| Abstract | BBC Radio 4 comedy is heard by over five million people every week in the UK. Selecting which comedy programmes are to be broadcast on the station is mainly the duty of a single person: Radio 4’s Commissioning Editor for Comedy. She faces a delicate task, since her personal choices will affect the listening experience of millions of people. A poor selection will potentially result in a waste of licence payers’ money, can have an effect on the perception of the station as a whole, and may even enrage the listeners. The BBC currently generates ‘objective’ numerical evaluations of audience responses in order to aid the commissioning decision-making process, but are the resultant figures useful? This thesis attempts to answer that question by investigating the suitability of this data in determining the quality of Radio 4 comedy programmes, using analysis of over 650,000 responses.
The key measure of objective audience evaluation of BBC broadcast programmes is the Appreciation Index (AI ), a weighted mean value derived from programme appreciation ratings on a 10-point scale from a panel of respondents. This metric is also published as an aggregate station-level score on a quarterly basis as a station performance indicator, used as a ‘meta score for channel quality’.
For BBC radio, there is no other recognised objective measure of programme performance that allows episode-level evaluation; the industry standard, RAJAR, does not allow sufficient granularity for audience size information at this level of detail. Thus BBC radio (in comparison to BBC television) has a particular need for the AI to provide a meaningful figure to facilitate programme evaluation.
Strict statistical theory dictates, however, that calculating a mean score using data taken from subjective ratings may not give useful results, particularly if the data spread is not unimodally distributed nor taken from a non-interval level scale. Given evidence that comedy is a divisive genre (and indeed as a constituent genre of Radio 4’s broadcasting there is desirability for it to be so), comedy may be particularly poorly represented by AI scores. Indeed, the analysis of the data shows that that the responses are not distributed in a fashion that allows the mean to be a useful measure of central tendency. Not only are the aggregate responses not spread in a unimodal distribution, individual respondents have been found to adopt patterns (types) of responses that differ from the topline distribution.
Academic investigations relating to programme appreciation ratings have been relatively scarce, partly due to the limited number of broadcasters that measure this aspect of audience research, as well as the tendency for those that do to refrain from disseminating the data. Where studies have actually been published, researchers have not addressed the issue of the shape of the data’s distribution nor the nature of its scale. This work’s original contribution to knowledge considers these aspects specifically and does so for radio comedy rather than for the more typically utilised television. |
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