Ingwer Borg Libros

Employee surveys are the central tool for accelerating strategic organization development. They allow managers and consultants to assess an organization on its "soft factors" such as leadership and employee engagement, leading to actions that reduce problems adn turn opportunities into tangible results. This practically oriented book details both the factors to be considered and the steps necessary for developing a successful employee survey process-- from administration to action. In doing so, the authors draw upon organizational psychology and survey methodology, as well as their wide practical experience with employee surveys in NOrth America, Europe, and multinationally. This book not only shows how to plan and execute smployee surveys, but also offers a host of models, methods, examples, and theory for what to do afterwards, including standard and nonstandard ddata analysis, presentations of results to top management, running workshops with managers and staff on the survey results, and planning and implementing actions. It also includes numerous practical tips and handy checklists that go far beyond simple " how-to_ recipes. Rather, all recommendations are discussed so that their rationale becomes transparent and adaptations can be made to optimally fit the needs of the particular organization.

This book introduces multidimensional scaling (MDS) and unfolding as data analysis techniques for applied researchers. MDS is used for the analysis of proximity data on a set of objects, representing the data as distances between points in a geometric space (usually of two dimensions). Unfolding is a related method that maps preference data (typically evaluative ratings of different persons on a set of objects) as distances between two sets of points (representing the persons and the objects, resp.).This second edition has been completely revised to reflect new developments and the coverage of unfolding has also been substantially expanded. Intended for applied researchers whose main interests are in using these methods as tools for building substantive theories, it discusses numerous applications (classical and recent), highlights practical issues (such as evaluating model fit), presents ways to enforce theoretical expectations for the scaling solutions, and addresses the typical mistakes that MDS/unfolding users tend to make. Further, it shows how MDS and unfolding can be used in practical research work, primarily by using the smacof package in the R environment but also Proxscal in SPSS. It is a valuable resource for psychologists, social scientists, and market researchers, with a basic understanding of multivariate statistics (such as multiple regression and factor analysis).

This book provides a comprehensive treatment of multidimensional scaling (MDS), a statistical technique used to analyze the structure of similarity or dissimilarity data in multidimensional space. There are many examples of such data, including intercorrelations of attitude items, direct ratings of similarity on choice objects, and trade indices for a set of countries. MDS models such data as distances between points in a geometric space of low dimensionality. This makes complex data sets accessible to visual exploration and thus makes it easier to see structure not obvious from the numbers. Other uses of MDS interpret the geometry and, in particular, the distance function as a psychological composition rule. The book may be used as an introduction to MDS for students in many areas, including statistics, psychology, sociology, political science, and marketing. The prerequisite is a two-semester course in statistics for the social or managerial sciences. The volume is also suited for various advanced courses on MDS, either with an emphasis on data analysis or a focus on the psychology of similarity. All the mathematics required for more advanced topics is developed systematically.

​First Steps.- The Purpose of MDS.- The Goodness of an MDS Solution.- Proximities.- Variants of Different MDS Models.- Confirmatory MDS.- Typical Mistakes in MDS.- MDS Algorithms.- Computer Programs for MDS.

Die Multidimensionale Skalierung (MDS) ist eine Familie von Verfahren, die Objekte des Forschungsinteresses durch Punkte eines mehrdimensionalen (meist: 2-dimensionalen) Raums so darstellen, dass die Distanz zwischen je zwei Punkten in diesem Raum einem gegebenen Nähe-, Abstands-, Ähnlichkeits- oder Unähnlichkeitswert dieser Objekte optimal entspricht. Der Zweck der MDS liegt meist in der Visualisierung der wesentlichen Struktur der Daten. Diese soll dem Auge für eine explorative oder Theorie testende Analyse zugänglich gemacht werden. Als Daten lassen sich in der MDS außerordentlich viele Messgrößen verwenden wie etwa Korrelationen der Objekte über ihre Ausprägungen auf verschiedenen Variablen; direkt erhobene globale Ähnlichkeitsratings für Paare von Objekten; oder Co-Occurrence-Koeffizienten, die erfassen, wie oft ein Ereignis zusammen mit einem anderen auftritt. Das Buch ist eine anwenderorientierte Einführung in die MDS. Es beschreibt die für die Praxis wichtigsten MDS-Modelle (inkl. Der konfirmatorischen MDS) konzeptionell, mit wenigen Formeln, und unter Verwendung typischer Beispiele. Es bietet zudem eine Beschreibung von zwei umfassenden Computerprogrammen für die MDS (PROXSCAL in SPSS, SMACOF in R). Ausführlich diskutiert werden zudem typische Anwenderfehler in der MDS.

Mitarbeiterbefragungen werden heute in vielen Unternehmen eingesetzt, um den strategischen und operativen Fortschritt zu messen, zu prognostizieren und zu beschleunigen. Der Band stellt in kompakter Form alle wesentlichen Überlegungen und Schritte einer erfolgreichen Mitarbeiterbefragung (MAB) praxisorientiert und wissenschaftlich fundiert dar. Von der Positionierung des Projektes bis zu nachfolgenden Aktionen oder Reaktionen wird die MAB-Technik Schritt für Schritt vorgestellt. Insbesondere wird auch auf die verschiedenen Chancen, Risiken und Kosten einer Mitarbeiterbefragung eingegangen. Das Buch soll den Personalspezialisten und den Linienmanager befähigen, eine Mitarbeiterberfragung anzulegen, gegebenenfalls selbst durchzuführen oder optimal zu nutzen.