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Part I: Data Analysis and Model-Building Basics
Here are some tips for analyzing data and interpreting the results, in terms of
the statistical procedures and techniques that you may use — at school, in
your job, and in everyday life. These tips are implemented and reinforced
throughout this book:
Be sure that the research question being asked is clear and definitive.
Some researchers don’t want to be pinned down on any particular set of
questions because they have the intent of mining the data (looking for
any relationship they can find, and then stating their results after the
fact). This can lead to overanalyzing the data, making the results subject
to skepticism by statisticians.
Double-check that you clearly understand the type of data being col-
lected. Is the data qualitative or quantitative? The type of data used
drives the approach that you take in the analysis.
Make sure that the statistical technique you use is designed to answer
the research question. If you want to make comparisons between two
groups and your data is quantitative, use a hypothesis test for two
means. If you want to compare five groups, use analysis of variance
(ANOVA). You can use this book as a resource to help you determine
the technique you need.
Look for the limitations of the data analysis. For example, if the
researcher wants to know whether negative political ads affect the popu-
lation of voters, and she bases her study on a group of college students,
you can find severe limitations here. For starters, student reactions to
negative ads don’t necessarily carry over to all voters in the population.
And even if the population were limited to all student voters, the stu-
dents from this particular class don’t represent all students. In this case,
it’s best to limit the conclusions to college students in that class (which
no researcher would ever want to do). Ultimately what needs to be
done is design the study so the sample contains a representation of the
intended population of all voters in the first place (a much more difficult
task, but well worth it).
One of the hardest parts of my job as a statistical consultant is dealing with
analyses after the design was already done — and done incorrectly. It’s much
better to put in a little work to get a good design together first, and then the
analysis will take care of itself.