Forum on design, sample-size, and power analysis. http://forums.stat.ucla.edu/list.php?11 Mon, 23 Nov 2009 22:49:14 -0800 Phorum 5.2.10 http://forums.stat.ucla.edu/read.php?11,135,135#msg-135 http://forums.stat.ucla.edu/read.php?11,135,135#msg-135
Thanks so much,

Dave]]> DaveK Design, Sample-Size, and Power Mon, 19 Oct 2009 14:07:32 -0700 http://forums.stat.ucla.edu/read.php?11,121,121#msg-121 http://forums.stat.ucla.edu/read.php?11,121,121#msg-121
Secondly- I don't want to overfit my model by throwing in too many unnecessary independent variables to test for. But how can you take out any potential variables (even after having possibly done a univariate test and found no strict correlation to the dependent response variable), without knowing of any potential interaction effects that could arise among variables being tested? One variable might be tested with a basic univariate test and thrown out as having no correlation to my response variable, but that variable WITH another one might have been important!

Thank you so much for your help...

-MP]]> MarcoP Design, Sample-Size, and Power Thu, 15 Oct 2009 11:13:50 -0700 http://forums.stat.ucla.edu/read.php?11,81,81#msg-81 http://forums.stat.ucla.edu/read.php?11,81,81#msg-81
I've attemtped to read some documentation on Rasch tests - the jargon is overwhelming and I cannot afford the time to become a psychometrician myself (nor do I care to do that, from what I have seen). I am looking for an intuitive explanation of how confidence intervals for individuals' test scores can be derived and what such confidence intervals mean. Given that repeated tests are not administered to any sample of students - a straightforward way of obtaining a confidence interval for the variation in test scores from one test to another - how is a confidence interval for an individual obtained from a single test administration?

The interpretation that they offer for the confidence interval is that "If [student name] were to take a similar test multiple times, the range of these scores would fall between xxx and yyy 80% of the time."

Thanks for any assistance you can provide.]]> dlehman Design, Sample-Size, and Power Thu, 17 Sep 2009 17:07:22 -0700 http://forums.stat.ucla.edu/read.php?11,55,55#msg-55 http://forums.stat.ucla.edu/read.php?11,55,55#msg-55
Thank you in advance for your inputs.

Eins]]> Eins Design, Sample-Size, and Power Mon, 15 Jun 2009 08:50:13 -0700 http://forums.stat.ucla.edu/read.php?11,47,47#msg-47 http://forums.stat.ucla.edu/read.php?11,47,47#msg-47 The problem we have is that the data are left-censored, that is, for some samples the amount detected in their blood was below the detection level(DL) of the machine. The literature is inconsistent on how to fill-in these nondetects(ND), some use ND=DL/2 or ND=0, while others will use imputation for these values. Since our samples do not have the same DL for each dioxin, we should use the generalized Wilcoxon Rank-Sum or the Gehan's statistic. How can we do these tests in R?
We believe using imputation is incorrect, because we have a low sample-size and choosing a distribution seems impractical with so few observations compared to our baseline NHANES, which has 600 or so observations. Do you have any suggestions for the analysis?]]> hilbertspaces Design, Sample-Size, and Power Sat, 09 May 2009 10:28:15 -0700 http://forums.stat.ucla.edu/read.php?11,38,38#msg-38 http://forums.stat.ucla.edu/read.php?11,38,38#msg-38
The data:

All variables are dichotomous including the criterion.
The criterion mean is estimated to be around 0.0157.

Expectations:

We are looking to detect a change to that mean as low as 5% with a sample size of 1200000 and be able to calculate each factors contribution to the change along with identifying the optimum configuration of factors and levels and estimating the results at specific confidence level intervals.


This is my question:

What would be the best type of analysis for the experiment results?

a) ANOVA
b) General Multivariate Linear Regression
c) Logistical Regression

Should I be considering any other type of analysis?


Thanks]]> bmillares Design, Sample-Size, and Power Tue, 21 Apr 2009 13:08:58 -0700 http://forums.stat.ucla.edu/read.php?11,32,32#msg-32 http://forums.stat.ucla.edu/read.php?11,32,32#msg-32
32 participants (16 in two groups) answered a question. Subsequent to answering this question, participants indicated whether their answer was based on a guess, a feeling or a memory - so a categorical variable with 3 levels. Each participant provided 22 of these categorical ratings. I thought initially, I could analyse the data by converting it into relative proportions and use a mixed analysis of variance with 1 between and 1 within. However, of course, the observations are not independent of one another. Moreover, they average out to 33.33% across the within-subject. It doesn't seem like a chi-square test is a good idea though, as each of the 22 for each subject would be more related to one another than any of the other observations. What analysis can I use - or is it only possible to explore this data?]]> jb421 Design, Sample-Size, and Power Sat, 26 Sep 2009 12:55:32 -0700 http://forums.stat.ucla.edu/read.php?11,31,31#msg-31 http://forums.stat.ucla.edu/read.php?11,31,31#msg-31
In one case, the rates are cost/effectiveness ratios such as REVENUE over EXPENSE, and these are typically much greater than 1, perhaps even in the thousands.

To get to my point, the most general case I have is FOLLOWERS over FOLLOWING which, as a rate, follows more of a power law with lots of between 0 and 1, few greater than that.

Is there a general method (probably non-parametric) I can use to compare two rates? In the cases where the rates are computed from samples, the samples are typically pretty small (10-20).]]> Ryan Rosario Design, Sample-Size, and Power Thu, 09 Apr 2009 09:33:24 -0700 http://forums.stat.ucla.edu/read.php?11,18,18#msg-18 http://forums.stat.ucla.edu/read.php?11,18,18#msg-18 Jose Hales-Garcia Design, Sample-Size, and Power Tue, 24 Mar 2009 08:31:18 -0700