The necessity of modeling is vital for the purpose of reasoning and diagnosing in complex systems, since the human mind might sometimes have a limited capacity and an inability to be objective. The chain graph (CG) class is a powerful and robust tool for modeling real-world applications. It is a type of probabilistic graphical models (PGM) and has multiple interpretations. Each of these interpretations has a distinct Markov property. This thesis deals with the multivariate regression chain graph (MVR-CG) interpretation. The main goal of this thesis is to implement and evaluate the results of the MVR-PC-algorithm proposed by Sonntag and Peña in 2012. This algorithm uses a constraint based approach used in order to learn a MVR-CG from data.In this study the MRV-PC-algorithm is implemented and tested to see whether the implementation is correct. For this purpose, it is run on several different independence models that can be perfectly represented by MVR-CGs. The learned CG and the independence model of the given probability distribution are then compared to ensure that they are in the same Markov equivalence class. Additionally, for the purpose of checking how accurate the algorithm is, in learning a MVR-CG from data, a large number of samples are passed to the algorithm. The results are analyzed based on number of nodes and average number of adjacents per node. The accuracy of the algorithm is measured by the precision and recall of independencies and dependencies.In general, the higher the number of samples given to the algorithm, the more accurate the learned MVR-CGs become. In addition, when the graph is sparse, the result becomes significantly more accurate. The number of nodes can affect the results slightly. When the number of nodes increases it can lead to better results, if the average number of adjacents is fixed. On the other hand, if the number of nodes is fixed and the average number of adjacents increases, the effect is more considerable and the accuracy of the results dramatically declines. Moreover the type of the random variables can affect the results. Given the samples with discrete variables, the recall of independencies measure would be higher and the precision of independencies measure would be lower. Conversely, given the samples with continuous variables, the recall of independencies would be less but the precision of independencies would be higher.

Modeling is a powerful tool that can be used to identify important relationships between organisms and their habitat (Guisan & Zimmermann, 2000). Understanding the dynamics of how the two relate to one another is important for conserving and managing ecosystems, but the extreme complexity of those ecosystems makes it very difficult to fully diagram. Unlike many other modeling techniques, Multivariate Regression Trees (MRTs) are not limited by a prior assumptions, pre-determined relationships, transformations, or correlations. MRTs have the power to provide both explanation and prediction of ecological data by producing simple models that are easy to interpret. This study proposed to use MRTs to evaluate and model relationships between Lucania parva and the environment and habitat of Florida Bay. Counts were transformed to presence-absence and abundance groupings. Models were first run using a variety of combination of response variables and all explanatory variables. Results of these models were used to select the best combination of response and explanatory variables in an effort to create a best fit model. Models indicated that Lucania parva populations are found in the dense (cover ≥50%), shallow water (<1.8 m) grass beds that occur in the western portion of Florida Bay. A best fit model was able to explain 63.7% of the variance with predictive error of 0.43.

One of the challenges in the structural biological community is processing the wealth of protein data being produced today; therefore, the use of computational tools has been incorporated to speed up and help understand the structures of proteins, hence the functions of proteins. In this thesis, protein structure investigations were made through the use of Multivariate Analysis (MVA), and Fourier Transformed Infrared (FTIR), a form of vibrational spectroscopy. FTIR has been shown to identify the chemical bonds in a protein in solution and it is rapid and easy to use; the spectra produced from FTIR are then analysed qualitatively and quantitatively by using MVA methods, and this produces non-redundant but important information from the FTIR spectra. High resolution techniques such as X-ray crystallography and NMR are not always applicable and Fourier Transform Infrared (FTIR) spectroscopy, a widely applicable analytical technique, has great potential to assist structure analysis for a wide range of proteins. FTIR spectral shape and band positions in the Amide I (which contains the most intense absorption region), Amide II, and Amide III regions, can be analysed computationally, using multivariate regression, to extract structural information. In this thesis Partial least squares (PLS), a form of MVA, was used to correlate a matrix of FTIR spectra and their known secondary structure motifs, in order to determine their structures (in terms of "helix", "sheet", “310-helix”, “turns” and "other" contents) for a selection of 84 non-redundant proteins. Analysis of the spectral wavelength range between 1480 and 1900 cm-1 (Amide I and Amide II regions) results in high accuracies of prediction, as high as R2 = 0.96 for α-helix, 0.95 for β-sheet, 0.92 for 310-helix, 0.94 for turns and 0.90 for other; their Root Mean Square Error for Calibration (RMSEC) values are between 0.01 to 0.05, and their Root Mean Square Error for Prediction (RMSEP) values are between 0.02 to 0.12. The Amide II region also gave results comparable to that of Amide I, especially for predictions of helix content. We also used Principal Component Analysis (PCA) to classify FTIR protein spectra into their natural groupings as proteins of mainly α-helical structure, or protein of mainly β-sheet structure or proteins of some mixed variations of α-helix and β-sheet. We have also been able to differentiate between parallel and anti-parallel β-sheet. The developed methods were applied to characterize the secondary structure conformational changes of an unfolding protein as a function of pH and also to determine the limit of Quantitation (LoQ).Our structural analyses compare highly favourably to those in the literature using machine learning techniques. Our work proves that FTIR spectra in combination with multivariate regression analysis like PCA and PLS, can accurately identify and quantify protein secondary structure. The developed models in this research are especially important in the pharmaceutical industry where the therapeutic effect of drugs strongly depends on the stability of the physical or chemical structure of their proteins targets; therefore, understanding the structure of proteins is very important in the biopharmaceutical world for drugs production and formulation. There is a new class of drugs that are proteins themselves used to treat infectious and autoimmune diseases. The use of spectroscopy and multivariate regression analysis in the medical industry to identify biomarkers in diseases has also brought new challenges to the bioinformatics field. These methods may be applicable in food science and academia in general, for the investigation and elucidation of protein structure.

After a 100-year history, traditional zoning practices are being challenged as a contributing factor in a number of social, heath and economic problems facing cities in the United States. In this context, form based codes have emerged as a possible alternative way for cities to guide development. Growing out of the New Urbanist movement, form based codes frequently mix uses, allow for a greater variety of housing types and encourage development that is both denser and more compact. Despite an established literature which links land-use regulations, and zoning in particular, to fiscal outcomes, the impacts that form based codes have on public finance in the growing number of cities which have adopted them has yet to be fully investigated. The goal of this research is to examine if and how form based codes alter property tax and sales tax generation in the cities that adopt them. To examine the relationship between form based codes and public finance a series of two multivariate regression analyses were conducted using historic property and sales tax data. The first regression analysis was performed using the full list of 122 cities which have adopted form based standards from between 1984 and 2009. In an attempt to limit the diversity of sample cities and improve the ability to generalize results a second regression analysis was performed using a smaller list of 47 cities with populations between 50,000 and 200,000 thousand that had adopted form based standards between 1984 and 2009. The results of the first analysis established that a statistically significant positive relationship existed between the presence of form based standards which were implemented citywide and observed property tax revenue both in total and on a per capita basis. Similarly, a statistically significant positive relationship between the presence of form based standards implemented at the neighborhood level and total property tax revenue was observed. No significant relationship was found between the presence of neighborhood level standards and per capita property tax revenue. Further no significant relationship was found between form based standards and sales tax revenue. In general, these findings support the theory that form based codes and the development they allow, does alter the amount of property tax a city collects, but does not support the theory that form based codes affect sales tax revenues by facilitating the development of a more conducive urban, walkable environment or for any other reason. The results of the second regression analysis using data from cities with populations between 50,000 and 200,000 showed a significant positive relationship between the presences of citywide form based standards and total property tax revenue and per capita property tax revenue. Analysis of sales tax data showed a positive relationship between total sales tax revenue and the presence of form based standards at the neighborhood level. No other significant relationship between form based standards and sales tax revenue was observed. Similar, to analysis of all cities, the results for cities with population of 50,000 to 200,000 support the theory that form based codes and the development they allow does alter the amount of property tax a city collects, and that form based codes do not affect sales tax revenues except in the case of codes adopted at the neighborhood level, where a generally positive relationship was identified at the 10% confidence interval. Following this multivariate regression analysis, a case study of Saratoga Springs, New York was completed. Located in the far reaches of the Albany Metropolitan Area, Saratoga Springs developed as a popular tourist destination in the mid 1800’s. After experiencing economic decline in line with that of its peer cities in the mid to late 20th century, Saratoga Springs has experience a boom and now boast some of the highest home values in Upstate New York. In 2003 the city was one of the first in country to adopt form based standards, which have guided a significant amount of development in the city’s historic downtown as the city re-emerged as a popular tourist destination. Since the adoption of form based standards in Saratoga Springs both property tax and sales tax receipts have doubled.

Many species are sensitive to a light in ultraviolet spectrum. Some species have surface patterns that reflect ultraviolet light. These markings have been observed in many animal taxa; butterflies (Lepidoptera) are no exception. UV-reflectance in butterflies has been primarily connected to sexual selection and in this respect it has been a subject of many studies. In my work I propose an alternative view to this phenomenon. The aim of my work is to reveal how a particular environmental factors influence the morphospace of UV- reflectant patterns and wing shape of the Gonepteryx rhamni (Pieridae). The effect of various environmental factors (latitude, longitude, altitude, mean annual temperature, mean annual precipitation, normalized difference vegetation index - NDVI or net primary productivity - NPP) on wing morphospace was tested using the methods of Geometric morphometrics. I have also studied shape variability among the males and females, specimens from different locations and differences in morphospace of several G. rhamni subspecies. The dataset used in this analysis includes 118 males and 67 females from the Palearctic ecozone. The effect of almost all environmental (except to NDVI and NPP) predictors on shape of the UV-pattern and wing margin was significant in the case of males. In the...

本研究以不動產估價技術規則第19條第7項與第20條之規定，引用相似無關迴歸模式、多變量迴歸模式與典型相關分析等計量模式，對金融機構所做的擔保品估價進行驗證、預測及控制分析。 擔保品估價中會產生兩價，即擔保品的評估市場價格與評估擔保值（價），大部分的人都認為兩價存在一個比率關係。傳統的迴歸分析估價模式係由一組價格影響因素影響一個不動產價格，上述情形是否可能由同一組價格影響因素影響兩個不動產價格？本研究實證結果顯示，在95％統計信賴水準下，有兩個不動產價格受同一組價格因素影響的結果。既然驗證存在同一組價格影響因素影響兩個不動產價格，是否有更具效率的計量估價模式呢？典型相關分析係透過兩組變項之相關關係建構計量模式，除可再度驗證同一組價格影響因素影響兩個不動產價格，並可如同因素分析或主成份分析的功能，對兩組變項各做變項縮減的工作，達到對變項去蕪存菁的效果。 / This thesis is based on Article 19 No 7 and Article 20 of the Real Estate Appraisal Regulation. Seemingly Unrelated Regression Model, Multivariate Regression Model and Econometric Model and so on econometric model are applied. In addition, collateral valuations done by financial institutions are verified, predicted and analyzed. In collateral valuations, there are two-value references: assessed market value and assessed accommodation value. Majority believe that there is a ratio between these two values. The traditional regression analysis of the valuation model is having one set of pricing factors to have impact on the real estate price. However, is it possible that one set of pricing factors will affect two real estate prices? The findings approve that, under statistical confidence level with 95%, more than two real estate prices can be influenced by one set of pricing factors. Further more, this thesis also examines if there are other econometric valuation models to be applied? The canonical correlation analysis is to build a calculation model to analyze correlation between two variables. Other than examining one set of pricing factors can influence two real estate prices, this analysis also provides a similar function of the factor analysis or principal analysis to reduce variables caused by two sets of variable.

En écologie, dans le cadre par exemple d’études des services fournis par les écosystèmes, les modélisations descriptive, explicative et prédictive ont toutes trois leur place distincte. Certaines situations bien précises requièrent soit l’un soit l’autre de ces types de modélisation ; le bon choix s’impose afin de pouvoir faire du modèle un usage conforme aux objectifs de l’étude. Dans le cadre de ce travail, nous explorons dans un premier temps le pouvoir explicatif de l’arbre de régression multivariable (ARM). Cette méthode de modélisation est basée sur un algorithme récursif de bipartition et une méthode de rééchantillonage permettant l’élagage du modèle final, qui est un arbre, afin d’obtenir le modèle produisant les meilleures prédictions. Cette analyse asymétrique à deux tableaux permet l’obtention de groupes homogènes d’objets du tableau réponse, les divisions entre les groupes correspondant à des points de coupure des variables du tableau explicatif marquant les changements les plus abrupts de la réponse. Nous démontrons qu’afin de calculer le pouvoir explicatif de l’ARM, on doit définir un coefficient de détermination ajusté dans lequel les degrés de liberté du modèle sont estimés à l’aide d’un algorithme. Cette estimation du coefficient de détermination de la population est pratiquement non biaisée. Puisque l’ARM sous-tend des prémisses de discontinuité alors que l’analyse canonique de redondance (ACR) modélise des gradients linéaires continus, la comparaison de leur pouvoir explicatif respectif permet entre autres de distinguer quel type de patron la réponse suit en fonction des variables explicatives. La comparaison du pouvoir explicatif entre l’ACR et l’ARM a été motivée par l’utilisation extensive de l’ACR afin d’étudier la diversité bêta. Toujours dans une optique explicative, nous définissons une nouvelle procédure appelée l’arbre de régression multivariable en cascade (ARMC) qui permet de construire un modèle tout en imposant un ordre hiérarchique aux hypothèses à l’étude. Cette nouvelle procédure permet d’entreprendre l’étude de l’effet hiérarchisé de deux jeux de variables explicatives, principal et subordonné, puis de calculer leur pouvoir explicatif. L’interprétation du modèle final se fait comme dans une MANOVA hiérarchique. On peut trouver dans les résultats de cette analyse des informations supplémentaires quant aux liens qui existent entre la réponse et les variables explicatives, par exemple des interactions entres les deux jeux explicatifs qui n’étaient pas mises en évidence par l’analyse ARM usuelle. D’autre part, on étudie le pouvoir prédictif des modèles linéaires généralisés en modélisant la biomasse de différentes espèces d’arbre tropicaux en fonction de certaines de leurs mesures allométriques. Plus particulièrement, nous examinons la capacité des structures d’erreur gaussienne et gamma à fournir les prédictions les plus précises. Nous montrons que pour une espèce en particulier, le pouvoir prédictif d’un modèle faisant usage de la structure d’erreur gamma est supérieur. Cette étude s’insère dans un cadre pratique et se veut un exemple pour les gestionnaires voulant estimer précisément la capture du carbone par des plantations d’arbres tropicaux. Nos conclusions pourraient faire partie intégrante d’un programme de réduction des émissions de carbone par les changements d’utilisation des terres. / In ecology, in ecosystem services studies for example, descriptive, explanatory and predictive modelling all have relevance in different situations. Precise circumstances may require one or the other type of modelling; it is important to choose the method properly to insure that the final model fits the study’s goal. In this thesis, we first explore the explanatory power of the multivariate regression tree (MRT). This modelling technique is based on a recursive bipartitionning algorithm. The tree is fully grown by successive bipartitions and then it is pruned by resampling in order to reveal the tree providing the best predictions. This asymmetric analysis of two tables produces homogeneous groups in terms of the response that are constrained by splitting levels in the values of some of the most important explanatory variables. We show that to calculate the explanatory power of an MRT, an appropriate adjusted coefficient of determination must include an estimation of the degrees of freedom of the MRT model through an algorithm. This estimation of the population coefficient of determination is practically unbiased. Since MRT is based upon discontinuity premises whereas canonical redundancy analysis (RDA) models continuous linear gradients, the comparison of their explanatory powers enables one to distinguish between those two patterns of species distributions along the explanatory variables. The extensive use of RDA for the study of beta diversity motivated the comparison between its explanatory power and that of MRT. In an explanatory perspective again, we define a new procedure called a cascade of multivariate regression trees (CMRT). This procedure provides the possibility of computing an MRT model where an order is imposed to nested explanatory hypotheses. CMRT provides a framework to study the exclusive effect of a main and a subordinate set of explanatory variables by calculating their explanatory powers. The interpretation of the final model is done as in nested MANOVA. New information may arise from this analysis about the relationship between the response and the explanatory variables, for example interaction effects between the two explanatory data sets that were not evidenced by the usual MRT model. On the other hand, we study the predictive power of generalized linear models (GLM) to predict individual tropical tree biomass as a function of allometric shape variables. Particularly, we examine the capacity of gaussian and gamma error structures to provide the most precise predictions. We show that for a particular species, gamma error structure is superior in terms of predictive power. This study is part of a practical framework; it is meant to be used as a tool for managers who need to precisely estimate the amount of carbon recaptured by tropical tree plantations. Our conclusions could be integrated within a program of carbon emission reduction by land use changes.

En écologie, dans le cadre par exemple d’études des services fournis par les écosystèmes, les modélisations descriptive, explicative et prédictive ont toutes trois leur place distincte. Certaines situations bien précises requièrent soit l’un soit l’autre de ces types de modélisation ; le bon choix s’impose afin de pouvoir faire du modèle un usage conforme aux objectifs de l’étude. Dans le cadre de ce travail, nous explorons dans un premier temps le pouvoir explicatif de l’arbre de régression multivariable (ARM). Cette méthode de modélisation est basée sur un algorithme récursif de bipartition et une méthode de rééchantillonage permettant l’élagage du modèle final, qui est un arbre, afin d’obtenir le modèle produisant les meilleures prédictions. Cette analyse asymétrique à deux tableaux permet l’obtention de groupes homogènes d’objets du tableau réponse, les divisions entre les groupes correspondant à des points de coupure des variables du tableau explicatif marquant les changements les plus abrupts de la réponse. Nous démontrons qu’afin de calculer le pouvoir explicatif de l’ARM, on doit définir un coefficient de détermination ajusté dans lequel les degrés de liberté du modèle sont estimés à l’aide d’un algorithme. Cette estimation du coefficient de détermination de la population est pratiquement non biaisée. Puisque l’ARM sous-tend des prémisses de discontinuité alors que l’analyse canonique de redondance (ACR) modélise des gradients linéaires continus, la comparaison de leur pouvoir explicatif respectif permet entre autres de distinguer quel type de patron la réponse suit en fonction des variables explicatives. La comparaison du pouvoir explicatif entre l’ACR et l’ARM a été motivée par l’utilisation extensive de l’ACR afin d’étudier la diversité bêta. Toujours dans une optique explicative, nous définissons une nouvelle procédure appelée l’arbre de régression multivariable en cascade (ARMC) qui permet de construire un modèle tout en imposant un ordre hiérarchique aux hypothèses à l’étude. Cette nouvelle procédure permet d’entreprendre l’étude de l’effet hiérarchisé de deux jeux de variables explicatives, principal et subordonné, puis de calculer leur pouvoir explicatif. L’interprétation du modèle final se fait comme dans une MANOVA hiérarchique. On peut trouver dans les résultats de cette analyse des informations supplémentaires quant aux liens qui existent entre la réponse et les variables explicatives, par exemple des interactions entres les deux jeux explicatifs qui n’étaient pas mises en évidence par l’analyse ARM usuelle. D’autre part, on étudie le pouvoir prédictif des modèles linéaires généralisés en modélisant la biomasse de différentes espèces d’arbre tropicaux en fonction de certaines de leurs mesures allométriques. Plus particulièrement, nous examinons la capacité des structures d’erreur gaussienne et gamma à fournir les prédictions les plus précises. Nous montrons que pour une espèce en particulier, le pouvoir prédictif d’un modèle faisant usage de la structure d’erreur gamma est supérieur. Cette étude s’insère dans un cadre pratique et se veut un exemple pour les gestionnaires voulant estimer précisément la capture du carbone par des plantations d’arbres tropicaux. Nos conclusions pourraient faire partie intégrante d’un programme de réduction des émissions de carbone par les changements d’utilisation des terres. / In ecology, in ecosystem services studies for example, descriptive, explanatory and predictive modelling all have relevance in different situations. Precise circumstances may require one or the other type of modelling; it is important to choose the method properly to insure that the final model fits the study’s goal. In this thesis, we first explore the explanatory power of the multivariate regression tree (MRT). This modelling technique is based on a recursive bipartitionning algorithm. The tree is fully grown by successive bipartitions and then it is pruned by resampling in order to reveal the tree providing the best predictions. This asymmetric analysis of two tables produces homogeneous groups in terms of the response that are constrained by splitting levels in the values of some of the most important explanatory variables. We show that to calculate the explanatory power of an MRT, an appropriate adjusted coefficient of determination must include an estimation of the degrees of freedom of the MRT model through an algorithm. This estimation of the population coefficient of determination is practically unbiased. Since MRT is based upon discontinuity premises whereas canonical redundancy analysis (RDA) models continuous linear gradients, the comparison of their explanatory powers enables one to distinguish between those two patterns of species distributions along the explanatory variables. The extensive use of RDA for the study of beta diversity motivated the comparison between its explanatory power and that of MRT. In an explanatory perspective again, we define a new procedure called a cascade of multivariate regression trees (CMRT). This procedure provides the possibility of computing an MRT model where an order is imposed to nested explanatory hypotheses. CMRT provides a framework to study the exclusive effect of a main and a subordinate set of explanatory variables by calculating their explanatory powers. The interpretation of the final model is done as in nested MANOVA. New information may arise from this analysis about the relationship between the response and the explanatory variables, for example interaction effects between the two explanatory data sets that were not evidenced by the usual MRT model. On the other hand, we study the predictive power of generalized linear models (GLM) to predict individual tropical tree biomass as a function of allometric shape variables. Particularly, we examine the capacity of gaussian and gamma error structures to provide the most precise predictions. We show that for a particular species, gamma error structure is superior in terms of predictive power. This study is part of a practical framework; it is meant to be used as a tool for managers who need to precisely estimate the amount of carbon recaptured by tropical tree plantations. Our conclusions could be integrated within a program of carbon emission reduction by land use changes.

[pt] Dado o surgimento de uma nova ordem econômica, as empresas em todo o mundo perceberam que precisam estar comprometidas com a sustentabilidade. As pressões externas vão desde o governo, com a criação de regulamentações socioambientais, até os empregados e a sociedade - mídia, ONGs e clientes - que estão cada vez mais conectados, atentos e exigentes a essas questões. Empresas sustentáveis devem satisfazer as necessidades do presente (gerar lucro) sem comprometer o futuro (respeitando o meio ambiente e os preceitos de responsabilidade social). A indústria de manufatura, foco dessa dissertação, tem muito a contribuir para a sustentabilidade, pois impacta socio-economico-ambientalmente os locais onde opera, de forma significativa. Geração de gases de efeito estufa e de resíduos tóxicos estão entre os grandes vilões, mas não se limitando a eles. No âmbito interno, as empresas necessitam absorver o conceito de sustentabilidade no seu processo de produção, a partir de práticas de gestão ambiental relacionadas, por exemplo, à otimização do uso dos recursos ambientais (ex. reuso de água e utilização de energias alternativas), à redução de gases poluentes e às alternativas para descarte de resíduos; assim como às práticas de gestão social tais como medidas para aumentar saúde e segurança no ambiente de trabalho e criação de programas ligados ao bem estar dos funcionários. As ações, porém, devem ser ampliadas para toda a cadeia do processo e devem ser adotadas medidas colaborativas com os fornecedores para que sejam comprometidos e também responsáveis. No entanto, para a empresa se tornar sustentável, investimentos adicionais e aumento de custos são necessários para incluir em sua estrutura pessoal e processos responsáveis pelo incremento da sustentabilidade, seja ela econômica, social ou relacionada ao meio ambiente, o chamado triple bottom line, em inglês. / [en] Companies worldwide realized that being committed to sustainability is becoming a source to competitive advantage. Empirical evidence exists in the literature validating a positive link of sustainable manufacturing practices with organizational performance. However, there is a lack of rigorous empirical studies directly examining the impact of both environmental and social practices on operational manufacturing performance, especially in four main competitive operational capabilities: cost delivery, quality, and flexibility. This study analyses these relationships with literature review and the backdrop of the resource-based view and of the natural resource-based view of the firm. For this purpose, structural equation modeling (SEM) is used to build the measurement model and hierarchical stepwise multiple regression is used to test the research hypotheses. The data used were obtained from the sixth round of the International Manufacturing Strategy Survey (IMSS-VI) which includes responses from 931 manufacturing plants within the assembly industry in 22 countries. Our findings suggest that internal and external sustainability management practices are complementary. Manufacturing plants can increase their quality and flexibility performance, by implementing internal sustainable practices, such as water and energy consumption reduction, environmental and social certifications, work/life balance policies and sustainability communication, and can increase their cost efficiency and delivery performance by promoting supplier s sustainability management. Overall, this study contributes to the investigation of strategies for sustainable management, highlighting important implications for both practice and future research.

There is a growing demand for studies applying quantitative methods to large-scale data sets for the purpose of evaluating the effects of educational reforms (UVK, 2010). In this thesis the statistical method, Propensity Score Analysis (PSA), is presented and explored in the evaluating context of an extensive educational initiative within science and technology education; the Science and Technology for All-program (NTA). The research question put forward reads; under what conditions are PSA-analyses a useful method when measuring the effects from a school improvement initiative in S &amp; T? The study considers the use of PSA when looking for long-term effects that could be measured, what to take into consideration to be able to measure this, and how this could be done. The baseline references (outcome variables) used in order to measure/evaluate the long-term effects from the studied program is students’ achievements in the national test (score and grades) and their grades in year 9. Some findings revealed regarding the object of study (long-term effects from using NTA) are also presented. The PSA method is found to be a useful tool that makes it possible to create artificial control groups when experimental studies are impossible or inappropriate; which is often the case in school education research. The method opens up for making use of the rich source of registry data gathered by authorities. PSA proves reliable and relatively insensitive to the effects of covariates and heterogeneous effecter if the number of samples is large enough. The use of PSA (or other statistical methods) also makes it possible to measure outcomes several years after treatment. There are issues of concern when using PSA. One is the obvious demand for organized collection of measurement data. Another issue of concern is the choice of outcome variables. In this study the chosen outcome variables (pupils’ score and grading in national tests and grades in year 9) open up for discussions regarding aspects that might not be reflected/measured in national tests and/or teachers’ grading. Findings regarding the long-term effects from using NTA) show significantly positive effects in physics on test scores (average increase 16.5%) and test grades, but not in biology and chemistry. In this study no significant effects are found for course grades. PSA approach has proved to be a reliable method. There is however a limitation in terms of the method's ability to capture more subtle aspects of learning. A combination of quantitative and qualitative approach when studying long-term effects from educational intervention is therefore suggested. / <p>QC 20131120</p>