On Sun, 11 Dec 2022 21:48:27 +0800, Jinsong Zhao <
jsz...@yeah.net>
wrote:
> (me)
< snip stuff >
>> Your contrasts when including interactions will have correlations,
>> so the regression results that you look at for main effects should
>> /not/ include the interaction effects.
>>
>
>Thanks a lot for the reply and instructions. I do not understand well
>about coding contrasts. My knowledge of statistics, which I only learned
>introductory statistics during my college, is very limited. If you could
>point me to some textbooks about this kind of statistical analysis, I
>would really appreciate it.
>
Okay, you have had one intro course in statistics. Did you take a
lot of math, including matrix theory? I did. Basic calculus
certainly helped me understand distributions and tests. In my
first job, as a computer programmer, I was mentored by a
statistician -- but I was able to help HIM with, for instance,
canonical correlation and factor analysis, which are 'merely'
particular manipulations of matrices. (And regression is one
simple case of canonical correlation.)
The intro-to-stats that I took in psychology was the most worthless
course of my career, for content. However, it partly prepared me for
consulting with psychologists who know no math.
I don't have any idea how much you know. It would be
malpractice of a sort to point you at a text and say, Good Luck.
There are SO MANY ways to go wrong.
Maybe read up first, in some intro-to-regression book in your
area. After you have read enough to understand the language,
hire a consultant for /few/ hours of consulting. Let them design
and run the analyses, and interpret them.
Ask questions; float your ideas, and see how close you came;
ask THEM for what texts they recommend.
Having worked as a statistical consultant, I can say that the
time-consuming part of the task is often "cleaning up the data."
Missing values? Out of range values? - What do you want to do?
You should be able to clean the data, and quickly show them
you have done that step. Plots and graphs of bivariate
distributions are good for providing assurance that there are
no hidden problems.
- Professors at universities are apt to be willing to moonlight.
Someone who has published relevant research would be best.
A theorist who has never dealt with real data might be terrible.
(I'm recalling a fine theorist who made several fine posts here,
back during the Bush administration. Reef_fish Bob was
autistic-spectrum: bad at context, and hostile to people as
a reflex.)
--
Rich Ulrich