Authors:Cathelijn J. F. Waaijer, Cornelis A. van Bochove, and Nees Jan van Eck
multidimensional scaling. An in-depth comparison of the two techniques is provided by Van Eck et al. (submitted). The comparison shows that in general the VOS technique provides more satisfactory representations of data sets than the multidimensional scaling
In Table 3 it becomes immediately clear that tow different elements are of importance in this analysis. This is clearly illustrated in the Fig. 1 a–e, which are the graphical representations of the data in Table 3 . A first observation is related
when inventors update their own personal cognitive representations, versus adjusting cognitive representations due to the prior efforts of others. More nuanced data for testing would also be preferred. Second, future research could examine more of the
representations of the functions (of puzzle-solving and truth-finding) carried by the networks.
For example, some journals function to reproduce the specialty of analytical chemistry, while others reproduce sociology. Note that specialized knowledge is
This paper presents a methodology to aggregate multidimensional research output. Using a tailored version of the non-parametric
Data Envelopment Analysis model, we account for the large heterogeneity in research output and the individual researcher preferences
by endogenously weighting the various output dimensions. The approach offers three important advantages compared to the traditional
approaches: (1) flexibility in the aggregation of different research outputs into an overall evaluation score; (2) a reduction
of the impact of measurement errors and a-typical observations; and (3) a correction for the influences of a wide variety
of factors outside the evaluated researcher’s control. As a result, research evaluations are more effective representations
of actual research performance. The methodology is illustrated on a data set of all faculty members at a large polytechnic
university in Belgium. The sample includes questionnaire items on the motivation and perception of the researcher. This allows
us to explore whether motivation and background characteristics (such as age, gender, retention, etc.,) of the researchers
explain variations in measured research performance.