Breakout sessions for both R and Stata users show how to use panel data from IPUMS PMA to model contraceptive adoption and discontinuation during the first year of the pandemic.
A new feature makes it easy to locate facilities surveyed in multiple rounds of SDP data collection. Plus, new Client Exit Interview data are now available!
PMA panel members report their contraceptive use, reasons for discontinuation, and pregnancy status for each month leading up to the Female Questionnaire. Here's how to get up and running with your first Time-to-Event analysis.
Heatmaps and alluvial plots make it easy to compare key family planning indicators over time and across multiple populations sampled by PMA panel surveys.
We show how to reproduce measures featured in PMA panel reports, and how to visualize significant differences between groups in figures built with ggplot2.
Your guide to inclusion criteria, loss to follow-up, and key technical variables for Family Planning panel data from IPUMS PMA.
Getting started with new "long" and "wide" data formats in R.
This month's data release includes Phase 2 panel data from 6 samples, plus Phase 1 panel data from 3 new countries.
We use multilevel models to examine factors related to women's satisfaction with family planning care received from the service delivery facilities in Kenya.
Service quality questions are included in both Client Exit Interview and Family Planning surveys from PMA. We show how to match similar questions across surveys and compare the results with faceted graphics.
SDP surveys include data on the availability, cost, and demand for up to 13 family planning methods at each facility. Use one generic formula to explore independent models for client counseling on each method.
Conflict data provides important context for access to family planning services.
Women receiving family planning services assess their care in a new data series from PMA.
Where to find example code for all of the key concepts we've covered so far.
We've learned how to build key indicators with spatially referenced nutrition data from PMA. Now let's see how researchers have used them.
Remotely sensed daily precipitation data is an incredible resource for understanding rainfed food systems.
Use WHO guidelines to calculate Minimum Dietary Diversity (MDD), Minimum Meal Frequency (MMF), and Minimum Acceptable Diet (MAD).
It's now much easier to perform row-wise operations with pre-grouped data!
Nutrition surveys are available for women, young children, and health service providers in their area.
The PMA COVID-19 survey is part of a broader panel study. We discuss how to merge it with the baseline survey, and how to specify sample weights and cluster information with the new svyVGAM package.
Explore IPUMS PMA data with R right in your browser, or download Stata code for offline practice
You can compare levels of trust and efficacy for 13 information sources in 4 countries with data from the new PMA COVID-19 survey. Let's talk data visualization options.
A guide to bar charts for Likert-type psychometric scales built with ggplot2.
Showcasing the gtsummary package for sample descriptive statistics, weighted population estimates, and model summary output.
A new panel study promises insights into the impact of COVID-19 on family planning and reproductive health.
How to visualize patterns in migration data using alluvial plots, line plots, and density plots.
R and Stata code with video from an event held at the Population Association of America 2021 Annual Meeting.
Five outstanding undergraduate research projects integrate dynamic data visualization with spatial analysis and narrative.
Use tidyr::pivot_longer to reshape wide data into a long format.
Summary and source code from a recent article using data from Ethiopia.
Get details on new variables related to labor & delivery services, antenatal care, vaccinations, facility shipment schedules, and more!
Analyzing women's contraceptive use while considering service delivery point and spatial contextual factors.
How to integrate external spatial data with PMA data.
Map spatial variation in the service delivery environment across enumeration areas.
Create aggregate measures for women living the areas served by SDPs
Use dplyr::across to summarize variables with a similar naming pattern.
SDP samples are not nationally representative. Learn how to use them to describe the health service environment experienced by individuals.
How to download an IPUMS PMA data extract and start using it in R
How to download R for free and install some of the R packages used on this blog
If you see mistakes or want to suggest changes, please create an issue on the source repository.
Developed by IPUMS at the University of Minnesota
Licensed under the Mozilla Public License Version 2.0
Site built with Distill for R Markdown