A non-technical guide.
You've heard that
predicting an outcome (finding indicators) is not the same as
explaining it (establishing cause and effect). But how to interpret these sorts of research
findings -- or how to get started with your own explanatory analysis? You can start here. Guaranteed
to help "or your money back."
on interpretation errors that are terribly difficult to avoid, involving conventional significance testing.
sample research questions
Sometimes it's useful--and encouraging--to see
the range of questions
one might address using statistics.
This article contrasts two approaches to uncovering the reasons behind enrollment, application, and other key decisions made
by students and their parents. You may be surprised at how much more effective some innovative
"derived importance" techniques prove as compared to traditional "stated importance" methods.
Abstract and link to paper.
A quick summary of a brainstorming tool. It's a helpful way to start a process of investigating the factors that could drive decisions.
This paper of 30-odd pages explores the ingenious yet usually flawed methods that have been devised for statistical detection
of copying on multiple-choice exams. The methods of Angoff, Crawford, Belleza & Belleza, and
Kling are examined in depth, with some attention also given to the work of Frary, Wollack and
Cohen, and others.
Abstract and link to paper.
It's easy to confuse these two. Unable to find a graphical representation that explained the difference to my satisfaction,
I created the set of charts and brief commentary available
Think it's easy to sort out the differences between ANOVA and ANCOVA? This look at empirical and conceptual considerations
may make you think again. It includes a flow chart, glossary, and commentary and is available
A two-page guide for those who are familiar with the sometimes wondrous method of factor analysis and looking for suggestions
to guide the classic decision points: on applicability, method of extraction, number of factors,
and rotation. Available in
Eleven reasons why teachers shouldn't be judged based on student test scores.
In case you're not laughing at the very idea, here are some
Occasional commentary on other researchers' methods and analyses. Disclaimer: some of this
critique may be crabby, cranky, cross, cantankerous, or curmudgeonly.
Ralph Lengler and Martin J. Eppler's
Periodic Table of Visualization Methods
is a gorgeous, ingenious, highly informative single-page display of about a hundred types of charts and diagrams
for visualizing data, concepts, strategies, and more. Hovering your cursor over any "element"
in this table will bring up a colorful and instructive example.
An excellent resource for data graphics you can create in the R software, including very useful code examples.
Michael Friendly of Toronto's York University presents a nice smorgasbord of visual confections.
Some good lessons and also some good laughs.
A fine place to ask targeted questions on statistics or data visualization, or to browse
through searchable questions, answers, and commentary.
A fun, intricate infographic for fans of psychology and behavioral economics research. From the people at visualcapitalist.com.
I welcome any
comments you would like to make, publicly or privately.
copyright 2007-2018 by roland b. stark.