Alexia Prskawetz (61 results)

Condition: Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

paperback. Condition: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

Broschiert. Condition: Gut. Auflage: 1. 473 Seiten Einband und Kanten minimal berieben, sonst gut erhalten. LIEFERZEITEN / DELIVERY TIMES: DEUTSCHLAND 5 - 14 Tage EUROPA/EUROPE: 7 - 30 Tage/Days USA/WELTWEIT/WORLDWIDE: 14 - 60 Tage/Days (!!!) +++ Sprache: Deutsch Gewicht in Gramm: 600.

Condition: New. In English.

Condition: New. In English.

Condition: Sehr gut. Zustand: Sehr gut | Seiten: 251 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Condition: New. In English.

Condition: New.

Condition: New. pp. 224.

Condition: New. pp. 244.

Condition: New. pp. 260.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The central aim of many studies in population research and demography is to explain cause-effect relationships among variables or events. For decades, population scientists have concentrated their efforts on estimating the 'causes of effects' by applying standard cross-sectional and dynamic regression techniques, with regression coefficients routinely being understood as estimates of causal effects. The standard approach to infer the 'effects of causes' in natural sciences and in psychology is to conduct randomized experiments. In population studies, experimental designs are generally infeasible.In population studies, most research is based on non-experimental designs (observational or survey designs) and rarely on quasi experiments or natural experiments. Using non-experimental designs to infer causal relationships-i.e. relationships that can ultimately inform policies or interventions-is a complex undertaking. Specifically, treatment effects can be inferred from non-experimental data with a counterfactual approach. In this counterfactual perspective, causal effects are defined as the difference between the potential outcome irrespective of whether or not an individual had received a certain treatment (or experienced a certain cause). The counterfactual approach to estimate effects of causes from quasi-experimental data or from observational studies was first proposed by Rubin in 1974 and further developed by James Heckman and others.This book presents both theoretical contributions and empirical applications of the counterfactual approach to causal inference.…

Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | This book is the outcome of a project that started with the organisation of theTopicalWorkshopon¿Agent-BasedComputationalModelling. AnInst- ment for Analysing Complex Adaptive Systems in Demography, Economics and Environment¿ at the Vienna Institute of Demography, December 4-6, 2003. The workshop brought together scholars from several disciplines, all- ing both for serious scienti?c debate and for informal conversation over a cup co?ee or during a visit to the wonderfulmuseums of Vienna. One of the nicest features of Agent-Based Modelling is indeed the opportunity that scholars ?nd a common language and discuss from their disciplinary perspective, in turn learning from other perspectives. Given the success of the meeting, we found it important to pursue the purpose of collecting these interdisciplinary contributions in a volume. In order to ensure the highest scienti?c standards for the book, we decided that all the contributions (with the sole exception of the introductory chapter) should have been accepted conditional on peer reviews. Generoushelpwasprovidedbyreviewers,someofwhomwereneither directly involved in the workshop nor in the book. All this would not have been possible without the funding provided by the Complex Systems N- work of Excellence (Exystence) funded by the European Union, the Vienna Institute of Demography of the Austrian Academy of Sciences, Universit` a Bocconi, and ARC Systems Research GmbH, and the help of the wonderful sta? ofthe Vienna Institute of Demography(in particular,Ani Minassianand Belinda Aparicio Diaz). Agent-Based Modelling is important, interesting and also fun¿we hope this book contributes to showing that. Milano Francesco C.…

Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The central aim of many studies in population research and demography is to explain cause-effect relationships among variables or events. For decades, population scientists have concentrated their efforts on estimating the 'causes of effects' by applying standard cross-sectional and dynamic regression techniques, with regression coefficients routinely being understood as estimates of causal effects. The standard approach to infer the 'effects of causes' in natural sciences and in psychology is to conduct randomized experiments. In population studies, experimental designs are generally infeasible.In population studies, most research is based on non-experimental designs (observational or survey designs) and rarely on quasi experiments or natural experiments. Using non-experimental designs to infer causal relationships-i.e. relationships that can ultimately inform policies or interventions-is a complex undertaking. Specifically, treatment effects can be inferred from non-experimental data with a counterfactual approach. In this counterfactual perspective, causal effects are defined as the difference between the potential outcome irrespective of whether or not an individual had received a certain treatment (or experienced a certain cause). The counterfactual approach to estimate effects of causes from quasi-experimental data or from observational studies was first proposed by Rubin in 1974 and further developed by James Heckman and others.This book presents both theoretical contributions and empirical applications of the counterfactual approach to causal inference.…

Condition: New. In English.

Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condition: New.

Condition: New. In English.

Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This edited collection explores the links between human capital (both in the form of health and in the form of education), demographic change, and economic growth. Using empirical as well as theoretical perspectives, the authors investigate several important issues in the context of human capital, namely population ageing, inequality, public policy, and long-term economic development. Ultimately, they demonstrate that the accumulation of human capital is of crucial importance to long-run economic growth.…

Taschenbuch. Condition: Neu. Human Capital and Economic Growth | The Impact of Health, Education and Demographic Change | Alberto Bucci (u. a.) | Taschenbuch | xxxii | Englisch | 2021 | Springer | EAN 9783030216016 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Condition: New. pp. XXXII, 361 47 illus., 18 illus. in color. 1st ed. 2019 edition NO-PA16APR2015-KAP.

Paperback. Condition: Brand New. 396 pages. 8.27x5.83x0.93 inches. In Stock.

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Hardcover. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Condition: New. Warum bleibt der Kinderwunsch vieler Paare unerfuellt, waehrend sich andere gegen Kinder entscheiden? Das Buch praesentiert die Ergebnisse einer Arbeitsgruppe der Berlin-Brandenburgischen Akademie der Wissenschaften und der Leopoldina zu den Gruenden niedriger .