Which reviews carry the most weight? The influence of message and source factors in online word-of-mouth messages

Online reviews are one example of electronic word of mouth messaging (eWOM), which research has shown plays an important role in purchasing decisions for consumers. Yet most eWOM research has ignored the potential effect of specific message and source features within the messages themselves. This study used a narrative lens to explore how the presence of a similar and explicitly identified character influences perceived trustworthiness of the reviews, as well as overall attitudes and purchase intention toward products. A mixed design experiment was conducted to test effects of character presence, as well as types of appeal, positive and negative valence, and product involvement in online review messages. Character presence was found to increase perceived trustworthiness and brand attitudes, but only for low-involvement products. Rational appeals and negative valenced reviews were also seen as more trustworthy, yet these main effects were complicated by a three-way interaction that suggests the effects of message features in eWOM reviews are complex and require more research to explore their nuances

Information Splitting for Big Data Analytics

Many statistical models require an estimation of unknown (co)-variance parameter(s) in a model. The estimation usually obtained by maximizing a log-likelihood which involves log determinant terms. In principle, one requires the \emph{observed information}--the negative Hessian matrix or the second derivative of the log-likelihood---to obtain an accurate maximum likelihood estimator according to the Newton method. When one uses the \emph{Fisher information}, the expect value of the observed information, a simpler algorithm than the Newton method is obtained as the Fisher scoring algorithm. With the advance in high-throughput technologies in the biological sciences, recommendation systems and social networks, the sizes of data sets---and the corresponding statistical models---have suddenly increased by several orders of magnitude. Neither the observed information nor the Fisher information is easy to obtained for these big data sets. This paper introduces an information splitting technique to simplify the computation. After splitting the mean of the observed information and the Fisher information, an simpler approximate Hessian matrix for the log-likelihood can be obtained. This approximated Hessian matrix can significantly reduce computations, and makes the linear mixed model applicable for big data sets. Such a spitting and simpler formulas heavily depends on matrix algebra transforms, and applicable to large scale breeding model, genetics wide association analysis.Comment: arXiv admin note: text overlap with arXiv:1605.0764

Anderson Localization for Schr\"odinger Operators with Monotone Potentials over Circle Homeomorphisms

In this paper, we prove pure point spectrum for a large class of Schr\"odinger operators over circle maps with conditions on the rotation number going beyond the Diophantine. More specifically, we develop the scheme to obtain pure point spectrum for Schr\"odinger operators with monotone bi-Lipschitz potentials over orientation-preserving circle homeomorphisms with Diophantine or weakly Liouville rotation number. The localization is uniform when the coupling constant is large enough.Comment: 17 page

Using Lexicons Obtained from Online Reviews to Classify Computer Games

This paper presents a new method for characterizing computer games based on lexicons obtained from online game reviews. Inspired by the lexical approach to define personality traits(Ashton, 2007), we hypothesize that game players would have used natural language in describing computer games and play experience over the time and the fundamental traits of computer games would be encapsulated in player’s languages. Therefore, the traits of computer games could be explored by investigating descriptive terms within game reviews

Assessing Fit of Item Response Models for Performance Assessments using Bayesian Analysis

Assessing IRT model-fit and comparing different IRT models from a Bayesian perspective is gaining attention. This research evaluated the performance of Bayesian model-fit and model-comparison techniques in assessing the fit of unidimensional Graded Response (GR) models and comparing different GR models for performance assessment applications.The study explored the general performance of the PPMC method and a variety of discrepancy measures (test-level, item-level, and pair-wise measures) in evaluating different aspects of fit for unidimensional GR models. Previous findings that the PPMC method is conservative were confirmed. In addition, PPMC was found to have adequate power in detecting different aspects of misfit when using appropriate discrepancy measures. Pair-wise measures were found more powerful in detecting violations of unidimensionality and local independence assumptions than test-level and item-level measures. Yen's Q3 measure appeared to perform best. In addition, the power of PPMC increased as the degree of multidimensionality or local dependence among item responses increased. Two classical item-fit statistics were found effective for detecting the item misfit due to discrepancies from GR model boundary curves.The study also compared the relative effectiveness of three Bayesian model-comparison indices (DIC, CPO, and PPMC) for model selection. The results showed that these indices appeared to perform equally well in selecting a preferred model for an overall test. However, the advantage of PPMC applications is that they can be used to compare the relative fit of different models, but also evaluate the absolute fit of each individual model. In contrast, the DIC and CPO indices only compare the relative fit of different models.This study further applied the Bayesian model-fit and model-comparison methods to three real datasets from the QCAI performance assessment. The results indicated that these datasets were essentially unidimensional and exhibited local independence among items. A 2P GR model provided better fit than a 1P GR model, and a two-dimensional model was also not preferred. These findings were consistent with previous studies, although Stone's fit statistics in the PPMC context identified less misfitting items compared to previous studies. Limitations and future research for Bayesian applications to IRT are discussed