121 research outputs found
MĂłdszerek a kvantitatĂv társadalomkutatási paradigmákban
A kvantitatĂv társadalomkutatás paradigmái nem csak a kutatási problĂ©mákat, hanem a mĂłdszereket tekintve is behatárolják a szĂłba jöhetĹ‘ alternatĂvák körĂ©t. Vagyis, bár ritkán reflektálunk erre, a statisztikai eszközök közötti választás nem pusztán technikai kĂ©rdĂ©s. HasonlĂłan ritkán merĂĽl fel az a szempont, hogy maga az eszköz is hatást gyakorolhat az általa szolgált tudományra, annak szemlĂ©letĂ©re, fogalmaira, kĂ©rdĂ©sfeltevĂ©seire. ĂŤrásomban ezeket az álláspontokat igyekszem nĂ©hány – köztĂĽk Bourdieu korrespondenciaelemzĂ©sĂ©t, a mobilitáskutatás paradigmáit, a Big Data társadalomkutatási felhasználását Ă©s általában a mĂłdszertani innováciĂłk diffĂşziĂłját Ă©rintĹ‘ – pĂ©ldával alátámasztani
„I just ran two million regressions”, avagy mĂłdszertani paradigmák a kvantitatĂv társadalomkutatásban
Kuhn Ăłta elfogadott nĂ©zet, hogy a kĂĽlönbözĹ‘ korok paradigmatikus elmĂ©letei nem csak tartalmilag, hanem mĂłdszertanilag is kĂĽlönböznek egymástĂłl. Ennek ellenĂ©re a mĂłdszert alkalmazĂł tudomány (elĹ‘adásomban: a kvantitatĂv társadalomkutatás) vagy maguk a mĂłdszerek (elĹ‘adásomban: a statisztikai eszközök) ritkán reflektálnak erre. HasonlĂłan ritkán merĂĽl fel az a szempont, hogy magának a statisztikának is önállĂł, a társadalomtudományoktĂłl rĂ©szben fĂĽggetlen törtĂ©nete van, saját paradigmákkal, melyek talán az általa szolgált tudományra, annak szemlĂ©letĂ©re, fogalmaira, kĂ©rdĂ©sfeltevĂ©seire is hatást gyakorolnak. ElĹ‘adásomban nĂ©hány pĂ©ldával igyekszem alátámasztani ezt az álláspontomat
A scoping review on the use of natural language processing in research on political polarization: trends and research prospects
As part of the “text-as-data” movement, Natural Language Processing (NLP) provides a computational way to examine political polarization. We conducted a methodological scoping review of studies published since 2010 ( n = 154) to clarify how NLP research has conceptualized and measured political polarization, and to characterize the degree of integration of the two different research paradigms that meet in this research area. We identified biases toward US context (59%), Twitter data (43%) and machine learning approach (33%). Research covers different layers of the political public sphere (politicians, experts, media, or the lay public), however, very few studies involved more than one layer. Results indicate that only a few studies made use of domain knowledge and a high proportion of the studies were not interdisciplinary. Those studies that made efforts to interpret the results demonstrated that the characteristics of political texts depend not only on the political position of their authors, but also on other often-overlooked factors. Ignoring these factors may lead to overly optimistic performance measures. Also, spurious results may be obtained when causal relations are inferred from textual data. Our paper provides arguments for the integration of explanatory and predictive modeling paradigms, and for a more interdisciplinary approach to polarization research
Rejoinder : On the Application of Discrete Marginal Graphical Models
Tematic issue on marginal modeling. Rejoinder to dicussants
Discrete Graphical Models in Social Mobility Research - A Comparative Analysis of American, Czechoslovakian and Hungarian Mobility before the Collapse of State Socialism
Variants of path models have been widely used for the analysis of the social status attainment process. The methods presented here differ from earlier approaches in several ways. Social status is considered a categorical variable and path models are developed starting from graphical models, using the marginal log-linear approach. Overall model fit may be tested by standard techniques. Under these models, the status attainment process is completely characterized by a set of parameters that measure the strengths of the relevant effects. This is in sharp contrast with estimating and interpreting ad hoc parameters, without paying attention to overall model fit and to other effects influencing the process. The method is applied to the social status attainment process in the USA, Hungary and Czechoslovakia at the end of the last century, and shows that policies in the latter socialist countries to prevent status inheritance had little success
On the application of discrete marginal graphical models
Graphical models are defined by general and possibly complex conditional independence assumptions and are well suited to model direct and indirect associations and effects that are of central importance in many problems of sociology. Such relevance is apparent in research on social mobility. This article provides a unified view of many of the graphical models discussed in a largely scattered literature. The marginal modeling framework proposed here
relies on parameters that capture aspects of associations among the variables that are relevant for the graph and, depending on the substantive problem at hand, may lead to deeper insight than other approaches. In this context,
model search, which uses a sequence of nested models, means the restriction of increasing subsets of parameters. As a special case, general path models for categorical data are introduced. These models are applied to the social
status attainment process, generating substantive results and gaining new insights into the difference between liberal and conservative welfare systems. To help others use these models, all details of the analyses are posted on the Web site for this article at http://nemethr.web.elte.hu/discrete-graphical-models/. Researchers can thus easily modify the analyses to their own data and models
A kĂ©rdezĹ‘biztosok hatása a politikai közvĂ©lemĂ©ny-kutatások eredmĂ©nyeire: bizonyĂtĂ©kok Ă©s magyarázatok
A kĂ©rdezĹ‘biztosok hatását hagyományosan a közvĂ©lemĂ©ny-kutatások során kapott válaszokat potenciálisan torzĂtĂł tĂ©nyezĹ‘k közĂ© sorolják, Ă©s a kutatások elĹ‘kĂ©szĂtĂ©sekor jelentĹ‘s erĹ‘feszĂtĂ©seket is tesznek e hatás kontrollálására. A tĂ©nyleges kĂ©rdezĹ‘i hatás mĂ©rĂ©sĂ©re azonban eddig kevĂ©s kĂsĂ©rlet szĂĽletett. Tanulmányunkban arra kerestĂĽnk választ, befolyásolja-e, s ha igen, milyen mĂ©rtĂ©kben a kĂ©rdezĹ‘ a politikai közvĂ©lemĂ©ny-kutatások eredmĂ©nyĂ©t, továbbá rĂ©szleges magyarázatot is prĂłbáltunk adni a hatás lĂ©trejöttĂ©re. A problĂ©ma közvetlenĂĽl kapcsolĂłdik a választási földrajz Ă©rdeklĹ‘dĂ©si terĂ©ben állĂł jelensĂ©gnek, a lakĂłhely politikai preferenciára gyakorolt hatásának a mĂ©rĂ©sĂ©hez is, ugyanis a lakĂłhely hatásába a kĂ©rdezĹ‘i hatás is belejátszik. Egyik legfontosabb eredmĂ©nyĂĽnk szerint, bár a lakĂłhely hatása is jelentĹ‘s, a kĂ©rdezĹ‘ hatása legalább ekkora vagy nagyobb. A kĂ©rdezĹ‘biztosok eltĂ©rĹ‘ pártpreferenciája vagy demográfiai jellemzĹ‘i rĂ©szben megmagyarázzák ezt a kĂ©rdezĹ‘i heterogenitást; a pártpreferencia hatása egyĂ©bkĂ©nt olyan irányĂş, hogy a kĂ©rdezett preferenciája a kĂ©rdezĹ‘Ă©hez idomul. BizonyĂtĂ©kot találtunk arra, hogy a hatás nem csupán pártpreferenciával kapcsolatos kĂ©rdĂ©sek esetĂ©n áll fenn, sĹ‘t, mĂ©rtĂ©ke nĂ©hány más kĂ©rdĂ©s esetĂ©n nagyságrenddel nagyobb, Ă©s stabilnak tűnik az a mintázat is, hogy a kĂ©rdezĹ‘i hatás meghaladja a földrajzi kontextus hatását
How to measure musical preference on Facebook? Evidence from a mixed-method data collection
More and more digital data is available for social science analysis. This
provides new ways of measuring several concepts. But when we start using new
data sources, we have to understand how the new data source could be processed
and how it could be analysed effectively. It is especially for Facebook data
since there is no established gold standard analysis-framework. However,
researchers have in-depth knowledge on how to measure different concepts using
survey data. Thus, cross-referencing Facebook data with survey data is a
reasonable way to support Facebook data analysis at different decision points.
In this paper, we present how music preference could be measured by Facebook
data and how survey data could support the selection of main indicators. Based
on our results, we provide some general suggestions for Facebook data
processing and indicator operationalization.Comment: 35 pages 3 figures, 6 table
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