This report will examine the approach taken by the British Study (BES). The report begins by placing the BES in its historical, social and political context and the specific approach it takes to achieve its stated aims. Next, the report focuses on how the BES gathers its data and why this approach has been taken. Lastly, the paper demonstrates that this approach ensures that the large election samples gathered are representative of the wider population, which allows the study to critique other narratives and provide a detailed study into the social phenomena of general elections.
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The British Election Study is a set of ‘nationally representative probability sample surveys’ conducted immediately after every general election. The BES began collecting data in 1964 with the aim to provide an objective analysis of the changing determinants behind election results across Britain, such as major shifts in voter preference, declining party membership, attitudes towards politics and explanations of electoral outcomes (BES, 2017). Public interest in elections has grown significantly since 1964 with elections now generating 24-hour news cycles with large viewing figures centred around the discourse of the national democratic process (Lamond & Reid, 2018). The BES state that the media benefit from there ‘non-partisan analysis and interpretation’ of a ‘high-quality data source’ and that journalists enhance public understanding of the data via there informed commentary (BES, 2017). Politicians also benefit from the study due to their increased understanding of voter attitudes towards policy and reasons for voter choice (UMAN, 2018). When politicians are elected as members of parliaments (MPs), they are bound (in constitutional theory) to represent those in their constituencies, meaning they have the power to directly affect the lives of the citizenry through voting in new laws or raising issues that affect their constituents; sometimes resulting in significant social, cultural and economic shifts for large parts of the electorate (Reeve & Ware, 2016).
Given the complexity of the phenomena being researched, the quantitative approach taken by the BES is the most appropriate, as a large-scale quantitative approach not only describes the outcome of the elections but also enable the BES to provide a credible insight into the more complex phenomena of changing patterns to the electorates attitudes and behaviours (Kent, 2015) (BES, 2017). The BESs systematic capture, order, count, rank and calibration of socio-political characteristics of samples of the population, is exemplified in the BES cross-sectional and longitudinal panel research, which uses waves of data to study any changes to electoral data or voter preference (BES, 2017). Ruspini (2002) refers to such longitudinal approaches as prospective and retrospective surveys: in this case, pre-and post-election waves measuring partisan attachment to particular political parties (Sanders, et al., 2007). This consistency of questioning allows the BES to offer an explanation of causal links between variables of survey data and provides a strong theoretical grounding for robust findings (Sanders, et al., 2007). In a climate of cuts to public service and the need for legitimate evidence bases, longitudinal data measurement is a tool that can be used to ensure that government policies are efficient and popular – particularly when measuring how much money has been spent to achieve specific political goals (ACSS, 2018). There is also the benefit of what Ruspini (2002, p.24) refers to as the ‘sleeper effect’, which measures the connection between events and social transitions that are separated in time, e.g., the same experience followed by different adaptations can potentially lead to different trajectories (the same idea becoming more persuasive over time). This is reflected in the BES longitudinal data, due to the advantage of being able to measure the evolution of political preferences and electoral behaviours over differing periods of time (Scarbrough, 2000) (BES, 2017).
Despite these positive, the BESs need for public and professional recognition is not a circumstance detached from socio-economic context and evolving political conditions (Johnson, 1989). Dilnot & Blastland (2007, p.106) remind us that numbers are ‘pure’ but ‘counting never is’, and in order for the BES to maintain its standing as a major political data resource to inform political and media discourse, it must maintain an orthodoxy of methodological conservatism that enables ease of use by the public relations industry and popular media outlets (Scarbrough, 2000) (Dunleavy, 1979). This does not change the BESs questions and stated aims but may affect the way the data is counted, interpreted and analysed. If this context is ignored, the outcomes presented may not be any more than numbers creating an illusion of what is being measured (Dilnot & Blastland, 2007).
Scarbrough (2000) argues that the BES would largely accept this criticism, as much of these problems arise from the limitations inherent to quantitative and longitudinal based methodologies. These limitations include: the longitudinal constraints of maintaining capability over long periods of time, the quantitative problem of remaining free from publication or media bias; and the ability to clearly communicate uncertainties associated with the results of the survey (Jerrim & de Vries, 2015).
The BES gathers pre-election and post-election data using representative national in-person and internet surveys. The post-election cross section surveys are accompanied by panel studies (Johnson, et al., 2005) (Sanders, et al., 2007). All the surveys are based on representative probability sampling in order to accurately reflect the wider population, although not everybody in Britain has an equal chance of being selected (Scarbrough, 2000). In the context of the Scottish referendum in 2014, the 2015 Pre-Election study included an enhancement funded under the Future of UK and Scotland initiative, which added 55,000 additional interviews to the study. This resulted in in a deliberate oversampling of Scottish constituents in order to achieve a Scottish boost to the samples (BES, 2017) (Johnson, et al., 2005). Jerrim & de Vries (2015) suggest that this could result in one type of uncertainty (sampling variation) taking precedence over another – meaning that one finding could be observed by chance due to the fact that they have data from random individuals rather than the whole British Electorate (Gorard, 2010). This limitation is acknowledged in the BES online literature, and a strong focus is given to the importance of weighting the data in a way which takes into account the over-sampling of Scottish respondents in their online surveys (BES, 2016). This type of design enables the BES to capture relevant variables that determine how different parts of the electorate vote, it’s also used to facilitate modelling effects of the contexts in which these changes occur once the election has taken place (Whiteley, 2013) (Sanders, et al., 2007). The electorate are asked fixed questions for both the pre-and post-election survey’s which helps measure the BESs objective of measuring political preferences and electoral behaviours over time (Whiteley, 2013) (ACSS, 2018).
Despite this casual explanation of the outcomes of elections offered by the BES, ‘measurement is not passive; it influences what is measured’ (Dilnot & Blastland, 2007, p 106). The BESs fixed questions reflect the context of the mid-1960s, which is problematic due to the participants personal and professional lives being subject to socio-political, cultural and economic changes that may not have been present at the beginning of the study (Scarbrough, 2000). There is also the issue of physiological change to the sample size known as ‘attrition’ which has been cited in the BES technical reports (Johnson, et al., 2005) (Ruspini, 2002). Attrition occurs when individuals leave the study due to a death, incapacity or just a refusal to participate. This results in a cumulative thinning of the data which is not random, as those who started the study are not the same as those who finish, resulting in a potential bias to the longitudinal data (Ruspini, 2002). As with many surveys, the BES uses a grossing up weight to compensate for factors that can result in a particular group being over-represented. This approach makes it easier to describe what is being measured by the electorate and the prevalence of any changing social phenomena (UK Data Service, 2014) (BES, 2017). The weight for the pre-election study was designed to fit population estimates and included factors designed to address unequal selection probabilities which arose due to the deliberate oversampling of Scotland, Wales, marginal constituencies in England; and the selection of one person per house (giving small households and single dwellings a higher selection probability) (Johnson, et al., 2005) (Sanders, et al., 2007). 4,161 respondents were interviewed during the BES 2005 pre-election survey, but only 3,226 (77.5 per cent) on the post-election survey (Johnson, et al., 2005) . In an attempt to reduce attrition, the BES used a top-up sample of fresh respondents in the post-election survey to maintain the sample size and reduce the potential of bias. The weighting strategy of the post-election survey was weighted to correct for unequal selection probabilities, non-response bias between waves, and calibration weighting to reflect a sample of the population estimates (Johnson, et al., 2005) (Sanders, et al., 2007). The population average is likely to be very close to the sample size due the size of the survey (Albers, 2017), however there will always be some level of sampling error within the data as it would not be practicable or realistic to survey more than a sample of the population (Dilnot & Blastland, 2007).
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One key finding from the BES longitudinal approach helped highlight a relative increase in the popularity of the Labour Party leader, Jeremy Corbyn, during the 2017 general election (BES, 2017). The BESs pre-election survey reported that the Conservatives, led by Theresa May, had a lead over Labour of around 27 to 41 percent (BES, 2017). A specific question that the electorate was asked was who would make the more plausible ‘leader’: Theresa May had an average lead of 1.3 percent (4.8 percent), Jeremy Corybyn had an average score of 3.5 percent (BES, 2017). The 2017 general election was appropriately named by sections of the media as the ‘Battle of Brexit’ (Helm, 2017), alluding that the 2017 election outcome had already been decided in the 2016 vote to leave the European union (Anushka & Walker, 2017). The BES quantitative approach gave a different explanation regarding the electorates preferences and behavers, as the post-election data demonstrated that the Labour Party had won over 54 percent of electorate from other political parties – compared to 19 percent for the Conservative Party. The Labour Party had also won 50 percent of voters who had not registered a preference before the campaign (BES, 2017). This data suggests that the reason that the Labour Party had gained so much support was due to the ‘strong leadership’ demonstrated by Jeremy Corbyn (Prosser, et al., 2018) – a narrative that was at odds with the nation’s popular broadcast and print media during at the and during the election (Cammaerts, et al., 2016).
Another example of the BES data being at odds with broadcast and print media, was some of the media’s subtle realignment of their acceptance of Corbyns success; due to the popular narrative that his strong performance was only due to a surge in youth turnout (between 18 and 24) – what commonly became known as a the youthqauke (Prosser, et al., 2018). This suggests that Labours strong performance was entirely due to young voters mobilised by Corbyns promise to reduce tuition fees or in opposition to Brexit (Agerholm, 2017) (BES, 2017). Parts of the media claimed that the youth turnout was as high as 72 percent (no evidence was provided for this claim) (Bond & Robson, 2017) (Prosser, et al., 2018) . This is problematic as people aged 18 to 24 only make 11 percent (5.2 million) of the electorate, and only one third of this demographic attend University (BES, 2017). According to the BES (2017) data, the Labour party won 3.5 million more votes than they did in 2015, and there were around 1.2 million more young voters taking part in the 2017 election. Even with the assumption that every single 18 to 24-year-old voted for Labour this would still only account for around one third of Labours gains in 2017 (BES, 2017) (Prosser, et al., 2018). Despite what appears to be some of the popular media attempting to use ‘force to squeeze reality into boxes that don’t fit’ Blastland and Dilnot (t. p22). The BES quantitative longitudinal approach has not only successful in describing the social context of the 2017 election, but accounted for the patterns and reasons for the change within the British electorates voting preferences (Prosser, et al., 2018).
This report has examined the approach taken by the BES and demonstrated the benefits of conducting a quantitative longitudinal approach to investigate social issues; arguing that the data generated has been largely successful in analysing and understanding British voting behaviour. It has highlighted the importance of understanding the limitations of this approach whilst analysing the data but has demonstrated the strengths of the longitudinal approach in measuring social patterns over time. The BES approach to sampling and weighting ensures that the large pre-election and post-election samples are representative of the wider population and this allows them to successfully critique media narratives due to their analyses of trends over time and focus on specific variables relevance to the British electorates behaviour. These components enable to BES to provide a persuasive detailed study into the social phenomena relative to General elections.
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