Patterns of evidence use in Nigerian policymaking: insights from latent class analysis

Toyib Aremu et al.

Policy Science2026https://doi.org/10.1007/s11077-026-09600-4article
AJG 1ABDC B
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0.50

What the paper says

This article empirically develops a typology of evidence users in Nigeria. While evidence informed policymaking promises more effective decision making and preventing policy failures, our understanding of policymakers’ profile with respect to preferred evidence types remains fragmented. This knowledge can better influence strategies to address gaps between research and policy. Drawing on a recent original survey of 196 senior policy officers across 13 federal ministries and both chambers of Nigeria’s legislative arm, we applied latent class analysis to 11 evidence-use indicators which produced three distinct user profiles: non-users (26%) characterized by minimal familiarity or use across evidence types, non-systematic users (54%) dominated by those with familiarity but limited active use across types of evidence, and eclectic users (20%) defined by high familiarity and regular use of both systematic and non-systematic evidence. Multinomial logistic regression shows that having a doctorate is linked to being more likely in the non-systematic or eclectic user groups, while each year of experience increases the chance of being in the non-systematic group. These findings reveal systematic variation in evidence engagement among policy analysts and legislative assistants and underscore the role of advanced training and experience in shaping policy analytical capacity.

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https://doi.org/https://doi.org/10.1007/s11077-026-09600-4

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@article{toyib2026,
  title        = {{Patterns of evidence use in Nigerian policymaking: insights from latent class analysis}},
  author       = {Toyib Aremu et al.},
  journal      = {Policy Science},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s11077-026-09600-4},
}

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0.50

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F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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