Response Style of Rating Scales: The Effects of Data Collection Mode, Scale Format, and Acculturation.

Rating scales are popular for measuring attitudes, but response style, a source of measurement error associated with this type of question, can result in measurement bias in important attitudinal measures. Although numerous research efforts have been devoted to this topic, there are still some overlooked areas. This dissertation intends to fill three gaps in the literature on response style. Chapter 1 investigates the effects of face-to-face and Web survey on acquiescent response style (ARS) and extreme response style (ERS) using the 2012 American National Election Studies (ANES) data. Using the latent class analysis approach, I find that: 1) both ARS and ERS exist in both face-to-face and Web survey; 2) face-to-face respondents demonstrate more ARS and ERS than Web respondents; 3) the effect of mode on ERS is larger for black respondents than for white and Hispanic respondents. Chapter 2 compares ERS with respect to the format of response scale, specifically agree-disagree (A/D) and item specific (IS) scales. This study analyzes a between- and within-subject experiment embedded in the 2012 ANES. Using latent class factor analysis, I reached the following three major findings: 1) ERS exists in both A/D and IS scale formats; 2) ERS shows a slightly different pattern between the two scale formats; 3) when analyzing ERS within subjects across two waves, there is only a single ERS latent class variable for both scale formats, after controlling for the correlation within respondents. Chapter 3 utilizes the 2003 Detroit Arab American Study to examine the impact of acculturation of Arab Americans on ERS. The results indicate that less acculturated respondents are more prone to ERS than more acculturated respondents, and this is especially true for the 5-point rating response scales as compared to 3-point rating scales. This phenomenon can be explained by the fact that less acculturated respondents identify more strongly with honor-based collectivist cultures that value decisive and assertive answers since this is a way of showing one’s unambiguous attitude and standing, an important quality in such a culture. The language of the interview primes the relevant cultural norms and therefore mediates the relationship between acculturation and ERS.PhDSurvey MethodologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111591/1/liumn_1.pd

Graph Neural Networks for Recommender Systems

In recent years, a new type of deep learning models, Graph Neural Networks (GNNs), have demonstrated to be a powerful learning paradigm when applied to problems that can be described via graph data, due to their natural ability to integrate representations across nodes that are connected via some topological structure. One of such domains is Recommendation Systems, the majority of whose data can be naturally represented via graphs. For example, typical item recommendation datasets can be represented via user-item bipartite graphs, social recommendation datasets by social networks, and so on. The successful application of GNNs to the field of recommendation, is demonstrated by the state of the art results achieved on various datasets, making GNNs extremely appealing in this domain, also from a commercial perspective. However, the introduction of graph layers and their associated sampling techniques significantly affects the nature of the calculations that need to be performed on GPUs, the main computational accelerator used nowadays: something that hasn't been investigated so far by any of the architectures in the recommendation literature. This thesis aims to fill this gap by conducting the first systematic empirical investigation of GNN-based architectures for recommender systems, focusing on their multi-GPU scalability and precision speed-up properties, when using different types of hardware

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

Distributed Representations for Compositional Semantics

The mathematical representation of semantics is a key issue for Natural Language Processing (NLP). A lot of research has been devoted to finding ways of representing the semantics of individual words in vector spaces. Distributional approaches --- meaning distributed representations that exploit co-occurrence statistics of large corpora --- have proved popular and successful across a number of tasks. However, natural language usually comes in structures beyond the word level, with meaning arising not only from the individual words but also the structure they are contained in at the phrasal or sentential level. Modelling the compositional process by which the meaning of an utterance arises from the meaning of its parts is an equally fundamental task of NLP. This dissertation explores methods for learning distributed semantic representations and models for composing these into representations for larger linguistic units. Our underlying hypothesis is that neural models are a suitable vehicle for learning semantically rich representations and that such representations in turn are suitable vehicles for solving important tasks in natural language processing. The contribution of this thesis is a thorough evaluation of our hypothesis, as part of which we introduce several new approaches to representation learning and compositional semantics, as well as multiple state-of-the-art models which apply distributed semantic representations to various tasks in NLP.Comment: DPhil Thesis, University of Oxford, Submitted and accepted in 201

Methods for Modelling Response Styles

Abstract Ratings scales are ubiquitous in empirical research, especially in the social sciences, where they are used for measuring abstract concepts such as opinion or attitude. Survey questions typically employ rating scales, for example when persons are asked to self-report their perceptions of films or their job satisfaction. Yet, using a rating scale is subjective. Some persons may use only the middle of the rating scale, whilst others choose to use only the extremes. Consequently, persons with the same opinion may very well answer the same survey question using different ratings. This leads to the response style problem: How can we take into account that different ratings can potentially have different meanings to different persons when analyzing such data? This dissertation makes methodological and empirical contributions towards modelling rating scale data while accounting for such differences in response styles. The general approach is to identify individuals in the data which exhibit similar response styles, and to extract substantive information only within such groups. These elements naturally lead to the synthesis of cluster analysis and dimensionality reduction methods. In order to identify these response styles, responses to multiple survey questions are used to assess within-subject rating scale usage. Both non-parametric and parametric approaches are formulated and studied, and accompanying open-source software implementations are made available. The added value of using the developed algorithms is illustrated by applying these to empirical data. Applications range from sensometrics and brand studies, to psychology and political science

Effects of Cognitive Style on Food Perception and Eating Behavior

Within the fields of psychology, notably cultural psychology, the analytic-holistic cognitive style theory has been introduced, developed, fine-tuned, and validated across a wide range of situations, stimuli, and populations. This research, combined with recent applications of the analytic-holistic theory, suggests that the differences in analytic or holistic tendencies of individuals in food, sensory, and consumer tests can impact food perception and associated behaviors. This dissertation aimed to investigate the impact of analytic-holistic cognitive styles of consumers in food situations. The first objective to accomplish this goal was to conduct exploratory research to identify if and where the analytic-holistic theory may be applicable across areas of the consumer food experience. The second objective was then to replicate one of the paramount differences of analytic and holistic groups by investigating the effect of the eating environment and how analytic-holistic cognitive styles may mediate this effect. The third objective was to identify where and how analytic and holistic groups differ in their responses to standard sensory evaluation tasks. Finally, the fourth objective was to develop and validate an analytic-holistic measurement tool that could accurately separate participants based on their analytic-holistic tendencies in food-related situations. Through completing these objectives, it was first found and continuously supported that analytic and holistic groups have significantly different perceptions of and reactions to food stimuli and food experiences. In addition, the completed studies also provide evidence that the two cognitive style groups subsequently have significantly different response data in sensory evaluation tasks, while also showing indications the current methodology to separate consumers based on analytic-holistic tendencies is not the most accurate within food-related applications. Finally, the completed studies were able to show that a food-related analytic-holistic measurement tool could be adequately developed and had superior performance to the existing assessment tool in validation testing. Combining the studies within this dissertation offers valuable insights to food science, sensory, and consumer researchers across academia and industry by showing the necessity of accounting for analytic-holistic consumer differences in their respective fields. Moreover, this dissertation provides these researchers a new, more accurate measurement tool to allow them to easily and accurately separate analytic and holistic groups within their own research. To conclude, this dissertation offers ample evidence for the importance of accounting for analytic-holistic differences in food-related consumer testing through a variety of studies showing significant differences between analytic and holistic consumer groups in terms of food perception and food-related behavior

Learning Criteria and Evaluation Metrics for Textual Transfer between Non-Parallel Corpora

We consider the problem of automatically generating textual paraphrases with modified attributes or stylistic properties, focusing on the setting without parallel data (Hu et al., 2017; Shen et al., 2017). This setting poses challenges for learning and evaluation. We show that the metric of post-transfer classification accuracy is insufficient on its own, and propose additional metrics based on semantic content preservation and fluency. For reliable evaluation, all three metric categories must be taken into account. We contribute new loss functions and training strategies to address the new metrics. Semantic preservation is addressed by adding a cyclic consistency loss and a loss based on paraphrase pairs, while fluency is improved by integrating losses based on style-specific language models. Automatic and manual evaluation show large improvements over the baseline method of Shen et al. (2017). Our hope is that these losses and metrics can be general and useful tools for a range of textual transfer settings without parallel corpora