Armed Conflict and Religious Adherence Across Countries: A Time Series Analysis

2021 ◽  
Author(s):  
Nohemi Jocabeth Echeverría Vicente ◽  
Kenneth Hemmerechts ◽  
Dimokritos Kavadias
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Armed Conflict ◽  
Sociology Of Religion ◽  
Fundamental Question ◽  
Religious Revival ◽  
Comparative Sociology ◽  
National Differences ◽  
History Of ◽  
Cross National

Abstract A fundamental question in the comparative sociology of religion is: What are the drivers of cross-national differences in religiosity? The existential insecurity argument raises the expectation of higher levels of religiosity in contexts of social crisis. We test this argument against countries’ armed conflict experiences, employing global longitudinal data on religious adherence over almost half a century. We did not find evidence of religious revival when measuring the consequences of armed conflict with a 5-year lag, indicating that armed conflict-related social crises do not tend to lead to sudden changes in the religious adherence of a country. However, we did find more consistent indications of a higher proportion of religious people when using accumulated measurements of armed conflict, highlighting the importance of investigating the armed conflict history of a country when assessing its religious consequences. Our results show that countries with a more devastating experience of armed conflict tend to present higher proportions of religious adherence in comparison with countries with a less devastating armed conflict history. We concluded that armed conflict tends to partially drive religious persistence in societies that have experienced it, and that the pace at which this takes place is gradual rather than immediate.

scholarly journals Highly comparative time-series analysis: the empirical structure of time series and their methods

2013 ◽  
Vol 10 (83) ◽  
pp. 20130048 ◽  
Author(s):  
Ben D. Fulcher ◽  
Max A. Little ◽  
Nick S. Jones
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Scientific Data ◽  
Series Data ◽  
Series Analysis ◽  
Analysis Methods ◽  
Scientific Disciplines ◽  
History Of ◽  
Reduced Representations

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

Paradoxes of Pluralism, Privilege, and Persecution: Explaining Christian Growth and Decline Worldwide

2021 ◽  
Author(s):  
Nilay Saiya ◽  
Stuti Manchanda
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Population Growth ◽  
Growth Rates ◽  
The Other ◽  
Faith Traditions ◽  
Statistical Approaches ◽  
State Relations ◽  
Christian Population ◽  
Cross National

Abstract This article examines the effect of church–state relations on rates of Christian population growth or decline worldwide. It makes the paradoxical argument that contexts of both pluralism and persecution do not impede Christian growth rates. In these environments, Christians do not have the luxury of becoming complacent. On one hand, pluralism means that Christianity must actively compete with other faith traditions in order to gain and maintain adherents. On the other hand, persecution can, paradoxically, sometimes strengthen Christianity by deepening attachments to faith and reinforcing solidarity among Christians. Rather, it is a third type of relationship—privilege, or state support for Christianity—that corresponds to the greatest threat to growth in Christianity. Countries where Christianity is privileged by the state encourage apathy and the politicization of religion, resulting in a less dynamic faith and the overall decline of Christian populations. We test these propositions using a cross-national, time-series analysis of a global sample of countries from 2010 to 2020. Our findings provide support for our theory that Christianity suffers in contexts of privilege but not in environments of pluralism or persecution. The finding is robust to a number of model specifications and statistical approaches.

Time‐Series Analysis

2009 ◽  
Author(s):  
Jon Pevehouse ◽  
Jason D. Brozek
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Data Aggregation ◽  
Multivariate Analyses ◽  
Temporal Stability ◽  
Var Models ◽  
Series Analysis ◽  
History Of ◽  
General Time ◽  
Analysis Of Time Series

This article discusses time-series methods such as simple time-series regressions, ARIMA models, vector autoregression (VAR) models, and unit root and error correction models (ECM). It specifically presents a brief history of time-series analysis before moving to a review of the basic time-series model. It then describes the stationary models in univariate and multivariate analyses. The nonstationary models of each type are addressed. In addition, various issues regarding the analysis of time series including data aggregation and temporal stability are considered. Before concluding, the article briefly reports the time-series techniques in the context of panel data. In general, time-series analysis can help improve the understanding of the political world.

scholarly journals Income Inequality from 1960–2012: A Brief Time-Series History of Capital and Labour

2014 ◽  
Vol 22 (1) ◽  
pp. 88-101
Author(s):  
Eoin Flaherty
Keyword(s):  
Time Series ◽  
Income Inequality ◽  
Time Series Analysis ◽  
National Income ◽  
Financial Sector ◽  
Total Income ◽  
Series Analysis ◽  
Top Incomes ◽  
History Of ◽  
The 1960S

This commentary examines two principal forms of inequality and their evolution since the 1960s: the division of national income between capital and labour, and the share of total income held by the top 1 per cent of earners. Trends are linked to current discussions of inequality drivers such as financialisation, and a brief time-series analysis of the effects of trade and financial sector growth on top incomes is presented.

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