Categorical Classification: Either mutually exclusive coding
where coders will select the single best classification for the unit
of analysis, or non-exclusive coding where coders will select all
categories relevant to the unit of analysis
Dimension / Affect Coding: Coders will score each unit of
analysis on a relevant dimension (scale). Scores are specific to the
dimensions on which they are evaluated. Any numerical value can be
used to establish the affect scale, but it is essential to carefully
define what each discrete score represents. For example, on an
affect scale {
,
, 0, 1, 2} coders should be provided with
specific examples of what constitutes a
as opposed to a
score, and so on. Coders should be given a single dimension, a
choice of dimensions, or multiple dimensions on which to code each
document. Each dimension should be a single, exhaustive scale
containing all relevant positions. Some examples of dimensions:
``Economic liberalism / conservatism,'' ``Leadership ability,''
``Hawk/Dove,'' etc. In single dimension coding, coders are only
given one dimension on which to code, so each unit of analysis will
be coded on that dimension or coded as irrelevant. In multiple
dimension coding, coders are given a list of dimensions, and code
the unit of analysis on all relevant dimensions.
Develop coding infrastructure: Consider whether hand coding done
with computer assistance would be helpful, or whether keeping track
in a spreadsheet or on paper is sufficient. Commercial options
include programs like Atlas.ti, Nud*ist, Xsight, or EZ-text. Some
projects develop their own computer programs for collecting coding
data. The selection of a coding interface can have a dramatic effect
on the results. For example, see discussion in Kwon, Shulman & Hovy (2006).