Translated Literature in the Bulgarian Middle Ages as a Social and Cultural Phenomenon

The study reviews the Medieval Bulgarian translations from Greek as a multi-centennial process, preconditioned by the constant contacts between Byzantium and its Slavonic neighbor and dependant on the historical and cultural circumstances in Medieval Bulgaria. The facts are discussed from the prospective of two basic determining factors: social and cultural environment (spiritual needs of the age, political and cultural ideology, translationsʼ initiator, centers of translation activities, degree of education/literacy). The chronological and typological analysis of the thematic and genre range of the translated literature enables the outlining of five main stages: (1) Cyrillo-Methodian period (the middle of the 9th centuty – 885) – reception of the corpus needed for missionary purposes; (2) The First Bulgarian Tsardom period (885–1018) – intensive translation activities, founding the Christian literature in Bulgaria; (3) The period of The Byzantine rule (1018–1185) – a standstill in the translation activities and single translations of low-level literature texts; (4) The Second Bulgarian Tsardom – the period of Asenevtsi dynasty (the late 12th and the 13th centuries) – a partial revision of the liturgical and paraliturgical books; (5) The Second Bulgarian Tsardom – the Athonite-Tarnovo period (the 14th – early 15th century) – extensive relations with Byzantium and alignment to the then-current Byzantine models, intensifications of the translations flow and a broad range of the translation stream. (taken from: http://www.ceeol.com/aspx/issuedetails.aspx?issueid=fb876e89-ce0b-48a8-9373-a3d1e4d579a6&articleId=3056800e-cac7-4138-959e-8813abc311d9, 10.12.2013)

Predictive Margins and Marginal Effects in Stata

Tables of estimated regression coefficients, usually accompanied by additional information such as standard errors, t-statistics, p-values, confidence intervals or significance stars, have long been the preferred way of communicating results from statistical models. In recent years, however, the limits of this form of exposition have been increasingly recognized. For example, interpretation of regression tables can be very challenging in the presence of complications such as interaction effects, categorical variables, or nonlinear functional forms. Furthermore, while these issues might still be manageable in the case of linear regression, interpretational difficulties can be overwhelming in nonlinear models such as logistic regression. To facilitate sensible interpretation of such models it is often necessary to compute additional results such as marginal effects, predictive margins, or contrasts. Moreover, smart graphical displays of results can be very valuable in making complex relations accessible. A number of helpful commands geared at supporting these tasks have been recently introduced in Stata, making elaborate interpretation and communication of regression results possible without much extra effort. Examples of such commands are -margins-, -contrasts-, and -marginsplot-. In my talk, I will discuss the capabilities of these commands and present a range of examples illustrating their use.

Survey estimation and marginal effects in Stata

Stata is a general purpose software package that has become popular among various disciplines such as epidemiology, economics, or social sciences. Users like Stata for its scientific approach, its robustness and reliability, and the ease with which its functionality can be extended by user written programs. In this talk I will first give a brief overview of the functionality of Stata and then discuss two specific features: survey estimation and predictive margins/marginal effects. Most surveys are based on complex samples that contain multiple sampling stages, are stratified or clustered, and feature unequal selection probabilities. Standard estimators can produce misleading results in such samples unless the peculiarities of the sampling plan are taken into account. Stata offers survey statistics for complex samples for a wide variety of estimators and supports several variance estimation procedures such as linearization, jackknife, and balanced repeated replication (see Kreuter and Valliant, 2007, Stata Journal 7: 1-21). In the talk I will illustrate these features using applied examples and I will also show how user written commands can be adapted to support complex samples. Complex can also be the models we fit to our data, making it difficult to interpret them, especially in case of nonlinear or non-additive models (Mood, 2010, European Sociological Review 26: 67-82). Stata provides a number of highly useful commands to make results of such models accessible by computing and displaying predictive margins and marginal effects. In my talk I will discuss these commands provide various examples demonstrating their use.