Keywords: a Vocabulary of Culture and Society

Therefore, the seeker after the truth is not one who studies the writings of the ancients and, following his natural disposition, puts his trust in them, but rather the one who suspects his faith in them and questions what he gathers from them, the one who submits to argument and demonstration, and not to the sayings of a human being whose nature is fraught with all kinds of imperfection and de‹ciency. Thus the duty of the man who investigates the writings of scientists, if learning the truth is his goal, is to make himself the enemy of all that he reads, and, applying his mind to the core and margins of its content, attack it from every side. He should also suspect himself as he performs his critical examination of it, so that he may avoid falling into either prejudice or leniency. (Ibn al-Haytham)1

국제정치이론 = Theory of international politics

This research focuses on how price changes influence the observed pattern of brand competition. The paper begins with a basic utility model formulation and examines the implications of three major classes of preference distributions on the expected patterns of competition. A price-tier model is proposed to operationalize the theory and to allow predictive testing. The price-tier model is estimated on 28 brands across four product categories.The results show a specific asymmetric pattern of price competition. Higher-price, higher quality brands steal share from other brands in the same price-quality tier, as well as from brands in the tier below. However, lower-price, lower-quality brands take sales from their own tier and the tier below brands, but do not steal significant share from the tiers above. The results are consistent with a bimodal preference distribution, with the regular price indifference point being located toward the lower-quality end of the preference distribution for the categories analyzed.

Price-Induced Patterns Of Competition

Advertising : principles and practice

Getting the books philosophy of social science the philosophical foundations of social thought now is not type of inspiring means. You could not isolated going in the manner of ebook increase or library or borrowing from your contacts to retrieve them. This is an certainly easy means to specifically get lead by on-line. This online declaration philosophy of social science the philosophical foundations of social thought can be one of the options to accompany you gone having additional time.

Philosophy of Social Science: The Philosophical Foundations of Social Thought

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Information, Debate, and Opinion Change On-line: Deliberative transformation of opinion in computer-mediated discussion

Propensity score methods, whose foundation is the design-based approach of the Rubin Causal Model, prevail today in the causal inference field for observational studies. The methods are largely applied in the fields of economics, medicine, education, political science, psychology, management and business. Such a designbased approach for causal inference from observational studies aims to mend an observational (non-randomised) design so as to approximate a randomised experiment design and, in this sense, it strictly follows the rationale of experimental designs. We extend the use of propensity score methods also to observational designs where the nature of observed data does not allow us to estimate reliably causal effects; thus, we can only estimate conditional associations. We show, with an application, that the use of the design-based approach, particularly for small sample studies, is more trustworthy than model-based approaches (i.e., regression analyses), when estimating conditional associations. Propensity score methods consist of: (i) the design phase, which is outcome free and aims to balance a study design with respect to observed covariates (e.g. removes selection bias in observational designs in order to mimic randomised experiment designs when estimating causal effects), and; (ii) the analysis phase, which uses the outcome data in order to estimate causal effects or conditional associations, to perform additional covariate adjustments, and to carry out sensitivity analysis for obtained causal effect estimates. The development of propensity score methods over the past three decades has resulted in guidance on estimating causal effects from large observational data sets. However, the question of "how large" a data set should be, remains mostly unanswered. Because the social sciences and medical research often face relatively small samples, the investigation of minimum sample size requirements for reliably estimating causal effects from observational designs, remains a highly important topic. Based on the published research on propensity score methods with small samples, treated samples that consists of less than 100 units have not yet been sufficiently investigated. The thesis thus investigates minimum sample size requirements that enable a reliable estimation of causal effects. We carry out a series of simulation studies examining a variety of small treated sample sizes (samples smaller than 100), group ratios (i.e., ratio between the samples of control and treated units), different selection mechanisms (i.e., the level of initial covariate imbalances between treated and control groups), different numbers of observed covariates, and the performance of different matching algorithms (i.e., greedy and optimal). We also examine performance of moderately large treated samples (i.e., samples consisting of 200 and 500 units) in order to compare the behaviour of small versus moderately large treated samples with propensity score methods. Two simulation study extensions are performed to investigate: (i) whether different correlation structures (weaker versus stronger) between the observed covariates and the outcome variable has an impact on propensity score studies; and (ii) whether different classes of the outcome variable (binary versus continuous) affect treatment effect estimates with small samples differently from larger samples. The simulation results show that the success of propensity score study (i.e., successful removal of selection bias) with small treated samples primarily depends on a sufficiently large pool of control units. However, the required size of a control group depends on: (i) the size of a treated group; (ii) the number of observed covariates; and (iii) the strength of the selection mechanism, measured by the initial imbalances in observed covariates between the treated and control groups. The simulation study’s findings demonstrate that small treated samples (as small as 8 = t n ) perform as good as moderately large treated samples (i.e., t n of 200 or 500) at removing selection bias from observational study designs as long as the group ratio is sufficiently large, and the treatment assignment mechanism is strongly ignorable. Of course, the treatment effect estimates derived from small treated samples are much less precise in comparison to those obtained by moderately large treated samples, due to much larger treatment effect standard errors in cases of small treated samples. However, the size of treatment effect standard errors in small sample studies does not depend only on the overall sample size but also on the number of observed covariates and the matching algorithm used in the propensity score study. On the other hand, the number of observed covariates (i.e., 30 , 20 , 15 , 10 = p ) and the matching algorithm used has a negligible effect on the size of treatment effect standard errors in moderately large sample studies. The use of different matching algorithms has a negligible effect on the minimum required group ratio for removing selection bias from observational designs with moderately large samples, whereas a tiny effect with small samples (i.e., optimal matching algorithm on average performs slightly better – on average requiring smaller group ratios for removing selection bias). At the same time, the propensity score study with optimal matching algorithm for small samples resulted on average in slightly smaller treatment effect standard errors, which was not the case for moderately large samples. An application of the simulation study results with real observational data (LaLonde 1986) additionally supports our conclusions regarding the performance of propensity score methods with small samples. The second application of real observational data (Luthar, et al. 2011) provides an example of a study where research questions might be causal but due to the nature of observed data, we are not able to estimate, reliably, causal effects. Hence, only conditional associations between an indicator variable and an outcome variable, given a set of substantively interesting covariates, can be estimated. This application is particularly interesting because: (i) it shows the use of propensity score methods to estimate conditional associations, and; (ii) it shows why design-based approaches are much more trustworthy, in comparison to the model-based approaches (e.g., regression analysis), when estimating conditional associations, particularly in small sample studies.

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Univerza v ljubljani fakulteta za drubene vede

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Information, Debate, and Opinion Change On-line: Deliberative transformation of opinion in computer-mediated discussion

Univerza v ljubljani fakulteta za drubene vede