Experience in introduction of English terminology in engineering lessons: methodology and evaluation

This communication explains a experience for the introduction of English terminology in a technical degree of higher education. We present the methodology and assessment procedures used to evaluate the way the students perceived the introduction of terminology in English in two different subjects from 3rd and 5th year courses of a Computer Science degree in which English was not the vehicular language. We propose a strategy based on two main pillars, namely: 1) The design of materials, explanations, and exams, paying particular attention to the way in which the specific terminology was exposed to the students, and 2) The assessment of the impact in the students by means of the analysis of the feedback trough a set of enquiries. Our experience showed that the students responded very positively to the introduction of English terminology, and presented an affirmative feedback about the impact that an improvement of their linguistic abilities would have in their future work. Further, we present statistics regarding the use of English as the vehicular language for technical reports, which is envisaged as very useful by the students. Finally, we propose a set of questions for further debate which are centered in the role that English terminology should pay in technical degrees, and about the way in which universities should deploy resources in English languages within the different Syllabus

Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression