Imprecision in respondent recall can cause response heaping in frequency data for particular
values (e.g., 5, 10, 15). In human dimensions research, heaping can occur for
variables such as days of participation (e.g., hunting, fishing), animals/fish harvested,
or money spent on licenses. Distributions with heaps can bias population estimates
because the means and totals can be inflated or deflated. Because bias can result in
poor management decisions, determining if the bias is large enough to matter is important.
This note introduces the logic and flow of a deheaping program that estimates
bias in means and totals when people use approximate responses (i.e., prototypes). The
program can make estimates even when spikes occur due to bag limits. The program is
available online, and smooths heaps at multiples of 5 (numbers ending in 5 and 0) and
7 (e.g., 7, 14, 21), and produces standard deviations in estimates