Health Sciences
Intersectionality
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The utility of quantitative methods for intersectionality: a real world example using the Born in Bradford’s Better Start (BiBBS) cohort dataset
Project aims
This project was a PhD study, completed in December 2023. The aim of the PhD was to explore methods to enhance knowledge about the engagement of a diverse and deprived population with early years interventions.
Project overview
The study explored different methods of analysing quantitative data, to see how good they were at capturing how combinations of sociodemographic characteristics related to whether a mother participated in a range of free pregnancy and early years interventions.
These analyses were used as a case study to explore if the methods were effective for carrying out quantitative analysis within the intersectionality framework. The aim was not to critique intersectionality, but to acknowledge that it is becoming more widely used in health and care research and so researchers need to understand which are the ‘right tools for the job’. The study identified the strengths and limitations of the methods when applied in practice to the Born in Bradford’s Better Start (BiBBS) dataset, which acted as a case study.
Research inclusion: women, children and families
Born in Bradford’s Better Start (BiBBS): The BiBBS experimental birth cohort was set up to evaluate the effectiveness of Better Start Bradford (BSB), an initiative delivering free pregnancy and early years interventions to women and their families in three areas of Bradford between 2015-2025. The programme aimed to improve outcomes and change the future for children and families living in these areas, through interventions focusing on diet and nutrition, social and emotional development, and speech, language and communication.
Quantitative methods used: A baseline logistic regression analysis was compared to a range of approaches: regression with interactions, latent class analysis (LCA) followed by regressions, intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA), decision trees and qualitative comparative analysis (QCA). In each, a range of key sociodemographic variables was explored to see what each analysis would uncover about their relationship with mothers’ participation in the BSB interventions.
Key findings
Combinations of migrant status, ethnicity, and English language ability were related to differences in participation in the BSB interventions. Findings underlined the importance of exploring experiences along multiple axes; analysing each of these features individually did not allow for the same level of nuance or understanding.
Each method had strengths and limitations when applied in practice, and none of the methods was judged to be the single ‘best’ approach to quantitative intersectionality, though I-MAIHDA was the most effective method for describing how participation differed across sub-groups of women in the data. LCA was valuable in encouraging a more nuanced understanding of the study population and challenging assumptions such as what it may mean to be a part of a community that is labelled as ‘deprived’.
Funding
The PhD was funded by the National Institute for Health Research (NIHR) Yorkshire and Humber Applied Research Collaborative (ARC) within the Minority Ethnic Health and Inequalities workstream of the Early Life Prevention theme.
Associated work and further information:
The full thesis is available at White Rose eTheses Online
A paper expanding on the I-MAIHDA analysis was published in 2024 and can be viewed online (open access):