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"Methodology (Data Analysis)"
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Multilingual text analysis : challenges, models, and approaches
Text analytics (TA) covers a very wide research area. Its overarching goal is to discover and present knowledge - facts, rules, and relationships - that is otherwise hidden in the textual content. The authors of this book guide us in a quest to attain this knowledge automatically, by applying various machine learning techniques. This book describes recent development in multilingual text analysis. It covers several specific examples of practical TA applications, including their problem statements, theoretical background, and implementation of the proposed solution. The reader can see which preprocessing techniques and text representation models were used, how the evaluation process was designed and implemented, and how these approaches can be adapted to multilingual domains.
Book
Visualization in Bayesian workflow
Bayesian data analysis is about more than just computing a posterior distribution, and Bayesian visualization is about more than trace plots of Markov chains. Practical Bayesian data analysis, like all data analysis, is an iterative process of model building, inference, model checking and evaluation, and model expansion. Visualization is helpful in each of these stages of the Bayesian workflow and it is indispensable when drawing inferences from the types of modern, high dimensional models that are used by applied researchers.
Journal Article
Thematic Analysis
As qualitative research becomes increasingly recognized and valued, it is imperative that it is conducted in a rigorous and methodical manner to yield meaningful and useful results. To be accepted as trustworthy, qualitative researchers must demonstrate that data analysis has been conducted in a precise, consistent, and exhaustive manner through recording, systematizing, and disclosing the methods of analysis with enough detail to enable the reader to determine whether the process is credible. Although there are numerous examples of how to conduct qualitative research, few sophisticated tools are available to researchers for conducting a rigorous and relevant thematic analysis. The purpose of this article is to guide researchers using thematic analysis as a research method. We offer personal insights and practical examples, while exploring issues of rigor and trustworthiness. The process of conducting a thematic analysis is illustrated through the presentation of an auditable decision trail, guiding interpreting and representing textual data. We detail our step-by-step approach to exploring the effectiveness of strategic clinical networks in Alberta, Canada, in our mixed methods case study. This article contributes a purposeful approach to thematic analysis in order to systematize and increase the traceability and verification of the analysis.
Journal Article
Twitter : a digital socioscope
How can Twitter data be used to study individual-level human behavior and social interaction on a global scale? This book introduces readers to the methods, opportunities, and challenges of using Twitter data to analyze phenomena ranging from the number of people infected by the flu, to national elections, to tomorrow's stock prices. Each chapter, written by leading domain experts in clear and accessible language, takes the reader to the forefront of the newly emerging field of computational social science.
Book
Handling Missing Values in Longitudinal Panel Data With Multiple Imputation
This article offers an applied review of key issues and methods for the analysis of longitudinal panel data in the presence of missing values. The authors consider the unique challenges associated with attrition (survey dropout), incomplete repeated measures, and unknown observations of time. Using simulated data based on 4 waves of the Marital Instability Over the Life Course Study (n = 2,034), they applied a fixed effect regression model and an event-history analysis with time-varying covariates. They then compared results for analyses with nonimputed missing data and with imputed data both in long and in wide structures. Imputation produced improved estimates in the event-history analysis but only modest improvements in the estimates and standard errors of the fixed effects analysis. Factors responsible for differences in the value of imputation are examined, and recommendations for handling missing values in panel data are presented.
Journal Article
Demystifying Content Analysis
Objective. In the course of daily teaching responsibilities, pharmacy educators collect rich data that can provide valuable insight into student learning. This article describes the qualitative data analysis method of content analysis, which can be useful to pharmacy educators because of its application in the investigation of a wide variety of data sources, including textual, visual, and audio files.
Findings. Both manifest and latent content analysis approaches are described, with several examples used to illustrate the processes. This article also offers insights into the variety of relevant terms and visualizations found in the content analysis literature. Finally, common threats to the reliability and validity of content analysis are discussed, along with suitable strategies to mitigate these risks during analysis.
Summary. This review of content analysis as a qualitative data analysis method will provide clarity and actionable instruction for both novice and experienced pharmacy education researchers.
Journal Article
Polychoric versus Pearson correlations in exploratory and confirmatory factor analysis of ordinal variables
Given that the use of Likert scales is increasingly common in the field of social research it is necessary to determine which methodology is the most suitable for analysing the data obtained; although, given the categorization of these scales, the results should be treated as ordinal data it is often the case that they are analysed using techniques designed for cardinal measures. One of the most widely used techniques for studying the construct validity of data is factor analysis, whether exploratory or confirmatory, and this method uses correlation matrices (generally Pearson) to obtain factor solutions. In this context, and by means of simulation studies, we aim to illustrate the advantages of using polychoric rather than Pearson correlations, taking into account that the latter require quantitative variables measured in intervals, and that the relationship between these variables has to be monotonic. The results show that the solutions obtained using polychoric correlations provide a more accurate reproduction of the measurement model used to generate the data.
Journal Article
Thinking about the Coding Process in Qualitative Data Analysis
Coding is a ubiquitous part of the qualitative research process, but it is often under-considered in research methods training and literature. This article explores a number of questions about the coding process which are often raised by beginning researchers, in the light of the recommendations of methods textbooks and the factors which contribute to an answer to these questions. I argue for a conceptualisation of coding as a decision-making process, in which decisions about aspects of coding such as density, frequency, size of data pieces to be coded, are all made by individual researchers in line with their methodological background, their research design and research questions, and the practicalities of their study. This has implications for the way that coding is carried out by researchers at all stages of their careers, as it requires that coding decisions should be made in the context of an individual study, not once and for all.
Journal Article
Humble Chief Executive Officers' Connections to Top Management Team Integration and Middle Managers' Responses
In this article, we examine the concept of humility among chief executive officers (CEOs) and the process through which it is connected to integration in the top management team (TMT) and middle managers' responses. We develop and validate a comprehensive measure of humility using multiple samples and then test a multilevel model of how CEOs' humility links to the processes of top and middle managers. Our methodology involves survey data gathered twice from 328 TMT members and 645 middle managers in 63 private companies in China. We find CEO humility to be positively associated with empowering leadership behaviors, which in turn correlates with TMT integration. TMT integration then positively relates to middle managers' perception of having an empowering organizational climate, which is then associated with their work engagement, affective commitment, and job performance. Findings confirm our hypotheses based on social information processing theory: humble CEOs connect to top and middle managers through collective perceptions of empowerment at both levels. Qualitative data from interviews with 51 CEOs provide additional insight into the meaning of humility among CEOs and differences between those with high and low humility.
Journal Article