Data Analysis In Social Sciences Research Articles

Predicting an unfavorable outcome in patients with liver failure associated with obstructive jaundice syndrome: a prospective observational study

INTRODUCTION: Specialized scales developed to assess survival and mortality of patients after liver transplantation have not been validated when applied to liver failure in the setting of obstructive jaundice. OBJECTIVE: To determine the prognostic value of multiparametric scoring scales and to identify predictors of unfavorable outcome in liver failure due to obstructive jaundice. MATERIALS AND METHODS: 53 patients with confirmed liver failure due to obstructive jaundice of benign etiology who underwent biliary decompression were included in this prospective, observational, single-center study. Depending on the outcome two groups were distinguished: with favorable (n = 27, 51 %) and with unfavorable outcome (n = 26, 49 %). The following time points were defined for the dynamic evaluation of the course of the disease: day 1, day 3, day 7, and day 11 after decompression. Statistical processing of the data was performed using IBM SPSS Statistics 22 software for social science data analysis. RESULTS: The sensitivity and specificity of the studied scales in predicting liver failure due to obstructive jaundice were calculated on the basis of receiver operating characteristic (ROC) analysis. The AUROC for the SOFA was 0.862, for the MELD — 0.882, for the APACHE II — 0.864, for the Child-Turcotte-Pugh — 0.813. The sensitivity and specificity values for the SOFA were 78.9 and 77.8 %, for the MELD — 80.8 and 79.7 %, for the APACHE II — 57.7 and 81.9 %, and for the Child-Turcotte-Pugh — 75.1 and 70.9 %, respectively. Albumin was an independent predictor of unfavorable outcome (AUC ROC 0.909, p = 0.01), sensitivity 88.6 %, specificity 85.2 %. CONCLUSIONS: The scales presented in the study and the biomarker candidate “albumin” showed significant prognostic ability, but low values of sensitivity and specificity (less than 80 %) in some points of the study require the search for additional predictors of unfavorable outcome of liver failure due to obstructive jaundice.

Quality Textbooks for Quality Research

The book review considers two handbooks “How to collect data in qualitative field research” and “Practices of qualitative data analysis in social sciences” published by the National Research University Higher School of Economics in 2020 and 2023, respectively. The handbooks compensate for the acute shortage of Russian educational texts on qualitative data collection and analysis in sociology. They can be effectively used in teaching research disciplines and in research practice. Both handbooks have all the characteristics of a contemporary educational texts. Their authors have relevant practical experience in the issues under discussion and provide particular examples and practical recommendations. Both texts are well structured. Both include many educational and methodological elements (not only literature and test questions, but summaries, keywords, practical tasks, links to extensive online resources). The discussed issues are presented at the level of contemporary approaches adopted both in domestic and foreign theory and practice; and this is reflected in citations, references and the considered cases.

The application of structural equation model in psychological research

The application of structural equation model in psychological research

The Practices of Qualitative Data Analysis In Social Sciences: The Textbook / Ed. By E.V. Polukhina. Moscow: Id VSHE, 2023. 383 P. Reviewed By O.R. Mikhaylova

The Practices of Qualitative Data Analysis In Social Sciences: The Textbook / Ed. By E.V. Polukhina. Moscow: Id VSHE, 2023. 383 P. Reviewed By O.R. Mikhaylova

Learning to See: Convolutional Neural Networks for the Analysis of Social Science Data

AbstractWe provide an introduction of the functioning, implementation, and challenges of convolutional neural networks (CNNs) to classify visual information in social sciences. This tool can help scholars to make more efficient the tedious task of classifying images and extracting information from them. We illustrate the implementation and impact of this methodology by coding handwritten information from vote tallies. Our paper not only demonstrates the contributions of CNNs to both scholars and policy practitioners, but also presents the practical challenges and limitations of the method, providing advice on how to deal with these issues.

Application of Cluster Analysis in Research on the Spatial Dimension of Penalised Behaviour

This paper is focused on some of the possibilities of the use of cluster analysis (clustering) in criminology and the sociology of law. Cluster analysis makes it possible to divide even a large dataset into a specified number of subsets in such a way that the resulting subsets are as homogenous as possible, and at the same time differ from each other substantially. When analysing geographical data, e.g. describing the location of crimes, the result of cluster analysis is a division of a territory into a certain number of coherent areas based on an objective criterion. The division of the territory under study into smaller parts is more insightful when the clustering method is applied compared to an arbitrary division into official administrative units. The paper provides a detailed description of hierarchical cluster analysis methods and an example of using the Ward’s hierarchical method and the k-means combinational method to divide data on crime reports in the city of Baltimore between 2014 and 2019. The analysis demonstrates that the resulting division differs considerably from the administrative division of Baltimore, and that increasing the number of groups emerging as a result of cluster analysis leads to an increase of variance of variables describing the structure of crime in individual parts of the city. The divisions obtained using clustering are used to verify the hypothesis on differences in crime structure in different areas of Baltimore. The main aim of the paper is to encourage the use of modern methods of data analysis in social sciences and to present the usefulness of cluster analysis in criminology and the sociology of law research.

Data Analysis In Social Sciences: Comparison between Quantitative and Qualitative Research

Data Analysis In Social Sciences: Comparison between Quantitative and Qualitative Research

Exploratory Analysis for Big Social Data Using Deep Network

Exploratory analysis is an important way to gain understanding and find unknown relationships from various data sources, especially in the era of big data. Traditional paradigms of social science data analysis follow the steps of feature selection, modeling, and prediction. In this paper, we propose a new paradigm that does not require feature selection so that data can speak for itself without manually picking out features. Besides, we propose using the deep network as a methodology to explore previously unknown relationships and capture complexity and non-linearity between target variables and a large number of input features for big social data. The new paradigm tends to be a relatively generic approach that can be widely used in different scenarios. In order to validate the feasibility of the paradigm, we use country-level indicators forecasting as a case study. The process includes: 1) data collection and preparation and 2) modeling and experiment. The data collection and preparation part builds a data warehouse and conducts the extract-transform-load process to eliminate data format inconsistency. The modeling and experiment part includes model setup and model structures change to achieve relatively high accuracy on prediction results at both model level and case level. We find some patterns about network capacity modification and the influence of time interval difference on the test results, whereas both of them deserve further research.

Opening up to Big Data: Computer-Assisted Analysis of Textual Data in Social Sciences

Two developments in computational text analysis may change the way qualitative data analysis in social sciences is performed: 1. the availability of digital text worth to investigate is growing rapidly, and 2. the improvement of algorithmic information extraction approaches, also called text mining, allows for further bridging the gap between qualitative and quantitative text analysis. The key factor hereby is the inclusion of context into computational linguistic models which extends conventional computational content analysis towards the extraction of meaning. To clarify methodological differences of various computer-assisted text analysis approaches the article suggests a typology from the perspective of a qualitative researcher. This typology shows compatibilities between manual qualitative data analysis methods and computational, rather quantitative approaches for large scale mixed method text analysis designs. URN: http://nbn-resolving.de/urn:nbn:de:0114-fqs1302231

An effect size primer: A guide for clinicians and researchers.

Increasing emphasis has been placed on the use of effect size reporting in the analysis of social science data. Nonetheless, the use of effect size reporting remains inconsistent, and interpretation of effect size estimates continues to be confused. Researchers are presented with numerous effect sizes estimate options, not all of which are appropriate for every research question. Clinicians also may have little guidance in the interpretation of effect sizes relevant for clinical practice. The current article provides a primer of effect size estimates for the social sciences. Common effect sizes estimates, their use, and interpretations are presented as a guide for researchers.

Responding to the Quantitative Literacy Gap Among Students in Sociology Courses

The Integrating Data Analysis (IDA) approach to undergraduate education developed by the American Sociological Association (ASA) and the Social Science Data Analysis Network (SSDAN) has been embedded in the undergraduate sociology curriculum at Lehman College, The City University of New York (CUNY), since 2003. This study draws on student and faculty assessment data from the fall 2004 and spring 2006 semesters to evaluate the effectiveness of this initiative. The results show that students in courses with an IDA component significantly improved their performance on quantitative skills tests. Efforts to engage students in active learning through the use of computers were associated with increased student comfort and greater interest in working with data. In turn, students who were comfortable working with data exhibited especially high levels of quantitative skill. Although students who were taught a wide variety of data analysis skills reported greater interest in working with data, those who were taught a more limited range of skills achieved higher performance scores. Likewise, students who were required to complete assignments and undertake graded examinations that tested a wide range of quantitative skills had less interest in working with data but achieved greater improvements in their test scores.

Teaching and Learning Guide for: A Window on the ‘New’ Sociology of Childhood

Teaching and Learning Guide for: A Window on the ‘New’ Sociology of Childhood

Using multi dimensional scaling to analyse software engineers' de‐motivators for SPI

AbstractWe present analysis of factors that de‐motivate software practitioners from supporting software process improvement (SPI) initiatives, using a multidimensional scaling (MDS) technique. Our findings are based on an empirical study involving nearly 200 software practitioners from 13 UK companies. Our aim is to provide managers with a better understanding of the obstacles that prevent practitioners from supporting SPI. This insight should help SPI managers establish more effective SPI implementation strategies. In this article we re‐introduce the use of MDS techniques in SPI research. MDS is a social science data analysis technique designed to generate a rich visual understanding of human issues. By using MDS we found evidence to suggest distinct clusters of de‐motivators exist for different staff groups. Copyright © 2007 John Wiley & Sons, Ltd.

Integrating Data Analysis (IDA): Working With Sociology Departments to Address the Quantitative Literacy Gap

The NSF-funded Integrating Data Analysis (IDA) Project undertaken by the American Sociological Association (ASA) and the Social Science Data Analysis Network sought to close the quantitative literacy gap for sociology majors. Working with twelve departments, the project built on lessons learned from ASA's Minority Opportunities through School Transformation (MOST) Program, where curricular change was a departmental (not individual faculty) agenda. A critical mass of faculty in each IDA department developed and used quantitative modules in lower division courses. This article reports on the steps to develop departmental curricular change and the important results for student learning and future professional success.

The Data Documentation Initiative

Effective secondary analysis of social science data requires good documentation. Especially because Internet access has become standard, the problems of reading and understanding the contents of data files have become acute. Resolving these problems requires standards for documenting data, as well as standard formats for both data and documentation that can be read and displayed by computers and software anywhere in the world. To define a documentation standard, representatives of North American and European survey research and data archive organizations have created a Data Documentation Initiative (DDI). This article discusses the value and significance of that effort for the social sciences.

A half century of longitudinal methods in social gerontology: evidence of change in the journal.

With a focus on the use of longitudinal data, this study reviews trends in the quantitative analysis of social science data on aging during the past half century. A content analysis was performed on 227 articles from 12 volumes that were systematically sampled from the Journal of Gerontology: Social Sciences to examine change in the type of data and quantitative methods used (1946-2000). Cross-sectional analysis remains the single most frequent type of study, but the publication of analyses based on longitudinal panel data increased appreciably over the five decades studied. There was little increase in the use of repeated cross-sectional analysis. Despite the widespread use of cross-sectional analysis, interest in data with more than one occasion of measurement has grown among social scientists who are reviewing for and publishing in the JOURNAL. Given the longitudinal data now available, social science research on aging should give more explicit attention to three issues: attrition, change in repeated measures of independent variables, and models to account for many waves of data.

Software Process Improvement Motivators: An Analysis using Multidimensional Scaling

In this paper we present an analysis of software practitioners' motivations for software process improvement (SPI). Our findings are based on an empirical study of SPI in 13 software companies where we conducted focus groups with nearly 200 software practitioners. Our aim is to better understand how companies can maximise practitioner support for SPI. This insight should help SPI managers establish more effective SPI implementation strategies. In this paper we introduce the use of multidimensional scaling (MDS) in SPI research. MDS is a social science data analysis technique designed to generate a rich visual understanding of human issues. By using MDS we found evidence to suggest distinct clusters of punitive and rewarding SPI motivators. Furthermore our analysis also suggests that different clusters of motivations exist for different staff groups.

Empirical models of farmer behaviour using psychological, social and economic variables. Part I: linear modelling

The analysis of social science data is increasingly dependent on the construction of mathematical models to predict and explain behaviour. This and the following paper have two objectives. The first objective is to describe some of the results of a recent survey on farmer decision-making and to present a number of models which have been developed using the data from this survey. The second objective is to use these models as a framework for a general discussion of the wide range of techniques available for the modelling of psychological, social and economic data. The classification of models into categories such as mechanistic and empirical, and the range of mathematical techniques available are described. The use of linear modelling is illustrated with examples from the data.

Metadata for units of measure in social science databases

Statistical social science databases present many unique data management problems. Among them is the fact that computer-aided use of statistical social science data is subject to serious but preventable errors in data manipulation and analysis, particularly given the increasing number of non-expert users. Two incremental prototype solutions to the problem of correctly manipulating units of measure when manipulating data are described. These implementations use rudimentary metadata representing measurement-theoretical and category attribute information to support error checking of data derivations common in research and reference using social science data. The specification of the metadata structures and the implementation of its processing is informed by relevant principles from literature in statistical and scientific data models, dimensional analysis, and knowledge representation with formal ontology. Design parameters useful in the effort to establish appropriate variable-level metadata specifications for social science databases used in digital environments are discussed. The integrated use of the proposed metadata appears to be a promising method of better supporting the manipulation and analysis of social science data. While measurement theoretical metadata has further application to the cataloging and retrieval of collections of statistical databases, the importance is argued of metadata for social science databases to serve users beyond these purposes.

Avoiding Model Selection in Bayesian Social Research

Raftery's paper addresses two important problems in the statistical analysis of social science data: (1) choosing an appropriate model when so much data are available that standard P-values reject all parsimonious models; and (2) making estimates and predictions when there are not enough data available to fit the desired model using standard techniques. For both problems, we agree with Raftery that classical frequentist methods fail and that Raftery's suggested methods based on BIC can point in better directions. Nevertheless, we disagree with his solutions because, in principle, they are still directed off-target and only by serendipity manage to hit the target in special circumstances. Our primary criticisms of Raftery's proposals are that (1) he promises the impossible: the selection of a model that is adequate for specific purposes without consideration of those purposes; and (2) he uses the same limited tool for model averaging as for model selection, thereby depriving himself of the benefits of the broad range of available Bayesian procedures. Despite our criticisms, we applaud Raftery's desire to improve practice by providing methods and computer programs for all to use and applying these methods to real problems. We believe that his paper makes a positive contribution to social science, by focusing on