-
Line width can be set by using STYLE sheets.
So for example by making an internal STYLE sheet
we can use "width:50%" to change to a half width text line.
See this example of the results.
<HEAD>
<TITLE>HCI Project 2 Line Length Example</TITLE>
<STYLE>
P.quarter {width:25%}
P.half {width:50%}
P.full {width:100%}
</STYLE>
</HEAD>
and use <P CLASS=half>the text lines</P>
for example.
- You need enough samples (i.e., variable
values) for each subject, with enough of a change each time, to
be able to produce a noticable effect in your performance
metrics, and have enough data points to provide credible
statistical analysis, but not so many that each subject takes
forever to complete the experiment. It's a tradeoff you need to
make and explain.
-
If you use "time" you'll need to find some way to normalize across
tasks. i.e., in order to be comparable, every reading task might need to
have the same number of words. Using Speed helps with this
problem.
-
In order to test "comprehension" you'll need to administer a
simple test after each reading task. If you use exactly the same text
for every reading task in your experiment (i.e., for every
variation of your variable) then you will get a Learning Effect,
and comprehension should improve over time. However, if you use very
different texts for each task then you run the risk of that
factor causing the change in the performance measure, rather than
the one you are changing.
i.e., the text can't be the same, or too different.
You need to find a way to deal with this problem.
- In order to get statistically significant
results in an experiment that makes detailed measurements you'd
probably need at least 30 subjects. I don't expect you to go to
those lengths: but only 3-4 subjects isn't enough. However, if
instead this was a usability test that observes subjects
interacting with an interface, using as few as 5 participants
should give a reasonable evaluation of the main strengths and
weaknesses of the interface.
- The use of Simple Linear Regression Analysis may not be
appropriate as the relationship may not be linear, but it is used here
as a first approximation to more detailed analysis.
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