Suchergebnisse

Intro -- Preface -- Acknowledgements -- Contents -- About the Authors -- 1 The Importance of Selecting the Most Effective Teachers -- 1.1 Strengthening the Teacher Workforce -- 1.2 Teacher Selection is a 'Quick Win' -- 1.3 Why Teacher Selection Matters -- 1.4 The Costs and Benefits of Teacher Selection -- 1.5 Purpose and Overview of the Book -- 1.6 Chapter Summary -- References -- Part I Identifying the Characteristics of Effective Teachers -- 2 What Does 'Teacher Effectiveness' Look like? -- 2.1 Teacher Effectiveness -- 2.2 Measuring Teacher Effectiveness -- 2.3 Trajectories of Teacher Effectiveness -- 2.4 Teacher Effectiveness and Related Outcomes -- 2.5 Are Effective Teachers Born or Made? -- 2.6 Chapter Summary -- References -- 3 The Role of Individual Attributes in Teacher Selection -- 3.1 Individual Attributes -- 3.2 The Distinction Between Cognitive and Non-Cognitive Attributes -- 3.3 Individual Attributes and Teacher Effectiveness -- 3.4 Research on Attributes Related to Teacher Effectiveness -- 3.5 Inductive, Deductive, and Integrated Approaches -- 3.6 Cross-Cultural Perspectives: Which Attributes Are Universal? -- 3.7 Chapter Summary -- References -- Part II Selection Methods and Practices: Issues and Uses -- 4 Issues and Challenges in Selection -- 4.1 Test Quality and Theoretical Models Underpinning Selection -- 4.2 Adverse Impact -- 4.3 Applicants' Perceptions of Selection Procedures -- 4.4 Faking -- 4.5 Coaching Effects -- 4.6 Chapter Summary -- References -- 5 Selection Practices and Methods in Other Fields -- 5.1 Selection Practices in Other Fields -- 5.2 Selection Methods and Their Evidence -- 5.3 What Can We Learn from Selection in Other Fields? -- 5.4 Chapter Summary -- References -- Part III Teacher Selection: Past, Present, and Future -- 6 Teacher Selection: History and Current Practices.

In dem Beitrag geht es um die Frage, aus welchen Informationsbestandteilen und mit welcher Reichweite Schlüsse über die historische Realität und / oder die Struktur historischer Gesellschaften gezogen werden. Es wird erörtert, welche Einflußgrößen und Faktoren sich als Minderung der Aussagemöglichkeiten gegenüber einer gewählten oder vorgegebenen empirischen Basis auf den Forschungsprozeß und die darin erzielten Ergebnisse auswirken. Aus der Vielzahl von Quellen werden hypothetische Beispiele herausgegriffen, anhand derer die Selektivität aufgrund der Quellenlage gezeigt wird. Die Analyse von Beispielen zeigt, daß immer nur die innere Logik des quantitativen Forschungsansatzes prüfbar ist, der auf der Basis des maschinenlesbaren Datensatzes in eine statistische Analyse mündet. In einem zweiten Teil werden die Auswahltechniken betrachtet, die die Möglichkeit bieten, verkleinerte Abbilder von Grundgesamtheiten oder Zielgesamtheiten methodisch kontrolliert und nachvollziehbar zu erstellen. Die wichtigsten Vorgehensweisen werden vorgestellt. Drei Grundrichtungen der Auswahltechniken werden unterschieden: (1) Wahrscheinlichkeitsauswahl bzw. systematische Auswahl; (2) bewußte Auswahl; (3) willkürliche (undefinierte, unklare) Auswahl. (KW)

AbstractSample selection arises when the outcome of interest is partially observed in a study. A common challenge is the requirement for exclusion restrictions. That is, some of the covariates affecting missingness mechanism do not affect the outcome. The drive to establish this requirement often leads to the inclusion of irrelevant variables in the model. A suboptimal solution is the use of classical variable selection criteria such as AIC and BIC, and traditional variable selection procedures such as stepwise selection. These methods are unstable when there is limited expert knowledge about the variables to include in the model. To address this, we propose the use of adaptive Lasso for variable selection and parameter estimation in both the selection and outcome submodels simultaneously in the absence of exclusion restrictions. By using the maximum likelihood estimator of the sample selection model, we constructed a loss function similar to the least squares regression problem up to a constant, and minimized its penalized version using an efficient algorithm. We show that the estimator, with proper choice of regularization parameter, is consistent and possesses the oracle properties. The method is compared to Lasso and adaptively weighted $$L_{1}$$

L
1

penalized Two-step method. We applied the methods to the well-known Ambulatory Expenditure Data.