Journal of the Royal Statistical Society Series A (Statistics in Society) (J R STAT SOC A STAT)

Publisher: Royal Statistical Society (Great Britain), Wiley

Journal description

Datasets relating to articles published in the four series of the Journal of the Royal Statistical Society are available online. Please click here Statistics in Society publishes original papers whose primary appeal lies in their subject-matter rather than in their technical statistical content to encourage clear statistical thinking on issues of importance to society. The journal's particular focus is on statistics as applied to social issues and this is interpreted broadly to include all disciplines which take people as their subject-matter. Thus education sociology medicine psychology the law demography government and politics economics and social geography all fall within its remit. The journal welcomes contributions from workers in central and local government or in business as well as academics and researchers in relevant disciplines. Papers should generally have a substantial statistical component but innovative statistical methods are not essential. Papers containing mathematical exposition are acceptable provided that this is relevant and that explanations are presented in clear English. Review papers are encouraged. The journal also welcomes relevant methodological papers with illustrative applications involving appropriate data. Such papers could include discussions of methods of data collection and of ethical issues.

Current impact factor: 1.57

Impact Factor Rankings

2015 Impact Factor Available summer 2015
2013 / 2014 Impact Factor 1.573
2012 Impact Factor 1.361
2011 Impact Factor 2.11
2010 Impact Factor 2.57
2009 Impact Factor 1.69
2008 Impact Factor 1.484
2007 Impact Factor 1.654
2006 Impact Factor 1.547
2005 Impact Factor 1.075
2004 Impact Factor 0.796
2003 Impact Factor 1.068
2002 Impact Factor 1.315
2001 Impact Factor 1.532
2000 Impact Factor 1.277
1999 Impact Factor 0.804
1998 Impact Factor 1.962
1997 Impact Factor 1.556

Impact factor over time

Impact factor

Additional details

5-year impact 2.29
Cited half-life 0.00
Immediacy index 0.50
Eigenfactor 0.01
Article influence 1.71
Website Journal of the Royal Statistical Society - Series A: Statistics in Society website
Other titles Journal of the Royal Statistical Society., Journal of the Royal Statistical Society. Series A, Statistics in society, Statistics in society
ISSN 0964-1998
OCLC 42017027
Material type Document, Periodical, Internet resource
Document type Internet Resource, Computer File, Journal / Magazine / Newspaper

Publisher details


  • Pre-print
    • Author can archive a pre-print version
  • Post-print
    • Author cannot archive a post-print version
  • Restrictions
    • 12 months embargo
  • Conditions
    • Some journals have separate policies, please check with each journal directly
    • On author's personal website, institutional repositories, arXiv, AgEcon, PhilPapers, PubMed Central, RePEc or Social Science Research Network
    • Author's pre-print may not be updated with Publisher's Version/PDF
    • Author's pre-print must acknowledge acceptance for publication
    • On a non-profit server
    • Publisher's version/PDF cannot be used
    • Publisher source must be acknowledged with citation
    • Must link to publisher version with set statement (see policy)
    • If OnlineOpen is available, BBSRC, EPSRC, MRC, NERC and STFC authors, may self-archive after 12 months
    • If OnlineOpen is available, AHRC and ESRC authors, may self-archive after 24 months
    • Publisher last contacted on 07/08/2014
    • This policy is an exception to the default policies of 'Wiley'
  • Classification
    ​ yellow

Publications in this journal

  • Journal of the Royal Statistical Society Series A (Statistics in Society) 01/2015; 178(1). DOI:10.1111/j.1467-985X.2014.12096_1.x
  • [Show abstract] [Hide abstract]
    ABSTRACT: Within an area of sub‐Saharan Africa termed ‘the meningitis belt’, meningococcal meningitis epidemics are a major public health concern. The epidemic control strategy that is currently utilized is reactive, such that a vaccination programme is initiated in a district once a predefined weekly incidence threshold has been exceeded. We report progress towards the development of an early warning system based on statistical modelling of district level weekly incidence data. Four modelling approaches are considered and their forecasting performances are compared by using weekly epidemiological data from Niger for the period 1986–2007. We conclude that the models under consideration are advantageous in different situations. The three‐state Markov model described in which observed incidence is categorized according to policy‐defined thresholds gives the most reliable short‐term forecasts, whereas the dynamic linear model proposed, using log‐transformed weekly incidence as the response variable, gives more reliable predictions of annual epidemics.
    Journal of the Royal Statistical Society Series A (Statistics in Society) 06/2014; 177(3). DOI:10.1111/rssa.12033
  • Journal of the Royal Statistical Society Series A (Statistics in Society) 02/2014; 177(2). DOI:10.1111/rssa.12050_4
  • [Show abstract] [Hide abstract]
    ABSTRACT: There are several challenges to testing the effectiveness of group therapy-based interventions in alcohol and other drug use (AOD) treatment settings. Enrollment into AOD therapy groups typically occurs on an open (rolling) basis. Changes in therapy group membership induce a complex correlation structure among client outcomes, with relatively small numbers of clients attending each therapy group session. Primary outcomes are measured post-treatment, so each datum reflects the effect of all sessions attended by a client. The number of post-treatment outcomes assessments is typically very limited. The first feature of our modeling approach relaxes the assumption of independent random effects in the standard multiple membership model by employing conditional autoregression (CAR) to model correlation in random therapy group session effects associated with clients' attendance of common group therapy sessions. A second feature specifies a longitudinal growth model under which the posterior distribution of client-specific random effects, or growth parameters, is modeled non-parametrically. The Dirichlet process prior helps to overcome limitations of standard parametric growth models given limited numbers of longitudinal assessments. We motivate and illustrate our approach with a data set from a study of group cognitive behavioral therapy to reduce depressive symptoms among residential AOD treatment clients.
    Journal of the Royal Statistical Society Series A (Statistics in Society) 06/2013; 176(3). DOI:10.1111/j.1467-985X.2012.12002.x