data

Study: One-third of U.S. children with harmful lead exposures going unidentified

kkrisberg — Fri, 05 May 2017 17:53:35 +0000

Study: One-third of U.S. children with harmful lead exposures going unidentified

Right now, according to public health officials, about half a million U.S. kids have blood lead levels that could harm their health. However, new research finds many more children — hundreds of thousands more — are likely going unidentified.

In a study published last week in Pediatrics, researchers estimated that while 1.2 million cases of elevated blood lead levels (EBLL) likely occurred between 1999 and 2010, only 607,000 were reported to the Centers for Disease Control and Prevention. That data gap not only means kids are likely going without needed treatment and services, but that public health officials don’t have all the data they need to ensure prevention and remediation efforts reach everyone who needs them. And if — as the study suggests — public health officials don’t have complete data on where lead poisoning threats exist, it makes it difficult to pinpoint the sources of exposure and prevent additional poisonings in the future.

According to CDC, there’s no safe blood lead level in children, and even low levels of lead have been shown to impact IQ, attention and academic achievement. Other effects of childhood lead poisoning include damage to the nervous system, slowed growth and development, behavioral issues, and hearing and speech problems. Research has found that investing in lead hazard control reaps significant benefits, both in preventing the high cost of special care and services and in protecting a child’s opportunity to succeed academically, which is often a predictor of future health, prosperity and longevity. In California alone, research shows that EBLL results in lost earnings of up to $11 billion over the lifetime of children born each year.

Study co-author Eric Roberts, a pediatrician and co-principal investigator of the California Environmental Health Tracking Program at the Public Health Institute, said that because the standards determining who should be tested for lead vary across states, he and colleagues had a hunch many at-risk and impacted children were being overlooked. According to the Pediatrics study, the American Academy of Pediatrics recommends clinicians refer to state and local lead screening guidelines, “but these recommendations are often difficult for clinicians to access and commonly defer to practitioners’ individual evaluations of EBLL risk in the communities they serve, a task for which few are equipped.” And while laws require lead testing for Medicaid and WIC enrollees, researchers said the requirement often goes unenforced. That means childhood lead testing is mostly left to the discretion of physicians.

“We did observe almost universally that when you ask state or local public health to quantify the amount of lead poisoning, they provide you with numbers of lead-exposed kids identified by physicians — and that always struck me as a circular argument,” Roberts told me. In other words, in communities where physicians do a lot of testing, the resulting data underscores the need to sustain and possibly expand such testing; if little testing is done, the resulting data can cause physicians to incorrectly believe testing isn’t necessary.

He added: “Right now, folks are just assuming that certain communities are at low risk. But the default should be for testing, especially for children 12 to 24 months old. …We now assume no risk until it’s found, and that policy is clearly failing to protect kids.”

To conduct the study, Roberts and colleagues used National Health and Nutrition Examination Survey (NHANES) data to estimate state-level EBLL prevalence among children ages 12 months to 5 years between 1999 and 2010. They then compared those estimates to diagnostic case numbers reported to CDC. They found that in 23 of the 39 states where data was available, more than half of children with EBLL weren’t being identified. Nationwide, according to the study, only 64 percent — or 607,000 cases — of children with EBLL were identified and reported to CDC.

Between 1999 and 2010, researchers estimated that about 1.2 million children had EBLL, but only those 607,000 cases were identified and reported to CDC. The researchers noted that about 45 percent of unreported cases happened in years when the particular state wasn’t reporting such data to CDC, and about 55 percent of unreported cases weren’t reported due to “incomplete case ascertainment.” At the regional level, the greatest number of reported EBLL cases occurred in the Midwest and Northeast, while the highest number of overall cases occurred in the South. At the state-level, 23 states reported fewer than half of expected EBLL cases, while 11 states reported less than 20 percent of expected cases. For example, in California, the study estimated that only 37 percent of children with EBLL were identified.

Researchers noted that because of changes to NHANES data collection in 2010, clinician testing has become the only source of information on EBLL prevalence. In addition, CDC’s Childhood Lead Poisoning and Healthy Homes Program had its budget cut in 2012 from $29 million to $2 million, which means states have much less capacity to do lead-related surveillance, outreach, education, prevention, assessment and enforcement.

Co-authors Roberts, Daniel Madrigal, Jhaqueline Valle, Galatea King and Linda Kite write:

American Academy of Pediatrics policy…explicitly defers to state and local health departments to decide when children should undergo BLL testing. This policy is based on two assumptions, however: that well-resourced public health agencies will communicate effectively with providers, enabling them to make data-directed decisions about when to test for EBLL, and that statutory requirements for testing would  be accompanied by mechanisms  and resources for enforcement. Both of these assumptions have proven to be false, with the effect  that large numbers of children  with EBLL (indeed, the majority  of these children in many parts of  the country) have been missed by clinicians.

“I think the fundamental message here for public health is that we need to realize that simply enumerating the number of kids found by pediatric care providers is not a scientific basis for knowing whether kids are at risk or not,” Roberts told me. “(Lead exposure) is a much wider-spread problem than we’ve been willing to admit.”

Roberts noted that while the most common route of child lead exposure is lead-based house paint — and that risk has been drastically reduced in recent decades — the drinking water crisis in Flint shows that lead remains a serious threat to children’s health. In fact, he said as aging infrastructures deteriorate, children could face new lead exposure risks, which is all the more reason for better surveillance, testing and data.

On the clinical side, he said groups like the American Academy of Pediatrics can make a difference by strengthening their practitioner guidelines for childhood lead testing, with the understanding that local public health data might not provide a complete picture of who’s at risk and who should be tested.

“What we’re doing when we allow kids to be exposed to lead is we’re sacrificing some portion of their futures,” Roberts said. “There’s a cost to doing nothing.”

For a copy of the new lead study, visit Pediatrics.

Kim Krisberg is a freelance public health writer living in Austin, Texas, and has been writing about public health for 15 years. Follow me on Twitter — @kkrisberg.

kkrisberg Fri, 05/05/2017 - 13:53

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Researchers identify thousands of fracking spills, highlight why data is critical to prevention

kkrisberg — Wed, 01 Mar 2017 20:51:54 +0000

Researchers identify thousands of fracking spills, highlight why data is critical to prevention

In 2015, the U.S. Environmental Protection Agency released a report finding 457 fracking-related spills in eight states between 2006 and 2012. Last month, a new study tallied more than 6,600 fracking spills in just four states between 2005 and 2014. But, as usual, the numbers only tell part of the story.

Not every spill counted in that new number represents a spill of potentially harmful materials or even a spill that made contact with the environment. In fact, the study’s goal wasn’t to tally an absolute number of fracking spills. Instead, researchers set out to collect available spill data and then drill down (no pun intended) into the details to unearth common patterns and characteristics. And it’s those commonalities that can reveal the larger story of how to prevent such spills — which often contain health-harming chemicals — from happening in the first place.

“When you look across spills, what are the risk factors, in what stage of a well’s life are you most likely to see a spill, are we more likely to see a spill from a well that’s already experienced one, are there changes in the law or in enforcement that drive more spills,” asked study co-author Kate Konschnik, founding director of the Harvard Law School’s Environmental Policy Initiative. “We wanted to see what the larger story told us about risk.”

The study, which was published in February in Environmental Science & Technology, comes from a working group convened by the Science for Nature and People Partnership and is part of a larger line of research trying to assess the risks that unconventional oil and gas (UOG) production, commonly referred to as fracking, pose to water resources. For instance, Konschnik and her colleagues published a different study in December that assessed the environmental risk of UOG spills by determining their distances from nearby streams. In the more recent study, Konschnik told me that there were two overriding goals: to look for trends in spill data and to tease out what kinds of spill data may be most useful in making UOG development safer.

To conduct the study, Konschnik and colleagues analyzed spill data from 2005 to 2014 at more than 31,400 UOG wells in Colorado, New Mexico, North Dakota and Pennsylvania. They included spill data related to the full UOG production cycle, including storage and transportation, rather than focusing only on the fracturing stage. The study only included UOG production wells, not fracking disposal wells, which are used to store the often chemical-laden wastewater that comes back up to the surface during drilling. Researchers tried to include 11 other states in the study, but the data was either incomplete or too difficult to access.

Overall, researchers found 6,648 spills across the four states during the nine-year study period. That number exceeds the EPA findings by so much because the study included the entire fracking life cycle, whereas EPA only examined spills explicitly related to the fracturing stage. (“UOG is growing in scale and intensity…and a fairer examination of risk is to look at releases throughout (a well’s) entire life,” Konschnik told me.) North Dakota reported the most spills and the highest overall spill rate at about 12 percent. Pennsylvania reported 1,293 spills (4.3 percent), New Mexico had 426 (3.1 percent) and Colorado had 476 (1.1 percent). Across the four states, wells that experienced multiple spills contributed to a larger proportion of spills, indicating that prior spills may be an indicator of future spills.

Researchers also examined yearly spill rates, finding that fluctuations were “likely” shaped by changes in state reporting requirements, “demonstrating how state policies directly impact efforts to identify and accurately assess UOG risk, their causes and potential mitigating remedies.” For example, when North Dakota switched reporting requirements from verbal to written, spill rates increased by up to 4 percent. And in Pennsylvania, annual spill rates increased as more inspectors were hired, the study found. Across all four states, the greatest spill risk occurred during the first three years of a well’s existence.

Spill volumes ranged from 1 gallon to up to 991,000 gallons. In addition to 46 freshwater spills, the total volume of spills associated with fracking chemicals, solutions and flowback ranged from more than 99,000 gallons in Pennsylvania to more than 203,000 gallons in Colorado. The most common pathways for spills, according to the study, were storage tanks and pits as well as flowlines. Spills related to transportation were also prevalent across the four states, with most associated with loading and unloading. As for what caused the spills, only Colorado and New Mexico explicitly asked for such information during reporting. In examining the available causal data, researchers found that human error and equipment failures were the most common culprits.

One of the study's biggest takeaways was the importance of data reporting as well as the challenge of varying reporting requirements. For example, in Colorado, reporting requirements are triggered for any fracking spill of 42 gallons or more that escapes secondary containment. While in New Mexico, reports are required for spills greater than 25 barrels or if an operator thinks a spill might endanger water quality or public health. Study co-authors Konschnik, Lauren Patterson, Hannah Wiseman, Joseph Fargione, Kelly Maloney, Joseph Kiesecker, Jean-Philippe Nicot, Sharon Baruch-Mordo, Sally Entrekin, Anne Trainor and James Saiers write:

Further improving reporting requirements and processes for reporting will facilitate states’ and companies’ efforts to identify risks for certain types of spills and take action to mitigate some of the identified risk factors. To the extent that this information is publicly available and searchable, operators can use it to remove or mitigate risk factors to improve environmental performance and avoid higher insurance premiums.

Assembling these data electronically within a centralized database would allow state regulators and other stakeholders to identify trends, including the most common spill pathways and causes, as well as identify the wells or operators associated with unusually high spill rates. Making this information publicly available and providing it in an easy, usable format would allow operators, insurance companies, and citizen monitoring groups  to assess the largest and most prevalent risks and respond accordingly. This paper illustrates the benefits of having  available and accessible data.

Konschnik said that “without question,” the study reveals that many spills are likely preventable. For example, she said enhanced training or simple reminder signage could help prevent the human errors underscoring a significant portion of spills identified in the study. Other interventions are even simpler. For instance, she said the study’s data indicated that wildlife caused some of the spills, which could mean operators simply need to fence off areas to reduce spill risks.

As for the health hazards of such spills, this study doesn’t address that question. But Konschnik did say that current data — and, of course, more robust datasets — could help pinponit areas where public health monitoring is needed.

“Our data can be used as an indicator of where more research can be done,” she said. “If we had more robust data that was publicly available, you could dig much deeper…this is one piece of the puzzle in which a more granular view of spills data married with some community health assessment data and monitoring data could help determine whether or when there are risks to exposure.”

For residents living in fracking regions, finding spill data on one’s own can be quite difficult, Konschnik said. As such, she and her colleagues created an interactive website anyone can use to learn more about fracking spills and their causes — check it out here.

“UOG really is the wave of the future — that’s where we’ll see growth,” Konschnik told me. “And so these spills might be more representative of what we’ll see in the future.”

For a full copy of the study, visit Environmental Science & Technology.

Kim Krisberg is a freelance public health writer living in Austin, Texas, and has been writing about public health for 15 years.

kkrisberg Wed, 03/01/2017 - 15:51

Accuracy, precision, and significance: The misery of cholera

pgleick — Wed, 13 Feb 2013 13:36:42 +0000

Accuracy, precision, and significance: The misery of cholera

We’re bombarded with numbers every day. But seeing a number and understanding it are two different things. Far too often, the true “significance” of a figure is hidden, unknown, or misjudged. I will be returning to that theme often in these blog posts in the context of water, climate change, energy, and more. In particular, there is an important distinction between accuracy and precision.

Here is one example – reported cases of cholera worldwide. Cholera is perhaps the most widespread and serious water-related disease, directly associated with the failure to provide safe drinking water and adequate sanitation. Billions of people lack this basic human right and suffer from illness as a result. Millions die unnecessary deaths.

The scourge of cholera. December 1912. Le Petit Journal, Bibliothèque nationale de France. [This image is in the public domain.]

The World Health Organization has reported that in 2011 (the last year for which comprehensive data are available) 58 countries reported 589,854 cases of cholera.

OK, I see that number, but what does it mean? Is it accurate? Is it precise?

Accuracy and precision are not the same things. In the field of science and data, “accuracy” is typically considered to be a measure of how close a number is to that quantity’s true value.

“Precision” is a term with two relevant meanings. The first describes the degree to which repeated efforts to do, or measure, something will produce the same results. The second meaning is a measure of the relative accuracy with which any given number can be represented, and is typically expressed through the use of “significant figures.”

Take, for example, the number 123. This has three significant figures. The implication is that the actual number is not 122 or 124, but 123 precisely, with a margin of error of a half of the last place (in this case 0.5). If the actual precision of measurement is not this small, then perhaps this number should be represented as 120 (with two significant figures), or even 100 (with only one significant figure).

[A minor aside: the number 100 could have 1, 2, or 3 significant figures – we don’t know unless it is stated explicitly. One way to do this is to use decimal notation. The number 100. (with the decimal point) has three significant figures, and can also be expressed as 1.00 x 10².]

Any particular data can be accurate, precise, both, or neither.

So, back to cholera. This number of cases -- 589,854 -- seems very precise. It is reported to six significant figures – a very high degree of precision.

In fact, however, this number is an example of “false precision” – it is presented in a way (with six significant figures) that implies, incorrectly, a higher degree of both precision and accuracy than reality warrants.

Why? First, it is entirely possible that this number is exactly the sum (i.e., it is precise) of the number of cases of cholera reported to WHO by the 58 reporting countries. But experts on water-related disease note the following:

Many countries around the world do not report water-related diseases at all. As noted above, in 2011 only 58 countries reported cholera. We know cholera occurred in countries not reporting.
Most cholera outbreaks are not detected. Thus, even countries reporting cholera underreport.
There is no agreed-upon standard definition for determining if a case of extreme or acute watery diarrhea is “cholera” or a different illness that presents the same way.
Health surveillance systems (i.e., medical systems for tracking, recording, and reporting disease) vary dramatically from country to country in their quality and completeness.
Some major countries, known to have extensive and severe cholera outbreaks, typically report zero instances of cholera because they either fear the stigma associated with the failing to provide adequate water systems or they hide cholera cases by labeling them as something else (such as acute watery diarrhea).

Thus, this highly precise number is neither precise nor accurate. Indeed, it is grossly inaccurate. The WHO acknowledges this, and indeed, believes the officially reported cases could represent only a small fraction of the actual number that occurs. Taking these uncertainties into account, WHO estimates that there are as many as 10 times more cases than are actually reported. A more detailed statistical analysis recently suggested that overall there are around 2.8 million cases of cholera every year (with an uncertainty range of 1.2 to 4.3 million) and about 91,000 deaths (with an uncertainty range of 28,000 to 140,000).

So, beware misleading numbers. The officially reported estimates of cholera cases are neither precise (despite six significant figures), nor accurate.

Finally, there is another aspect to “significance.” That is the importance of the figure in some context. In this sense, the cholera numbers may be neither accurate nor precise, but they are significant. They tell the story of a horrible and unnecessary situation – a deadly, crippling, and preventable disease that is the result of our failure to provide safe water and sanitation to all the population on the planet. Cholera is completely preventable – we've effectively eliminated it in the United States and other industrialized countries by putting in place wastewater treatment and water purification systems. Let’s improve our data collection and reporting system, so we know, accurately, the extent of the problem, and then let’s move quickly to do what is necessary to reduce and eliminate cholera.

Peter Gleick

pgleick Wed, 02/13/2013 - 08:36

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water resources

I disagree. WHO said "In 2011, a total of 58 countries from all continents reported 589 854 cases of cholera to WHO". The number of cases REPORTED is a direct count, and use of 6 significant figures is appropriate.

It's true that this number probably greatly underestimates the number of cholera cases that occurred, but WHO never claimed to be reporting that. The number they reported is both accurate and appropriately precise.

Yes, as I noted, the WHO number is certainly "precise" based only on the "reported" data, but it is neither precise nor accurate in the context of the overall cholera situation. I don't agree, however, that the number is "accurate" in any definition of the word!

Similarly, it's annoying when press converts a rounded number (pounds, kilometers) to another (dollars, miles) and states every digit of "accuracy".

Great discussion; and absolutely important both at conceptual and fatual levels.
The key message "Let’s improve our data collection and reporting system......to reduce and eliminate cholera.". There is a need to think about changing the whole game (not just the rules of the game) in developing countries about data collection and reporting systems. Rural areas of developing countries have a significant population with marginal health infrastructure. Many institutions try to play with data to show the "progress", by presenting the cases at lower sides. Other think (including educated staff in rural health centers etc) that it is just a cosmetic requirement, and has nothing to do with any improvement. I can mention many factors which I noted during field work in such areas; however, two things are important to improve data collection and reported systems (I am sharing for discussion here)
1- Awareness at all levels (from causes and identification of such diseases to reporting)
2- Involvement of nutral institution to collect such data (such as colleges, universities, and other research-based institutions with some sort of mechanism depending upon the local condition); because many health units prefer to report figures showing "better progress" for their adminstration and the government.
My Question is : What mechanism do you suggest to achieve the target of "improvement in data collection and reporting system"?, both in rural and urban areas of developing countries ?

Rosie Redfield's objection is entirely valid — as written, the claim is most likely both very accurate and very precise. This being the case, then the issue becomes whether or not the reporting of that number, or its reporting in the context of civil discourse, is presented (implicitly or explicitly) as a claim about all outbreaks of cholera or the WHO reporting of cholera. And in that context, Gleick's criticisms are relevant.

Two things:

First, surely Paulos's 1988 book, "Innumeracy" is extremely relevant to this blog within the wider context of education and civil discourse.

Second, "significance" is a term of art in mathematics and science elsewhere than in "significant figures" and nearly with as much importance and more timeliness: statistical significance. Statistical significance is frequently misunderstood and misused within science itself and even more egregiously in civil discourse (science journalism, mostly). I suspect, though perhaps wrongly, that this isn't what Gelick had in mind for his blog, but it's just as vital and relevant. What statistician Andrew Gelman has written on the topic is a good start.

Great comment, thanks. And good hint about Paulos's book. I may at times talk about statistical significance -- it is certainly often misunderstood and misused. I've often thought that a required statistics course in high school would be far more useful in life than some of the standard high school math requirements...

I'm ambivalent about stats in high school. On the one hand, I'm strongly in favor of including some probability and intro statistics into the high school math curriculum for liberal arts purposes; i.e., as an essential part of a basic education and with regard to numeracy in the context of citizenship.

On the other hand, in terms of practical utility (particularly vocational), I've read quite a few complaints from working scientists about their undergraduate statistics coursework. (In that it was much less useful than it could have been, and misleading in important respects.) But the same can be said about, well, everything in the high school curriculum and, indeed, much of the undergraduate curriculum in most subjects.

At that point we're grappling with the conflict between vocational and liberal arts education at the secondary level — we've pretty much agreed in the US to strongly favor a liberal arts approach to secondary education (and as an end to itself), but particularly in the context of certain vocations (including science) there's always some discussion favoring a more practical, technical approach.

Personally, I'd favor an even stronger universal liberal arts approach at the secondary level with an additional year or even two, with a decreased liberal arts emphasis and increased technical emphasis at the undergraduate level. But I know that's never going to happen and we'll continue to muddle through with compromises that don't do any particular thing very well.

That said, my preferred approach would then include probability and stats in high school taught as part of a broad, liberal arts math education; and then much, much better (and more) stats education for all the undergraduates who will actually be doing statistics.

The Status of Simulations

drorzel — Wed, 29 Dec 2010 09:19:54 +0000

The Status of Simulations

Most of what would ordinarily be blogging time this morning got used up writing a response to a question at the
Physics Stack Exchange. But having put all that effort in over there, I might as well put it to use here, too...

The question comes from a person who did a poster on terminology at the recently concluded American Geophysical Union meeting, offering the following definition of "data":

Values collected as part of a scientific investigation; may be qualified as 'science data'. This includes uncalibrated values (raw data), derived values (calibrated data), and other transformations of the values (processed data).

In response, he got a note saying:

You have a bias here towards observational data. Need to recognize that a lot of data comes from models and analyses.

The question is phrased as, basically, "What constitutes 'data?'" but really it's about the status given to simulation results within science.

This is, of course, a politically loaded question, which is probably why it got this response at the AGU, where there are people who work on climate change issues. Given the concerted effort in some quarters to cast doubt on the science of climate change in part by disparaging models and simulations as having lesser status, scientifically, it's not surprising that people would be a little touchy about anything that seems to lean in that direction.

As for the actual status of models and simulations, that varies from (sub)field to (sub)field, more or less in accordance with how difficult it is to interpret experimental or observational data. My own field of experimental Atomic, Molecular, and Optical physics has a fairly clear divide between experiment and theory, largely because the experiments we do are relatively unambiguous: an atom either absorbs light or doesn't, or it's either in this position or that one. We do need simulations to compare to some experiments, but there's never much question that those are theory, and not part of the experiment. The correspondence between things like density distributions observed in experiments and those generated by simulations is often close to perfect, differing only by a bit of noise in the experimental data.

When you get to nuclear and particle physics, where the detectors are the size of office buildings, the line gets a little fuzzier. The systems they use to detect and identify the products of collisions between particles are so complicated that it's impossible to interpret what happens without a significant amount of simulation. As a result, experimental nuclear and particle physicists spend a great deal of time generating and analyzing simulated data, in order to account for issues of detector efficiency and so on. I don't think they would call these results "data" per se, but computational simulation is an absolutely essential part of experimental physics in those fields, and those simulations are accorded more status than they would be in AMO physics. Experimental nuclear particle physicists spend almost as much time writing computer code as theoretical AMO physicists, at least from what I've seen.

The situation gets even more complicated when you get to parts of physics that are fundamentally observational rather than experimental. If you're a particle physicist, you can repeat your experiments millions or billions of times, and build up a very good statistical understanding of what happens. If you're an astrophysicist or a geophysicist, you only get one data run-- we have only one observable universe, and only one Earth within it to study. You can't rewind the history of the observable universe and try it again with slightly different input parameters. Unless you do it in a simulation.

My outsider's understanding of those fields is that simulation and modeling is accorded a much higher status than in my corner of physics, just out of necessity. If you want to use a physical model to explain some geological or astrophysical phenomenon, the only way you can really do it is by running a whole lot of simulations, and showing that the single reality we observe is a plausible result of your models. Correctly interpreting and establishing correspondences between simulations and observation is a subtle and complicated business, and constitutes a huge proportion of the work in those communities.

I don't know that many astrophysicists, and even fewer geophysicists, so I don't know the terminology they use. My impression of the astrophysics talks I've seen is that they wouldn't put such simulation results on the same level as observational data or experiments, but then my sample isn't remotely representative. It may well be that there are fields in which model results are deemed "data" in the local jargon.

Of course, part of the reason for moving this over here is that there will be many more geology/ climate science types hanging around ScienceBlogs than there are at the Physics Stack Exchange, so there's a good chance of getting some clarification from within the relevant communities. And even extending the question to other fields outside the physical sciences-- I know even less about biology than geophysics, so for all I know this is a question that comes up there, too. If you work in a field where simulation results are commonly termed "data," leave a comment and let me know.

drorzel Wed, 12/29/2010 - 04:19

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Physical Sciences

I don't understand - what part of that definition leans toward observational data, or away from simulations and analysis? What part of it says that "Values collected as part of a scientific investigation" can't come from a model?

In other words, how could one improve the definition to incorporate the response, without simply saying "Data can include values derived through observations, modelling, or analysis?" (I see that sort of statement as redundant - if the rest of the definition is written poorly, then the caveat will contradict the definition; if written well, then it's superfluous.)

For the record, I'm a lawyer, not a scientist.

I am a climate scientist. Complex numerical modeling of climate system is a giant neighborhood in the climate science community. There are a great many scientists who work almost entirely within the world of numerical climate models. Many of them refer to the output of climate models as "data". This has been exacerbated by the release of huge archives of climate model output on public websites (the CMIP archive hosted by LLNL) which allows anyone to download and analyze the output from more than 20 climate models. This is accomplished in much the same fashion that large observational groups, such as NASA, share publicly-funded observational data. The CMIP archive has been a great development for opening up the world of climate modeling to any interested party (and to valuable scrutiny by any interested party). But it has, in my opinion, blurred the line between data and simulation output.

My opinion is that the outputs of numerical models should not be referred to as data. Our technical language, particularly in the earth sciences, should distinguish between observations of the one true realization of the earth's climate and the representations of possible (but not necessarily likely or even plausible) climate states that the models spit out.

None of this should be taken as demoting the "status of simulations". I work with models and observations. They both have their place in climate science.

To some extent, this is a pragmatic issue: Data is data, simulated or not, when you get to the part where you put the data into the equation/software/visualization system.

But we do have a strong cultural bias, with deep roots and a valid raison d'etre, to refer to information we collect from observation as data. THAT kind of data has a special place in the "Scientific Method" and all that.

One problem with this is that simulation results are sometimes seen, within various fields (certainly in bioanthropology) as "made up data" and "made up data" is sometimes very very evil. If, that is, you called it observation and really just made it up.

Even the term "simulated data" has a negative connotation. Perhaps "simulation data" is the ideal term that Eric seems to imply that we need.

I have more than once found myself in a room full of screeching anthropologists insisting that bootstrapping was unethical because it involved made up data. Fortunately, they were my students in my classroom so I had a chance of doing something about it.

Chad: "Given the concerted effort in some quarters to cast doubt on the science of climate change in part by disparaging models and simulations as having lesser status, scientifically..."

Can any scientist really doubt that models and simulations have lesser status, scientifically, then observational/experimental data? This is THE cornerstone of empiricism and science.

If your model says jumping from your window is safe and your experiment ends up with you dead on the pavement will anyone argue that your model got it right?

Yeah, there is a whole spectrum here, since extracting data from observations/experiments usually takes some kind of models, but the general rule is pretty simple - each step away from direct observation, each layer of theory, model or simulation, lowers the reliability of the conclusions.

For a trivial example, lets say you measure voltage at some point in a light detecting circuit and your meter shows 10 Volts. The statement that the meter showed 10 Volts is the most reliable one, the statement that there were 10V potential there in the circuit is less reliable since it assumes your meter is working as intended, the statement that it means 20mA current was flowing between those points is even less reliable since it relies on the correct knowledge of the impedance and many other aspects of the circuit, finally the statement that it means the light intensity was such and such is even less reliable since it assumes the circuit is working as intended, and so on, each step away from the empirical fact that the meter showed 10V involves additional assumptions and models which lowers the reliability of the conclusion.

each step away from direct observation, each layer of theory, model or simulation, lowers the reliability of the conclusions.

I'd have to strongly disagree with this. What if you're dealing with some process where the only data you have is very far removed from the system? (I'm thinking specifically of things like behavior of Earth's magnetic field.) Models can do a lot to illuminate the mechanisms that cause the behavior because we can observe what is happening with the model while we cannot ever directly observe what is happening inside the planet: we can only see the generated field and examine historical evidence for what it might have looked like in the past (which is already a somewhat contentious debate). In reality, those observations doesn't tell us how the field is generated, and the equations are non-linear and work over huge ranges of scales. There's no straight-forward way to describe the behavior as only the simplest cases are intuitive. The models, however, are extremely illuminating and are increasingly reliable as more computational power and thus better resolution become available.

"each step away from direct observation, each layer of theory, model or simulation, lowers the reliability of the conclusions."

This is hogwash; and for reasons more fundamental than suggested by Cherish. An observation that lacks a theory to explain it doesn't provide much understanding.

@5 & 6: I interpret Paul's statement as "the more complicated the model (once you include all the intermediate layers of theory)..."

To use Cherish's magnetic field example, if you *could* observe what's happening inside the Earth and built a model based on that, it would likely be better than one based on modeling the unobservable. Chad says essentially the same thing re: particle physics.

Eric-- I don't think anyone's arguing that observation without theory is more meaningful. Paul's statement that "The statement that the meter showed 10 Volts is the most reliable one" is accurate. It's not terribly meaningful, but it is more reliable.

This sort of issue comes up all the time in financial modeling as well. It's always very tempting to use the result of a [smoothed w/ nice boundary behavior] model as input to another one, but you should avoid that where possible.

You two (Cherish and Eric) completely missed the point, I am not talking about understanding I am talking about *reliability*. All the understanding a model has to offer is worthless if it's reliability is close to zero or undefined.

As a professional data torturer, I don't see any difference in principle between "real" and simulated data: it's all information that is to be processed. There are even statistical techniques for dealing specifically with simulations.

Within biology, I think simulations aren't necessarily seen as better or worse, but just as telling us something different: the model is an abstraction, an idealisation, of the Real World.

Speaking to amplify Cherish's point and to address Paul's point, I recall doing several experiments as an undergrad physics student in which the values I was actually recording were far, far removed from the values I was trying to determine.

The fact is, you really can't directly measure the energy levels of the nucleons in the atoms of a sample, but if you have the Standard Model from QM you can take the values you can measure (which in a typical MÃ¶ssbauer spectroscopy experiment would be things like histogram data from an event detector and the linear velocity of your solenoid) and derive those energy levels.

I understand that the Standard Model is currently considered incomplete or inadequate, but it's nevertheless valuable because it makes accurate predictions which are usable to physicists and engineers alike. Without the mathematical models made possible by the Standard Model, these kinds of calculations would be impossible, and if any of the assumptions or relationships within the Standard Model were found to be grossly incorrect or inaccurate, any data derived through its equations would be rendered meaningless, just as the model itself would collapse like a proverbial house of cards.

But we have enough confidence in this particular model, despite its flaws, that we don't anticipate such a drastic outcome; after all, we engineer semiconductor devices of staggering complexity which push quantum limits, and we are engineering quantum computers and cryptography systems using those same models. Similarly, we know that General Relativity has shortcomings, but we don't omit relativistic corrections from our GPS software. (At least clock-skew is easier to directly measure, assuming you trust how your atomic clock works...)

Consider the field of astronomy, where the things you're measuring are even more remote than in any other scientific discipline. Trying to determine something as simple as distance to some object can be fraught with difficulty, and many estimates have been revised as our tools have gotten better. Still, determining distance relies upon a ladder or hierarchy of metrics, with overlap between the different ranges for each technique at the astronomer's disposal â the overlap is what gives you confidence that the results for one distance estimate are valid when other objects can be measured using both that and a different method. As long as the methodology for determining some astrophysical results is well-published, so that the model can be tested and analyzed, there's no reason to reject the calculated results derived by an astrophysicist in favor of the raw observational data from an astronomer. (But don't throw away the raw data! Someone has to check the work, or maybe even do fresh analysis.)

If we were to apply Chad's reasoning to the fields of astronomy and astrophysics, I suspect there would be precious few trustworthy conclusions one could draw about anything.

I am also a climate scientist. It's not uncommon to refer to model output as "data", especially by modelers and software engineering types. I do data-model comparisons, and I try to distinguish between "model output" and "data". (Although "data" can mean either direct observations or observational "data products" that involve modeling). When I work with statisticians they often refer to everything, observations and model output, as "data", and this can be confusing.

Regarding the primacy of observations over theory, fine, I understand the perspective: reality is of course the ultimate arbiter of whether a theory is right or wrong. But, in practice, observational data are not always more "reliable" than theory. (Sparse data, high noise, uncontrolled confounding factors, unknown systematic biases, poorly characterized relationships between measured and inferred quantities, etc.) Not every field of science has abundant clean data that can be measured out to ten decimal places.

Consequently, this makes "falsification" much harder. You can't always just say that data and theory are "inconsistent" and therefore the theory is ruled out. You also have to wonder whether the data are flawed, because in reality it's not just theory that can be wrong, but data too. (Or, at least, the data don't necessarily mean what you think.)

Rather than "falsification", I prefer to think of science as iterative theory refinement. This is a broad process which involves looking at the totality of both observational and theoretical evidence, the credibility of individual measurements and calculations, and overall coherency. Does the balance of evidence undermine the credibility of a theory, or support it?

Paul, how is creating a model based on the fundamental laws of physics (i.e. Maxwell's equations and the laws governing fluid dynamics) that creastes a field similar to that observed at the surface of the planet unreliable? When were the laws of physics invalidated?

I'm not sure what you're getting at with 'reliability'. Do you mean that there are errors in measurements when you have to infer some of the information? That's true, but you also can make a reasonable estimate about what the error is. If a model based on fundamental laws is producing results that are close to the empirical data, I'd have a hard time believing it isn't valid. Further, any program of that sort of complexity has to be validated at many levels. It has to be able to successfully produce results that can be observed before you throw it at a problem that complicated. If you can't first reproduce results from a ball of spinning ferromagnetic fluid from a lab, then obviously you aren't going to tackle a planetary interior until you know the code works correctly.

It seems to me that what you are saying is that models have no basis in reality. That is false.

I mostly agree with FB's take. I admire his (her?) attempt to distinguish "model output" from "data".

Contrary to Chad's original article, I'd claim that this nomenclature issue occasionally pops up in AMO physics as well. I've heard theorists talk about the correspondence between "theory" (their analytic model) and "data" (the results of their numerical simulation). Which drives me nuts.

Admittedly this is a bit of a different issue than what goes on with nuclear/particle/climate physics. But we need to update the language to differentiate between data (which, according to my religious beliefs, is something you can only get from an experiment) and the results of simulations.

I think it works better if you consider data as evidence, since the point of collecting data is to support an argument. Scientific argument follows the rules of rhetoric whether one is forensic, arguing the past, or hortatory, arguing the future. Real world observations are essential for forensic argument, since the argument is about what caused what and what happened. On the other hand, the results of simulations can be used for arguing about past or about the future.

It makes perfect sense to use the results of a simulation as evidence, but it is evidence about the behavior of the model. It has to be combined with evidence of real world observations to support arguments about the behavior of the real world.

P.S. The mathematician Gian Carlo Rota considered mathematical proofs to be evidence which could be used to understand mathematical entities. It was rarely obvious which statements were the axioms and which the theorems, but whichever were chosen, the structure of the proof could provide valuable insight.

I'm a computer scientist. Everything is data!

I find some of the opinions expressed above deeply disturbing; it is hugely important that we maintain a clear separation between observational and experimental data from the Real World, and numbers generated by computer simulations.

Isaac Newton appreciated the distinction when he promoted the primacy of experiments and poo-pooed those who tried to argue about the structure of nature without experimenting. Let's not go backwards now.

In the computer science sense, of course all numbers are "data". That's just a different use of the term and should not trouble us or cause confusion. But a "datum" (from the Latin, meaning "a given [thing]") in science should be a privileged item.

Data is never completely raw - the example of the reading on a voltmeter is a good one - but there is a world of difference between a measured item on a super-strong well-verified inferential chain and a number puffed out of a computer using the researchers' pet models.

For example, there is a difference between the straightforward algorithms that recover particle tracks from detectors and the more contentious calculations that aggregate these to infer the existence of particles that cannot be observed as directly.

And of course data needs to be "cleaned" sometimes; raw data is privileged but not necessarily pure. A voltmeter can be lose its calibration, a rainfall collector can fill with rubbish, a recorder can record the wrong number.

A recent post somewhere on ScienceBlogs from a climate scientist whinged that it was unreasonable to demand publication of models for review, and referred to the informal process of aggregation and "verification" of models, and adjustment of parameters. The implication was that the plebs should accept that the masters of climate science look on this work and pronounce it satisfactory. Very, very unconvincing. With that post, and this one, I have become even more unhappy about the status of climate science; most of us have neither the expertise nor time to dig deep and to find that many think that the numerical diarrhea generated by computer models (fiddle factors adjusted to the researchers' preference) might be accorded the same respect as careful measurements is very worrying.

I suspect the climate scientists are generally correct. But I don't trust the climate science community. There is none of the rigor demanded of medical trials or epidemiology in their work.

Sam,

If you think that the commenters here have claimed that computer models and measured data are equivalent, I seriously suggest that you re-read the comments.

What commenters have argued against is the position that theory and modeling are useless or unreliable (as you imply with your "numerical diarrhea" comment), or that measured data is always more reliable than theory.

(note -- I'm the one who posted the question to stackexchange that spawned this discussion)

Re: Sam C's comment (#16) : we actually have models of how sensors degrade over time and how sensitivity in one waveband is related to sensitivity of the light from the test lamp (imagine a 15 year old telescope that's been pointed at the sun -- it gets some burn-in on the CCD [1], and being that it's in space, you can't go and swap it out for a fresh one), and that's used to generate the calibrated data which most scientists use without question.

For newer missions (eg, SDO), except for EVE, the data is only being released after calibration. (and we had to fight for a reversible calibration, and not data which had a point spread function applied and forced to integers)

... at the very least, I likely need to differentiate between 'simulations' and 'models', as the term 'model' is used to describe formulas, simulations, specific runs of simulations, multi-dimensional maps, etc.

And for publication, I think that editors should require that at the very least, the peer reviewers have access to the underlying data used in the analysis, and information about where it came from and how it was processed. As right now, peer-reviewed papers are the main metric of productivity (ie, tenure, etc.), if we can get the gate-keepers on board, I think we have a chance with fixing the culture that accepts a lack of data transparency.

[1] http://umbra.nascom.nasa.gov/eit/eit_guide/offpoint.htm

Sam, you're totally right. And while we're at it, let's stop using computer models to predict the weather, engineering prescription drugs, designing cell phone towers, finding mineral deposits under the ground, developing an understanding of cancer and other biological processes...all that stuff. Obviously if it's made on computers, it must be junk and useless, right?

You may not realize it, but nearly every field of science and engineering uses computers to model complicated processes. Take a look at Nvidia's site (http://research.nvidia.com/) as one example and you can see the breadth of fields that use large-scale computing. Most of these fields take for granted that they can use computers to solve their problems, and most don't do anything differently that climate science. It's just that climate science has been under intense scrutiny, so everyone assumes that it is somehow flawed because it uses computers.

The inputs to these processes use 'real' data, and the outputs need to be measured and evaluated against observational data. Generally, we know that these models can produce some very good science and engineering because you're living with the results every day. For those who use their modeling to tackle less pragmatic or immediate subjects, peer review comes in to evaluate the validity. If you're modeling something with an obnoxiously unrealistic parameter regime, it's fair to say that an observant reviewer is going to ask you what you were thinking. Further, some fields employ benchmarks: if your simulation cannot produce a required set of phenomena, you don't meet the benchmark and your results can't be trusted.

Most modelers don't use 'fiddle factors to adjust to the researchers preference'. They evaluate the system at a set of regimes to see how the system responds. This is good science because it's important to know where your system works, where it doesn't, and whether the results are unexpected due to the model or the physical system itself.

Using computer does have it's drawbacks as there will never be enough computational power to have full resolution of all systems at reasonable timescales. However, arguing that computer models produce useless or highly suspect data because of this is like saying we should never make macroscopic approximations to systems and should always rely on quantum mechanics to figure out what's going to happen because it's so much more precise.

Interesting to see some cultural divide here. Looks to me that your attitude depends on how complicated is your measuring device and the phenomena you are trying to describe. On the one extreme there is the "voltmeter" people, who seem to ignore any layer of interpretation involved in their measurements, thinking about measuring as a simple and unambiguous process. Nice work if you can get itâ¦On the other extreme, high energy experimentalists are confronted with a couple of GB of data per secondâ¦Both the collsions and the measuring device are extremely complicated systems, and big part of the effort is figuring out what constitutes of a measurement, what is noise and what is signal, in other words how to interpret the data (which means lots and lots of modelling and simulations). I expect this is a big part in climate science (about which I don't know all that much).

I'd argue that this layer of interpretation exists always in any measurement. When the measuring device is simple and/or well-understood this can be done in an intuitive oral-tradition kind of way. When the system is complicated enough this layer has to be explicitly considered and systematized. I'd also argue that such systematic approach makes the measurement more, not less, relaible. But, I'm only a theorist, and I admit to not having any strong opinions on what the scientific method ought to be...

Sam (#16): The publishing of climate model code issue is a strawman. The codes for most major climate models are publicly available and have been for many years - at least those developed in the US. Indeed the models have many tuning parameters (which are really physical coefficients, such as ice crystal fall speeds and turbulent mixing coefficients, not merely unphysical knobs). Many are chosen based on observations, rather than whim, however the most crucial parameters (those that determine the properties and behavior of clouds) are only observed with huge uncertainty bars (note that these measurement uncertainties persist in spite of tremendous rigor in the measurement strategy). Therefore the modelers are only helped marginally by the observational community. Nevertheless, the sensitivity of the model results to tuning parameter values across the range of values within the measurement uncertainty has been probed and provides a powerful constraint (though not a complete constraint) on the range of possible future warming for a given increase in greenhouse gases. All of this well documented in the IPCC reports and the scientific literature. If this is not clearly conveyed by a few voices in the blogs, then that is unfortunate. But the peer-reviewed literature and the assessments upon which they rest clearly document the uncertainties related to climate modeling.

Asking for the same level of rigor in climate science as in medical trials is not particularly helpful. Medical trials rely on carefully controlled experiments with an experimental group and a control group. Doing so for the climate system would be nice. But alas, we have only one Earth. I would be interested to know how the epidemiology community is a better example of rigor. They, like climate science, are inhibited by a lack of carefully controlled experiments.

I am a theorist/computational scientist who works closely with experimentalists. We always refer to "numerical/simulation data" and "experimental data". They are both data, and neither is flawless; both come with assumptions and approximations. Theory and numerics start from a hypothesis of "why" something happens and try the describe a particular phenomenon; you test the "why" (our understanding) by testing the prediction of the phenomenon with the experimental observation of the phenomenon. This process is at the core of the scientific method, so I don't understand people who think so little of theory/simulation.

"the experiments we do are relatively unambiguous: an atom either absorbs light or doesn't, or it's either in this position or that one." Well... dogs know that quantum mechanics allows the superposition of states, right ?

Rare Sharing of Data Led to Results on Alzheimer's

ably — Mon, 16 Aug 2010 10:20:35 +0000

Rare Sharing of Data Led to Results on Alzheimer's

From the New York Times

The key to the Alzheimer's project was an agreement as ambitious as its goal: not just to raise money, not just to do research on a vast scale, but also to share all the data, making every single finding public immediately, available to anyone with a computer anywhere in the world.

No one would own the data. No one could submit patent applications, though private companies would ultimately profit from any drugs or imaging tests developed as a result of the effort.

ably Mon, 08/16/2010 - 06:20

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open science

data

open science

On offense

razib — Fri, 30 Oct 2009 05:51:41 +0000

On offense

Ruchira Paul has a post up, "Religious, superstitious, nonsense" and other harsh words. The point at issue is the fact that a teacher who expressed anti-Creationist views in harsh tones was sued. Ruchira asks somewhat rhetorically as to the sort of things parochial schools say about other religions and atheists. The bigger issue is one of public decorum, and decorum is very contextual. When my 7th grade teacher had us read Medea she explained a bit about the context of Greek society, including the nature of their religion. She spoke of "their gods" and "our God." Her reference to "our God" was absolutely ecumenical, and in the most general of tones, while her reference to "their gods" was clinical and disrespectful. Disrespectful because she perceived Greek paganism to be superstitious, if interesting, nonsense, and said so (I agreed with her, but my own sentiments were a bit more catholic). Ludicrous on the face of it was her stance, which she made plain. There were no Greek pagans to protest.

The issue and dynamic are general. In the ancient world the Jews and Christians were considered atheistic because they denied the existence of all gods but their own.This was an offense to the pagan majority, who did not exhibit reciprocal disbelief. I have talked to Hindus who are hurt by the exclusive and atheistic stance by the Abrahamic religions about other gods (from the perspective of a non-Abrahamist much of the scriptures of those religions and the writings of their most respected divines are hate screeds). In any case, with the rise of Christianity to be called heathen or pagan was an offense, and the old gods became blasphemous demons (and naturally fundamentalist Christians are wont to identify Hindu deities such as Shiva with demonic figures in the Book of Revelation). When the Chinese encountered Christianity (again) in the 16th and 17th centuries, in particular its Catholic forms, the exact same accusations of cannibalism which were common in the ancient world reemerged. Outside of the proper cultural context, a plain explanation of transubstantiation seems more offensive than sacred.

One can expand the point outside of religion. In much of pre-modern Europe a bare breast was reputedly less sexually charged than exposed shoulders or legs. Sexuality is not totally culture variant, even societies where nudity is common have norms (e.g., notice that Amazonian women in the older National Geographic specials never squat). But there's enough variation that software differs from society to society. The human mind operates on autopilot much of the time, and internalizes particular cues and contexts, and fires programs which come preloaded. When put into an exotic circumstance it takes some time to adjust, and when two individuals come together when there are cultural differences confusion can often ensue (immigrants may often never become totally acculturated).

Back to religion. In the World Values Survey there's a question about how much you "Trust People Of Other Religions." There are 4 responses, trust complete, trust a little, not trust very much, and not trust at all. I created an index of trust, whereby the above response were coded as 0, 1, 2 and 3. So if 100% in a country did not trust at all, the value would be 3. Below are the responses for nations in WVS wave 5. I've ordered them. You might be surprised.

		Index
	Sweden	0.976
	New Zealand	0.986
	France	1.009
	Norway	1.096
	Great Britain	1.131
	Mali	1.15
	Finland	1.152
	United States	1.189
	Canada	1.201
	Australia	1.255
	Switzerland	1.286
	Andorra	1.294
	South Africa	1.305
	Rwanda	1.329
	Argentina	1.372
	Trinidad and Tobago	1.402
	Burkina Faso	1.455
	Uruguay	1.484
	Ghana	1.502
	Taiwan	1.583
	Serbia	1.591
	Poland	1.601
	Brazil	1.601
	Netherlands	1.617
	Spain	1.629
	India	1.643
	Ethiopia	1.644
	South Korea	1.646
	Bulgaria	1.649
	Indonesia	1.675
	Georgia	1.689
	Germany	1.702
	Ukraine	1.732
	Zambia	1.74
	Italy	1.744
	Malaysia	1.763
	Chile	1.786
	Egypt	1.794
	Thailand	1.842
	Colombia	1.848
	Russian Federation	1.849
	Viet Nam	1.85
	Slovenia	1.891
	Romania	1.907
	Jordan	1.951
	Mexico	1.987
	Turkey	1.988
	Cyprus	1.998
	Moldova	2.02
	Morocco	2.085
	Peru	2.111
	China	2.112

As Muslims go the people of Mail have a reputation of being atypically chilled out. These data would support that. At the bottom of the list are nations where religion, nationalism, and their intersection are rife. No surprise. But what about Vietnam and China? East Asian nations are arguably even more secular than the Nordic countries, and unlike Sweden they don't really have a recent history of religiosity which gave way to secularity. They think religion is weird, and Communism didn't help change that attitude at all (both China and Korea broke the power of the Buddhist orders nearly 1,000 years ago, while Oda Nobunaga was a great monk-slayer).

razib Fri, 10/30/2009 - 01:51

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Who are the conservative Democrats? (part 2)

razib — Thu, 20 Aug 2009 22:28:14 +0000

Who are the conservative Democrats? (part 2)

A question below:

I'm curious about the demographics of this category, specifically their geographic distribution, religion and ethnicity.

First, I limited the sample to whites to remove confounds of ethnicity. Interestingly, in the GSS in the period between 1998-2008 24% of black Democrats/lean Democrats considered themselves conservatives, as opposed to 18% of whites. This surprised me, I generally remove blacks from he GSS sample in politics so had no data to fill in the gap where intuition lay. It does reiterate my suspicion that personal assertions of political ideology are less important than revealed preferences as determined by voting patterns. Another way of characterizing the racial breakdown is that while 21% of liberal + moderate Democrats were black, 28% of conservative Democrats were black.

As for the rest of the question, that's rather easy to explore. I simply combined the liberals and moderates into one category, placed the conservatives into other, and cross-referenced them when other variables. For your information, "SEI" = socioeconomic index below. The short of it is that conservative Democrats are less intelligent and educated, more Protestant and religious, less affluent, and of course, more Southern. This is for whites only again.

	Liberal + Moderate	Conservative
Northeast	86.5	13.5
Midwest	84.5	15.5
South	73.8	26.2
West	86.8	13.2
18-30	87.6	12.4
31-45	84.6	15.4
46.65	80.1	19.9
65-	76.6	23.4
SEI 17-37	76.7	23.3
SEI 37-57	80.8	19.2
SEI 57-77	86.9	13.1
SEI 77-97	92.1	7.9
Male	80.7	19.3
Female	83.3	16.7
Less Than High School	73	27
High School	80.2	19.8
Junior College	78.5	21.5
Bachelor	88.8	11.2
Graduate	93.8	6.2
Dumb (WORDSUM 0-4)	70.1	29.9
Normal (WORDSUM 5-7)	79.1	20.9
Smart (WORDSUM 8-10)	88.9	11.1
Atheist/Agnostic	94.7	5.3
Higher Power	92.9	7.1
Believe in God Sometimes	83.4	16.6
Believe with Doubts	85	15
Know God Exists	78.1	21.9
Protestant	78.3	21.7
Catholic	80.5	19.5
Jewish	92.7	7.3
No Religion	91.9	8.1
Never Married	87.8	12.2
Married	80.4	19.6
Divorced	82.8	17.2
Income > $25,000	84.9	15.1
Income < $25,000	78.8	21.2
Voted Clinton 96	83.3	16.7
Voted Dole 96	71.4	28.6
Voted Perot 96	76.4	23.6

razib Thu, 08/20/2009 - 18:28

Tags

Culture

data

Politics

One large class of conservative Democrats can be explained by the residual Confederate / New Deal / ethnic loyalty of some white Southerners. At the local level in the South the Democratic Party still does fairly well on this basis.

The black conservative Democrats presumably are churchy social conservatives whose loyalty is based on the New Deal plus the civil rights movement.

Two rather contradictory loyalties.

For me (an economic liberal for whom the social issues are secondary) this is just more evidence of the way that ethnic appeals and peripheral issues screw up politics. For a long time in American politics (most of 1865-1932, except during the Bryan era) the D/R divide was almost purely ethnic and regional: Northern Protestants were R, Catholics and Southern Protestants were D. Substantive political issues rarely came up.

Tom Rees on Thinking Allowed

razib — Thu, 20 Aug 2009 01:50:42 +0000

Tom Rees on Thinking Allowed

It was a pleasure to hear Tom Rees, one of my favorite religion & data bloggers, on Thinking Allowed, one of my regular podcasts. Epiphenom is of particular interest because Tom Rees offers up original data and analysis, instead of anecdote laced speculation. As they say, if you enjoy Thinking Allowed, you might also be interested in In Our Time....

razib Wed, 08/19/2009 - 21:50

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Culture

data

religion

Women are more fundamentalist because they are more religious

razib — Fri, 07 Aug 2009 11:49:42 +0000

Women are more fundamentalist because they are more religious

A comment below asks:

Well, good for you for getting me to click through by using an interesting post title. But how do you know women who "know god exists" aren't assuming a female god?

In a vacuum of all knowledge about this sort of topic this is a reasonable question. But there's plenty of social science data showing that American women tend to be more religiously conservative & "orthodox" as a whole than men (in contrast to female ministers or rabbis, who are more likely to be progressive than their male counterparts from what I gather). But I decided to see how textually "conservative" men and women were who "know God exists" (a subset of the population) were in relation to each other. Data below....

Bible Word of God + "Knows God Exists"

	Male	Female	Diff.	% Diff.	95-interval male	95-interval female
All	45.1	48	2.9	6%	42.7-47.5	46.1-49.9
White	43.5	44.6	1.1	3%	40.8-46.3	42.5-46.6
Black	53.1	62.2	9.1	17%	47.7-58.6	58.4-66.1
College+	21	29.5	8.5	40%	17.4-24.6	25.9-33.1
No college	51.2	52.2	1	2%	48.5-53.9	50.1-54.3
Liberal	39.1	42.7	3.6	9%	34.5-43.7	38.3-47
Conserv.	47.7	53.5	5.8	12%	43.7-51.7	50.6-56.5

Since these data had smaller N's than the previous ones I put in the 95 percent confidence intervals. I bolded those with college educations because only in that case do the intervals not overlap. My general conclusion here is the difference in fundamentalism between men and women is more a difference in religiosity, and once men and women reach a certain level of religious commitment there isn't much of a difference in outlook. If I expanded the sample to those who "believe in God but have doubts," the sex difference crops up again, but this is because so many more men than women fall into this category proportionally.

(I checked these results with attitudes toward evolution, and they align well, but the sample sizes were even smaller so I left that out)

razib Fri, 08/07/2009 - 07:49

Tags

Culture

data

GSS

This is surprising given the derogatory manner in which the Bible refers to women. Here's an example from the book of Timothy:

In like manner, women also in decent apparel: adorning themselves with modesty and sobriety, not with plaited hair, or gold, or pearls, or costly attire: 10 But, as it becometh women professing godliness, with good works. 11 Let the woman learn in silence with all subjection. 12 But I suffer not a woman to teach, nor to use authority over the man: but to be in silence. 13 For Adam was first formed; then Eve. 14 And Adam was not seduced; but the woman, being seduced, was in the transgression. 15 Yet she shall be saved through child bearing; if she continue in faith and love and sanctification with sobriety.

Dear MythBusters: Please share your data

rallain — Thu, 14 May 2009 10:57:05 +0000

Dear MythBusters: Please share your data

I think the Mythbusters have a wonderful opportunity for educational outreach. Take this week's episode. One myth was to see if arrows fired from a moving horse penetrated more than arrows fired from a standing position. They first did this with real horses, but they said the data was not convincing.

I am pretty sure they had more than 10 trials recorded (there was a glimpse of the notebook). I would love to see this data and find (or let students find) the standard error of these measurements. This would be a great exercise to see how this whole uncertainty thing works.

As long as I am asking for stuff. How about you (the MythBusters) also provide your slow motion videos in a format that can be used with video analysis? There is a ton of great stuff there. One problem is that I never really know the frame rate of their videos.

Adam, Jamie, please contact me when you have this set up. I will owe you a beverage.

PS - if this data is already out there and I just have not seen it, sorry.

rallain Thu, 05/14/2009 - 06:57

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