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We present a study of the dependencies of shear bias on simulation (input)and measured (output) parameters, noise, point-spread function anisotropy,pixel size, and the model bias coming from two different and independent galaxyshape estimators. We used simulated images from Galsim based on the GREAT3control-space-constant branch, and we measured shear bias from a model-fittingmethod (gFIT) and a moment-based method (Kaiser-Squires-Broadhurst). We showthe bias dependencies found on input and output parameters for both methods,and we identify the main dependencies and causes. Most of the results areconsistent between the two estimators, an interesting result given thedifferences of the methods. We also find important dependences on orientationand morphology properties such as flux, size, and ellipticity. We show thatnoise and pixelization play an important role in the bias dependencies on theoutput properties and galaxy orientation. We show some examples of model biasthat produce a bias dependence on the Sersic index n as well as a differentshear bias between galaxies consisting of a single Sersic profile and galaxieswith a disc and a bulge. We also see an important coupling between severalproperties on the bias dependences. Because of this, we need to study severalmeasured properties simultaneously in order to properly understand the natureof shear bias. This paper serves as a first step towards a companion paper thatdescribes a machine learning approach to modelling shear bias as a complexfunction of many observed properties.

Shear measurement bias I: dependencies on methods, simulation parameters and measured parameters