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RESUMO Neste artigo implementou-se o modelo de satisfação ECSI (European Customer Satisfaction Index) em uma das principais empresas brasileiras fornecedoras de Enterprise Resource Planning (ERP). O ECSI analisa o relacionamento da satisfação com seus antecedentes (imagem, expectativa, qualidade e valor) e conseqüentes (reclamação e lealdade). Para esta pesquisa, o modelo original foi modificado, retirando-se o construto reclamação. A pesquisa contou com duas fases: na exploratória foram realizadas entrevistas em profundidade e grupo de foco. Na fase descritiva foi realizado um e-survey, com 684 casos válidos. As escalas foram adaptadas de outros estudos, exceto para o construto qualidade, em que uma nova escala foi proposta e validada. O tratamento estatístico foi apoiado por técnicas multivariadas, incluindo a Modelagem de Equações Estruturais. Constatou-se que existe uma relação positiva forte entre imagem, qualidade e satisfação na propensão dos clientes à lealdade. Os resultados da pesquisa são discutidos, bem como suas implicações para futuras pesquisas e para a prática gerencial. ABSTRACT This research implemented the ECSI model (European Customer Satisfaction Index), in one of the most important Brazilian organization, that supplies ERP systems. The ECSI analyses the relationship among satisfaction with its antecedents (image, expectative quality and value) and its outcomes (complaint and loyalty). The original model was modified, as the complaint construct was dropped from the model. The methodology was implemented using a two phase design. The exploratory phase was developed applying focus groups and in deep interviews. At the descriptive phase, an e-survey was accomplished, obtaining 684 valid cases. The scales were obtained from previous studies, except the quality scale, that was developed specifically for this study. The data was analyzed using structural equation modeling and multivariate techniques. It was observed that there is a strong positive impact on loyalty, which was provided by image, quality and satisfaction. Finally, the results are discussed, as the implications for managers and future research.

Plant breeders always face the challenge to select the best individuals. Selection methods are required that maximize selection gain based on available data. When several crosses have been made, the BLUP procedure achieves this by combining phenotypic data with information on pedigree relationships via an index, known as family-index selection. The index, estimated based on the intra-class correlation coefficient, exploits the relationship among individuals within a family relative to other families in the population. An intra-class correlation coefficient of one indicates that the individual performance can be fully explained based on the family background, whereas an intra-class correlation coefficient of zero indicates the performance of individuals is independent of the family background. In the case, the intra-class correlation coefficient is one, family-index selection is considered. In the case the intra-class correlation coefficient is zero, individual selection is considered. The main difference between individual and family-index selection lies in the adjustment in estimating the individual’s effect depending on the intra-class correlation coefficient afforded by the latter. Two examples serve to illustrate the application of the BLUP method. The efficiency of individual and family-index selection was evaluated in terms of the heritability obtained from linear mixed models implementing the selection methods by suitably defining the treatment factor as the sum of individual and family effect. Family-index selection was found to be at least as efficient as individual selection in Dianthus caryophyllus L., except for flower size in standard carnation and vase life in mini carnation for which traits family-index selection outperformed individual selection. Family-index selection was superior to individual selection in Pelargonium zonale in cases when the heritability was low. Hence, the pedigree-based BLUP procedure can enhance selection efficiency in production-related traits in P. zonale or shelf-life related in D. caryophyllus L.

This is the peer reviewed version of the following article: McIsaac, M. A. and Cook, R. J. (2014), Response-dependent two-phase sampling designs for biomarker studies. Can J Statistics, 42: 268–284. doi: 10.1002/cjs.11207, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/cjs.11207/references. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

Background In resource-limited settings, monitoring and evaluation (M&E) of antiretroviral treatment (ART) programs often relies on aggregated facility-level data. Such data are limited, however, because of the potential for ecological bias, although collecting detailed patient-level data is often prohibitively expensive. To resolve this dilemma, we propose the use of the two-phase design. Specifically, when the outcome of interest is binary, the two-phase design provides a framework within which researchers can resolve ecological bias through the collection of patient-level data on a sub-sample of individuals while making use of the routinely collected aggregated data to obtain potentially substantial efficiency gains. Methods Between 2005–2007, the Malawian Ministry of Health conducted a one-time cross-sectional survey of 82,887 patients registered at 189 ART clinics. Using these patient data, an aggregated dataset is constructed to mimic the type of data that it routinely available. A hypothetical study of risk factors for patient outcomes at 6 months post-registration is considered. Analyses are conducted based on: (i) complete patient-level data; (ii) aggregated data; (iii) a hypothetical case–control study; (iv) a hypothetical two-phase study stratified on clinic type; and, (v) a hypothetical two-phase study stratified on clinic type and registration year. A simulation study is conducted to compare statistical power to detect an interaction between clinic type and year of registration across the designs. Results Analyses and conclusions based solely on aggregated data may suffer from ecological bias. Collecting and analyzing patient data using either a case–control or two-phase design resolves ecological bias to provide valid conclusions. To detect the interaction between clinic type and year of registration, the case–control design would require a prohibitively large sample size. In contrast, a two-phase design that stratifies on clinic and year of registration achieves greater than 85% power with as few as 1,000 patient samples. Conclusions Two-phase designs have the potential to augment current M&E efforts in resource-limited settings by providing a framework for the collection and analysis of patient data. The design is cost-efficient in the sense that it often requires far fewer patients to be sampled when compared to standard designs.