreover, the degree of bias was consistent for both the maximum likelihood and unweighted least squares estimates. The standard errors of the estimates were found to be inflated, making significance tests unreliable. The analysis of mixed matrices produced average estimates that more closely approximated the model parameters except in the case where the dichotomous variables were skewed in opposite directions. However, since goodness-of-fit statistics and standard errors are not available in LISREL when tetrachoric and polyserial correlations are used, the unbiased estimates are not of practical significance. Until alternative computer programs are available that employ distribution-free estimation procedures that consider the skewness and kurtosis of the variables, researchers are ill-advised to employ LISREL in the estimation of structural equation models containing skewed categorical manifest variables.<br>Ph. D.

In order to perform a specific function, a protein needs to fold into the proper structure. Prediction the protein structure from its amino acid sequence has still been unsolved problem. The main focus of this thesis is to develop new approach on the protein structure modeling by means of differential geometry and integrable theory. The start point is to simplify a protein backbone as a piecewise linear polygonal chain, with vertices recognized as the central alpha carbons of the amino acids. Frenet frame and equations from differential geometry are used to describe the geometric shape of the protein linear chain. Within the framework of integrable theory, we also develop a general geometrical approach, to systematically derive Hamiltonian energy functions for piecewise linear polygonal chains. These theoretical studies is expected to provide a solid basis for the general description of curves in three space dimensions. An efficient algorithm of loop closure has been proposed.

Structural equation models combine factor analysis models and multivariate regression models to estimate associations between observed variables and unobserved variables. The main achievement of this Capstone Project is the understanding of structural equation models and application of the models to real-world data. In this report, we reviewed structural equation models and several prerequisite topics. We performed a simulation study to compare maximum likelihood structural equation model estimation versus two-stage sequential estimation using multiple linear regression and maximum likelihood factor analysis. The simulation study confirmed that confidence intervals produced by structural equation models are valid and those obtained by two-stage sequential estimation are largely inaccurate. We applied structural equation models to an educational data comparing the efficacy of teaching conditions on learning scientific inquiry skills among 177 middle school students in Fitchburg, Massachusetts using a computer simulated science microworld. Application of structural equation models to the educational data showed that there were no significant differences in test score gains between three learning conditions, while controlling for latent factors measured by survey responses.

In the present study, parameter estimates, standard errors and chi-square statistics were compared using normal and elliptical estimation methods given three research conditions: population data contamination (10%, 20%, and 30%), sample size (100, 400, and 1000), and kurtosis (kappa =1,10, 20).

Thesis (Ph. D.)--Aerospace Engineering, Georgia Institute of Technology, 2007.<br>Bauchau, Olivier, Committee Member ; Hodges, Dewey, Committee Chair ; Singhose, William, Committee Member ; Costello, Mark, Committee Member ; Flannery, Raymond, Committee Member.

<p>The field of inverse problems is an area in applied mathematics that is of great importance in several scientific and industrial applications. Since an inverse problem is typically founded on non-linear and ill-posed models it is a very difficult problem to solve. To find a regularized solution it is crucial to have <em>a priori</em> information about the solution. Therefore, general theories are not sufficient considering new applications.</p><p>In this thesis we consider the inverse problem to determine the beam bending stiffness from measurements of the transverse dynamic displacement. Of special interest is to localize parts with reduced bending stiffness. Driven by requirements in the wood-industry it is not enough considering time-efficient algorithms, the models must also be adapted to manage extremely short calculation times.</p><p>For the developing of efficient methods inverse problems based on the fourth order Euler-Bernoulli beam equation and the second order string equation are studied. Important results are the transformation of a nonlinear regularization problem to a linear one and a convex procedure for finding parts with reduced bending stiffness.</p>

Population aging has been an increasing in many societies during the last century, andespecially in China this issue has become one of the most urgent social phenomenonin the recent twenty years. Meanwhile, being healthy matters to the senior populationthe most. The main purpose of this paper is to investigate how to measure Chineseelderly health condition, and what the main factors are influencing their health. Thedata of this paper is from the China Health and Retirement Longitudinal Study(CHARLS). A structural equation model(SEM) was established to verify therelationship between different influencing factors and the elderly health. The latentvariables in this model were pre-studied by both exploratory factor analysis andconfirmatory factor analysis. The conclusion based on this data is elderly health canbe measured in four aspects physical condition, emotional condition, body functionand pain. The significant influencing effects of each aspects of health are time sharing,exercise, family environment and lifestyle.

La ricerca svolta si è configurata come indagine sul vetro strutturale. Il vetro è un materiale nuovo nel campo dei materiali strutturali, in quanto fino a poco tempo fa veniva utilizzato solo in finestrature e/o facciate continue. Invece negli ultimi anni si è visto che il vetro è sempre più utilizzato per parti direttamente strutturali, quali pavimentazioni, scale, balaustre, pensiline, coperture, ecc. In questi casi il vetro deve comportarsi come un materiale da costruzione a tutti gli effetti, quale il calcestruzzo armato o l'acciaio. Guardandolo da questo punto di vista, si vede come siano necessarie delle normative nell'ambito del calcolo del vetro strutturale. A tale proposito, si sono prese in considerazione le normative presenti in campo europeo ed italiano. Negli ultimi anni infatti si è sentita l'esigenza anche in Italia di una normativa completa sul vetro strutturale (come già presente in molti stati europei), senza dover sempre ricorrere all'universo delle norme UNI, molto complete, ma altrettanto dispersive. Per elaborare quindi un documento unitario, si è istituita presso il CNR una commissione volontaria per la redazione di tali normative, in cui siamo entrati a far parte nell'ambito del gruppo "modelli". La nostra indagine comunque si è incentrata sulla caratterizzazione del vetro strutturale nel modo più ampio possibile, guardandolo dal punto di vista della progettazione, delle prove sul materiale, dei modelli matematici. La progettazione si è incentrata sulla ricerca e sviluppo di un elemento strutturale facilmente producibile e vendibile in diverse configurazioni e soluzioni. La scelta è caduta sulla progettazione di una trave reticolare in vetro ed acciaio inossidabile. Caratteristiche principali di quest'elemento sono: la modularità, poiché la trave è costituita da un modulo base ripetibile fino alla lunghezza complessiva di 6.90 m; la possibilità di ottenere configurazioni curvilinee, in quanto gli elementi del modulo base possono ruotare reciprocamente; la trasportabilità, poiché ruotando gli elementi il modulo può "appiattirsi" ed essere trasportato più agevolmente. La trave è stata studiata staticamente e dinamicamente in varie configurazioni possibili ed al termine si è provveduto al deposito di un brevetto italiano. Per quanto riguarda le prove sul materiale, abbiamo condotto prove sia in campo statico che in campo dinamico. In campo statico abbiamo svolto delle prove a compressione semplice, prima senza strumentazione necessaria per gli spostamenti poi aggiungendo la strumentazione stessa. In questo modo abbiamo potuto analizzare il meccanismo di rottura del vetro, notando che i campioni a nostra disposizione in vetro laminato composto da tre strati di vetro non subiscono una rottura fragile, bensì si forma nel grafico stress-strain una sorta di pianerottolo plastico dovuto alla presenza del PVB. Le prove di tipo dinamico si sono svolte sia con l'utilizzo di accelerometri e martello strumentato, sia con l'uso di un vibrometro laser. Scopo principale di tali test è stato la comprensione del comportamento dell'interstrato e delle sue caratteristiche meccaniche. Utilizzando diversi metodi di identificazione dinamica, abbiamo ricavato i parametri modali principali quali frequenze proprie, smorzamenti modali e forme modali. I test hanno riguardano tre diversi tipi di campioni: uno in vetro monolitico, uno in vetro laminato a due strati ed uno in vetro laminato a tre strati. Come ci aspettavamo, il campione in vetro monolitico si comporta esattamente come una trave in vibrazione libera, il campione a due strati si comporta ai primi modi come se il PVB realizzasse una connessione perfettamente rigida tra gli strati di vetro, rendendo quindi il comportamento ancora analogo a quello di una trave monolitica, mentre il campione a tre strati presenta alcune anomalie di comportamento, in quanto le frequenze proprie si abbassano rispetto a quelle del due strati invece che aumentare. Abbiamo cercato in letteratura alcune possibili spiegazioni di questo fenomeno, deducendo che il fattore "temperatura" è quello che maggiormente influisce sul comportamento del PVB e che, essendo stato il campione a tre strati l'unico a subire alcuni cicli di variazioni considerevoli di temperatura, sia possibile una sua variazione di comportamento dovuta alla temperatura. Ultimo aspetto esposto è stata la trattazione del vetro laminato in ambito teorico. Utilizzando il metodo di espansione asintotica, abbiamo ricavato le pulsazioni proprie di un multistrato composto da materiali a comportamento elastico lineare con forte contrasto delle proprietà meccaniche, quali il vetro ed il PVB. Con l'utilizzo di un parametro piccolo, ", abbiamo descritto il comportamento limite del multistrato, individuandone le pulsazioni alle basse ed alle medie frequenze. Ciò è stato possibile utilizzando due diverse espansioni asintotiche per la pulsazione [omega]. In conclusione, si è condotta un'indagine sul vetro strutturale in modo più ampio possibile, toccando diverse tematiche e cercando di sollevare molte problematiche per rendere il vetro sempre più simile ad un materiale da costruzione a tutti gli effetti.<br>The research activity has been con gured as an investigation on structural glass. Glass is a new material if placed in the field of structural materials, because until recently it was used mainly for glazing and/or curtain walls. Instead, in recent years, we have seen that the glass is increasingly used for structural parts, such as flooring, staircases, balustrades, canopies, roofing, etc. In all these cases, the glass has to behave as a building material for all purposes, such as concrete or steel. Looking at it from this point of view, it is evident the need and the utility of regulations in the calculation of structural glass. In this regard, we considered the standards present in European and Italian systems. In recent years, the need in Italy for comprehensive legislation on the structural glass (as already present in many European countries) is very urgent, without having to resort each time to the universe of UNI, very complete, but just as widespread. Thus, to elaborate a standard unified document, a voluntary committee has set up at the CNR for the drafting of these regulations, and here we joined in the "models" group. Our investigation, however, focused on the characterization of structural glass as widely as possible, looking from the point of view of design, testing of materials, mathematical models. The design has focused on research and development of a structural element, easy to produce and sell in different configurations and solutions. The choice was on the design of a truss made of glass and stainless steel. Key features of this element are: modularity, since the beam consists of a base module repeatable until a total length of 6.90 m, the possibility of curve configurations, since the elements of the basic module can rotate mutually, and the portability, since turning the elements, the module will "flatten out" and can be transported more easily. The beam has been studied in terms of static and dynamic conditions in various configurations and at the end of the design was merged in an Italian patent. Regarding the tests on structural glass, we conducted tests in both static and dynamic eld. In statics, we have performed simple compression tests, first without necessary equipment for displacement data and then adding the instrumentation. In this way we could analyze the failure mechanism of glass, noting that our samples of laminated glass (consisting of three layers of glass) do not undergo brittle failure, but in the stress-strain graph a kind of plastic landing appeared, due to presence of PVB. The dynamic tests have taken place with the use of accelerometers and manual hammering, and then by the use of a laser vibrometer. The main aim of these tests was to understand the behavior of the interlayer and its mechanical properties. Using different methods of dynamic identification, we obtained the modal parameters, such as the natural frequencies, the modal damping and the mode shapes. The tests involved three different typologies of samples: a monolithic glass, a laminated glass composed by two layers of glass and a laminated glass composed by three layers of glass. As expected, the monolithic glass behaves just like a beam in free vibration. The two-layer sample behaves at first modes as if the PVB will achieve a perfectly rigid connection between the layers of glass, thus making the behavior similar to that of a monolithic beam. The three-layer sample has some behavior anomalies, because its frequencies are lower than those of the two-layer sample, instead of increasing. We searched in literature some possible explanations for this phenomenon, arguing that the factor "temperature" is one that most a ects the behavior of PVB. The three-layer sample was the only one that undergo cycles of considerable temperature variations, and it is possible a behavior change due to temperature. Last exposed issue was the treatment of laminated glass from theoretical point of view. Using the method of asymptotic expansion, we obtained the natural frequencies of a multi-layer element composed by linear elastic materials with strong contrast in mechanical properties, such as glass and PVB. With the use of a small parameter, [epsilon], we described the limit behavior of the multi-layer, identifying its pulsations at low and medium frequencies. This was achieved using two different asymptotic expansions for the pulsation [omega]. In conclusion, we conducted an investigation into the structural glass as wide as possible, touching on various themes and trying to raise many issues to make the glass more and more similar to a building material for all purposes.

This dissertation addresses research questions related to defining a smart city and the associated activities. The general research question is explored in the dissertation via the conduct of three related studies. The finding from these three investigations are presented in the results section as 3 essays that collectively examine the smart city phenomena as it has emerged within the City. Essay 1 assesses building municipal open data capability. The study proposed an Opendata Roadmap Framework to enhance the organization's dynamic capability. The results provide a valuable practical framework to help cities develop open data capability. The results also provide a comparative study or benchmark for similar initiatives with other regional cities and within the nation. Essay 2 measures the residents' understanding and beliefs about smart cities. This portion of the research used a qualitative method that included interviewing residents and city officials to understand their definition of a smart city and what they believe makes a city smart. The interviews focused on understanding resident engagement because it is an important characteristic of a smart city. The gap between the city officials and residents understanding was examined. In addition, the interviews help identify essential factors associated with smart cities like trust in government, perceived security, perceived privacy, trust in technology, and perceived monetary value. Essay 3 examines the acceptance of smart city technologies and factors that affect the adoption of such technologies. This essay uses the insights from the other two essays to propose a smart city Unified Theory of Acceptance and Use of Technology (UTAUT) extension labelled Smart City UTAUT (SC-UTAUT). The new proposed model was tested using a survey method. The 1,786 valid responses were used to test the proposed structural equation model using Smart PLS. Results show a significant relationship between trust in technology, trust in government, perceived monetary value, effort expectancy, self-efficacy, and behavioral intention. The dissertation concludes with a summary of how the three essays make a cumulative contribution to the literature as well as providing practical guidance for becoming a smart city.

I modelli ad equazioni strutturali con variabili latenti hanno subito un notevole sviluppo negli ultimi anni. Partendo dai pionieri delle due macro-definizioni di modelli con variabili latenti, Covariance Structure Analysis e Component Analysis, con LISREL e PLS-PM come le tecniche più importanti, diverse estensioni e miglioramenti sono stati proposti. Inoltre, per i modelli di analisi di ridondanza, che fanno parte della Component Analysis, ma hanno solo variabili endogene osservate, sono stati proposti nuovi metodi in letteratura per affrontare più di un gruppo di variabili osservate esogene, con equazioni lineari semplici ed un'ottimizzazione unificata del problema. La critica principale, che è stata affrontata di recente in nuovi filoni di letteratura riguardanti i modelli ad equazioni strutturali, è l'incapacità parziale di questi sistemi di equazioni di modellizzare indicatori categoriali. Sono stati proposti diversi metodi a tale scopo, in PLS-PM e LISREL rispettivamente, che sfruttano metodi di Optimal Scaling o l’algoritmo EM nel processo di ottimizzazione. Per l’analisi di ridondanza, con variabili endogene solo osservate, la possibilità di estendere le procedure di stima a variabili qualitative è notevolmente meno ostacolata da restrizioni del modello, ancor di più nel modello di analisi di ridondanza estesa, con più di un blocco di variabili esogene. Questo lavoro presenta una nuova stima di modelli di analisi di ridondanza estesa in presenza di variabili endogene binarie o categoriali, con due principali tecniche di stima: Iterated Reweighed Least Squares, e Gradient Descent con backpropagation tramite reti neurali. Per questi ultimi, recenti sviluppi nei modelli ad equazioni strutturali con reti neurali saranno esaminati, e la nuova tecnica sarà quindi introdotta.<br>Structural Equation Models with latent variables have considerably developed in recent years. Starting from the pioneers of the two most prominent ways of defining models with latent variables, namely Covariance Structure Analysis and Component Analysis, with LISREL and PLS-PM as the most famous techniques, several extensions and improvements have been put forward. Moreover, for Redundancy Analysis models, which are part of the Component Analysis framework, but have only observed endogenous variables, new methods have been proposed in literature to deal with more than one group of exogenous observed variables, with simple linear equations and a unified optimization problem. One main criticism, that has been dealt with recently in new strands of literature regarding Structural Equation Modeling, is the partial inability of these systems of linear equations to deal with categorical indicators. Several methods have been proposed, in PLS-PM and LISREL respectively, either related to Optimal Scaling, or adapting the EM algorithm to the particular case under examination. In the Redundancy Analysis framework, with only observed endogenous variables, the possibility of extending the estimation procedures to a qualitative setting is considerably less hampered by model restrictions, even more so in the Extended Redundancy Analysis model, with more than one block of exogenous variables. This work will hence present a new estimation of Extended Redundancy Analysis models in presence of binary or categorical endogenous variables, with two main estimation techniques: Iterated Reweighed Least Squares, and Gradient Descent with backpropagation in an Artificial Neural Network architecture. For the latter, recent developments in Structural Equation Models in the neural networks setting will be firstly examined, and the new technique will be subsequently introduced.