Science on the Periphery. The spanish Reception of Nuclear Energy: an Attempt at Modernity?

During the Cold War, Franco’s dictatorship attempted to use technological transfer and nuclear technology to strengthen the geopolitical influence of his Government in Spain. This paper studies this process, with particular reference to the role played by German scientists as advisers to the Spanish nuclear project.     

Informatik-Patente

Informatik-Patente

Informatik-Patente     

Is Education Getting Lost in University Mergers?

Mergers are common phenomena in higher education institutions. Improving educational quality is typically one of the stated goals of university mergers. Yet, little information exists about how merging institutions approach this goal. This paper presents results from a study of planning documents created prior to four mergers in the Finnish higher education system. These documents show that there was little concrete attention given to the educational issues related to the mergers. Most attention was placed on administrative issues and issues related to research. When educational issues were mentioned, it was almost always in the form of vague goals with few details provided about how the goals were to be reached. We conclude that this lack of attention to the educational aspect of mergers is a significant weakness of the planning process in these mergers.     

The First Year at School: A Quantitative Investigation of the Attainment and Progress of Pupils∗

In England children must start school after their fifth birthday, but it is common for children to start when they are four in what is known as the Reception class. The Performance Indicators in Primary Schools (PIPS) project collected data on 1700 pupils’ early mathematics and pre/early reading levels at the start and end of their Reception year. The on‐entry assessment proved to be a good predictor of performance in reading and mathematics at the end of reception and the progress which each child made was estimated. This progress was found to vary considerably between schools and the variation was much greater than that typically found in school effectiveness studies. The data provided a unique opportunity to compare the progress of children who had, and had not, been to school. The Reception year was found to have had a major impact on the literacy and numeracy of children. Multi‐level models were employed for the analysis and from the models Effect Sizes were computed to assist in comparing the importance of variables in the study. This approach provides a mechanism for comparing the findings of school effectiveness studies with experimental studies and meta‐analyses.     

The roles of teacher efficacy in instructional innovation: its predictive relations to constructivist and didactic instruction

Constructivist instruction has been implemented in the current instructional innovation in Singapore. Large scale survey study was conducted to examine the roles of teacher efficacy in implementing the innovative constructivist instruction. The results showed that the positive correlation between teacher efficacy and constructivist instruction was stronger than the correlation between teacher efficacy and didactic instruction. The study suggests that policy makers and school leaders should try to improve teacher’s efficacy beliefs so that innovative instruction could be effectively implemented by school teachers.     

Measurement Bias Detection Through Factor Analysis

Assessing the correctness of a structural equation model is essential to avoid drawing incorrect conclusions from empirical research. In the past, the chi-square test was recommended for assessing the correctness of the model but this test has been criticized because of its sensitivity to sample size. As a reaction, an abundance of fit indexes have been developed. The result of these developments is that structural equation modeling packages are now producing a large list of fit measures. One would think that this progression has led to a clear understanding of evaluating models with respect to model misspecifications. In this article we question the validity of approaches for model evaluation based on overall goodness-of-fit indexes. The argument against such usage is that they do not provide an adequate indication of the “size” of the model's misspecification. That is, they vary dramatically with the values of incidental parameters that are unrelated with the misspecification in the model. This is illustrated using simple but fundamental models. As an alternative method of model evaluation, we suggest using the expected parameter change in combination with the modification index (MI) and the power of the MI test.     

Principles and Practice of Scaled Difference Chi-Square Testing

We highlight critical conceptual and statistical issues and how to resolve them in conducting Satorra–Bentler (SB) scaled difference chi-square tests. Concerning the original (Satorra & Bentler, 2001 Satorra, A. and Bentler, P. M. 2001. A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66: 507–514. [Crossref], [Web of Science ®] , [Google Scholar]) and new (Satorra & Bentler, 2010 Satorra, A. and Bentler, P. M. 2010. Ensuring positiveness of the scaled chi-square test statistic. Psychometrika, 75: 243–248. [Crossref], [Web of Science ®] , [Google Scholar]) scaled difference tests, a fundamental difference exists in how to compute properly a model's scaling correction factor (c), depending on the particular structural equation modeling software used. Because of how LISREL 8 defines the SB scaled chi-square, LISREL users should compute c for each model by dividing the model's normal theory weighted least-squares (NTWLS) chi-square by its SB chi-square, to recover c accurately with both tests. EQS and Mplus users, in contrast, should divide the model's maximum likelihood (ML) chi-square by its SB chi-square to recover c. Because ML estimation does not minimize the NTWLS chi-square, however, it can produce a negative difference in nested NTWLS chi-square values. Thus, we recommend the standard practice of testing the scaled difference in ML chi-square values for models M ₁ and M ₀ (after properly recovering c for each model), to avoid an inadmissible test numerator. We illustrate the difference in computations across software programs for the original and new scaled tests and provide LISREL, EQS, and Mplus syntax in both single- and multiple-group form for specifying the model M ₁₀ that is involved in the new test.