More on Multidimensional Scaling and Unfolding in R: smacof Version 2

The smacof package offers a comprehensive implementation of multidimensional scaling (MDS) techniques in R. Since its first publication (De Leeuw and Mair 2009b) the functionality of the package has been enhanced, and several additional methods, features and utilities were added. Major updates include a complete re-implementation of multidimensional unfolding allowing for monotone dissimilarity transformations, including row-conditional, circular, and external unfolding. Additionally, the constrained MDS implementation was extended in terms of optimal scaling of the external variables. Further package additions include various tools and functions for goodness-of-fit assessment, unidimensional scaling, gravity MDS, asymmetric MDS, Procrustes, and MDS biplots. All these new package functionalities are illustrated using a variety of real-life applications

Individual differences in assimilation resistance and affective responses in problem solving

FSW - Gezinsopvoeding - Ou

Improving Users' Acceptance in Recommender System

Ph.DDOCTOR OF PHILOSOPH

Making Sense of Direction: Proximity and Order in Asymmetric Paired Comparison Data

In a square asymmetric matrix, the relationships among objects in the lower triangular half-matrix, differ from the relationships among the same objects in the upper triangular half. Square, asymmetric matrices can arise in similarity and preference data, when the direction of comparison is important. An asymmetric matrix can be rendered symmetric by averaging corresponding entries above and below the main diagonal. The difference between the original and the symmetric matrix is purely asymmetric, or skew-symmetric. The symmetric and skew-symmetric pans are orthogonal. An eigenvector-eigenvalue decomposition analyses the asymmetries into rank 2 skew-symmetric matrices, having an optimum least squares fit to the asymmetries (Gower, 1977). In this dissertation I derive an alternating least squares, nonmetric analogue of the canonical decomposition of asymmetry, suitable for ordinal-level data. In simulation studies, the nonmetric version gives better metric and nonmetric recovery, than does the canonical decomposition, when the asymmetries have been distorted by a range-compressing monotonic transform. The nonmetric technique appears to out-perform the canonical decomposition in detecting simplexes, and possibly in recovering multiplicative bias coefficients. However, canonical decomposition gives superior recovery after range-expanding monotonic transforms, and in the presence of error. An eigenvalue ratio test is proposed for determining the number of eigenvectors to extract in the canonical decomposition. The test quantifies changes in the slope of the log eigenvalue plot. In simulation studies the test appears to maintain its anticipated Type I error rate. The test is under-powered , which may help it to extract only well-identified eigenvectors. Finally, directional similarity judgments were collected for all possible pairs of exemplars of two semantic categories. The exemplars differed in typicality. After Tversky (1977) this should produce asymmetries related to the typicality. No asymmetries were found, however. Power analysis indicated that a correlation ratio for the asymmetries of .05 could have been detected 90% of the time. An extreme groups analysis also did not indicate asymmetry. The first eigenvector underlying the symmetric data, however, was highly correlated with typicality. Hence, Tversky\u27s model was not supported

Optimal Data Collection For Informative Rankings Expose Well-Connected Graphs

Given a graph where vertices represent alternatives and arcs represent pairwise comparison data, the statistical ranking problem is to find a potential function, defined on the vertices, such that the gradient of the potential function agrees with the pairwise comparisons. Our goal in this paper is to develop a method for collecting data for which the least squares estimator for the ranking problem has maximal Fisher information. Our approach, based on experimental design, is to view data collection as a bi-level optimization problem where the inner problem is the ranking problem and the outer problem is to identify data which maximizes the informativeness of the ranking. Under certain assumptions, the data collection problem decouples, reducing to a problem of finding multigraphs with large algebraic connectivity. This reduction of the data collection problem to graph-theoretic questions is one of the primary contributions of this work. As an application, we study the Yahoo! Movie user rating dataset and demonstrate that the addition of a small number of well-chosen pairwise comparisons can significantly increase the Fisher informativeness of the ranking. As another application, we study the 2011-12 NCAA football schedule and propose schedules with the same number of games which are significantly more informative. Using spectral clustering methods to identify highly-connected communities within the division, we argue that the NCAA could improve its notoriously poor rankings by simply scheduling more out-of-conference games.Comment: 31 pages, 10 figures, 3 table

Application of Natural Language Processing to Determine User Satisfaction in Public Services

Research on customer satisfaction has increased substantially in recent years. However, the relative importance and relationships between different determinants of satisfaction remains uncertain. Moreover, quantitative studies to date tend to test for significance of pre-determined factors thought to have an influence with no scalable means to identify other causes of user satisfaction. The gaps in knowledge make it difficult to use available knowledge on user preference for public service improvement. Meanwhile, digital technology development has enabled new methods to collect user feedback, for example through online forums where users can comment freely on their experience. New tools are needed to analyze large volumes of such feedback. Use of topic models is proposed as a feasible solution to aggregate open-ended user opinions that can be easily deployed in the public sector. Generated insights can contribute to a more inclusive decision-making process in public service provision. This novel methodological approach is applied to a case of service reviews of publicly-funded primary care practices in England. Findings from the analysis of 145,000 reviews covering almost 7,700 primary care centers indicate that the quality of interactions with staff and bureaucratic exigencies are the key issues driving user satisfaction across England