Measuring and Correcting Response Heaping Arising From the Use of Prototypes

Abstract

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.

Detecting, estimating, and correcting for biases in harvest data

“…In general, bias is reduced among more memorable events (Sudman et al ) and harvest data for large animals (Chu et al , Beaman et al a ), and hunting male moose (525–725 kg) in Alaska usually involves complex coordination and multiple hunters. Also, we suspect that heaping bias of estimated effort was not as large as initially expected because there are many permits in which moose hunters reported the days around the peak (e.g., shouldering; see Beaman et al ). The reason for the shouldering is unknown and could be addressed in future research.…”

“…We observed artificial peaks in hunting effort due to heaping, similar to observations made by others in wildlife and leisure research (Vaske et al , ; Beaman et al a ). However, with the use of a statistical program developed for heaping (Beaman et al ), we determined the bias in heaping was minimal. In general, bias is reduced among more memorable events (Sudman et al ) and harvest data for large animals (Chu et al , Beaman et al a ), and hunting male moose (525–725 kg) in Alaska usually involves complex coordination and multiple hunters.…”

“…Details of the heaping procedure and program used in SAS are available, with extensive documentation online (http://auctorresearch.net/deheap/). Beaman et al () presented logic behind the deheaping program used in this research.…”

Number Preference as a Source of Measurement Error in the U.S. National Forest Inventory

“…NP, sometimes referred to as digit preference [19], leads to an excessive grouping together, or heaps, of observations at specific values. For example, consider a survey asking respondents how many days they spent vacationing last year, with responses showing an unusually high frequency at 14 days.…”