Determinants of course completions in vocational education and training: Evidence from Australia

Completion rates in Australian vocational education and training (VET) are notoriously low. While there are conventional reasons such as issues with course, health, institutional factors, financial and family problems and dissatisfaction with the training experience, more VET specific explanations have included that students may discontinue their studies when they have obtained the specific skills they were seeking or they have gained employment. This present study seeks to examine whether the original intention of students at the time of enrollment along with satisfaction and the benefit that could be obtained from completion have any bearing on completion patterns. We model the probability of intending to complete from the 2011 Student Intentions Survey and the perform an out of sample prediction of students' intention on respondents to the 2011 Student Outcomes Survey. Subsequently a logistic regression model predicting actual completion is developed utilising student intentions, various components of satisfaction, completion pay-offs and some demographic and educational variables.. Main findings of this study include that while students' initial intentions to complete increase actual completion probabilities, the overwhelming determinants of completion are high annual hours of enrollment and the enrollment in higher VET qualifications. Furthermore, the benefits to completion and satisfaction with the training play only minor roles in shaping completion patterns. This study contributes to the existing body of knowledge about completions in VET by including the original completion intention of students in the examination of completion patterns

C++ Klassen zur Linearen Regression bei fehlenden Kovariablen

In diesem Bericht werden C++ Klassen zu linearen Modellen mit fehlenden Werten in der Kovariablenmatrix X vorgestellt. Diese Klassen implementieren erste verwendbare Modelle wie Zero Order Regression, First Order Regression oder modified First Order Regression und dienen als Ausgangsbasis für weitere Modellklassen. Die hier vorgestellten Klassen können in Simulationsstudien oder für konkrete Datensätze verwendet werden

Effectiveness of Anti-Bullying Programs in Schools

Undergraduate Applie

Regulation of Cydia pomonella in the Northern German climate

Population control of the codling moth (Cydia pomonella) is of great importance in ecological fruit-growing. In the work described here we attempted (1) to determine the efficacy of plant protection preparations certified for organic fruit production, and (2) to develop a suitable application strategy. We found that a Granulovirus (Madex3), Bacillus thuringiensis (Xentari) and Neem Azal were able to provide effective control of C. pomonella. The reduced efficacy of Madex 3 as reported e.g. from some Southern German orchards could not be observed in our trials

Modified First Order Regression, eine Simulationsstudie

In diesem Bericht werden verschiedene Imputationsmechanismen fuer fehlende Kovariablen in einem linearen Regressionsmodell mit zwei Kovariablen untersucht. Hierbei ist eine der Kovariablen vollstaendig beobachtet, die andere nur teilweise. Die betrachteten Imputationsmechanismen sind Zero Order Regression (ZOR), First Order Regression (FOR), First Order Regression plus random noise (FOR+) und Modified First Order Regression (MFOR)