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320 CHAPTER 11 Analyzing qualitative data

11.4.3.3 Subjective versus objective coders
You should be aware of the advantages and disadvantages of using subjective or
objective coders and their impact on coding reliability. When the coders are the same
people who developed the coding scheme, and in many cases they also design the
study and collect the data, they are called subjective or inside coders. When the cod-
ers are not involved in the design of the study, the data collection, or the development
of the coding scheme, they are called objective or outside coders.
There are pros and cons of both approaches. Because subjective coders are usu-
ally the researchers themselves, they know the literature well and have substantial
knowledge and expertise in the related topic. That knowledge and specialty can help
them understand the terms and concepts provided by participants and detect the un-
derlying themes in the text. They also require minimal training since they developed
the coding scheme themselves. However, the fact that they have already worked so
closely with the data becomes a disadvantage during the actual coding. The pre-
acquired knowledge may constrain their abilities to think beyond the established
concepts in their mind. Sometimes they may form hidden meanings of the coding
without being aware of it. The consequence is that the reliability reported by subjec-
tive coders may be inflated (Krippendorff, 1980).
On the contrary, objective coders usually do not have preacquired knowledge of
the subject and, therefore, may be more open to potential instances in the data. The
reliability reported by objective coders is less likely to be inflated. However, their
lack of domain knowledge and expertise may also hinder their ability to accurately
understand the data and detect interesting instances. In addition, objective coders
usually need a substantial amount of training and the entire process can be very
costly.
In practice, it is very common for studies to use subjective coders for content
analysis and this approach is usually considered acceptable as long as the appropriate
procedure is followed and reported, along with the reliability measures.

11.5 ANALYZING MULTIMEDIA CONTENT

Multimedia data has become prevalent in our daily life thanks to the rapid ad-
vances in affordable portable electronic devices and storage technologies.
Researchers can collect a large quantity of image, audio, and video data at fairly
low cost. Multimedia information such as screen shots, cursor movement tracks,
facial expressions, gestures, pictures, sound, and videos provide researchers
an amazingly rich pool of data to study how users interact with computers or
com puter-related devices.
Multimedia information also presents substantial challenges for data analysis.
In order to find interesting patterns in the interactions, the image, audio, and video
data need to be coded for specific instances (i.e., a specific gesture, event, or sound).
Without the support of automated tools, the researcher would have to manually go
through hours of audio or video recordings to identify and code the instances of