Subscales in the National Student Survey (NSS): Some considerations on their structure

Pollet, Thomas V.; Shepherd, Lee

doi:10.1080/0309877x.2022.2060069

Cited by 2 publications

(2 citation statements)

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“…Outliers show that some institutions are under and overperforming in the UK sector in terms of levels of dissatisfaction, representing many students who have invested in the experience. It is acknowledged that our findings are derived from the original UK NSS survey instrument (see Richardson, 2005 ), and we cannot account for more recent turbulence in the outcomes of the new survey instrument or impacts on outcomes of the new survey dimensions (see Pollett & Shepherd, 2022 ). We suggest that it is worth the risk here of perpetrating neoliberal ideologies by providing our anonymised table of institutional disagreement rankings.…”

Section: Discussionmentioning

confidence: 99%

“…The current study utilises the full lifetime of the original UK survey instrument to provide a longitudinal view of the metric outcomes, drawing on 12 years of returns to explore consistency and patterns over time. The revised instrument in 2017 was very similar in general terms but added new survey dimensions, including learning opportunities, learning communities, and the student voice (Pollet & Shepherd, 2022 ). The core components of the original survey instrument were retained, often with identical or very similar wording (Callender et al, 2014 ).…”

Section: Methodsmentioning

confidence: 99%

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Metrics of student dissatisfaction and disagreement: longitudinal explorations of a national survey instrument

Langan

Harris

2023

High Educ

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This study explores dissatisfaction and neutrality metrics from 12 years of a national-level undergraduate student survey. The notion of dissatisfaction is much less prevalent in the narratives surrounding student survey outcomes, and the underpinning metrics are seldom considered. This is despite an increasingly vociferous debate about ‘value for money’ of higher education and the positioning of students as consumers in a marketised sector. We used machine learning methods to explore over 2.7 million national survey outcomes from 154 institutions to describe year-on-year stability in the survey items that best predicted dissatisfaction and neutrality, together with their similarity to known metric predictors of satisfaction. The widely publicised annual increases in student ‘satisfaction’ are shown to be the result of complex reductions in the proportions of disagreement and neutrality across different survey dimensions. Due to the widespread use of survey metrics in university league tables, we create an anonymised, illustrative table to demonstrate how UK institutional rankings would have differed if dissatisfaction metrics had been the preferred focus for reporting. We conclude by debating the tensions of balancing the provision of valuable information about dissatisfaction, with perpetuating negative impacts that derive from this important subset of the survey population.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%