Thurstonian Scaling of Compositional Questionnaire Data

To prevent response biases, personality questionnaires may use comparative response formats. These include forced choice, where respondents choose among a number of items, and quantitative comparisons, where respondents indicate the extent to which items are preferred to each other. The present article extends Thurstonian modeling of binary choice data (Brown & Maydeu-Olivares, 2011a) to “proportion-of-total” (compositional) formats. Following Aitchison (1982), compositional item data are transformed into log-ratios, conceptualized as differences of latent item utilities. The mean and covariance structure of the log-ratios is modelled using Confirmatory Factor Analysis (CFA), where the item utilities are first-order factors, and personal attributes measured by a questionnaire are second-order factors. A simulation study with two sample sizes, N=300 and N=1000, shows that the method provides very good recovery of true parameters and near-nominal rejection rates. The approach is illustrated with empirical data from N=317 students, comparing model parameters obtained with compositional and Likert scale versions of a Big Five measure. The results show that the proposed model successfully captures the latent structures and person scores on the measured traits

Estimation and optimal designing under latent variable models for paired comparisons studies via a multiplicative algorithm

We consider:<BR/> 1. The problem of estimating the parameters of latent variable models such as the Bradley Terry or Thurstone Model by the method of maximum likelihood, given data from a paired comparisons experiment. The parameters of these models can be taken to be weights which are positive and sum to one;<BR/> 2. The problem of determining approximate locally optimal designs for good estimation of these parameters; i.e of determining optimal design weights which are also positive and sum to one

Introducing a Pairwise Comparison Scale for UX Evaluations with Preschoolers

This paper describes the development and validation of a pairwise comparison scale for user experience (UX) evaluations with preschoolers. More particularly, the dimensionality, reliability and validity of the scale are discussed. The results of three experiments among almost 170 preschoolers show that user experience cannot be measured quantitatively as a multi-dimensional construct. In contrast, preschoolers’ UX should be measured directly as a one-dimensional higher order construct. This one-dimensional scale encompassing five general items proved to be internally consistent and valid providing evidence of a solid theory-based instrument to measure UX with preschoolers.status: publishe

Defining the gap between research and practice in public relations programme evaluation - towards a new research agenda

The current situation in public relations programme evaluation is neatly summarized by McCoy who commented that 'probably the most common buzzwords in public relations in the last ten years have been evaluation and accountability' (McCoy 2005, 3). This paper examines the academic and practitioner-based literature and research on programme evaluation and it detects different priorities and approaches that may partly explain why the debate on acceptable and agreed evaluation methods continues. It analyses those differences and proposes a research agenda to bridge the gap and move the debate forward

Learning Combinations of Multiple Feature Representations for Music Emotion Prediction

Music consists of several structures and patterns evolving through time which greatly influences the human decoding of higher-level cognitive aspects of music like the emotions expressed in music. For tasks, such as genre, tag and emotion recognition, these structures have often been identified and used as individual and non-temporal features and representations. In this work, we address the hypothesis whether using multiple temporal and non-temporal representations of different features is beneficial for modeling music structure with the aim to predict the emotions expressed in music. We test this hypothesis by representing temporal and non-temporal structures using generative models of multiple audio features. The representations are used in a discriminative setting via the Product Probability Kernel and the Gaussian Process model enabling Multiple Kernel Learning, finding optimized combinations of both features and temporal/ non-temporal representations. We show the increased predictive performance using the combination of different features and representations along with the great interpretive prospects of this approach

The scale grid: Some interrelations of data models

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45699/1/11336_2005_Article_BF02296299.pd

The contribution of an orthogonal multiple factor solution to multiple correlation

A method is indicated by which multiple factor analysis may be used in determining a number, r , and then in selecting r “predicting” variables out of n variables so that each of the remaining n-r variables may be predicted almost as well from the r variables as it could be predicted from all the n —1 variables.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45732/1/11336_2005_Article_BF02288494.pd