Relevant bibliographies by topics / Sociology – Statistical methods

Academic literature on the topic 'Sociology – Statistical methods'

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Published: 4 June 2021
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Journal articles on the topic "Sociology – Statistical methods"

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McCleary, Richard, Larry V. Hendges, Ingram Olkin, Brian Mullen, Robert Rosenthal, Wiilam H. Yeaton, and Paul M. Wortman. "Statistical Methods for Meta-Anlaysis." Contemporary Sociology 16, no. 1 (January 1987): 105. http://dx.doi.org/10.2307/2071249.

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Roush, F. W. "Essentials of statistical methods." Mathematical Social Sciences 27, no. 1 (February 1994): 119–20. http://dx.doi.org/10.1016/0165-4896(94)00734-9.

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Fraser, Mark W., Jeffrey M. Jenson, David Kiefer, and Chirapat Popuang. "Statistical methods for the analysis of critical life events." Social Work Research 18, no. 3 (September 1994): 163–77. http://dx.doi.org/10.1093/swr/18.3.163.

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Westover, Jonathan H. "Empirically Examining Research Questions in the Social Sciences: Statistical Research Methods in Comparative International Sociology." International Journal of Science in Society 1, no. 1 (2009): 93–98. http://dx.doi.org/10.18848/1836-6236/cgp/v01i01/51465.

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Ralston, Kevin. "‘Sociologists Shouldn’t Have to Study Statistics’: Epistemology and Anxiety of Statistics in Sociology Students." Sociological Research Online 25, no. 2 (December 19, 2019): 219–35. http://dx.doi.org/10.1177/1360780419888927.

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Worry about learning maths and statistics has been widely researched internationally but very little of this work has focussed on sociology. It is well documented that sociology students can be reluctant to engage with statistical methods. This article provides an exploration of the relationship between anxiety of statistics and its antecedents in sociology students. The analyses presented are based upon data collected from over 30 universities in the UK and is the most comprehensive sample of its type. The primary aim of this article is to analyse whether the perceived epistemological legitimacy of statistics, among sociology students, is associated with reported statistics anxiety. The results show that epistemological legitimacy is highly associated with reported statistics anxiety. Confidence in maths is also strongly associated with statistics anxiety. The implications of acknowledging these and other pedagogical issues in teaching quantitative research methods are complex and layered. Measures capturing whether students accept the epistemological legitimacy of statistical methods should be routinely incorporated in research examining statistics anxiety.
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LAITIN, DAVID D. "INTEGRATION OF RESEARCH AND THEORY IN THE PERSPECTIVE OF JOHN GOLDTHORPE." European Journal of Sociology 45, no. 3 (December 2004): 411–16. http://dx.doi.org/10.1017/s0003975604001511.

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THIS REMARKABLE COLLECTION of (mostly reprinted) essays is first and foremost a defense of the statistically-based research program that has motivated Professor Goldthorpe's work through a distinguished career. In defending the statistical methodology that has become a standard in his corner of sociology, he also sets sharp limits to what can be contributed through qualitative methods. He then offers a challenge for future statistical analysts to address more directly that program's theoretical foundations. The dozen essays range widely in questions of methods and substance — in this review I provide no general summary but rather highlight a dual plea he makes to his profession: to marry macro statistical work to micro theory; and to press colleagues who engage in case study, historical, and ethnographic work to think more rigorously about what inferences can be correctly drawn from their low-n studies.
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GELMAN, ANDREW, and DONALD B. RUBIN. "Evaluating and Using Statistical Methods in the Social Sciences." Sociological Methods & Research 27, no. 3 (February 1999): 403–10. http://dx.doi.org/10.1177/0049124199027003004.

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Williamson, John B., and S. K. Kachigan. "Statistical Analysis: An Interdisciplinary Introduction to Univariate and Multivariate Methods." Teaching Sociology 16, no. 2 (April 1988): 216. http://dx.doi.org/10.2307/1317431.

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Bhatta, Tirth. "Book Review: Making Sense of Statistical Methods in Social Research." Teaching Sociology 39, no. 2 (April 2011): 212–14. http://dx.doi.org/10.1177/0092055x11403291.

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Handcock, Mark S., and Martina Morris. "2. Relative Distribution Methods." Sociological Methodology 28, no. 1 (August 1998): 53–97. http://dx.doi.org/10.1111/0081-1750.00042.

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We present an outline of relative distribution methods, with an application to recent changes in the U.S. wage distribution. Relative distribution methods are a nonparametric statistical framework for analyzing data in a fully distributional context. The framework combines the graphical tools of exploratory data analysis with statistical summaries, decomposition, and inference. The relative distribution is similar to a density ratio. It is technically defined as the random variable obtained by transforming a variable from a comparison group by the cumulative distribution function (CDF) of that variable for a reference group. This transformation produces a set of observations, the relative data, that represent the rank of the original comparison value in terms of the reference group's CDF. The density and CDF of the relative data can therefore be used to fully represent and analyze distributional differences. Analysis can move beyond comparisons of means and variances to tap the detailed information inherent in distributions. The analytic framework is general and flexible, as the relative density is decomposable into the effect of location and shape differences, and into effects that represent both compositional changes in covariates, and changes in the covariate-outcome variable relationship.
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Dissertations / Theses on the topic "Sociology – Statistical methods"

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Neylan, Julian School of History &amp Philosophy of Science UNSW. "The sociology of numbers: statistics and social policy in Australia." Awarded by:University of New South Wales. School of History and Philosophy of Science, 2005. http://handle.unsw.edu.au/1959.4/31963.

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This dissertation presents an historical-sociological study of how governments of the modern western state use the language and techniques of quantification in the domain of social policy. The case material has an Australian focus. The thesis argues that by relying on techniques of quantification, governments risk introducing a false legitimacy to their social policy decisions. The thesis takes observed historical phenomena, language and techniques of quantification for signifying the social, and seeks meaningful interpretations in light of the culturally embedded actions of individuals and collective members of Australian bureaucracies. These interpretations are framed by the arguments of a range of scholars on the sociology of mathematics and quantitative technologies. The interpretative framework is in turn grounded in the history and sociology of modernity since the Enlightenment period, with a particular focus on three aspects: the nature and purpose of the administrative bureaucracy, the role of positivism in shaping scientific inquiry and the emergence of a risk consciousness in the late twentieth century. The thesis claim is examined across three case studies, each representative of Australian government action in formulating social policy or providing human services. Key social entities examined include the national census of population, housing needs indicators, welfare program performance and social capital. The analysis of these social statistics reveals a set of recurring characteristics that are shown to reduce their certainty. The analysis provides evidence for a common set of institutional attitudes toward social numbers, essentially that quantification is an objective technical device capable of reducing unstable social entities to stable, reliable significations (numbers). While this appears to strengthen the apparatus of governmentality for developing and implementing state policy, ignoring the many unarticulated and arbitrary judgments that are embedded in social numbers introduces a false legitimacy to these government actions.
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Ellis-Woroch, Barbara. "A statistical examination of the relationship between workplace anger and hatred." Thesis, Capella University, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3565740.

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The purpose of this quantitative, survey-based study was to investigate the relationship between anger and hatred in an effort to learn more about how to understand and curb the problem of workplace violence. In particular, perfectionism was examined as a possible mediator of the relationship between anger and hatred. The study was conducted on a sample of 1,192 people. The statistical techniques of correlation, partial correlation, linear regression, and principal components analysis (PCA) were used to examine the relationship between variables in the study. The following conclusions were reached: (a) The correlation between hatred and anger was significant (p < .001) but weak (R = .298), and these values changed slightly (R=.221) when controlling for the influence of perfectionism; (b) PCA revealed hatred and anger to be highly distinct from each other; (c) PCA revealed that the sub-scales of anger and hatred were sensitive to differences in how anger and hatred are felt and expressed; (d) the relationship between anger and motivation was not significant (p = < .001); (e) the relationship between hatred and motivation was not significant (p = < .001); (f) anger and hatred were not multicollinear in their association with motivation; and (f) for younger subjects, anger was a weaker predictor of hatred, while for older subjects anger was a stronger predictor of hatred. Based on these findings, the main conclusion of the study is that existing theories that associate anger and hatred might require revision based on further analysis of the differences between anger and hatred

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Zimdars, Anna. "Challenges to meritocracy? : a study of the social mechanisms in student selection and attainment at the University of Oxford." Thesis, University of Oxford, 2007. http://ora.ox.ac.uk/objects/uuid:0e9cf555-a921-4134-baf4-ce7114795f36.

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Sprague, William Webb. "Wood's Method -- a Method for Fitting Leslie Matrices from Age-Sex Population Data, with some Practical Applications." Thesis, University of California, Berkeley, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3616579.

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This dissertation is dedicated to an exploration of "Wood's Method" -- a novel approach to fitting demographic transition matrices to age and sex population count data. Demographic transition matrices, otherwise known as "Leslie matrices," are extensively used to forecast population by age, sex, and other characteristics. Our implementation of Wood's Method simplifies the creation of age and sex population forecasts greatly by reducing the amount of data necessary to create a demographic transition matrix. Furthermore, the method can be used to infer a demographic component of change (one of migration, fertility, or mortality) if the other two components are specified.

In Chapter One, we introduce Wood's Method, as well as showing some illustrative examples. In Chapter Two, we evaluate the accuracy of Wood's Method by crossvalidating age and sex specific forecasts for 3,120 US counties. In Chapter Three, we present a simpler, alternative derivation of Wood's Method with an extensive example and show some extensions to the method made possible by this new formulation. In Chapter Four, we use the method to examine migration rates at the US County level and show important results regarding clustering of migration. Each chapters is independent of the others, but should be read in order.

To our knowledge, this is the first time Wood's Method has been used for forecasting human populations. We hope to show its viability as a forecasting and analysis method and sketch directions for further research.

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Fosse, Ethan. "Cultural Continuity and the Rise of the Millennials: Generational Trends in Politics, Religion, and Economic Values." Thesis, Harvard University, 2015. http://nrs.harvard.edu/urn-3:HUL.InstRepos:17463122.

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Díaz, Cereceda Cristina. "Efficient models for building acoustics : combining deterministic and statistical methods." Doctoral thesis, Universitat Politècnica de Catalunya, 2013. http://hdl.handle.net/10803/134507.

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Modelling vibroacoustic problems in the field of building design is a challenging problem due to the large size of the domains and the wide frequency range required by regulations. Standard numerical techniques, for instance finite element methods (FEM), fail when trying to reach the highest frequencies. The required element size is too small compared to the problem dimensions and the computational cost becomes unaffordable for such an everyday calculation. Statistical energy analysis (SEA) is a framework of analysis for vibroacoustic problems, based on the wave behaviour at high frequencies. It works directly with averaged magnitudes, which is in fact what regulations require, and its computational cost is very low. However, this simplified approach presents several limitations when dealing with real-life structures. Experiments or other complementary data are often required to complete the definition of the SEA model. This thesis deals with the modelling of building acoustic problems with a reasonable computational cost. In this sense, two main research lines have been followed. In the first part of the thesis, the potential of numerical simulations for extending the SEA applicability is analysed. In particular, three main points are addressed: first, a systematic methodology for the estimation of coupling loss factors from numerical simulations is developed. These factors are estimated from small deterministic simulations, and then applied for solving larger problems with SEA. Then, an SEA-like model for non-conservative couplings is presented, and a strategy for obtaining conservative and non-conservative coupling loss factors from numerical simulations is developed. Finally, a methodology for identifying SEA subsystems with modal analysis is proposed. This technique consists in performing a cluster analysis based on the problem eigenmodes. It allows detecting optimal SEA subdivisions for complex domains, even when two subsystems coexist in the same region of the geometry. In the second part of the thesis, the sound transmission through double walls is analysed from different points of view, as a representative example of the complexities of vibroacoustic simulations. First, a compilation of classical approaches to this problem is presented. Then, the finite layer method is proposed as a new way of discretising the pressure field in the cavity inside double walls, especially when it is partially filled with an absorbing material. This method combines a FEM-like discretisation in the direction perpendicular to the wall with trigonometric functions in the two in-plane directions. This approach has less computational cost than FEM but allows the enforcement of continuity and equilibrium between fluid layers. It is compared with experimental data and also with other prediction models in order to check the influence of commonly assumed simplifications. Finally, a combination of deterministic and statistical methods is presented as a possible solution for dealing with vibroacoustic problems consisting of double walls and other elements. The global analysis is performed with SEA, and numerical simulations of small parts of the problem are used to obtain the required parameters. Combining these techniques, a realistic simulation of the vibroacoustic problem can be performed with a reasonable computational cost.
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Miller, Jean Anne. "Naturalism & Objectivity: Methods and Meta-methods." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/28329.

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The error statistical account provides a basic account of evidence and inference. Formally, the approach is a re-interpretation of standard frequentist (Fisherian, Neyman-Pearson) statistics. Informally, it gives an account of inductive inference based on arguing from error, an analog of frequentist statistics, which keeps the concept of error probabilities central to the evaluation of inferences and evidence. Error statistical work at present tends to remain distinct from other approaches of naturalism and social epistemology in philosophy of science and, more generally, Science and Technology Studies (STS). My goal is to employ the error statistical program in order to address a number of problems to approaches in philosophy of science, which fall under two broad headings: (1) naturalistic philosophy of science and (2) social epistemology. The naturalistic approaches that I am interested in looking at seek to provide us with an account of scientific and meta-scientific methodologies that will avoid extreme skepticism, relativism and subjectivity and claim to teach us something about scientific inferences and evidence produced by experiments (broadly construed). I argue that these accounts fail to identify a satisfactory program for achieving those goals and; moreover, to the extent that they succeed it is by latching on to the more general principles and arguments from error statistics. In sum, I will apply the basic ideas from error statistics and use them to examine (and improve upon) an area to which they have not yet been applied, namely in assessing and pushing forward these interdisciplinary pursuits involving naturalistic philosophies of science that appeal to cognitive science, psychology, the scientific record and a variety of social epistemologies.
Ph. D.
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Munasib, Abdul Baten Ahmed. "Lifecycle of social networks a dynamic analysis of social capital accumulation /." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1121441394.

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Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains xiv, 130 p.; also includes graphics. Includes bibliographical references (p. 121-130). Available online via OhioLINK's ETD Center
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Bennett, Matthew. "A comparative study of volunteering and giving." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:d74fcc54-879d-442a-b01a-5b09ba8a2ff6.

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The main research question in this thesis explores why some people volunteer and give money compared to those that do not. The thesis builds on existing research that explains volunteering and giving, but is primarily concerned with showing how the social environment – or the context – in which people live can explain individual decisions to volunteer and give. This thesis answers three main research questions with this central theme in mind. First, how do the background characteristics of people explain whether they volunteer or give? Second, net of background characteristics of people, how does the social environment (context) that people live in explain volunteering and giving? Third, how do background characteristics of people and the social environment in which they live interact to explain volunteering and giving? Each of the four empirical chapters focuses on research questions that have received limited attention in the literature, while also utilizing relatively unique data, in relatively unique contexts. The main results of this study are as follows. Comparatively, the shared profile of a volunteer and charitable giver is someone who is middle aged, more educated, married, richer, healthier, and a religious service attendee. Contextual country characteristics also displayed an independent effect of these individual-level characteristics: religious diversity and belonging to a religious minority group was associated with a greater likelihood of volunteering, but are not associated with giving. Income inequality is associated with a decreased likelihood of volunteering and giving for respondents in developed countries, whereas the opposite is true for respondents in developing countries, supporting Wilkinson’s relative income hypothesis. A curvilinear relationship exists between national devoutness and volunteering, whereas a strong positive correlation exists between national devoutness and giving. Females are more likely to volunteer and give in societies that exhibit more gender equality; and the lower educated are more likely to volunteer in more educated societies, but that they are not more likely to give in these societies. There is no support for the idea that income disparities in volunteering and giving are exacerbated in more socially unequal societies. In England, there was no support for the idea that a stable residential area promotes volunteering and giving among adults, while neighbourhood deprivation and ethnic diversity were strong negative predictors of both behaviours. Among the youth demographic in England, religious diversity of schools is not associated with any form of civic engagement. Ethnic diversity is positively related to school extra curricular activities, but negatively with youth club participation. Youths attending private schools were more likely to take part in school-based extra-curriculars, but less likely to take part in out of school clubs and groups.
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Chu, Yue. "SVD-BAYES: A SINGULAR VALUE DECOMPOSITION-BASED APPROACH UNDER BAYESIAN FRAMEWORK FOR INDIRECT ESTIMATION OF AGE-SPECIFIC FERTILITY AND MORTALITY." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1609638415015896.

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Books on the topic "Sociology – Statistical methods"

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Applying social statistics: An introduction to quantitative reasoning in sociology. Lanham, Md: Rowman & Littlefield, 2010.

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Blöss, Thierry. Introduction aux méthodes statistiques en sociologie. Paris: Presses universitaires de France, 1999.

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Das Problem der Distanzbindungen in der hierarchischen Clusteranalyse. Frankfurt am Main: P. Lang, 1995.

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Stat-spotting: A field guide to identifying dubious data. Berkeley: University of California Press, 2008.

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Feigelman, William. Hands on sociology. New York: Longman, 1999.

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Yih-Jin, Young, ed. Hands-on sociology. 3rd ed. Boston: Pearson/Allyn & Bacon, 2006.

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Feigelman, William. Hands on sociology: Using computers to learn about sociology. New York: McGraw-Hill, 1993.

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Venedikov, Ĭordan. Statistika, sot͡s︡iologii͡a︡ i oshte neshto--. Sofii͡a︡: Informat͡s︡ionno obsluzhvane, 1992.

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1977-, Gibbs Julie, and Hyman Laura, eds. Social measurement through social surveys: An applied approach. Farnham: Ashgate Pub., 2009.

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Ferrando, Manuel García. Socioestadística: Introducción a la estadística en sociología. Madrid: Alianza, 1985.

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Book chapters on the topic "Sociology – Statistical methods"

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Oso, Laura, and Pablo Dalle. "Migration and Social Mobility Between Argentina and Spain: Climbing the Social Hierarchy in the Transnational Space." In Towards a Comparative Analysis of Social Inequalities between Europe and Latin America, 235–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48442-2_8.

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AbstractThis chapter analyses the relationship between migration and social mobility in Argentina and Spain from a transnational perspective focusing on two dimensions: the patterns of intergenerational social mobility of immigrants and natives in both countries; the social mobility strategies and trajectories of Galicians families in Buenos Aires and Argentinians, of Galician origin, who migrated to Galicia after the 2001 crisis. The chapter begins by contextualizing the migratory trends in Europe and Latin America. This is followed by a comparative study of how immigration impacts on the class structure and social mobility patterns in Argentina and Spain. Quantitative analysis techniques are used to study the intergenerational social mobility rates. The statistical analysis of stratification and social mobility surveys have been benchmarked against previous studies conducted in Argentina (Germani, G., Movilidad social en la sociedad industrial. EUDEBA, Buenos Aires, 1963; Dalle, P., Movilidad social desde las clases populares. Un estudio sociológico en el Área Metropolitana de Buenos Aires (1960–2013). CLACSO/Instituto de Investigaciones Gino Germani-UBA/CICCUS, Buenos Aires, 2016) and Spain (Fachelli, S., & López-Roldán, P., Revista Española de Sociología 26:1–20, 2017). Secondly, qualitative research methods are used to consider the social mobility strategies and class trajectories of migrant families. We analyse two fieldworks, developed in the framework of other research projects (based on 44 biographical and semi-structured interviews). These case studies were carried out with Galicians that migrated to Argentina between 1940 and 1960 and Argentinians, of Galician origin, who migrated to Galicia after the 2001 crisis.
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Ferrell, Jeff. "Beneath the Slab." In Drift. University of California Press, 2018. http://dx.doi.org/10.1525/california/9780520295544.003.0007.

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This chapter addresses method in a broad sense: as a way of orienting ourselves to the world around us. In this context, the chapter argues that the methods needed to make sense of a world adrift must embody uncertainty, ambiguity, and fragmentation if we are to avoid making drift into something it is not. Developing this sort of method, however, will require abandoning existing methods that are conceptualized as the solid, foundational slabs on which research is built. As fetishized exemplars of this slab-like approach, survey research and statistical analysis are particularly inappropriate for understanding a contemporary world of drift and drifters. Instead, the chapter suggests that we revisit early ethnographic research in sociology and related fields, and the long tradition of documentary photography and photodocumentary work, both of which embraced more fluid and less formalized methodological approaches.
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Biggiero, Lucio. "Network Analysis for Economics and Management Studies." In Foreign Direct Investments, 269–328. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2448-0.ch012.

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Sociology and other social sciences have employed network analysis earlier than management and organization sciences, and much earlier than economics, which has been the last one to systematically adopt it. Nevertheless, the development of network economics during last 15 years has been massive, alongside three main research streams: strategic formation network modeling, (mostly descriptive) analysis of real economic networks, and optimization methods of economic networks. The main reason why this enthusiastic and rapidly diffused interest of economists came so late is that the most essential network properties, like externalities, endogenous change processes, and nonlinear propagation processes, definitely prevent the possibility to build a general – and indeed even partial – competitive equilibrium theory. For this paradigm has dominated economics in the last century, this incompatibility operated as a hard brake, and presented network analysis as an inappropriate epistemology. Further, being intrinsically (and often, until recent times, also radically) structuralist, social network analysis was also antithetic to radical methodological individualism, which was – and still is – economics dominant methodology. Though culturally and scientifically influenced by economists in some fields, like finance, banking and industry studies, scholars in management and organization sciences were free from “neoclassical economics chains”, and therefore more ready and open to adopt the methodology and epistemology of social network analysis. The main and early field through which its methods were channeled was the sociology of organizations, and in particular group structure and communication, because this is a research area largely overlapped between sociology and management studies. Currently, network analysis is becoming more and more diffused within management and organization sciences. Mostly descriptive until 15 years ago, all the fields of social network analysis have a great opportunity of enriching and developing its methods of investigation through statistical network modeling, which offers the possibility to develop, respectively, network formation and network dynamics models. They are a good compromise between the much more powerful agent-based simulation models and the usually descriptive (or poorly analytical) methods.
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Biggiero, Lucio. "Network Analysis for Economics and Management Studies." In Relational Methodologies and Epistemology in Economics and Management Sciences, 1–60. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9770-6.ch001.

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Sociology and other social sciences have employed network analysis earlier than management and organization sciences, and much earlier than economics, which has been the last one to systematically adopt it. Nevertheless, the development of network economics during last 15 years has been massive, alongside three main research streams: strategic formation network modeling, (mostly descriptive) analysis of real economic networks, and optimization methods of economic networks. The main reason why this enthusiastic and rapidly diffused interest of economists came so late is that the most essential network properties, like externalities, endogenous change processes, and nonlinear propagation processes, definitely prevent the possibility to build a general – and indeed even partial – competitive equilibrium theory. For this paradigm has dominated economics in the last century, this incompatibility operated as a hard brake, and presented network analysis as an inappropriate epistemology. Further, being intrinsically (and often, until recent times, also radically) structuralist, social network analysis was also antithetic to radical methodological individualism, which was – and still is – economics dominant methodology. Though culturally and scientifically influenced by economists in some fields, like finance, banking and industry studies, scholars in management and organization sciences were free from “neoclassical economics chains”, and therefore more ready and open to adopt the methodology and epistemology of social network analysis. The main and early field through which its methods were channeled was the sociology of organizations, and in particular group structure and communication, because this is a research area largely overlapped between sociology and management studies. Currently, network analysis is becoming more and more diffused within management and organization sciences. Mostly descriptive until 15 years ago, all the fields of social network analysis have a great opportunity of enriching and developing its methods of investigation through statistical network modeling, which offers the possibility to develop, respectively, network formation and network dynamics models. They are a good compromise between the much more powerful agent-based simulation models and the usually descriptive (or poorly analytical) methods.
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Tits, Noé, Kevin El Haddad, and Thierry Dutoit. "The Theory behind Controllable Expressive Speech Synthesis: A Cross-Disciplinary Approach." In Human 4.0 - From Biology to Cybernetic. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.89849.

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As part of the Human-Computer Interaction field, Expressive speech synthesis is a very rich domain as it requires knowledge in areas such as machine learning, signal processing, sociology, and psychology. In this chapter, we will focus mostly on the technical side. From the recording of expressive speech to its modeling, the reader will have an overview of the main paradigms used in this field, through some of the most prominent systems and methods. We explain how speech can be represented and encoded with audio features. We present a history of the main methods of Text-to-Speech synthesis: concatenative, parametric and statistical parametric speech synthesis. Finally, we focus on the last one, with the last techniques modeling Text-to-Speech synthesis as a sequence-to-sequence problem. This enables the use of Deep Learning blocks such as Convolutional and Recurrent Neural Networks as well as Attention Mechanism. The last part of the chapter intends to assemble the different aspects of the theory and summarize the concepts.
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Rosenzweig, Cynthia, and Daniel Hillel. "Analysis of El Niño Effects: Methods and Models." In Climate Variability and the Global Harvest. Oxford University Press, 2008. http://dx.doi.org/10.1093/oso/9780195137637.003.0010.

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Knowledge of climate impacts is necessarily embedded in multifaceted, multiscaled contexts. The many facets include physical, ecological, and biological factors—as well as social, political, and economic ones—interacting on a spectrum of scales ranging from the individual to the household, the community, the region, the nation, and the world. Such complexities encompass natural as well as cultural aspects. Therefore, assessing the role of climate requires a comprehensive, integrated approach. Various methods and models have been proposed or developed to aid understanding of the relationships between agriculture and climate variability (and more specifically, ENSO) in regions around the world. Relevant methods include socioeconomic research techniques such as interviews and surveys; statistical analyses of climate and agronomic data; spatial analysis of remote-sensing observations; climate-scenario development with global and regional climate models and weather generators; and cropmodel simulations. Here we describe conceptual models that guide regional analysis, a framework of methods for regional studies, and examples of research in several agricultural regions that experience varying degrees of ENSO effects. Conceptual models are important because they can guide research and application projects and help physical, biological, and social scientists work together effectively within a common context. Equally important is the role of conceptual models in promoting effective interactions between researchers and agricultural practitioners. An early conceptual model for enhancing the usefulness of seasonal climate forecasts has been called the “end-to-end” approach (figure 5.1a). This model consists of a linear unidirectional trajectory in which El Niño events precipitate climate phenomena that, in turn, induce agronomic responses, with ensuing economic consequences. In disciplinary terms, the end-to-end trajectory begins with the physical sciences, proceeds to agronomy, and then to social science—primarily economics. The end-to-end model quickly evolved into an “end-to-multiple-ends” approach (figure 5.1b) because social science consists of many disciplines besides economics. Outcomes and insights regarding the use of seasonal climate forecasts differ, depending on whether the disciplines of economics, anthropology, political science, or sociology are involved. However, a weakness of these conceptual models is the absence of agricultural practitioners (e.g., farmers, planners, input providers, and insurers) in the research process.
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Thompson, Paul, Ken Plummer, and Neli Demireva. "Organising: creating research worlds." In Pioneering Social Research, 79–110. Policy Press, 2020. http://dx.doi.org/10.1332/policypress/9781447333524.003.0004.

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This chapter looks at how social research gradually became organized through the work of our pioneers. It starts by looking at the growth of both universities and academic disciplines (like anthropology and sociology) as key backgrounds for understanding the growth of organized research. A major section discusses a range of early research agencies — the Colonial Research Council, Political and Economic Planning (PEP), the Institute of Community Studies, the CSO (Central Statistical Office), the SSRC, Social Science Research Council, and the UK Data Archive. Some new university-based centres are also considered: medical social science at Aberdeen, methods at Surrey and the BCCS (Birmingham Centre for Contemporary Cultural Studies). There are brief discussions of the Banbury Study with Meg Stacey and Colin Bell; and the Affluent Worker study. The chapter closes with some pioneering work on quantitative research, longitudinal studies and the rise of computing.
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Goertz, Gary, and James Mahoney. "Introduction." In A Tale of Two Cultures. Princeton University Press, 2012. http://dx.doi.org/10.23943/princeton/9780691149707.003.0001.

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This book investigates the relationship between the quantitative and qualitative research traditions in the social sciences, with a particular focus on political science and sociology. It argues that the two traditions are alternative cultures with distinctive research procedures and practices, each having its own values, beliefs, and norms. The book considers the ways in which the traditions differ in terms of methodology, such as type of research question, mode of data analysis, and method of inference. It suggests that the two traditions draw on alternative mathematical foundations: quantitative research is grounded in inferential statistics (that is, probability and statistical theory), whereas qualitative research is (often implicitly) rooted in logic and set theory. This chapter discusses the book's approach to characterizing and comparing the two cultures of social science research and explains what is distinctive about qualitative research.
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Scafetta, N., and P. Grigolin. "Nonextensive Diffusion Entropy Analysis and Teen Birth Phenomena." In Nonextensive Entropy. Oxford University Press, 2004. http://dx.doi.org/10.1093/oso/9780195159769.003.0021.

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A complex process is often a balance between nonscaling and scaling components. We show how the nonextensive Tsallis g-entropy indicator may be interpreted as a measure of the nonscaling condition in time series. This is done by applying the nonextensive entropy formalism to the diffusion entropy analysis (DEA). We apply the analysis to the study of the teen birth phenomenon. We find that the number of unmarried teen births is strongly influenced by social processes that induce an anomalous memory in the data. This memory is related to the strength of the nonscaling component of the signal and is more intense than that in the married teen birth time series. By using a wavelet multiresolution analysis, we attempt to provide a social interpretation of this effect…. One of the most exciting and rapidly developing areas of modern research is the quantitative study of "complexity." Complexity has special interdisciplinary impacts in the fields of physics, mathematics, information science, biology, sociology, and medicine. No definition of a complex system has been universally embraced, so here we adopt the working definition, "an arrangement of parts so intricate as to be hard to understand or deal with." Therefore, the main goal of the science of complexity is to develop mathematical methods in order to discriminate among the fundamental microscopic and macroscopic constituents of a complex system and to describe their interrelations in a concise way. Experiments usually yield results in the form of time series for physical observables. Typically, these time series contain both a slow regular variation, usually called a "signal," and a rapid erratic fluctuation, usually called "noise." Historically, the techniques applied to processing such time series have been based on equilibrium statistical mechanics and, therefore, they are not applicable to phenomena far from equilibrium. Among the fluctuating phenomena, a particularly important place is occupied by those phenomena characterized by some type of self-similar or scaling-fractal structures [4]. In this chapter we show that the nonextensive Tsallis g-entropy indicator may be interpreted as a measure of the strength of the nonscaling component of a time series.
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Wang, Yingxu, and Guenther Ruhe. "The Cognitive Process of Decision Making." In Selected Readings on Strategic Information Systems, 237–50. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-090-5.ch016.

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Decision making is one of the basic cognitive processes of human behaviors by which a preferred option or a course of actions is chosen from among a set of alternatives based on certain criteria. Decision theories are widely applied in many disciplines encompassing cognitive informatics, computer science, management science, economics, sociology, psychology, political science, and statistics. A number of decision strategies have been proposed from different angles and application domains such as the maximum expected utility and Bayesian method. However, there is still a lack of a fundamental and mathematical decision model and a rigorous cognitive process for decision making. This article presents a fundamental cognitive decision making process and its mathematical model, which is described as a sequence of Cartesian-product based selections. A rigorous description of the decision process in real-time process algebra (RTPA) is provided. Real-world decisions are perceived as a repetitive application of the fundamental cognitive process. The result shows that all categories of decision strategies fit in the formally described decision process. The cognitive process of decision making may be applied in a wide range of decision-based systems such as cognitive informatics, software agent systems, expert systems, and decision support systems.
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Conference papers on the topic "Sociology – Statistical methods"

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Günay, Nergin. "Economic Science Considering with a Thermodynamic Perspective of a Physicist's Point of View." In International Conference on Eurasian Economies. Eurasian Economists Association, 2016. http://dx.doi.org/10.36880/c07.01559.

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Economy is a discipline by means of its structure which closely interests all humanities live non-stop whether they are directly related or not which in a relationship with mathematic as calculations, psychology as searching investor behaviors, sociology as searching social events, philosophy as structural reviews of the created environment and many kind of disciplines more. In this study based on a survey of the relevant literature, the common features of economy with physics is a supporter in the recent years are revealed. Concept passed into world literature as Econophysics or alias Econphysics is defined. Econophysics is a study field tries to find solutions to economic problem by using physical methods. The main tool is used by the econophysics are statistical and probability methods are taken from statistical physics frequently. Information related to implementation of the laws of thermodynamics which is the branch dealing with the energy and physical energy exchange economic problems are given. The laws of thermodynamics have a very general validity and they do not change depending on the characteristics of the studied system. In this regard, how thermodynamic physics are applied into economics practices are given in detail.
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Han, Yu, and Jie Tang. "Who to Invite Next? Predicting Invitees of Social Groups." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/519.

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Social instant messaging services (SMS) such as WhatsApp, Snapchat and WeChat, have significantly changed the way people work, live, and communicate, attracting increasing attention from multiple disciplinary including computer science, sociology, psychology, and physics. In SMS, social groups play a very important role in supporting communication among multiple users. An interesting question arises: what are the dynamic mechanisms underlying the group evolution? Or more specifically, in an existing group, who should be invited to join? In this paper, we formalize a novel problem of predicting potential invitees of groups. Employing WeChat, the largest social messaging service in China, as the source for our experimental data, we develop a probabilistic graph model to capture the fundamental factors that determine the probability of a user to be invited to a specific social group. Our results show that the proposed model indeed lead to statistically significant prediction improvements over several state-of-the-art baseline methods.
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Fonseca, Jaime. "Can We Reduce Students’ Negative Attitude Towards Math?" In InSITE 2007: Informing Science + IT Education Conference. Informing Science Institute, 2007. http://dx.doi.org/10.28945/3089.

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This study concerns the teaching/leaming experience of Data Analysis at the Higher Institute of Social and Political Sciences (ISCSP), Technical University of Lisbon, first in Sociology course, and next in Social Communication course. In both cases, Data Analysis subject was teaching/leaming of the discipline of Mathematics and Statistics for the Social Sciences. This study aims to find the effect of the use of new technologies on teaching/leaming the Data Analysis subject, and, more than that, it wants to know if this use can reduce the effect of negative experiences when learning Mathematics. From the used dataset, based on a questionnaire, we first profiled students, based on Latent Class Models; then we concluded that the negative attitude toward Mathematics’ learning until the 9 year (compulsory) schooling, influenced their performances on the Quantitative Methods (QM) subject, at the secondary level, but the same did not happened with the Data Analysis’ performance at University.
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