CHAPTER 9 - BIAS
9-1
DATA COLLECTION BIAS
WHAT IS BIAS?
Data Collection Bias is a small, relatively consistent error which may be introduced when
taking measurements on a Dipstick. Bias will have no effect at all on Roughness Indices
like IRI, but will have a very small effect on the slope of the profile, and a larger effect on
point elevations, particularly on long runs. The error is manifest in the form of a very
small tilt upwards of the profile – approximately 1/1000 inch per foot, or about 0.0047°.
This error can be introduced several different ways – by the texture of the pavement, by the
dirt and dust on the pavement, and to a much smaller degree, from very minor variations in
the electronic circuitry of each Dipstick.
Data Collection Bias is measured in inches (or mm) per step and entered in 5 place
decimals (.00000”). Data Collection Bias can vary from near zero to over .003” or more
per reading, and is always positive.
The source of bias cannot easily be distinctly separated and measured. However, the total
data collection bias is automatically removed by “Unboxing” any data collected in
“boxes,” and can easily be measured and compensated for in other cases by performing a
bias check and selecting "Apply to all Runs".
It is not always necessary to measure and compensate for data collection bias. If the sole
purpose of measurement is to calculate IRI, correcting for Data Collection Bias is rarely
needed. However, if the purpose of measurement is to establish exact point elevations, or
to produce high-quality longitudinal profiles, it is prudent to collect the data in “boxes” and
let the program correct for bias when “UNBOXing”. For greatest accuracy, box runs
should be terminated and restarted whenever surface texture changes. For example, if you
were measuring an asphalt highway and came to a concrete bridge, it would be prudent to
terminate one “Box” at the point where the highway transitions to concrete, and restart a
new “Box” at this point.
NOTE: When data is collected in “boxes” and “unboxed” prior to analysis, the Data
Collection bias is automatically removed and no other action is required of the user.
Otherwise, a single “Bias Run” may be collected and the resulting Bias applied to all Runs
collected on a road with the same texture and amount of dust/dirt.
Surface Roughness Bias and Bias from Dust or Grit
These are by far the largest two sources of Bias. Although Surface Roughness Bias and
Dirt or Dust Bias are the largest contributors to Data Collection Bias, it is practically
impossible to separate these two sources.
The very small amount of bias inherent in using the swivel or “Moon” feet results from when the
rough sandpaper pads on the bottom surface of the swivel foot come in contact with a rough or
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