Cross-Sectional Cancer Data Analysis
Budget: $30 – $250 NZD
I have a raw, row-structured dataset that will form the backbone of a cross-sectional study aimed at improving cancer prevention strategies. Before I can start writing, the numbers need to speak clearly. Using SPSS, I need a full analytical workflow that covers descriptive statistics, bivariate exploration, and a solid multivariable model.
Key study focuses
• Incidence rates of cancer
• Risk factors
• Preventive measures
Here’s what I expect:
1. Cleaned and well-documented SPSS file (variable labels, value labels, missing-data handling).
2. Descriptive summaries for all variables plus clear visualisations where they add value.
3. Bivariate tests (e.g., χ², t-tests, or non-parametric equivalents) linking the three focus areas to relevant demographics or exposures.
4. A multivariable model—logistic or linear as appropriate—with diagnostics and an easy-to-read table of adjusted estimates.
5. A concise interpretation of each result section that can drop straight into the Methods and Results of my manuscript.
If you’re comfortable navigating SPSS syntax, verifying statistical assumptions, and translating findings into plain language suitable for publication, I’d love to collaborate and move this project forward quickly.
Key study focuses
• Incidence rates of cancer
• Risk factors
• Preventive measures
Here’s what I expect:
1. Cleaned and well-documented SPSS file (variable labels, value labels, missing-data handling).
2. Descriptive summaries for all variables plus clear visualisations where they add value.
3. Bivariate tests (e.g., χ², t-tests, or non-parametric equivalents) linking the three focus areas to relevant demographics or exposures.
4. A multivariable model—logistic or linear as appropriate—with diagnostics and an easy-to-read table of adjusted estimates.
5. A concise interpretation of each result section that can drop straight into the Methods and Results of my manuscript.
If you’re comfortable navigating SPSS syntax, verifying statistical assumptions, and translating findings into plain language suitable for publication, I’d love to collaborate and move this project forward quickly.