StATS: Interesting web sites, publications, and quotes for the month of January (January 31, 2006) Category: Interesting stuff

Note: any quotations on this page have been moved to Category: Interesting quotes.

Frailty approach for the analysis of clustered failure time observations in dental research. Chuang SK, Cai T, Douglass CW, Wei LJ, Dodson TB. J Dent Res 2005: 84(1); 54-8. [Medline] [Abstract] [Full text] [PDF]

Introduction to Statistics Through Resampling Methods and R/S-PLUS. Good PI (2005) Wiley-Interscience, New York , NY. [BookFinder4U link]

[My comments] I don't have this book yet, but it seems to be a good resource to list for some of my web pages on randomization tests.

Annotated Survey Research Bibliography (N = 28). Jung BC. Accessed on 2006-01-10.

[My comments] A nice list that includes some of my favorite resources.

Strategically using General Purpose Statistics Packages: A Look at Stata, SAS and SPSS. Mitchell MN, Statistical Consulting Group UCLA Academic Technology Services Technical Report Series, December 15, 2005, Report Number 1, Version Number 1. Accessed on 2006-01-10.

[Abstract] This report describes my experiences using general purpose statistical software over 20 years and for over 11 years as a statistical consultant helping thousands of UCLA researchers. I hope that this information will help you make strategic decisions about statistical software { the software you choose to learn, and the software you choose to use for analyzing your research data.

Researchers Misunderstand Confidence Intervals and Standard Error Bars. Belia S, Fidler F, Williams J, Cumming G. Psychological Methods 2005, Vol. 10, No. 4, 389–396 2005: 10(4); 389-396.

[Abstract] Little is known about researchers’ understanding of confidence intervals (CIs) and standard error (SE) bars. Authors of journal articles in psychology, behavioral neuroscience, and medicine were invited to visit a Web site where they adjusted a figure until they judged 2 means, with error bars, to be just statistically significantly different (p <.05). Results from 473 respondents suggest that many leading researchers have severe misconceptions about how error bars relate to statistical significance, do not adequately distinguish CIs and SE bars, and do not appreciate the importance of whether the 2 means are independent or come from a repeated measures design. Better guidelines for researchers and less ambiguous graphical conventions are needed before the advantages of CIs for research communication can be realized.

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