Maximum likelihood estimation of the polychoric correlation coefficient

The polychoric correlation is discussed as a generalization of the tetrachoric correlation coefficient to more than two classes. Two estimation methods are discussed: Maximum likelihood estimation, and what may be called two-step maximum likelihood estimation. For the latter method, the thresholds are estimated in the first step. For both methods, asymptotic covariance matrices for estimates are derived, and the methods are illustrated and compared with artificial and real data.This paper was read at the 1978 European Meeting on Psychometrics and Mathematical Psychology in Uppsala, Sweden, June 1978.Research reported in this paper has been supported by the Bank of Sweden Tercentenary Foundation under project Structural Equation Models in the Social Sciences, project director Karl G. Jöreskog.     

Solutions to some nonlinear equations from nonmetric data

A method is presented to provide estimates of parameters of specified nonlinear equations from ordinal data generated from a crossed design. The analytic method, NOPE, is an iterative method in which monotone regression and the Gauss-Newton method of least squares are applied alternatively until a measure of stress is minimized. Examples of solutions from artificial data are presented together with examples of applications of the method to experimental results.This work was begun while the author was on sabbatical leave during 1970–71 at the Department of Mathematical Psychology, University of Nijmegen, the Netherlands, where discussions with E. E. Roskam on the problem were very helpful. Support was provided by Grant A0151 from the Natural Sciences and Engineering Council, Canada.     

Piecewise method of reciprocal averages for dual scaling of multiple-choice data

The proposed method handles the classical method of reciprocal averages (MRA) in a piecewise (item-by-item) mode, whereby one can deal with smaller matrices and attain faster convergence to a solution than the MRA. A new concept the principle of constant proportionality is introduced to provide an interesting interpretation for scaling multiple-choice data a la Guttman. A small example is presented for discussion of the technique.This study was supported by the Natural Sciences and Engineering Research Council Canada Grant (No. A4581) to S. Nishisato. The authors are indebted to reviewers for valuable comments.     

Exploring the interactions between modal options,destination access,and travel mood

A good understanding of subjectively experienced well-being in daily travel is critical for the design and evaluation of transportation policies and programs. Numerous studies have examined the relationship between how we travel and how we feel during travel. However, people’s travel mood is not only determined by their trip characteristics but also their potential mobility—the capacity of being mobile. Using the data collected via a smartphone application in Minneapolis-St. Paul area, we measure potential mobility in two aspects—modal options and destination access and reveal that people in households with better access to cars and alternative transportation modes are, all else equal, more likely to report more positive moods and fewer negative moods during travel. In addition, people living in neighborhoods with greater access to destinations are less likely to feel stress when traveling. We also find that associations between mode and mood are moderated by modal options and destination access. These findings confirm the importance of explicitly considering modal options and destination access in policy debates and planning initiatives addressing transportation and well-being. In doing so, they demonstrate the relevance of Sen’s Capabilities Approach to transportation planning.     

Adaptive sampling for active learning with genetic programming

Active learning is a machine learning paradigm allowing to decide which inputs to use for training. It is introduced to Genetic Programming (GP) essentially thanks to the dynamic data sampling, used to address some known issues such as the computational cost, the over-fitting problem and the imbalanced databases. The traditional dynamic sampling for GP gives to the algorithm a new sample periodically, often each generation, without considering the state of the evolution. In so doing, individuals do not have enough time to extract the hidden knowledge. An alternative approach is to use some information about the learning state to adapt the periodicity of the training data change. In this work, we propose an adaptive sampling strategy for classification tasks based on the state of solved fitness cases throughout learning. It is a flexible approach that could be applied with any dynamic sampling. We implemented some sampling algorithms extended with dynamic and adaptive controlling re-sampling frequency. We experimented them to solve the KDD intrusion detection and the Adult incomes prediction problems with GP. The experimental study demonstrates how the sampling frequency control preserves the power of dynamic sampling with possible improvements in learning time and quality. We also demonstrate that adaptive sampling can be an alternative to multi-level sampling. This work opens many new relevant extension paths.     

How Emotional Are Populists Really? Factors Explaining Emotional Appeals in the Communication of Political Parties

Are populists really more emotional than mainstream parties? The alleged link between populism and emotional communication has barely been subject to systematic empirical scrutiny. I use literature on populist communication and appraisal theory to generate expectations about which political parties use which emotional appeals. I test these claims by applying a novel emotional dictionary to a large set of text data including more than 700,000 press releases and tweets from three European countries. As expected, I find that populist parties use significantly more negative emotional appeals (anger, fear, disgust, sadness) and less positive emotional appeals (joy, enthusiasm, pride, hope) than mainstream parties. Furthermore, I find that political actors adapt the usage of emotional appeals to different purposes depending on the communication medium and the politicians' status level. This study entails important implications for the research on emotional appeals in politics and populist communication.     

Using bifactor models to identify faking on Big Five questionnaires

To identify faking, bifactor models were applied to Big Five personality data in three studies of laboratory and applicant samples using within‐subjects designs. The models were applied to homogenous data sets from separate honest, instructed faking, applicant conditions, and to simulated applicant data sets containing random individual responses from honest and faking conditions. Factor scores from the general factor in a bifactor model were found to be most highly related to response condition in both types of data sets. Domain factor scores from the faking conditions were found less affected by faking in measurement of Big Five domains than summated scale scores across studies. We conclude that bifactor models are efficacious in assessing the Big Five domains while controlling for faking.     

The development and validation of the Privacy and Data Security Concerns Scale (PDSCS)

This study describes the development and validation of the Privacy and Data Security Concerns Scale (PDSCS), designed to assess job applicants’ privacy and data security concerns. Validity evidence for the PDSCS was established with four steps including: item generation and content validation, item reduction, confirmatory factor analysis, and construct and criterion validity. Our findings supported the content and proposed factor structure of the nine‐item PDSCS. Specifically, the proposed three‐factor structure (with the subdimensions: Secure connection, inappropriate use of information, and mistrust in employer security practices) as well as a second‐order factor, (termed privacy and data security concerns) were confirmed. Additionally, evidence is presented for the criterion validity of the PDSCS and its subdimensions.