The Bootstrapping add on was practically rendered pointless when in version 21 they added bootstrapping nearly across the board. In addition, they offer a
simulation system that allows for montecarlo analysis. I would actually consider the simulation package myself. It’s now included in version 21 (at least in my version).
Everything that can be done in the menu system can typically be done in the syntax. Here is an example I generated for bootstrapping with a bias corrected
confidence interval and stratified by race.
BOOTSTRAP
/SAMPLING METHOD=STRATIFIED(STRATA=racem )
/VARIABLES TARGET=peaq1r INPUT= exp07 exp08
/CRITERIA CILEVEL=95 CITYPE=BCA NSAMPLES=10000
/MISSING USERMISSING=EXCLUDE.
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT peaq1r
/METHOD=ENTER exp07 exp08.
From: SPSSX(r) Discussion [mailto:[hidden email]]
On Behalf Of Margaret MacDougall
Sent: Thursday, March 14, 2013 2:18 PM
To: [hidden email]
Subject: Re: Bootstrap sampling for evaluating hypothesis tests
Thanks for your message. My interest in bootsrapping stems from the fact that I have developed a hyothesis test which can be justified on theoretical grounds for sufficiently large sample
size and I would like to see how well it behaves for a range of smaller sample sizes. I expect that this would entail generating multiple samples for which the null hypothesis is actually true and using these as a basis for assessing the probability of a Type
I error.
I have not used SPSS previously for bootstrapping but I am aware that the standard package offers some level of functionality in this area, via the existing menu commands and that there
is a bootstrapping add-on for SPSS. I am unclear how powerful the add-on is in erms of functionality or whether SPSS syntax could cover my needs.
From: "Poes, Matthew Joseph" <[hidden email]>
To: [hidden email]
Sent: Wednesday, 13 March 2013, 15:26
Subject: Re: Bootstrap sampling for evaluating hypothesis tests
I’ve used bootstrap resampling quite a bit, and I’ve used it in SPSS. I would need to know a bit more about what you are thinking here. Basically the way to accomplish
what you are trying to accomplish is to compare the findings of the bootrstrapped CI to the standard estimate and if they are consistent, then you can be more assured the findings are robust to type I error. In general bootstrapping is more robust in its
own right to type I error, so there is an argument that inconsistent results are meaningless, other than to say that the standard estimates were subject to bias and failed to meet certain assumptions. In other words, the bootstrapped estimate is always a
better estimate, so even if its inconsistent with the standard estimate, it still can be trusted in its own right.
From: SPSSX(r) Discussion [[hidden email]]
On Behalf Of Margaret MacDougall
Sent: Wednesday, March 13, 2013 10:14 AM
To: [hidden email]
Subject: Bootstrap sampling for evaluating hypothesis tests
I would be grateful to learn from any list users who have used SPSS successfully for bootstrap sampling to assess robustness to Type I errors of a proposed new hypothesis test.