I'm reading an interesting piece of research on anthropomorphism which
essentially states after a natural disaster if we use the term mother nature
when describing it, people will be less willing to contribute to relief efforts
(Humanizing nature could help the perceiver to conceive natural
There is software to determine this. One excellent and free app is G*Power.
http://www.psycho.uni-duesseldorf.de/abteilungen/aap/gpower3/
I would use the correlational study to give me an estimate of effect size. As
you describe, I would use that in the software to estimate my number of
Dear Tipsters,
There are various ways to plan sample size. When teaching this in research
methods, I divide the issues into two parts:
1. Estimation of population values.
Here, more is better but there are diminishing returns. Think of the fact that
we rarely see more than 1500 people in
Thanks Paul. I've downloaded G*Power. Question: the correlational component
of the study revealed r = -.21, p04 (higher tendency to humanize nature were
associated with a lower tendency to help victims of a natural disaster). The
next test will be an independent samples t-test.
How does
Hi
A couple of observations to add to what others have said.
First, note that the reported p (.06 or .0619, precisely) is for a
non-directional test. If the authors predicted the difference, directional p
is half of this (.031) and significant. This is consistent with the moderate
or large
Hi Michael:
Be careful with the effect size statistic that G*Power uses, sometimes
it is using rho. rho = .3 would be a medium effect size.
Ken
PS - It is surprising how underpowered are many of the experiments
reported in the journals.
I am assuming this was an independent samples t test where some participants
heard the mother nature language and others didn't. Using the d of .53 they
obtained as my estimate of what effect size they would be interested in
obtaining (or that they think would be worthwhile to note), it appears
I was going to stay out of this discussion but I have to address
a couple of points, one of which is made by Rick at the end
of his post:
(1) The major problem with power analysis is that it requires
one to have knowledge of POPULATION PARAMETERS,
that is, the means, the standard deviation, the
West Chester University invites applications for a tenure-track faculty
position in Clinical Psychology at the assistant professor level. Applicants
must have a Ph.D. in psychology, an active program of research with an adult
population, and the ability to mentor undergraduate and graduate
My two cents: Decide on what is the smallest effect that you would
consider to be of importance. If you think Type I and Type II errors are
equally serious, then set both alpha and beta to .05, that is, find N for 95%
power. G*Power does this with ease, but you are unlikely to like
Another interesting tidbit from Science Daily:
http://www.sciencedaily.com/releases/2013/08/130827122713.htm
--
Carol DeVolder, Ph.D.
Professor of Psychology
St. Ambrose University
518 West Locust Street
Davenport, Iowa 52803
563-333-6482
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It's hard to know what I would conclude. Are there other significant effects
found in the study with the small sample size? Power is not an issue if you
have statistical significance. I have a published paper in which one study has
an N of 8, four in each of the two groups. It was significant
On tonight's Jeopardy game the final question dealt with what area of Ireland
are there distinctive markings indicating Protestant and Catholic
neighborhoods.All three of the contestants wrote down Dublin.I wonder if they
have heard of William of Orange. Btw,I hosted a college student from
Donald Trump University
Please submit $4000 with youur application
michael
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