JiscMail Logo
Email discussion lists for the UK Education and Research communities

Help for PSYCH-POSTGRADS Archives


PSYCH-POSTGRADS Archives

PSYCH-POSTGRADS Archives


PSYCH-POSTGRADS@JISCMAIL.AC.UK


View:

Message:

[

First

|

Previous

|

Next

|

Last

]

By Topic:

[

First

|

Previous

|

Next

|

Last

]

By Author:

[

First

|

Previous

|

Next

|

Last

]

Font:

Proportional Font

LISTSERV Archives

LISTSERV Archives

PSYCH-POSTGRADS Home

PSYCH-POSTGRADS Home

PSYCH-POSTGRADS  February 2007

PSYCH-POSTGRADS February 2007

Options

Subscribe or Unsubscribe

Subscribe or Unsubscribe

Log In

Log In

Get Password

Get Password

Subject:

Re: Power analysis

From:

Kathryn Jane Gardner <[log in to unmask]>

Reply-To:

Kathryn Jane Gardner <[log in to unmask]>

Date:

Mon, 26 Feb 2007 15:19:29 +0000

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (106 lines)

Hi Jeremy,

Thanks for taking the time to answer my query. Your e-mail was helpful
and I'll be seeking out the papers you mention re: the D'Amico and SEM
methods. 

I was under the impression that DFA was preferable to logistic
regression as it has more power (or so I have read). Thus, when data
meets the correct assumptions, DFA is to be used. 

Jeremy, would you mind elaborating on what you mean exactly by "run
some simulations"?

I had a feeling point biserial r would be equivalent to normal pearsons
r power analyses. Regarding partial correlation though, you said to use
the calculations for regression, but would it not be hierarchical
regression, as the partial correlation procedure is analogous to the HMR
procedure ( i.e., looking for an effect after controlling for the
influence of another predictor or set of predictors)? 

Many thanks.
Kathryn



>>> "Jeremy Miles" <[log in to unmask]> 23/02/2007 17:59:09 >>>
On 23/02/07, Kathryn Jane Gardner <[log in to unmask]> wrote:
> Dear list,
>
> Does anybody know of any resources for power anaysis for two-group
> discriminant function analysis, or know how of the formula to do the
> power analysis? Either hand calculations/formula, internet based
> calculators or sample size tables etc in journals will do. I can't
seem
> to find anything, which is a shame as i've used power analysis for
all
> my other analyses.
>

Hi Kathryn

Unless you really, really have a good reason, then don't use
discriminant functions. Use logistic regression instead.  DFA is more
complex, and much more sensitive to distributional assumptions.
It's possible to do power analysis for logistic regression as well.

If you're really desperate, and need power for DFA, you could run some
simulations.  The problem is that the model parameters in DFA are a
bit fiddly and weird (at least, I always find them fiddly and weird).

> I also need to find out the power calculations for: point-biserial
> correlation, partial correlation & partial point-biserial
correlation.
>

Point biserial correlation is a correlation, so you can use standard
correlation power analysis.  Alternatively PB is just another way of
thinking about a t-test, so use t-test power instead.

For partial correlation, the p-value is the same as the p-value you
get for a regression (try it) so you can use standard regression
estimators.

If you're ever really desperate for power and you are doing something
weird, then there are three solutions.

1) Simulate it.  This can be a bit fiddly (especially in SPSS, it's
much easier in Stata or R).    The book Data Analysis Using Regression
and Multilevel/Hierarchical Models by Gelman and Hill describes how to
do this in R.  (It's not desperately straightforward, unless you're
already familiar with R.)

2) Use MANOVA.  Most analyses can be converted into a MANOVA design.
In SPSS MANOVA you can input summary statistics (means, correlations,
regressions) and analyses the data, to get a power for any sample
size.  There's a paper that describes this: 	D'Amico, E. J.,
Neilands,
T. B., & Zambarano, R. (2001). Power analysis for multivariate and
repeated measures designs: a flexible approach using the SPSS MANOVA
procedure. Behavior Research, methods, instruments and computers, 33,
479-484.  The book Statistical Power Analysis, by Murphy and Myers
expands on this.  Basically, every test is turned into an F test, and
they are all powered the same way.

3. Use a structural equation model.  Almost every analysis can be
thought of as a structural equation model, and power in structural
equation models is always estimated in the same way - by reducing
everything to a chi-square, and chi-squares are (relatively) easy to
work out the power for.  If you're already familiar with SEM, this is
a bit fiddly, if you're not, then it's very fiddly.  I wrote a paper
on this method, which can be found here:
http://www.biomedcentral.com/1471-2288/3/27/abstract 
Although I do a lot of power analysis, and I've only used it once
since I wrote the paper.  (That's how fiddly it is).  I use the
D'Amico method more.  (Coincidentally, Liz D'Amico works just down the
corridor from me now - I didn't know about her paper when I wrote
mine, or I might not have bothered.)

Jeremy




-- 
Jeremy Miles
Learning statistics blog: www.jeremymiles.co.uk/learningstats

Top of Message | Previous Page | Permalink

JiscMail Tools


RSS Feeds and Sharing


Advanced Options


Archives

April 2019
March 2019
February 2019
January 2019
December 2018
November 2018
October 2018
September 2018
August 2018
July 2018
June 2018
May 2018
April 2018
March 2018
February 2018
January 2018
December 2017
November 2017
October 2017
September 2017
August 2017
July 2017
June 2017
May 2017
April 2017
March 2017
February 2017
January 2017
December 2016
November 2016
October 2016
September 2016
August 2016
July 2016
June 2016
May 2016
April 2016
March 2016
February 2016
January 2016
December 2015
November 2015
October 2015
September 2015
August 2015
July 2015
June 2015
May 2015
April 2015
March 2015
February 2015
January 2015
December 2014
November 2014
October 2014
September 2014
August 2014
July 2014
June 2014
May 2014
April 2014
March 2014
February 2014
January 2014
December 2013
November 2013
October 2013
September 2013
August 2013
July 2013
June 2013
May 2013
April 2013
March 2013
February 2013
January 2013
December 2012
November 2012
October 2012
September 2012
August 2012
July 2012
June 2012
May 2012
April 2012
March 2012
February 2012
January 2012
December 2011
November 2011
October 2011
September 2011
August 2011
July 2011
June 2011
May 2011
April 2011
March 2011
February 2011
January 2011
December 2010
November 2010
October 2010
September 2010
August 2010
July 2010
June 2010
May 2010
April 2010
March 2010
February 2010
January 2010
December 2009
November 2009
October 2009
September 2009
August 2009
July 2009
June 2009
May 2009
April 2009
March 2009
February 2009
January 2009
December 2008
November 2008
October 2008
September 2008
August 2008
July 2008
June 2008
May 2008
April 2008
March 2008
February 2008
January 2008
December 2007
November 2007
October 2007
September 2007
August 2007
July 2007
June 2007
May 2007
April 2007
March 2007
February 2007
January 2007
December 2006
November 2006
October 2006
September 2006
August 2006
July 2006
June 2006
May 2006
April 2006
March 2006
February 2006
January 2006
December 2005
November 2005
October 2005
September 2005
August 2005
July 2005
June 2005
May 2005
April 2005
March 2005
February 2005
January 2005
December 2004
November 2004
October 2004
September 2004
August 2004
July 2004
June 2004
May 2004
April 2004
March 2004
February 2004
January 2004
December 2003
November 2003
October 2003
September 2003
June 2003
May 2003
April 2003
March 2003
February 2003
January 2003
December 2002
November 2002
October 2002
July 2002
June 2002
May 2002
April 2002
March 2002
February 2002
January 2002
December 2001
November 2001
October 2001
September 2001
August 2001
July 2001
June 2001
May 2001
April 2001
February 2001
January 2001
December 2000
November 2000
October 2000
September 2000
August 2000
July 2000
June 2000
May 2000
April 2000
March 2000
February 2000
January 2000
December 1999
November 1999
October 1999
September 1999
August 1999
July 1999
June 1999
May 1999
April 1999
March 1999
February 1999
January 1999
December 1998
November 1998
October 1998
September 1998


JiscMail is a Jisc service.

View our service policies at https://www.jiscmail.ac.uk/policyandsecurity/ and Jisc's privacy policy at https://www.jisc.ac.uk/website/privacy-notice

Secured by F-Secure Anti-Virus CataList Email List Search Powered by the LISTSERV Email List Manager