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Objective: The pain numerical rating scale (NRS) is widely used in pain research and clinical settings to represent pain intensity. For an individual with chronic pain, NRS reporting requires representation of a complex subjective state as a numeral. To evaluate the process of NRS reporting, this study examined the relationship between reported pain NRS levels and imagined painful events reported by study subjects. Design: A total of 149 subjects with chronic low back pain characterized by the NIH Research Task Force Recommended Minimal Dataset reported current pain NRS and provided imagined examples of painful experiences also attributing to these an NRS. We present a quantitative and qualitative analysis of the 797 pain examples provided by the study subjects. Results: Study subjects tended to be able to imagine both highly painful 10/10 events and non-painful events with relative agreement across subjects. While NRS for the pain examples tended to increase with example severity, for many types of examples there was wide dispersion around the mean pain level. Examination of pain examples indicated unexpected relationships between current pain and the intensity and nature of the imagined painful events. Conclusions: Our results indicate that the pain NRS does not provide a reliably interpretable assessment of current physical pain intensity for an individual with chronic pain at a specific moment. [ABSTRACT FROM AUTHOR]

Researchers in information science and related areas have developed various methods for analyzing textual data, such as survey responses. This article describes the application of analysis methods from two distinct fields, one method from interpretive social science and one method from statistical machine learning, to the same survey data. The results show that the two analyses produce some similar and some complementary insights about the phenomenon of interest, in this case, nonuse of social media. We compare both the processes of conducting these analyses and the results they produce to derive insights about each method's unique advantages and drawbacks, as well as the broader roles that these methods play in the respective fields where they are often used. These insights allow us to make more informed decisions about the tradeoffs in choosing different methods for analyzing textual data. Furthermore, this comparison suggests ways that such methods might be combined in novel and compelling ways. [ABSTRACT FROM AUTHOR]

How can a single person understand what's going on in a collection of millions of documents? This is an increasingly common problem: sifting through an organization's e-mails, understanding a decade worth of newspapers, or characterizing a scientific field's research. Topic models are a statistical framework that help users understand large document collections: not just to find individual documents but to understand the general themes present in the collection. This survey describes the recent academic and industrial applications of topic models with the goal of launching a young researcher capable of building their own applications of topic models. In addition to topic models' effective application to traditional problems like information retrieval, visualization, statistical inference, multilingual modeling, and linguistic understanding, this survey also reviews topic models' ability to unlock large text collections for qualitative analysis. We review their successful use by researchers to help understand fiction, non-fiction, scientific publications, and political texts. [ABSTRACT FROM AUTHOR]

Admixture models are a ubiquitous approach to capture latent population structure in genetic samples. Despite the widespread application of admixture models, little thought has been devoted to the quality of the model fit or the accuracy of the estimates of parameters of interest for a particular study. Here we develop methods for validating admixture models based on posterior predictive checks (PPCs), a Bayesian method for assessing the quality of fit of a statistical model to a specific dataset. We develop PPCs for five population-level statistics of interest: within-population genetic variation, background linkage disequilibrium, number of ancestral populations, between-population genetic variation, and the downstream use of admixture parameters to correct for population structure in association studies. Using PPCs, we evaluate the quality of the admixture model fit to four qualitatively different population genetic datasets: the population reference sample (POPRES) European individuals, the HapMap phase 3 individuals, continental Indians, and African American individuals. We found that the same model fitted to different genomic studies resulted in highly study-specific results when evaluated using PPCs, illustrating the utility of PPCs for model-based analyses in large genomic studies. [ABSTRACT FROM AUTHOR]