In Simple Words
Imagine testing whether an accounting class helps people manage money by asking them to estimate their monthly spending at the end of thirty days. To double-check if their end-of-month estimates are accurate or just wild guesses, a researcher checks in with them every single evening to log every penny spent that day, comparing the daily total against the monthly memory estimate.
Same Ghana tailors measured two ways
- Frequent visits
- Observed financial reports
- Monthly recall
- Similar average financial reports
Same tailors and month, measured two ways. Descriptive similarity; no formal paired-mean equality test or proof of absence of reporting bias.
See source →Research question
Evaluating business training interventions relies heavily on self-reported financial data, creating potential reporting bias if trained entrepreneurs record figures more accurately than control groups. High-frequency daily surveying is necessary to verify whether monthly recall data accurately reflects true microenterprise income without reporting distortions.
Participants
N = 150 tailors in Accra, Ghana.
Study Design
Comparative methodological evaluation tracking tailors every other day for one month (Round 4, averaging 23.9 days per tailor) versus standard 30-day monthly recall surveys (Round 5).
Results
Daily visits averaged 8.11 GH¢ cash in and 3.32 GH¢ cash out. Comparing monthly recall to aggregated daily data showed no statistically significant difference in revenue (p-value > 0.10, difference of -4.08 GH¢) or expenses (difference of 8.71 GH¢), proving that monthly recall surveys do not introduce strategic reporting bias in consulting evaluations, though daily data exhibited higher variance for record-keepers (squared difference 22,895 GH¢, p < 0.05).
Takeaway
Utilize standard monthly recall surveys for microenterprise financial evaluations, supplementing with high-frequency daily audits on a sub-sample to benchmark measurement variance.
Source and citation
See source →Read alongside the research
Plain-English glossary
Original explanations, informed by the references below. Examples are illustrations, not data from the studies above. The research text stays unchanged.
- Statistically significant
A label used when a result passes a chosen statistical threshold, often p < 0.05. It does not show how large or useful an effect is. A result that is not statistically significant does not prove there is no effect.
ExampleA study reports p = 0.03 against a 0.05 threshold. It passes that test, but the effect could still be too small to matter.
American Statistical Association · Statement on p-values ↗Read more about statistically significant →