Kepemimpinan Kontemporer di Ikatan Wanita Pengusaha Indonesia (Iwapi) Surabaya

Penelitian ini dilakukan untuk melihat kepemimpinan kontemporer yang dijalankan oleh Iwapi Surabaya. Jenis penelitian yang digunakan adalah metode kualitatif dengan metode pengumpulan data wawancara semi terstruktur dan observasi terbuka. Penen­tuan informan dengan purposive sampling. Uji keabsahan data menggunakan triangulasi sumber. Hasil penelitian menunjukkan bahwa pemimpin di Iwapi Surabaya telah menerapkan kepemimpinan transformasional, kepemimpinan sinergistik, dan kepemimpinan visioner di dalam pola kepemimpinan kontemporer. Namun, penerapan kepemimpinan kontemporer di Iwapi Surabaya ini memiliki kekurangan dalam meningkatkan konsiderasi pribadi, motivasi inspirasional, dan dalam melakukan kolaborasi cerdas

Do Cross-National Differences in the Costs of Children Generate Cross-National Differences in Fertility Rates?

Parity-specific probabilities of having a next birth are estimated from national fertility data and are compared with nation-specific costs of having children as measured by time-budget data, by attitude data from the International Social Survey Program, and by panel data on labor earnings and standard of living changes following a birth. We focus on five countries (the US, West Germany, Denmark, Italy, and the United Kingdom), whose fertility rates span the observed fertility range in the contemporary industrialized world and whose social welfare and family policies span the conceptual space of standard welfare-state typologies. Definitive conclusions are difficult because of the multiple dimensions on which child costs can be measured, the possibility that child costs affect both the quantum and the tempo of fertility, the relatively small fertility differences across industrialized nations, and the inherent small-N problem resulting from nation-level comparisons. Empirical analysis, however, supports the assertion that institutionally driven child costs affect the fertility patterns of industrialized nations.

Nonparametric predictive inference for future order statistics

Nonparametric predictive inference (NPI) has been developed for a range of data types, and for a variety of applications and problems in statistics. In this thesis, further theory will be developed on NPI for multiple future observations, with attention to order statistics. The present thesis consists of three main, related contributions. First, new probabilistic theory is presented on NPI for future order statistics; additionally a range of novel statistical inferences using this new theory is discussed. Secondly, NPI for reproducibility is developed by considering two statistical tests based on order statistics. Thirdly, robustness of NPI is introduced which involves a first adaptation of some of the robustness theory concepts within the NPI setting, namely sensitivity curve and breakdown point. In this thesis, we present NPI for future order statistics. Given data consisting of n real-valued observations, $m$ future observations are considered and predictive probabilities are presented for the $r$-th ordered future observation. In addition, joint and conditional probabilities for events involving multiple future order statistics are presented. We further present the use of such predictive probabilities for order statistics in statistical inference, in particular considering pairwise and multiple comparisons based on future order statistics of two or more independent groups of data. This new theory enables us to develop NPI for the reproducibility of statistical hypothesis tests based on order statistics. Reproducibility of statistical hypothesis tests is an important issue in applied statistics: if the test were repeated, would the same conclusion be reached that is rejection or non-rejection of the null hypothesis? NPI provides a natural framework for such inferences, as its explicitly predictive nature fits well with the core problem formulation of a repeat of the test in the future. For inference on reproducibility of statistical tests, NPI provides lower and upper reproducibility probabilities (RP). The NPI-RP method is presented for two basic tests using order statistics, namely a test for a specific value for a population quantile and a precedence test for comparison of data from two populations, as typically used for experiments involving lifetime data if one wishes to conclude before all observations are available. As every statistical inference has underlying assumptions about models and specific methods used, one important field in statistics is the study of robustness of inferences. The concept of robust inference is usually aimed at development of inference methods which are not too sensitive to data contamination or to deviations from model assumptions. In this thesis we use it in a slightly narrower sense. For our aims, robustness indicates insensitivity to small changes in the data, as our predictive probabilities for order statistics and statistical inferences involving future observations depend upon the given observations. We introduce some concepts for assessing the robustness of statistical procedures in the NPI framework, namely sensitivity curve and breakdown point. The classical breakdown point does not apply to our context as the predictive inferences are bounded, so we change the definition to suit our context. Most of our nonparametric inferences have a reasonably good robustness with regard to small changes in the data. Traditionally, in the robustness literature there has been quite a lot of emphasis and discussion on robustness properties of estimators for the location parameters. Thus, in our investigation of NPI robustness we also focus on differences in robustness of the mean and the median of the $m$ future observations, and see how they relate to the classical concepts of robustness of the median and mean

When do Domestic Alliances help Ventures Abroad? Direct and Moderating Effects from a Learning Perspective

While the importance of strategic alliances for new venture internationalization is well acknowledged, the effect of domestic partners remains less understood. Building on organizational learning theory\u27s vicarious learning arguments, we suggest that internationally experienced domestic partners positively influence new ventures\u27 international intensity. Moreover, acknowledging that ventures may have multiple learning sources, we argue that the effect is more pronounced when substituting for the lack of new ventures\u27 top management teams\u27 international experience, or when complementing the insights about foreign markets received from foreign alliance partners. The analysis of 194 publicly held new ventures largely supports our hypotheses

The Impact of Early Imprinting on Evolution of New Venture Networks

Given the argued importance of networks to new ventures, this paper is intended to fill a noted gap in the literature pertaining to the factors that influence the evolution of new ventures\u27 alliance networks. Drawing on the imprinting literature, we propose that one has to look beyond the first partner per se, and instead focus on the extant relationships the initial partner has with other firms. More specifically, we argue and find that the network size and centrality of a new venture\u27s initial alliance partner influence the subsequent size of the new venture\u27s network

A Gradient in Education Due to Health? Evidence from the Study of Health Behavior in School-Aged Children

Research exploring the relationship between education and health suggests that people with higher levels of schooling report better health. To emphasize health as a determinant of educational achievement, this article establishes a gradient in education by health among Canadian students. Using data from the 2006 Health Behaviour in School-aged Children (HBSC) study, the relationship between self-rated health and achievement is examined for 8,626 students from 131 schools. The variation of the gradient in education by health within and between schools suggests that increases in self-rated health are associated with increased achievement for students. Moreover, the within-school regression accounted for 2.7 % of the variation in achievement due to health, whereas the between-school regression slope accounted for 19.8% of the variation in achievement due to health. Inequalities in achievement associated with health were more pronounced between schools than within schools. Policy implications as they relate to the findings are discussed.La recherche portant sur le rapport entre l’éducation et la santé donne à penser que les gens les plus instruits se disent en meilleure santé. Afin de mettre en relief la santé comme facteur déterminant dans le niveau d’instruction, cet article développe une échelle liant le niveau de scolarité et la santé chez les élèves canadiens. Puisant dans des données de l’enquête sur les comportements liés à la santé chez les enfants d’âge scolaire (2006), nous examinons le rapport entre la santé et la scolarité telles que décrites par 8 626 élèves provenant de 131 écoles. La variation notée dans le rapport scolarité/santé à l’intérieur des écoles et entre elles donne à penser que plus l’état de santé déclaré est positif, plus le rendement est élevé chez les élèves. De plus, la régression au sein des écoles représente 2,7% de la variation dans le rendement attribuable à la santé, alors que la régression entre les écoles représente 19,8 % de la variation dans le rendement attribuable à la santé. Les inégalités dans le rendement associé à la santé étaient plus prononcées entre les écoles qu’au sein des écoles. Nous discutons des incidences sur la politique qui découlent de ces résultats

Studies on a Temperature-Sensitive Mutant of Feline Calcivirus

A temperature sensitive (ts) mutant of feline calicivirus (FCV) was isolated from a vaccine strain of FCV. Virus-infected feline embryo cells were treated with the mutagen 5-fluorouracil (5-FU). The resulting virus was cloned and 192 clones were tested for growth at 3

Face recognition using curvelet transform

This paper presents a new method for the problem of human face recognition from still images. This is based on a multiresolution analysis tool called Digital Curvelet Transform. Curvelet transform has better directional and edge representation abilities than wavelets. Due to these attractive attributes of curvelets, we introduce this idea for feature extraction by applying the curvelet transform of face images twice. The curvelet coefficients create a representative feature set for classification. These coefficients set are then used to train gradient descent backpropagation neural network (NN). A comparative study with wavelet-based, curvelet-based, and traditional Principal Component Analysis (PCA) techniques is also presented. High accuracy rate of 97 achieved by the proposed method for two well-known databases indicates the potential of this curvelet based curvelet feature extraction method