RESEARCH METHOD COHEN ok

240 INTERNET-BASED RESEARCH AND COMPUTER USAGE
245–6) also reports that greater variance in results
is likely in an Internet-based experiment than in a
conventional experiment due to technical matters
(e.g. network connection speed, computer speed,
multiple software running in parallel).
On the other hand, Reips (2002b: 247) also
reports that Internet-based experiments have
an attraction over laboratory and conventional
experiments:
They have greater generalizability because of
their wider sampling.
They demonstrate greater ecological validity as
typically they are conducted in settings that are
familiar to the participants and at times suitable
to the participant (‘the experiment comes to
the participant, not vice versa’), though, of
course, the obverse of this is that the researcher
has no control over the experimental setting
(Reips 2002b: 250).
They have a high degree of voluntariness,
such that more authentic behaviours can be
observed.
How correct these claims are is an empirical
matter. For example, the use of sophisticated
software packages (e.g. Java) can reduce
experimenter control as these packages may
interact with other programming languages.
Indeed Schwarz and Reips (2001) report that the
use of Javascript led to a 13 per cent higher
dropout rate in an experiment compared to an
identical experiment that did not use Javascript.
Further, multiple returns by a single participant
could confound reliability (discussed above in
connection with survey methods).
Reips (2002a, 2002b) provides a series of ‘dos’
and ‘don’ts’ in Internet experimenting. In terms of
‘dos’ he gives five main points:
Use dropout as a dependent variable.
Use dropout to detect motivational confounding
(i.e. to identify boredom and motivation
levels in experiments).
Place questions for personal information at
the beginning of the Internet study. Reips
(2002b) suggests that asking for personal
information may assist in keeping participants
in an experiment, and that this is part of the
‘high hurdle’ technique, where dropouts selfselect
out of the study, rather than dropping
out during the study.
Use techniques that help ensure quality in data
collection over the Internet (e.g. the ‘high
hurdle’ and ‘warm-up’ techniques discussed
earlier, subsampling to detect and ensure
consistency of results, using single passwords
to ensure data integrity, providing contact
information, reducing dropout).
Use Internet-based tools and services to
develop and announce your study (using
commercially produced software to ensure
that technical and presentational problems
are overcome). There are also web sites (e.g.
the American Psychological Society) that
announce experiments.
In terms of ‘don’ts’ Reips gives five main points:
Do not allow external access to unprotected
directories. This can violate ethical and
legal requirements, as it provides access to
confidential data. It also might allow the
participants to have access to the structure
of the experiment, thereby contaminating the
experiment.
Do not allow public display of confidential
participant data through URLs (uniform
resource locators, a problem if respondents
use the GET protocol, which is a way of
requesting an html page, whether or not one
uses query parameters), as this, again, violates
ethical codes.
Do not accidentally reveal the experiment’s
structure (as this could affect participant
behaviour). This might be done through
including the experiment’s details on a related
file or a file in the same directory.
Do not ignore the technical variance inherent
in the Internet (configuration details, browsers,
platforms, bandwidth and software might all
distort the experiment, as discussed above).
Do not bias results through improper use of
form elements, such as measurement errors,
where omitting particular categories (e.g.