Structural Equation Modeling is a common name for the statistical analysis of Structural Equation Models. Structural Equation Models are models that specify relationships between a set of variables and can be specified by means of path diagrams. A number of Structural Equation Modeling programs have been developed. These include, amongst others, AMOS, EQS, LISREL, Mx, RAMONA and SEPATH. A number of studies have been published on the use of some of the applications mentioned above. They include, amongst others, Brown (1986), Waller (1993) and Kano (1997). Structural Equation Models are increasingly being used in the social, economic and behavioral sciences. More and more people are therefore making use of one or more of the Structural Equation Modeling applications on the market. This study is performed with the aim of using each of the Structural Equation Modeling applications AMOS, EQS, LISREL, Mx, RAMONA and SEPATH for the first time and document the experience, joy and the difficulties encountered while using them. This treatise is different from the comparisons already published in that it is based on the use of AMOS, EQS, LISREL, Mx, RAMONA and SEPATH to fit a Structural Equation Model for peer influences on ambition, which is specified for data obtained by Duncan, Haller and Portes (1971), by myself as a first time user of each of the programs mentioned. The impressive features as well as the difficulties encountered are listed for each application. Recommendations for possible improvements to the various applications are also proposed. Finally, recommendations for future studies on the use of Structural Equation Modeling programs are made.

Magíster en Ingeniería de Negocios con Tecnología de Información / El conocimiento en la medicina se ha acumulado en artículos de investigación científica a través del tiempo, por consiguiente, se ha generado un interés creciente en desarrollar metodologías de minería de texto para extraer, estructurar y analizar el conocimiento obtenido de grandes volúmenes de información en el menor tiempo posible. En este trabajo se presenta un una metodología que permite lograr el objetivo anterior utilizando el modelo LDA (Latent Dirichlet Allocation). Esta metodología consiste en 3 pasos: Primero, reconocer tópicos relevantes en artículos de investigación científica médica de la Revista Médica de Chile (2012 2015); Segundo, identificar e interpretar la relación entre los tópicos resultantes mediante métodos de visualización (LDAvis); Tercero, evaluar características propias de las investigaciones científicas, en este caso, el financiamiento dirigido, utilizando los dos pasos anteriores. Los resultados muestran que esta metodología resulta efectiva, no sólo para el análisis de artículos de investigación científica médica, sino que también puede ser utilizado en otros campos de la ciencia. Adicionalmente, éste método permite analizar e interpretar el estado en el que se encuentra la investigación médica a nivel nacional utilizando como referente la Revista Médica de Chile. Dentro de este contexto es importante considerar los procesos de planificación, gestión y producción de la investigación científica al interior de los Hospitales que han sido estandartes de generación del conocimiento ya que funcionan como campus universitarios de tradición e innovación. Por la razón anterior, se realizará un análisis del entorno en el sector de la salud, su estructura y la posibilidad de aplicar la metodología propuesta en este trabajo a partir del planteamiento estratégico y el modelo de negocio del Hospital Exequiel González Cortés.

Le virus d’Epstein-Barr (EBV) est un herpèsvirus humain qui infecte plus de 90% de la population généralement de façon bénigne et asymptomatique. Cependant, de nombreuses données démontrent que ce virus peut également contribuer à certains processus de cancérisation. En effet, l’EBV est associé à de nombreuses pathologies malignes telles que le lymphome de Burkitt, le lymphome hodgkinien et le carcinome du nasopharynx. Dans la grande majorité de ces cancers associées à ce virus, l’EBV exprime un programme de latence de type II durant lequel la protéine LMP1 est exprimée. Elle est décrite comme l’oncogène majeur de l’EBV car son expression est nécessaire à la survie et à la prolifération des lignées transformées in vitro. Cette protéine membranaire est fonctionnellement apparentée aux membres de la famille des récepteurs du TNF. LMP1 est constitutivement active et son expression conduit à l’activation de voies de signalisation telles que les voies NF-κB, PI3K et des MAPK. L’activation de ces voies de signalisation cellulaire confère à LMP1 des propriétés oncogéniques, cependant, des effets toxiques liés à son expression ont également été décrits. Effectivement, LMP1 est capable d’induire l’apoptose dans différents types cellulaires. Dans ce contexte, nous avons d’abord développé et caractérisé, des variants dérivés de LMP1 constitués de sa partie C-terminale signalisatrice, complète ou partielle, fusionnée à la protéine GFP. Nous montrons que ces variants sont capables de séquestrer les protéines adaptatrices se fixant à LMP1 ou au récepteur TNFR1, et d’inhiber le signal et les phénotypes induits par ces derniers. Ces protéines à effet dominant négatif peuvent ainsi contrecarrer les effets transformants de LMP1 dans des modèles de latence II et III. Ces dominants négatifs peuvent aussi inhiber l’activation du TNFR1 et les phénotypes qui en découlent. Puis, nous avons étudié les propriétés de LMP1 en dehors d’un contexte infectieux et son rôle dans la transformation épithéliale. Nous démontrons que LMP1 induit la mort des cellules épithéliales MDCK mais certaines cellules outrepassent ses effets cytotoxiques générant des lignées qui expriment stablement LMP1 et dans lesquelles cet oncogène viral favorise la survie et exacerbe les phénotypes induits par le facteur de croissance HGF. Le caractère ambivalent de LMP1 pourrait limiter le pouvoir oncogène de l’EBV mais en contrepartie favoriser l’émergence de cellules résistantes à l’apoptose et capables de répondre de façon accrue à des facteurs de croissance. Nos travaux ont permis de mieux comprendre la dualité fonctionnelle de LMP1, d’une part ses effets oncogènes favorisant la survie cellulaire et d’autre part ses propriétés pro-apoptotiques, induites directement ou révélées suite à son inhibition, limitant la tumorigenèse. La caractérisation des mécanismes moléculaires impliquant LMP1 pourrait ainsi participer à la définition de potentielles stratégies thérapeutiques pour le traitement de cancers associés à l’EBV et où LMP1 est exprimée. / Epstein-Barr virus (EBV) is a human herpesvirus that infects more than 90% of worldwide population, generally asymptomatically. However, numerous studies show that EBV promotes tumorigenesis. Indeed, EBV infection is associated with many human malignancies including Burkitt’s lymphoma, Hodgkin’s lymphoma and nasopharyngeal carcinoma. In most of these cancers associated with EBV, it expresses latency II program in which the latent membrane protein 1 (LMP1) is expressed. LMP1 is described as the major EBV oncogene because its expression is necessary in vitro for survival and proliferation of transformed cell lines. This membrane protein is functionally related to members of the TNF receptors superfamily. LMP1 is constitutively active and its expression leads to activation of NF-κB, PI3K and MAPK signaling pathways. These activation confers oncogenic properties to LMP1, however, toxic effects associated with its expression are also described. Indeed, LMP1 can induce cell death in different cell types. In this context, we first developed and characterized LMP1 derivative variants consisting of its C-terminal signal, complete or partial, fused to GFP. We show that these variants are able to sequester adaptors binding to LMP1 and TNFR1, and inhibit signal and phenotypes induced by them. These proteins have dominant negative effect and may counteract LMP1 transformant properties in latency II cellular models. In addition, these dominant negatives impair TNFR1 signaling and associated phenotypes. Then, we studied LMP1 properties outside infectious context and its involvement in epithelial transformation. We show that LMP1 induces cell death in MDCK epithelial cells, but some go beyond its cytotoxic effects generating lines stably expressing LMP1 and in which this viral oncogene promotes survival and exacerbates HGF-induced phenotypes. Ambivalent character of LMP1 could limit the oncogenic potential of EBV but in return support the emergence of cells resistant to apoptosis and able to enhance growth factor responses. Our work allowed us to better understand the functional duality of LMP1 on the one hand its oncogenic effects favoring cell survival and other pro-apoptotic properties, induced directly or reveal by its inhibition, limiting tumorigenesis. Thus, characterization of molecular mechanisms involving LMP1 could participate in the definition of potential therapeutic strategies for treating cancers associated with EBV and where LMP1 is expressed.

Recently there has been a renewed interest in formative measurement and its role in properly specified models. Formative measurement models are difficult to identify, and hence to estimate and test. Existing solutions to the identification problem are shown to not adequately represent the formative constructs of interest. We propose a new two-step approach to operationalize a formatively measured construct that allows a closely matched common factor equivalent to be included in any structural equation model. We provide an artificial example and an original empirical study of privacy to illustrate our approach. Detailed proofs are given in an appendix.

Collaboration and sharing of information are the basis of modern social web system. Users in the social web systems are establishing and joining online communities, in order to collectively share their content with a group of people having common topic of interest. Group or community activities have increased exponentially in modern social Web systems. With the explosive growth of social communities, users of social Web systems have experienced considerable difficulty with discovering communities relevant to their interests. In this study, we address the problem of recommending communities to individual users. Recommender techniques that are based solely on community affiliation, may fail to find a wide range of proper communities for users when their available data are insufficient. We regard this problem as tag-based personalized searches. Based on social tags used by members of communities, we first represent communities in a low-dimensional space, the so-called latent semantic space, by using Latent Semantic Analysis. Then, for recommending communities to a given user, we capture how each community is relevant to both user’s personal tag usage and other community members’ tagging patterns in the latent space. We specially focus on the challenging problem of recommending communities to users who have joined very few communities or having no prior community membership. Our evaluation on two heterogeneous datasets shows that our approach can significantly improve the recommendation quality.

In this thesis focus is on developing an online social network application with a Peer-to-Peer infrastructure motivated by BestPeer++ architecture and BATON overlay structure. BestPeer++ is a data processing platform which enables data sharing between enterprise systems. BATON is an open-sourced project which implements a peer-to-peer with a topology of a balanced tree. We designed and developed the components for users to manage their accounts, maintain friend relationships, and publish their contents with privacy control and newsfeed, notification requests in this social network- ing application. We also developed a Hashtag Recommendation system for this social net- working application. A user may invoke a recommendation procedure while writing a content. After being invoked, the recommendation pro- cedure returns a list of candidate hashtags, and the user may select one hashtag from the list and embed it into the content. The proposed ap- proach uses Latent Dirichlet Allocation (LDA) topic model to derive the latent or hidden topics of different content. LDA topic model is a well developed data mining algorithm and generally effective in analyzing text documents with different lengths. The topic model is further used to identify the candidate hashtags that are associated with the texts in the published content through their association with the derived hidden top- ics. We considered different methods of recommendation approach for the pro- cedure to select candidate hashtags from different content. Some methods consider the hashtags contained in the contents of the whole social net- work or of the user self. These are content-based recommendation tech- niques which matching user’s own profile with the profiles of items.. Some methods consider the hashtags contained in contents of the friends or of the similar users. These are collaborative filtering based recommendation techniques which considers the profiles of other users in the system. At the end of the recommendation procedure, the candidate hashtags are or- dered by their probabilities of appearance in the content and returned to the user. We also conducted experiments to evaluate the effectiveness of the hashtag recommendation approach. These experiments were fed with the tweets published in Twitter. The hit-rate of recommendation is measured in these experiments. Hit-rate is the percentage of the selected or relevant hashtags contained in candidate hashtags. Our experiment results show that the hit-rate above 50% is observed when we use a method of recommendation approach independently. Also, for the case that both similar user and user preferences are considered at the same time, the hit-rate improved to 87% and 92% for top-5 and top-10 candidate recommendations respectively.

Circumscription and Compromise theory suggests self-concept and sex stereotype explain occupational preferences, including preferences for science, technology, engineering and mathematics (STEM). Support exists for sex differences between males and females in both science degrees and science careers. The main thrust of observed sex differences in science lies in the development of occupational interest, as it has been suggested females are encouraged away from science due to stereotypes and social pressure. The present study evaluates high school juniors and seniors (n = 295) to explore their preference for science as indicated by science motivation, attitude, academic experience, and interest. Latent Profile Analysis was used to model profiles of preferences for science with a person-centered approach. Then, the impact of self-concept variables was explored and four profiles of science interest were identified. Sex differences were identified based on science interest, but were not always in favor of males. Covariate analysis indicates vocabulary ability and personality as significantly different for students in the high science interest profile. Implications of these results and future research directions are discussed.

abstract: Organizational classifications are critical to a wide variety of stakeholders. Within the domain of higher education, researchers use established classifications for sample selection or within empirical models to account for unobserved organizational characteristics. Colleges and universities, as well as their political principals, often use classifications to form peer-groups and reference sets through which organizational performance is assessed. More broadly, classifications provide aspirational archetypes to an organizational field. Using American higher education as the empirical context, this dissertation introduces Latent Profile Analysis (LPA) as a method to identify the structure of an organizational field and to classify organizations within this structure. Using measures of model fit and concerns for interpretability, this investigation determined that 13 distinctive organizational designs are present in the field of American higher education. Derived groupings are compared to the 2018 Basic Classification from the Carnegie Classification of Institutions of Higher Education. Opportunities and challenges for operationalizing this derived classification are discussed. / Dissertation/Thesis / Doctoral Dissertation Public Administration and Policy 2020

Research on the dialects of English spoken within the United States shows variation regarding lexical, morphological, syntactic, and phonological features. Previous research has tended to focus on one linguistic variable at a time with variation. To incorporate multiple variables in the same analysis, this thesis uses a latent class analysis to perform a cluster analysis on results from the Harvard Dialect Survey (2003) in order to investigate what phonetic variables from the Harvard Dialect Survey are most closely associated with each dialect. This thesis also looks at how closely the latent class analysis results correspond to the Atlas of North America (Labov, Ash & Boberg, 2005b) and how well the results correspond to Joshua Katz's heat maps (Business Insider, 2013; Byrne, 2013; Huffington Post, 2013; The Atlantic, 2013). The results from the Harvard Dialect Survey generally parallel the findings of the Linguistic Atlas of North American English, providing support for six basic dialects of American English. The variables with the highest probability of occurring in the North dialect are ‘pajamas: /æ/’, ‘coupon: /ju:/’, ‘Monday, Friday: /e:/’ ‘Florida: /ɔ/’, and ‘caramel: 2 syllables’. For the South dialect, the top variables are ‘handkerchief: /ɪ/’, ‘lawyer: /ɒ/’, ‘pajamas: /ɑ/’, and ‘poem’ as 2 syllables. The top variables in the West dialect include ‘pajamas: /ɑ/’, ‘Florida: /ɔ/’, ‘Monday, Friday: /e:/’, ‘handkerchief: /ɪ/’, and ‘lawyer: /ɔj/’. For the New England dialect, they are ‘Monday, Friday: /e:/’, ‘route: /ru:t/’, ‘caramel: 3 syllables’, ‘mayonnaise: /ejɑ/’, and ‘lawyer: /ɔj/’. The top variables for the Midland dialect are ‘pajamas: /æ/’, ‘coupon: /u:/’, ‘Monday, Friday: /e:/’, ‘Florida: /ɔ/’, and ‘lawyer: /ɔj/’ and for New York City and the Mid-Atlantic States, they are ‘handkerchief: /ɪ/’, ‘Monday, Friday: /e:/’, ‘pajamas: /ɑ/’, ‘been: /ɪ/’, ‘route: /ru:t/’, ‘lawyer: /ɔj/’, and ‘coupon: /u:/’. One major discrepancy between the results from the latent class analysis and the linguistic atlas is the region of the low back merger. In the latent class analysis, the North dialect has a low probability of the ‘cot/caught’ low back vowel distinction, whereas the linguistic atlas found this to be a salent variable of the North dialect. In conclusion, these results show that the latent class analysis corresponds with current research, as well as adding additional information with multiple variables.

1 Latent criminality Abstract This submitted diploma thesis deals with the phenomenon of latent criminality. Latent criminality is the part of criminality that is for some reason not registered by the law enforcement authorities. The thesis itself consists of four consecutive chapters. The first chapter deals with the concept of crime. It discusses the different approaches to concept of crime in criminology and the indicators that are used to describe it. Furthermore, this chapter is dedicated to the division of crime into latent and registered crime and the sources of information about registered crime. In particular, it focuses on official crime statistics. In the second chapter, the issue of latent crime is discussed in more detail. It defines the concept of latency, in which cases it is considered being latent criminality and what its types are. Furthermore, the ratio of latent and registered crime, the rate and extent of latency are described. At the end the possible causes of its existence are presented, one of the main reasons being the failure to report the crime by its victims. The third chapter focuses on possible methods of detecting latent crime, where more space is subsequently devoted to the two most important methods, namely victimization surveys and self- report studies. For both of these...