Analysis: readiness and synthesis potential
This page assesses each dataset's readiness for secondary analysis and identifies where cross-dataset synthesis or meta-analysis may be feasible. Readiness scores reflect data quality, measurement detail, access routes and risk/protective factor coverage. Compatibility flags indicate potential for harmonisation across datasets. AHAH (an area-level index rather than a survey) is not scored.
How readiness is scored
Readiness is a 0-20 score based on eleven documented features: under-25 coverage, gambling measure coverage, named or validated gambling measures, public variable/question wording metadata, risk/protective factor breadth, validated risk/protective measures, longitudinal design, inequality variables, access route clarity, published research use and compatibility with other datasets. Risk/protective breadth and inequality coverage are read from the variables actually extracted for each dataset (for example area deprivation, income, ethnicity and disability), so compatible modules such as the Health Survey for England and the Scottish Health Survey score consistently.
What synthesis-ready means
Synthesis-ready datasets are those flagged A or B. They have named gambling measures that also appear in another dataset and enough overlapping risk/protective factor coverage to support harmonisation, pooled descriptive work, comparative analysis or meta-analysis after detailed checks of wording, scoring, age bands and survey timing.
Compatibility flags
- A: two or more shared named gambling measures and broad risk/protective factor overlap.
- B: at least one shared named gambling measure and moderate risk/protective factor overlap.
- C: gambling data are present, but synthesis potential needs more checking.
- D: limited current evidence for gambling-measure harmonisation.
Dataset readiness scores
Each dataset is scored 0–20 across eleven components reflecting how ready and useful it is for gambling-related secondary analysis. Higher scores indicate greater readiness for research use and cross-dataset synthesis.
Shared gambling measures across datasets
The matrix below shows which named gambling measures, such as PGSI, appear in each dataset. Columns are gambling measures and rows are datasets; repeated ticks identify potential harmonisation routes.
Measure overlap network
Datasets that share named gambling measures are connected. Thicker lines indicate more shared measures. Node size reflects the number of gambling variables in each dataset.
Risk/protective factor overlap
For datasets sharing gambling measures, this table shows which risk and protective factor domains are available in both, supporting the identification of cross-dataset research questions.