There has been much advancement in the field of public opinion research in the past few years, particularly with respect to the formation of policy attitudes in response to elite rhetoric, the translation of policy information into attitudes, and the biological foundations of policy attitudes. Much of the progress made in these areas of study can be attributed to the increased use of innovative, experimental methods and new data sources. Nonetheless, unresolved issues persist, such as whether there is an identifiable genetic basis of policy attitudes and the extent to which cultural versus partisan orientations drive opinions. This review will discuss both new findings in the field and identify areas that require further research. Adapted from the source document.
This article describes several approaches for estimating the benchmark dose (BMD) in a risk assessment study with quantal dose‐response data and when there are competing model classes for the dose‐response function. Strategies involving a two‐step approach, a model‐averaging approach, a focused‐inference approach, and a nonparametric approach based on a PAVA‐based estimator of the dose‐response function are described and compared. Attention is raised to the perils involved in data "double‐dipping" and the need to adjust for the model‐selection stage in the estimation procedure. Simulation results are presented comparing the performance of five model selectors and eight BMD estimators. An illustration using a real quantal‐response data set from a carcinogenecity study is provided.
Acknowledgements The authors are grateful to M. Scholes, D. Plaza-Bonilla, S. Menendez, P. Merino, S.C. Maris, H. Heller, D. Savvas, C. K. Kontopoulou, who were contacted and kindly supplied any missing information necessary for the meta-analysis. Special thanks to J.P.C. Eekhout for preparing Fig. 1 and F. Estellés for providing the basic data for the calculation of the fertilization in Spain. Also thanks to two anonymous reviewers for their helpful comments. M. L. Cayuela was supported by a 'Ramon y Cajal' research contract from the Spanish Ministry of Economy and Competitiveness. Thanks to Fundación Séneca, Agencia Regional de Ciencia y Tecnología de la Región de Murcia for support (grant number 19281/PI/14). Australian studies included in the meta-analysis were funded by the Australian Government, the Grains Research and Development Corporation, and the Department of Agriculture and Food WA. ; Peer reviewed ; Postprint ; Postprint ; Postprint ; Postprint ; Postprint
The social web has become a major repository of social and behavioral data that is of exceptional interest to the social science and humanities research community. Computer science has only recently developed various technologies and techniques that allow for harvesting, organizing and analyzing such data and provide knowledge and insights into the structure and behavior or people on-line. Some of these techniques include social web mining, conceptual and social network analysis and modeling, tag clouds, topic maps, folksonomies, complex network visualizations, modeling of processes on networks, agent based models of social network emergence, speech recognition, computer vision, natural language processing, opinion mining and sentiment analysis, recommender systems, user profiling and semantic wikis. All of these techniques are briefly introduced, example studies are given and ideas as well as possible directions in the field of political attitudes and mentalities are given. In the end challenges for future studies are discussed.
The tremendous impact of the novel coronavirus (COVID-19) on societal, political, and economic rhythms has given rise to a significant overall shift from pre- to post-pandemic policies. Restrictions, stay-at-home regulations, and lockdowns have directly influenced day-to-day urban transportation flow. The rise of door-to-door services and the demand for visiting medical facilities, grocery stores, and restaurants has had a significant impact on urban transportation modal demand, further impacting zonal parking demand distribution. This study reviews the overall impacts of the pandemic on urban transportation with respect to a variety of policy changes in different cities. The parking demand shift was investigated by exploring the during- and post-COVID-19 parking policies of distinct metropolises. The detailed data related to Melbourne city parking, generated by the Internet of things (IoT), such as sensors and devices, are examined. Empirical data from 2019 (16 March to 26 May) and 2020 (16 March to 26 May) are explored in-depth using explanatory data analysis to demonstrate the demand and average parking duration shifts from district to district. The results show that the experimental zones of Docklands, Queensbery, Southbanks, Titles, and Princess Theatre areas have experienced a decrease in percentage change of vehicle presence of 29.2%, 36.3%, 37.7%, 23.7% and 40.9%, respectively. Furthermore, on-street level analysis of Princess Theatre zone, Lonsdale Street, Exhibition Street, Spring Street, and Little Bourke Street parking bays indicated a decrease in percentage change of vehicle presence of 38.7%, 56.4%, 12.6%, and 35.1%, respectively. In conclusion, future potential policymaking frameworks are discussed that could provide further guidance in stipulating epidemic prevention and control policies, particularly in relation to parking regulations during the pandemic.
What visual features characterize online migration data visualizations, and what do they suggest for the politics of representing migration and informing public attitudes? Audiences increasingly encounter quantitative information through visualization, especially in digital environments. Yet visualizations have political dimensions that manifest themselves through "conventions," or shared symbols and practices conveying meaning. Using content analysis, I identify patterns of representation in a sample of 277 migration data visualizations scraped from Google Images. I find evidence of several conventions including appeals to objectivity and traceability as well as perspectives and units of analysis centered on states—particularly higher income migrant destinations. Then, by locating my analysis within the growing field of digital migration studies, I argue these conventions potentially shape public attitudes and understandings about migrants, and contribute to broader migration politics involving categorization and quantification that have relevance both on- and off-line.
Very often the data collected by social scientists involve dependent observations, without, however, the investigators having any substantive interest in the nature of the dependencies. Although these dependencies are not important for the answers to the research questions concerned, they must still be taken into account in the analysis. Standard statistical estimation and testing procedures assume independent and identically distributed observations, and they need to be modified for observations that are clustered in some way. Marginal models provide the tools to deal with these dependencies without having to make restrictive assumptions about their nature. In this paper, recent developments in the (maximum likelihood) estimation and testing of marginal models for categorical data will be explained, including marginal models with latent variables. The differences and commonalities with other ways of dealing with these nuisance dependencies will be discussed, especially with GEE and also briefly with (hierarchical) random coefficient models. The usefulness of marginal modeling will be illuminated by showing several common types of research questions and designs for which marginal models may provide the answers, along with two extensive real world examples. Finally, a brief evaluation will be given, including a discussion of shortcomings and strong points as well as computer programs and future work to be done.
Error analysis is a significant procedure used in English language to identify errors. The present study identifies various errors committed by intermediate medical students studying in the government college of Pakistan in their written English essays. It aims to explore and explain the most frequently committed errors in the English compositions of students. The current study uses English Essays as an instrument of data collection from a sample of 50 students studying in Government Agha Nizamuddin Girls Degree College Sukkur. Data was analyzed following three step process as designed by Corder's (1967) theory of Error analysis. Findings from the data analysis informed the researcher that students of aforementioned colleges commit errors in spelling, subject-verb agreement and use of tenses most frequently followed by misappropriate use of singular/plural, preposition, infinitives, word order and possessives.
The article presents analyzes results of local government's topographical spatial data archiving and exchange options and an overview of topographical work stages, which relates to data exchanging between institutions. The article examines topographical laws and local government's working methodology of spatial data.
The article presents analyzes results of local government's topographical spatial data archiving and exchange options and an overview of topographical work stages, which relates to data exchanging between institutions. The article examines topographical laws and local government's working methodology of spatial data.
The article presents analyzes results of local government's topographical spatial data archiving and exchange options and an overview of topographical work stages, which relates to data exchanging between institutions. The article examines topographical laws and local government's working methodology of spatial data.
The article presents analyzes results of local government's topographical spatial data archiving and exchange options and an overview of topographical work stages, which relates to data exchanging between institutions. The article examines topographical laws and local government's working methodology of spatial data.
In: Political analysis: PA ; the official journal of the Society for Political Methodology and the Political Methodology Section of the American Political Science Association, Volume 26, Issue 2, p. 230-239
In this paper, I introduce a Bayesian model for detecting changepoints in a time series of overdispersed counts, such as contributions to candidates over the course of a campaign or counts of terrorist violence. To avoid having to specify the number of changepoint ex ante, this model incorporates a hierarchical Dirichlet process prior to estimate the number of changepoints as well as their location. This allows researchers to discover salient structural breaks and perform inference on the number of such breaks in a given time series. I demonstrate the usefulness of the model with applications to campaign contributions in the 2012 U.S. Republican presidential primary and incidences of global terrorism from 1970 to 2015.
In: Political analysis: PA ; the official journal of the Society for Political Methodology and the Political Methodology Section of the American Political Science Association, Volume 13, Issue 4, p. 457-458
The articles in this special issue all use multilevel methods to study comparative political behavior. This is obviously a good thing, for both methodology and comparative politics. Clearly comparative politics means comparing things and not just studying nations other than the United States. This is equally true of micropolitical studies. These articles all do a very nice job of showing how one can do comparative micropolitics (and tie together micro and macro variables).
