P.Mean: Main page for the P.Mean website (created 2008-06-21)

P.Mean Website (created 1997-12-22, reborn at this location 2008-06-21)

News: "Data entry and data management issues with examples in IBM SPSS," Tuesday, August 24, 11am-noon CDT. Free webinar open to everyone. This training class will give you a general introduction to data management using IBM SPSS software. This class is useful for anyone who needs to enter or analyze research data. There are three steps that will help you get started with data entry for a research project. First, arrange your data in a rectangular format (one and only one number in each intersection of every row and column). Second, create a name for each column of data and provide documentation on this column such as units of measurement. Third, create codes for categorical data and for missing values. This class will show examples of data entry including the tricky issues associated with data entry of a two by two table and entry of dates. No statistical experience is necessary. No special hardware/software is needed. Details at www.pmean.com/webinars.

Welcome to the P.Mean website. Here are the most important links:

The most recent website entries (View all website entries for 2010, 2009, or 2008)

P.Mean: Standard operating procedures for a statistical consulting center (created 2010-07-30). I asked a question on one of the American Statistical Association message boards about how I setting up a consulting service at the University of Missouri-Kansas City (UMKC), where I work part-time. I wanted to develop some SOPs (Standard Operating Procedures) for this center that would supplement the guidance already available on the web. I asked if anyone else had SOPs (or anything similar) that I could look at so I wouldn't re-invent the wheel. I got a lot of responses.
P.Mean: When should research in a given area end? (created 2010-07-26). Someone asked a rather philosophical question, is there ever an end to research in a given area? Will there ever be a "last word" on a research topic. Here's what I wrote in response.
P.Mean: Sample chapter: The first three steps in selecting an appropriate sample size (created 2010-07-24). As I mentioned in an earlier webpage, I am talking to some publishers about writing a second book. The working title is "Jumpstart Statistics: How to Restart Your Stalled Research Project." Here's a tentative chapter from that book. It is not quite complete yet, but I'm hoping to finish it soon. One of your most critical choices in designing a research study is selecting an appropriate sample size. A sample size that is either too small or too large will be wasteful of resources and will raise ethical concerns.
P.Mean: Tentative table of contents for my second book (created 2010-07-24). As I mentioned in an earlier webpage, I am talking to some publishers about writing a second book. The working title is "Jumpstart Statistics: How to Restart Your Stalled Research Project." Here's a tentative table of contents.
P.Mean: Jumpstart Statistics, a proposal for my second book (created 2010-07-23). I want to talk to some publishers about writing a second book. Here is what I will propose to them.
P.Mean: Salary survey for Biostatisticians (created 2010-07-21). I am working part-time at UMKC in the Department of Informatic Medicine and Personalized Health. They like me and want me to increase my hours from 10 hours a week (25% time) to something more. I'll talk to them about this, but at the same time, I want to point out that my salary is not competitive with my peers. Here's a table from a recent survey on salaries, published in the Amstat News.
P.Mean: What is principal components analysis? (created 2010-07-19). I was asked to help someone who was reviewing a paper that used principal components analysis (PCA) as part of the statistical methodology. I have not yet seen the article, so I could only offer very general advice.
P.Mean: Another counter-intuitive probability problem (created 2010-07-04). A recent article in Science News, rekindled the two children problem and offered an odd twist. Here's the simple version. Suppose you have two children, one of whom is a boy. What is the probability that both children are boys? The obvious, but incorrect choice is 1/2. The correct answer is 1/3. How does this work?
P.Mean: Resources using Stack Overflow (created 2010-06-30) . A bunch of Internet resources fell into my lap all at once. Some of them relate to a new technology (Stack Overflow/Stack Exchange) that allows people to pose questions like an Interenet email discussion group, but it is web-based and has some of the capabilities associated with blogs and wikis.
P.Mean: The SPSS t-test is confusing (created 2010-06-29). I have always disliked how SPSS (now IBM SPSS) presented the output from their independent samples t-test. I want to explain why it is confusing and show you an alternative based on the general linear model.
P.Mean: Classic references in Statistics (created 2010-06-29). A prominent statistician, Christian Robert, listed some classic research papers in Statistics that he wanted to present to his students in a special readings class. This was commented on by another prominent statistician, Andrew Gelman. I'm not a prominent statistician, but that won't stop me from adding my two cents.
P.Mean: What I use for talks instead of Powerpoint (created 2010-06-28). Someone on LinkedIn asked a question about what technologies people use for their presentations (laptop, flipchart, or whiteboard). For most of my presentations, I use none of these technologies. Instead I create a webpage of my presentation and then print it and hand out copies.

Pages recently receiving major updates

P.Mean: Pilot study (created 1999-09-03, updated 2010-07-08). Dear Professor Mean, I am proposing a research study that will examine a complex intervention of diet, exercise, and behavioral modification for some of my pediatric patients who need to lose weight. I want to collect some data from a pilot study before I start the research study. How do I describe the pilot study in my protocol? -- Sophisticated Sarah
P.Mean: Confidence interval with zero events (created 2001-01-19, updated 2010-07-07). Dear Professor Mean, I was working with a colleague on some confidence intervals for the probability of an adverse event during several different types of operations. One of the proportions was zero, since the event never occurred. My friend computed a confidence interval and it went from zero to zero. I told him that this couldn't be right and computing a confidence interval with zero events is impossible. Isn't that right? -- Killjoy Karlina
P.Mean:: Testing for bimodality (May 3, 2005). This is an update and revision of age on my old site that has some broken links: www.childrensmercy.org/stats/weblog2005/Bimodality.asp. I have talked about bimodality before and it is a rather tricky thing. A recent discussion of tests of bimodality on edstat-l, though, yielded a few promising leads relating to the Dip test of Hartigan.
P.Mean: Design and analysis of pilot studies (created 2004-09-14, updated 2010-07-01). I've corrected a broken link on this article, which was originally published at my old website, www.childrens-mercy.org/stats/weblog2004/PilotStudy.asp. A colleague sent me a very nice paper, Design and analysis of pilot studies: recommendations for good practice. G. A. Lancaster, S. Dodd, P. R. Williamson. J Eval Clin Pract 2004: 10(2); 307-12 that covers some of the same ideas in my web page, Stats: Designing a pilot study. This is a very well researched article and has some excellent recommendations.
P.Mean: Examples of a fishbone diagram (created 2006-03-24). The fishbone diagram (also called the Ishikawa diagram, or the case and effect diagram) is a tool for identifying the root causes of quality problems. It was named after Kaoru Ishikawa, the man who pioneered the use of this chart in quality improvement in the 1960's. Surprisingly, I have had to hunt very hard to find any good examples of a fishbone diagram.
P.Mean: The first three steps in selecting an appropriate sample size (created 2009-07-20). I got an email last week from a client wanting to start a new research project looking at relationships between parenting beliefs and childhood behaviors. The description of the sorts of things to examine was quite elaborate, and it ended with the question "how many families would we need to have any significant differences if they exist?" Unfortunately, all the elaborate information provided did not include the information I would need to answer this question. Justifying a sample size usually involves three steps.

Interesting articles, books, quotes, or websites added to this site recently. (View all interesting articles, books, quotes, and websites for 2010, 2009 or 2008)

Anup Malani, Tomas J. Philipson. Push for more trials may hurt patients. Washington Examiner. 2010. Excerpt: "U.S. pharmaceutical companies are increasingly going abroad to conduct clinical trials required by the FDA. Recently, the Department of Health and Human Services released a report suggesting that the FDA lacks the resources to adequately monitor these foreign trials. Four of every five new drugs sold in the U.S. are tested in foreign trials, and the FDA inspects less than one in 10 of these. This is half the rate of inspection for domestic trials." [Accessed July 27, 2010]. Available at: http://www.washingtonexaminer.com/opinion/columns/Push-for-more-clinical-trials-may-hurt-patients-1002114-98875969.html.
H. Gilbert Welch, Lisa M. Schwartz, Steven Woloshin. The exaggerated relations between diet, body weight and mortality: the case for a categorical data approach. CMAJ. 2005;172(7):891-895. Excerpt: "Multivariate analysis has become a major statistical tool for medical research. It is most commonly used for adjustment — the process of correcting the main effect for multiple variables that confound the relation between exposure and outcome in an observational study. Any apparent relation between estrogen replacement and dementia, for example, should be adjusted for socioeconomic status, a variable that is known to relate both to access (and thus the likelihood of having received estrogen) and to measures of cognitive function (and thus the likelihood of being diagnosed with dementia). The capacity to account for numerous variables (e.g., income, education and insurance status) simultaneously constitutes a major advance in the ability of researchers to estimate the true effect of the exposure of interest. But this advance has come at a cost: the actual relation between exposure and outcome is increasingly opaque to readers, researchers and editors alike." [Accessed July 26, 2010]. Available at: http://www.ecmaj.com/cgi/content/full/172/7/891.
Phil Ender. Centering (ED230B/C). Excerpt: "Centering a variable involves subtracting the mean from each of the scores, that is, creating deviation scores. Centering can be done two ways; 1) centering using the grand mean and 2) centering using group means, which is also known as context centering." [Accessed July 26, 2010]. Available at: http://www.gseis.ucla.edu/courses/ed230bc1/notes4/center.html.
MediciGlobal. L2FU - Lost to Follow Up. Excerpt: "Patient drop outs in a clinical trial costs your company money. It can cost you the integrity of your study too! If it's important to recover patients lost from your clinical trial, you've come to the right place. Here, you'll read how L2FU's services can help you and how to begin finding patients today!" [Accessed July 26, 2010]. Available at: http://www.l2fu.com.
Steve Miller. Biostatistics, Open Source and BI – an Interview with Frank Harrell. Description: This article, published in Information Management Online, February 25, 2009, offers a nice interview with Frank Harrell, a leading proponent of modern statistical methods. Excerpt: "My correspondence with Frank provided the opportunity to ask him to do an interview for the OpenBI Forum. He graciously accepted, turning around deft responses to my sometimes ponderous questions in very short order. What follows is text for our questions and answer session. I trust that readers will learn as much from Frank’s responses as I did." [Accessed July 19, 2010]. Available at: http://www.information-management.com/news/10015023-1.html.
Karyn Heavner, Carl Phillips, Igor Burstyn, Warren Hare. Dichotomization: 2 x 2 (x2 x 2 x 2...) categories: infinite possibilities. BMC Medical Research Methodology. 2010;10(1):59. Abstract: "BACKGROUND: Consumers of epidemiology may prefer to have one measure of risk arising from analysis of a 2-by-2 table. However, reporting a single measure of association, such as one odds ratio (OR) and 95% confidence interval, from a continuous exposure variable that was dichotomized withholds much potentially useful information. Results of this type of analysis are often reported for one such dichotomization, as if no other cutoffs were investigated or even possible. METHODS: This analysis demonstrates the effect of using different theory and data driven cutoffs on the relationship between body mass index and high cholesterol using National Health and Nutrition Examination Survey data. The recommended analytic approach, presentation of a graph of ORs for a range of cutoffs, is the focus of most of the results and discussion. RESULTS: These cutoff variations resulted in ORs between 1.1 and 1.9. This allows investigators to select a result that either strongly supports or provides negligible support for an association; a choice that is invisible to readers. The OR curve presents readers with more information about the exposure disease relationship than a single OR and 95% confidence interval. CONCLUSION: As well as offering results for additional cutoffs that may be of interest to readers, the OR curve provides an indication of whether the study focuses on a reasonable representation of the data or outlier results. It offers more information about trends in the association as the cutoff changes and the implications of random fluctuations than a single OR and 95% confidence interval." [Accessed July 19, 2010]. Available at: http://www.biomedcentral.com/1471-2288/10/59.
Chris Corcoran, Louise Ryan, Pralay Senchaudhuri, et al. An Exact Trend Test for Correlated Binary Data. Biometrics. 2001;57(3):941-948. Abstract: "The problem of testing a dose-response relationship in the presence of exchangeably correlated binary data has been addressed using a variety of models. Most commonly used approaches are derived from likelihood or generalized estimating equations and rely on large-sample theory to justify their inferences. However, while earlier work has determined that these methods may perform poorly for small or sparse samples, there are few alternatives available to those faced with such data. We propose an exact trend test for exchangeably correlated binary data when groups of correlated observations are ordered. This exact approach is based on an exponential model derived by Molenberghs and Ryan (1999) and Ryan and Molenberghs (1999) and provides natural analogues to Fisher's exact test and the binomial trend test when the data are correlated. We use a graphical method with which one can efficiently compute the exact tail distribution and apply the test to two examples." [Accessed July 16, 2010]. Available at: http://dx.doi.org/10.1111/j.0006-341X.2001.00941.x.
Casey Olives, Marcello Pagano. Bayes-LQAS: classifying the prevalence of global acute malnutrition. Emerging Themes in Epidemiology. 2010;7(1):3. Abstract: "Lot Quality Assurance Sampling (LQAS) applications in health have generally relied on frequentist interpretations for statistical validity. Yet health professionals often seek statements about the probability distribution of unknown parameters to answer questions of interest. The frequentist paradigm does not pretend to yield such information, although a Bayesian formulation might. This is the source of an error made in a recent paper published in this journal. Many applications lend themselves to a Bayesian treatment, and would benefit from such considerations in their design. We discuss Bayes-LQAS (B-LQAS), which allows for incorporation of prior information into the LQAS classification procedure, and thus shows how to correct the aforementioned error. Further, we pay special attention to the formulation of Bayes Operating Characteristic Curves and the use of prior information to improve survey designs. As a motivating example, we discuss the classification of Global Acute Malnutrition prevalence and draw parallels between the Bayes and classical classifications schemes. We also illustrate the impact of informative and non-informative priors on the survey design. Results indicate that using a Bayesian approach allows the incorporation of expert information and/or historical data and is thus potentially a valuable tool for making accurate and precise classifications." [Accessed July 16, 2010]. Available at: http://www.ete-online.com/content/7/1/3.
Sylvia Sudat, Elizabeth Carlton, Edmund Seto, Robert Spear, Alan Hubbard. Using variable importance measures from causal inference to rank risk factors of schistosomiasis infection in a rural setting in China. Epidemiologic Perspectives & Innovations. 2010;7(1):3. Abstract: "BACKGROUND: Schistosomiasis infection, contracted through contact with contaminated water, is a global public health concern. In this paper we analyze data from a retrospective study reporting water contact and schistosomiasis infection status among 1011 individuals in rural China. We present semi-parametric methods for identifying risk factors through a comparison of three analysis approaches: a prediction-focused machine learning algorithm, a simple main-effects multivariable regression, and a semi-parametric variable importance (VI) estimate inspired by a causal population intervention parameter. RESULTS: The multivariable regression found only tool washing to be associated with the outcome, with a relative risk of 1.03 and a 95% confidence interval (CI) of 1.01-1.05. Three types of water contact were found to be associated with the outcome in the semi-parametric VI analysis: July water contact (VI estimate 0.16, 95% CI 0.11-0.22), water contact from tool washing (VI estimate 0.88, 95% CI 0.80-0.97), and water contact from rice planting (VI estimate 0.71, 95% CI 0.53-0.96). The July VI result, in particular, indicated a strong association with infection status - its causal interpretation implies that eliminating water contact in July would reduce the prevalence of schistosomiasis in our study population by 84%, or from 0.3 to 0.05 (95% CI 78%-89%). CONCLUSIONS: The July VI estimate suggests possible within-season variability in schistosomiasis infection risk, an association not detected by the regression analysis. Though there are many limitations to this study that temper the potential for causal interpretations, if a high-risk time period could be detected in something close to real time, new prevention options would be opened. Most importantly, we emphasize that traditional regression approaches are usually based on arbitrary pre-specified models, making their parameters difficult to interpret in the context of real-world applications. Our results support the practical application of analysis approaches that, in contrast, do not require arbitrary model pre-specification, estimate parameters that have simple public health interpretations, and apply inference that considers model selection as a source of variation." [Accessed July 16, 2010]. Available at: http://www.epi-perspectives.com/content/7/1/3.
C. Elizabeth McCarron, Eleanor Pullenayegum, Lehana Thabane, Ron Goeree, Jean-Eric Tarride. The importance of adjusting for potential confounders in Bayesian hierarchical models synthesising evidence from randomised and non-randomised studies: an application comparing treatments for abdominal aortic aneurysms. BMC Medical Research Methodology. 2010;10(1):64. Abstract: "BACKGROUND: Informing health care decision making may necessitate the synthesis of evidence from different study designs (e.g., randomised controlled trials, non-randomised/observational studies). Methods for synthesising different types of studies have been proposed, but their routine use requires development of approaches to adjust for potential biases, especially among non-randomised studies. The objective of this study was to extend a published Bayesian hierarchical model to adjust for bias due to confounding in synthesising evidence from studies with different designs. METHODS: In this new methodological approach, study estimates were adjusted for potential confounders using differences in patient characteristics (e.g., age) between study arms. The new model was applied to synthesise evidence from randomised and non-randomised studies from a published review comparing treatments for abdominal aortic aneurysms. We compared the results of the Bayesian hierarchical model adjusted for differences in study arms with: 1) unadjusted results, 2) results adjusted using aggregate study values and 3) two methods for downweighting the potentially biased non-randomised studies. Sensitivity of the results to alternative prior distributions and the inclusion of additional covariates were also assessed. RESULTS: In the base case analysis, the estimated odds ratio was 0.32 (0.13,0.76) for the randomised studies alone and 0.57 (0.41,0.82) for the non-randomised studies alone. The unadjusted result for the two types combined was 0.49 (0.21,0.98). Adjusted for differences between study arms, the estimated odds ratio was 0.37 (0.17,0.77), representing a shift towards the estimate for the randomised studies alone. Adjustment for aggregate values resulted in an estimate of 0.60 (0.28,1.20). The two methods used for downweighting gave odd ratios of 0.43 (0.18,0.89) and 0.35 (0.16,0.76), respectively. Point estimates were robust but credible intervals were wider when using vaguer priors. CONCLUSIONS: Covariate adjustment using aggregate study values does not account for covariate imbalances between treatment arms and downweighting may not eliminate bias. Adjustment using differences in patient characteristics between arms provides a systematic way of adjusting for bias due to confounding. Within the context of a Bayesian hierarchical model, such an approach could facilitate the use of all available evidence to inform health policy decisions." [Accessed July 14, 2010]. Available at: http://www.biomedcentral.com/1471-2288/10/64.
Julie Weed. Factory Efficiency Comes to the Hospital. The New York Times. 2010. Excerpt: "The program, called “continuous performance improvement,” or C.P.I., examines every aspect of patients’ stays at the hospital, from the time they arrive in the parking lot until they are discharged, to see what could work better for them and their families. Last year, amid rising health care expenses nationally, C.P.I. helped cut Seattle Children’s costs per patient by 3.7 percent, for a total savings of $23 million, Mr. Hagan says. And as patient demand has grown in the last six years, he estimates that the hospital avoided spending $180 million on capital projects by using its facilities more efficiently. It served 38,000 patients last year, up from 27,000 in 2004, without expansion or adding beds." [Accessed July 13, 2010]. Available at: http://www.nytimes.com/2010/07/11/business/11seattle.html.
Lehana Thabane, Jinhui Ma, Rong Chu, et al. A tutorial on pilot studies: the what, why and how. BMC Medical Research Methodology. 2010;10(1):1. Abstract: "Pilot studies for phase III trials - which are comparative randomized trials designed to provide preliminary evidence on the clinical efficacy of a drug or intervention - are routinely performed in many clinical areas. Also commonly know as "feasibility" or "vanguard" studies, they are designed to assess the safety of treatment or interventions; to assess recruitment potential; to assess the feasibility of international collaboration or coordination for multicentre trials; to increase clinical experience with the study medication or intervention for the phase III trials. They are the best way to assess feasibility of a large, expensive full-scale study, and in fact are an almost essential pre-requisite. Conducting a pilot prior to the main study can enhance the likelihood of success of the main study and potentially help to avoid doomed main studies. The objective of this paper is to provide a detailed examination of the key aspects of pilot studies for phase III trials including: 1) the general reasons for conducting a pilot study; 2) the relationships between pilot studies, proof-of-concept studies, and adaptive designs; 3) the challenges of and misconceptions about pilot studies; 4) the criteria for evaluating the success of a pilot study; 5) frequently asked questions about pilot studies; 7) some ethical aspects related to pilot studies; and 8) some suggestions on how to report the results of pilot investigations using the CONSORT format." [Accessed July 12, 2010]. Available at: http://www.biomedcentral.com/1471-2288/10/1.
Katharine Barnard, Louise Dent, Andrew Cook. A systematic review of models to predict recruitment to multicentre clinical trials. BMC Medical Research Methodology. 2010;10(1):63. Abstract: "BACKGROUND: Less than one third of publicly funded trials managed to recruit according to their original plan often resulting in request for additional funding and/or time extensions. The aim was to identify models which might be useful to a major public funder of randomised controlled trials when estimating likely time requirements for recruiting trial participants. The requirements of a useful model were identified as usability, based on experience, able to reflect time trends, accounting for centre recruitment and contribution to a commissioning decision. METHODS: A systematic review of English language articles using MEDLINE and EMBASE. Search terms included: randomised controlled trial, patient, accrual, predict, enrol, models, statistical; Bayes Theorem; Decision Theory; Monte Carlo Method and Poisson. Only studies discussing prediction of recruitment to trials using a modelling approach were included. Information was extracted from articles by one author, and checked by a second, using a pre-defined form. RESULTS: Out of 326 identified abstracts, only 8 met all the inclusion criteria. Of these 8 studies examined, there are five major classes of model discussed: the unconditional model, the conditional model, the Poisson model, Bayesian models and Monte Carlo simulation of Markov models. None of these meet all the pre-identified needs of the funder. CONCLUSIONS: To meet the needs of a number of research programmes, a new model is required as a matter of importance. Any model chosen should be validated against both retrospective and prospective data, to ensure the predictions it gives are superior to those currently used." [Accessed July 11, 2010]. Available at: http://www.biomedcentral.com/1471-2288/10/63.
John F. Hall. Journeys in Survey Research - Home. Excerpt: "Welcome to this new resource for researchers, students and others doing, or learning about, survey research and the analysis of survey data. You will find here a wealth of materials drawn from my 45 years of doing and teaching survey research." [Accessed July 9, 2010]. Available at: http://surveyresearch.weebly.com/.
Kristin L. Carman, Maureen Maurer, Jill Mathews Yegian, et al. Evidence That Consumers Are Skeptical About Evidence-Based Health Care. Health Aff. 2010;29(7):1400-1406. Abstract: "We undertook focus groups, interviews, and an online survey with health care consumers as part of a recent project to assist purchasers in communicating more effectively about health care evidence and quality. Most of the consumers were ages 18-64; had health insurance through a current employer; and had taken part in making decisions about health insurance coverage for themselves, their spouse, or someone else. We found many of these consumers' beliefs, values, and knowledge to be at odds with what policy makers prescribe as evidence-based health care. Few consumers understood terms such as "medical evidence" or "quality guidelines." Most believed that more care meant higher-quality, better care. The gaps in knowledge and misconceptions point to serious challenges in engaging consumers in evidence-based decision making." [Accessed July 8, 2010]. Available at: http://content.healthaffairs.org/cgi/content/abstract/29/7/1400.

The most recent personal entries (View all personal entries)

Steve, Cathy, and Nicholas -- Nicholas goes whale watching (created 2010-07-08). While in Juneau, we took a whale watching cruise. Here are some pictures of a humpback whale and other wildlife that we encountered during the cruise.
Steve, Cathy, and Nicholas -- Nicholas carries a big chunk of the Mendenhall glacier (created 2010-07-08). While in Juneau, we took a bus tour to Mendenhall Glacier. It was the first glacier that I saw on our Alaska tour, and it was quite impressive. Pieces of the glacier had fallen off into Mendenhall Lake and Nicholas found one that had drifted ashore. At first, I told Nicholas that he needed to leave the chunk of ice right there. But then we found that the park rangers had taken another chunk of ice from the lake and were displaying it near the visitors center. So Nicholas wanted to bring them his own chunk of ice.
Steve, Cathy, and Nicholas -- Letters I've written to the Kansas City Star (created 2010-07-02). One of my goals in life is to get a letter published on the Opinion pages of the Kansas City Star on a regular basis. They don't like to publish from any one writer more often than once a month. So far I've had mixed success, but I thought it would be interesting to post all of these letters on my website, both the ones that got published and the ones that didn't. I tried to note which efforts were successful and which were not, but I may not be 100% accurate.
Steve, Cathy, and Nicholas -- Nick makes his own Father's Day card (created 2010-07-01). We were on vacation in Alaska during Father's Day and I'm just getting around to posting various pictures of the trip. One thing unrelated to Alaska, but still worth showing is the Father's Day card that Nicholas made for me.
Steve, Cathy, and Nicholas -- Pictures of the Back Porch Cloggers (created 2010-06-08). In January of 2000, I took lessons on Appalachian Clogging from a group called the Back Porch Cloggers. I persisted through the entire fifteen weeks, graduated and then took the beginning courses a second time as well as continuing with the advanced group. Eventually, I got good enough that I was invited to be part of the performing group. The performing group would go monthly to various locations such as local festivals and to nursing homes and perform some of the more advanced dances. The leader of the Back Porch Clogges, John Hardin, retired in 2006, but the members of the group still kept meeting irregularly to keep practicing the dances so we wouldn't forget them. We also found time to hold several performances at the Deanna Rose Children's Farmstead. We do a few dances and then invite the children up (and any brave adults) to practice a few very simple clogging steps. Here are some pictures from a performance last year. I'll try to identify all the dancers, but will not name anyone who is hidden behind another dancer.

The most popular pages, excluding home page and various archive pages (last checked 2009-12-06)

This work is licensed under a Creative Commons Attribution 3.0 United States License. This page was written by Steve Simon and was last modified on 2010-07-30.