The science of statistics is all about differentiating signal from noise. This exercise is far from trivial: Although there is enough computing power in today’s laptops to churn out very sophisticated analyses, it is easily overlooked that data analysis is also a cognitive activity.

Numerical skills alone are often insufficient to understand a data set—indeed, number-crunching ability that’s unaccompanied by informed judgment can often do more harm than good.

This fact frequently becomes apparent in the climate arena, where the ability to use pivot tables in Excel or to do a simple linear regressions is often over-interpreted as deep statistical competence.

The graph below illustrates this problem with the global temperature data: although there is no question that the trend is increasing, it is always possible to cherry pick periods for analysis during which there is no significant increase in temperature. Of course, those “analyses” are a meaningless distraction from what is actually happening on our planet (the only one we’ve got, by the way).

Similar comments apply to some of the analyses reported in the blogosphere of our recent data on rejection of science and conspiracist ideation. We have already dealt with the “scamming” issue here and here, and we will not take it up again in this post.

Instead, we focus on the in-principle problems exhibited by some of the blog-analyses of our data. Two related problems and misconceptions appear to be pervasive: first, blog analysts have failed to differentiate between signal and noise, and second, no one who has toyed with our data has thus far exhibited any knowledge of the crucial notion of a latent construct or latent variable.

Let’s consider the signal vs. noise issue first. We use the item in our title, viz. that NASA faked the moon landing, for illustration. Several commentators have argued that the title was misleading because if one only considers level X of climate “skepticism” and level Y of moon endorsement, then there were none or only very few data points in that cell in the Excel spreadsheet.

Perhaps.

But that is drilling into the noise and ignoring the signal.

The signal turns out to be there and it is quite unambiguous: computing a Pearson correlation across all data points between the moon-landing item and HIV denial reveals a correlation of -.25. Likewise, for lung cancer, the correlation is -.23. Both are highly significant at p < .0000…0001 (the exact value is 10 ^-16, which is another way of saying that the probability of those correlations arising by chance is infinitesimally small).

What about climate? The correlation between the Moon item and the “CauseCO2” item is smaller, around -.12, but also highly significant, p < .0001.

Now you know why the title of our paper was “NASA faked the moon landing—Therefore (Climate) Science is a Hoax: An Anatomy of the Motivated Rejection of Science.” We put the “(climate)” in parentheses before “science” because the association between conspiracist ideation and rejection of science was greater for the other sciences than for climate science.

(As an intriguing aside, by the logic that’s been applied to our data by some critics, the larger correlations involving other sciences would suggest that AIDS researchers—keen to get their grants renewed?— scammed our survey to make AIDS deniers look bad.)

But we can do better than extract a signal by simple correlations.

Far better.

This brings us to our second issue, the role of latent variables.

To understand this concept, one must first consider the fact that on any cognitive test or survey, any one item, however well designed, will not provide an error-free measure of the psychological construct of interest. No single puzzle can tell you about a person’s IQ, no single question will reveal one’s personality, and no single moon landing will reveal a person’s propensity for conspiracist ideation.

So the correlations we just reported constitute a better signal than the noise that overwhelms a selected few cells of an Excel spreadsheet, but they are still “contaminated” by measurement error or item variance—that is, the data reflect the idisosyncracy of the particular item in addition to information about the construct of interest, in this case conspiracist ideation.

What to do?

Enter latent variable analysis, also known as structural equation modeling (SEM).

SEM is a technique that estimates latent constructs—that is, the hypothesized psychological construct of interest, such as intelligence or personality or conspiracist ideation. SEM does this by considering multiple items, thereby removing the measurement error that besets individual test items.

We cannot get into the details here, but basically SEM permits computation of the error-free associations between constructs, such as one’s attitudes towards science and one’s conspiracist ideation. It is because measurement error has been reduced or eliminated, that correlations between constructs are higher in magnitude than might be suggested by the pairwise correlations between items.

And because SEM removes measurement error, the associations between constructs are particularly resilient, as we showed earlier when all observations are removed that might conceivably represent “scamming.”

When the long-term temperature trend is ignored in favour of a few years of declining temperatures after a unique scorcher, this is missing the statistical forest not just for a tree but for a little twig on a tree.

Likewise, when the associations between latent constructs in our data are ignored in favour of one or two cells in an Excel spreadsheet, that’s missing the statistical forest not just for a little twig on a single tree but for a single leg of a pinebark beetle on that twig that’s eating its way northbound through the Rockies as the globe is warming.

Understanding people means to have a Theory of Mind. A model of other people’s thinking.

This may come as a surprise to some who mistakenly consider models to be something science should do without. Not only are models central to all scientific inquiry—ever heard of the heliocentric model of the solar system?—but without a model we could not understand other people, and not even ourselves.  

This Theory of Mind is a collection of beliefs of what other people believe or know, what they want, and how they most likely will act. If you have ever sat next to someone on a plane who’s telling you all about Barney’s last summer holiday, oblivious to the fact that you’ve never met or heard of Barney before, then you will understand the importance of a Theory of Mind and how its integrity is central to human interaction.

What does this have to do with our recent paper on the motivated rejection of science?

Everything.

We already established that if potentially “suspect” outlying observations are removed from our data, the correlations of greatest interest, between conspiracist ideation and rejection of science, retains its significance. So far, so good, but now we need to discuss the far-from-trivial issue of why anyone would consider those observations “suspect” (other than by their magnitude alone).

This brings us to the issue of “scamming”, the hypothesis that people completed our survey by “faking” their responses.

It turns out that any decision about “scamming” is a cognitive choice that rests on a model in one’s mind about other people’s behavior.

Let’s consider this hypothetical response profile:

4 4 4 4 4 4 4 4 4 4  … 1 1 1 1 1,

where the 4’s stand for endorsement  of conspiracy theories and the 1’s for rejection of climate-related items.

One might be tempted to conclude that this respondent was a “warmist” who “scammed” our survey by faking endorsement of conspiracy theories, perhaps in order to make “deniers” look like “nutters” (I am using caricaturizing labels in quotation marks, such as “warmist”, for succinctness; the discussion is impossible without succinct labels.)

Crucially, we must recognize that the judgment just made was a cognitive judgment that relies on a model—a model of what a response pattern would look like if someone faked the data. This cognitive model rests on (a) the tacit premise that no one could possibly be serious when endorsing all conspiracy theories, and it would include (b) the further deduction that anyone who does this must be faking the response. A further deduction (c) could be added that this faking was done in order to cast aspersions on people who reject (climate) science.

So, a cognitive model with one premise and at least one auxiliary assumption indeed suggests that this response pattern represents a “scammer.”

The crucial point is this: Identification of presumed scammers is a model-based inference, and there is no escaping that fact (e.g., trap questions don’t help because they could equally be scammed).

Moreover, because identification of “scammers” rests on a model-based inference, it should come as no surprise that there are multiple other cognitive models of at least equal plausibility that would lead to different conclusions: For example, the above response pattern is equally compatible with the model that (a) no one in their right mind would endorse all conspiracy theories, and therefore (b) some “deniers” are really “nutters” (again, caricaturizing labels are used for succinctness.).

We therefore come to opposing conclusions about the putative “scamming” responses based on two opposing models of what respondents were thinking while they were completing the survey.

There is no easy way to adjudicate between the two models.

It is for that reason that we removed all those responses to which one or the other of those cognitive models might apply. Given that the removal of “scammers” (or true “nutters”, on the alternative model) makes no difference to the significance of our correlations, we fortunately do not have to expend much further energy on this issue within the narrow context of our survey.

However, it is worth taking a broader view at the notion of “scamming” and the implications of various different cognitive models by considering other manifestations of climate denial (or endorsement) on the internet. Blog comments, after all, are potentially as anonymous as survey responses and they are therefore subject to precisely the same model-based interpretation as the response patterns in our data.

So let’s apply the above models to a few comments and other material harvested from the internet.

We begin with this one, reproduced verbatim below:

“Here is some photographic analysis for Apollo 11 showing that the moonwalking was in fact staged. There is nothing to oppose this analysis and no getting around it. If it conflicts with your irrational beliefs see a psychiatrist. But don’t be getting about running cover for the criminals that push these fraudulent and expensive undertakings on the public. These networks haven’t gone away and they are busier then ever. The CO2-warming scam is scientific fraud. And its a far bigger, more ambitious, and more expensive scam then the moon hoax ever was. 

http://www.youtube.com/watch?v=A6MvcIs4OcQ”

Applying the same cognitive model that suggests that participants scammed our survey, this comment was clearly written by a “warmist” who “scammed” the comment to make “deniers” look like “nutters.”

Some instances of climate denial, by the same logic that some have applied to our survey responses, are a “warmist” hoax.

Moving from comments to blog posts, here we have a certain Oliver Manuel:

“In 1972 I became uneasy about the politicalization of science, but I could not grasp the problem until Climategate emails and documents were released in November of 2009. … I have tentatively traced this back to secret, fear-driven agreements by the winners of the Second World War in 1945 and later by Kissinger, Zhou En-Lai, Chairman Mao, Brezhnev and Nixon in 1971.”

The same individual also recently sent me an email, which opened with: “On The Eleventh Anniversary of the 9-11-2011 Tragedy — Events leading to Climategate in Nov 2009.”

(Disclaimer: O. Manuel did not design our survey items.)

So is this individual a “nutter,” or is he a “warmist” posing as a “nutter” to make “deniers” look bad? On the cognitive model that some people have applied to our survey responses, the latter possibility should be favored.

Lest one think that Oliver Manuel is just a lone individual of questionable mental competence, it must be noted that climate “skeptic” Ian Plimer relies on Manuel’s bizarre theory, that the sun is largely composed of iron, in his principal work of fiction Heaven and Earth. This recent article opens up a door to a sordid and bizarre network of Manuel and associates whose responses to our survey are readily predicted.

Of course, they are all just “warmists” doing their stuff to make “deniers” look bad.

But why stop at blog posts?

Let’s examine some public utterances of well-known alleged climate deniers and see if they might be warmist scammers in disguise, doing their best to make deniers look like nutters.

A leading candidate for scammer-in-chief is Lord Christopher Monckton. Although he is commonly perceived to be the Vaudevillian poster boy of climate denial, some very serious questions about his true identity have been raised on Australian national TV.

Those questions hint at the possibility that Mr Monckton might be a scammer, an impression buttressed by his public concerns about President Obama’s place of birth.

Further evidence for Mr Monckton’s warmist mission to pose as scammer is provided by his public claim that NASA blew up its own satellite to prevent the climate hoax from being uncovered. This seems likely, given that NASA has had ample opportunity to hone its skills with the so-called “moon landing.”

We conclude that there is clear evidence that Mr Monckton is a warmist scammer trying to make climate denial look nuts.

And Mr Monckton is not alone; there appears to be a considerable number of such scammers out there, given that warmists-faking-nutty-denial-theories of exploding satellites can be found elsewhere.

Looks like they all scammed our survey.

We are convinced now.

Climate denial is a warmist hoax, perpetrated by the same scammers who faked our survey.

Data integrity is a central issue in all research, and internet-based data collection poses a unique set of challenges. Much attention has been devoted to that issue and procedures have been developed to safeguard against abuse. There have been numerous demonstrations that internet platforms offers a reliable and replicable means of data collection, and the practice is now widely accepted.

Nonetheless, each data set must be examined for outliers and “unusual” responses, and our recent paper on conspiracist ideation and the motivated rejection of science is no exception.

Perhaps unsurprisingly, after various unfounded accusations against us have collapsed into smithereens, critics of our work have now set their sights on the data. It has been alleged that the responses to our survey were somehow “scammed,” thereby compromising our conclusions.

Unlike the earlier baseless accusations, there is some merit in casting a critical eye on our data. Science is skepticism and our data must not be exempt from scrutiny.

As it turns out, our results withstand skeptical scrutiny. We will explain why in a series of posts that take up substantive issues that have been raised in the blogosphere in turn.

This first post deals with the identification of outliers; that is, observations that are unusual and deserve to be considered carefully.

Outlier detection and identification

Let’s begin by examining the variable of greatest interest in our paper, namely the indicators for “conspiracist ideation,” which is the propensity to endorse various theories about the world that are, to varying extents, demonstrably unfounded and absurd (there are some reasonably good criteria for what exactly constitutes a conspiracy theory but that’s not at issue here).

The full distribution of our conspiracy score (summed across the various items using a 4-point scale ) is shown in the figure below. Ignore the vertical red line for now.

For simplicity we are ignoring the space aliens for now (which formed a different indicator variable on their own), so the observations below represent the sum across 10 conspiracies (remember that the “convenience” theories involving AIDS and climate science are omitted from this indicator variable for the reasons noted in the paper).

Thus, a person who strongly disagreed with all conspiracies would score a 10, and someone who strongly agreed with them all would score a 40.

The figure invites several observations. First, the distribution is asymmetrical, with a longish upper tail. That is, most people tend to more or less reject conspiracies; their score falls towards the lower end of the scale.

Second there are several observations at the very top that may­—repeat may—represent aberrant observations. It is those extreme scores that critiques of our data have focused on, for example the very thorough analysis by Tom Curtis. The top two extreme data points are indeed unusual. But then again, one might (just) expect a few such extreme scores in a sample of more than 1,000 people given the shape of the distribution.

So how does one deal with this situation?

The first, and most important step is the recognition that once the data have been obtained, any identification of an observation as an “outlier”, and any decision to remove a subset of observations from analysis, almost inevitably involve a subjective decision. Thus, a valuable default stance is that all data should be retained for analysis. (There may be some clear-cut exceptions but the data in the above figure do not fall within that category).

There are two ways in which data analysts can deal with outliers: One is to remove them from consideration based on some criterion. There are many candidate criteria in the literature, which we do not review here because most retain an element of subjectivity. For our analysis, we therefore elected not to remove outliers by fiat, but we instead ensured that the inclusion or exclusion of any potential outliers has no substantive effect on the results.

That is, we examined the extent to which the removal of outliers made a difference to the principal result. In the case of our study, one principal result of interest involved the negative correlation between conspiracist ideation and acceptance of science. That is, our data showed that greater endorsement of conspiracy theories is associated with a greater tendency to deny the link between HIV and AIDS, lung cancer and tobacco, or CO2 emissions and global warming.

How resilient is this result to the removal of possible outliers?

The red line in the above figure answers that question. If all observations above that line (i.e., scores 25 or greater; there were 31 of those) are removed from the analysis, the link between the latent constructs for conspiracist ideation and rejection of climate science remains highly significant (specifically, the p-value is < .001), which means that the association is highly unlikely (less than 1 in 1000) to have arisen by chance alone.

In other words, if we discard the top 3% of the data, that is those part of the data which for conceptual reasons should arouse the greatest suspicion, our conclusions remain qualitatively unchanged.

Why discard anything above 25? Why not 29 or 30 or 18?

Because a score of 25 represents a person who disagrees with half of the theories and agrees with the other half (or some equivalent mix of strongly-agreeing and strongly-disagreeing responses). Lowering the cutoff further, thereby eliminating more observations, would eventually eliminate anyone who endorsed any of the theories—and guess what, that would defeat the whole point of the study. Conversely, there is no point in raising the cutoff because we already know what happens when all data are included.

We conclude with considerable confidence that when a highly conservative criterion (scores 25 or above) for outliers is used, the principal result remains qualitatively unchanged. Conspiracist ideation predicts rejection of science—not just climate science, but also the (even stronger) and even more strongly, rejection of the link between HIV and AIDS and the link between tobacco and lung cancer. [13/9: rephrased to clarify, the ‘more strongly’ refers to magnitude of regression weights.]

How does the elimination of outliers relate to the notion of “scamming”, which has stimulated so much interest in our data?

The answer is both obvious and also quite subtle: The obvious part is that the two folks at the very top of the above distribution strongly endorsed virtually all conspiracy theories. If they then also strongly rejected climate science, that would arguably constitute a profile of scamming—that is, those folks may have generated responses to create the appearance that “deniers” are “conspiracy nuts” (note the quotation marks: this discussion is almost impossible to write succinctly without labels, even if they are caricatures).

Now, we have dealt with the obvious bit about the “scamming” problem by throwing out not just those two people of greatest concern, but the top 3% of the distribution—that is, anyone who might remotely look and act like a “scammer” based on their responses to 10 conspiracy theories.

Remember—none of the significant correlations in our data disappear when those people are removed from consideration.

But now to the more subtle part: How would we know that someone who endorses all conspiracy theories but none of the science is actually a scammer? We have tacitly assumed that this somehow is evidence of scamming. But on what basis? Is there more to this than intuition?

This brings us to the fascinating issue of mental models of people’s behavior, which we will address in a future post.

The publication of my paper on conspiracist ideation was met with several nearly-instant accusations. First out of the gate was the claim that I did not contact 5 “skeptic” or “skeptic-leaning” blogs to link to the survey.

I initially did not release those names because I was concerned about the privacy issues involved, as I explained here. Because a release of names cannot be undone—whereas a delayed release harms no one—I decided to seek guidance from various institutions, foremost among them my own university, before deciding whether or not to release those names.

Shortly thereafter, the first of the 5 bloggers, Mr McIntyre, found his misplaced email.

This leaves us with 4 bloggers whose identity had to remain confidential until now.

I am pleased to report that I received advice from executives of the University of Western Australia earlier today, that no legal or privacy issues or matters of research ethics prevent publication of the names of those bloggers.

So here they are:

• Dr Roger Pielke Jr (he replied to the initial contact)
• Mr Marc Morano (of Climatedepot; he replied to the initial contact)
• Dr Roy Spencer (no reply)
• Mr Robert Ferguson (of the Science and Public Policy Institute, no reply)

It will be noted that all 4 have publically stated during the last few days/weeks that they were not contacted.

At this juncture one might consider a few intriguing questions:

1. When will an apology be forthcoming for the accusations launched against me? And how many individuals should now be issuing a public apology?

To explore the magnitude of this question we must take stock of public statements that have been made about my research. For example, one blogger considered it “highly suspect” whether I had contacted any “skeptic” sites.

Mr McIntyre expended time to locate and then publicize the name of the person within my university to whom complaints about my research should be addressed; time that we now know would have been better spent searching his inbox.

Another individual surmised that “the allegations will be widened to include a clear and deliberate intention to commit academic fraud.” 

Finally, another individual opined that “the lack of evidence that he tried to contact skeptic blogs” warranted the inference that none had been contacted—we now know that the presumed lack of evidence was actually evidence for a measure of carelessness or shoddy record keeping among the individuals contacted.

In light of such massive, and massively false, allegations numerous apologies ought to be forthcoming.

However, the fact-free echo machines of the internet sit awkwardly with the notion of civility and conversation of which apologies are an integral part. I therefore doubt that any such apologies will be forthcoming.

Instead, I predict that attention will now focus on some of the other accusations and theories.

After all, what better way to avoid learning from one’s errors than by chasing down another rabbit hole.

2. Why would the people who were contacted publically fail to acknowledge this fact?

Several hypotheses could be entertained but I prefer to settle for the simplest explanation.

It’s called “human error.” It simply means the 4 bloggers couldn’t find the email, didn’t know what to search for, or their inboxes were corrupted by a move into another building, to name but a few possibilities.

The only fly in the ointment in that hypothesis is that I provided search keys and exact dates and times of some correspondence.

3. Where do we go from here?

That’s easy. On to the next theory, of course.

One of the many adverse consequences of knee-jerk science rejection is the voluminous noise generated in response to certain events, such as the recent publication of my paper on rejection of science and conspiracist ideation. Whenever baseless accusations are launched, whether against me or other scientists, this detracts attention from other potentially substantive issues.

My inbox has been overflowing with messages relating to my paper, to the point where I can no longer guarantee a personal response to each message. Some emails raise good points and substantive scientific issues. Likewise, the comment stream on my earlier posts contain some interesting points, and I apologize for not being able to engage with the comments to the extent that I would like—I am however monitoring them so I can make a note of important insights.

I will endeavour to take up those substantive issues here as time permits. I consider the following points to be particularly worthy of discussion in connection with my forthcoming paper:

• The distinction between conspiracist ideation and meritorious criticism.
• Outlier detection and interpretation of extreme responses.
• The role of structural equation modelling and how it differs from Excel cross-tabulation.
• Details of the methodology and the supplementary online material.

I look forward to posting on those issues (roughly in the above order) in the near future.

I would do so sooner if my time weren’t also occupied with other, comparatively trivial matters, such as the identity of those “skeptic” bloggers whom I contacted for my study. I have several phone conversations scheduled for tomorrow, Monday, W.A. time, with the ethics committee at my university. I will report on the outcome as soon as a decision has been finalized.

I want to offer some further thoughts on the crucial notion of “triage”, that is, the separation of an intellectual signal from the noise of the echo chambers:

• One must differentiate between the organized purveyors and pushers of science denial on the one hand, and the “consumers” of such denial on the other. While the former legitimately attract moral scorn because their conduct causes much human pain, the latter are in a very different category. This distinction can be brought into sharp focus by considering AIDS denial: The purveyors of pseudo-scientific nonsense who convinced South Africa’s President Mbeki that antiretroviral drugs were “racist” medicine deserve little other than moral contempt. Their actions have killed—330,000 people in South Africa alone, based on the peer-reviewed literature—and their actions continue to kill.

  The sick and desperate people who turn to the purveyors of denial to deal with their tragic illness, by contrast, deserve not contempt but compassion, however ill-informed and counter-productive their actions may have been. The triage between the perpetrators and the victims of science denial is, alas, frequently very difficult and I can only highlight that dilemma without being able to resolve it.

  In this context, it is of interest that my forthcoming paper on the rejection of science found a stronger link between conspiracist ideation and the rejection of sciences other than climate science (including rejection of the link between HIV and AIDS). To date, however, this fact has been overshadowed by the eager self-immolation of the climate-denial community, who has seen fit to respond to my paper with more conspiracist ideation than my modest survey could have ever uncovered.

• There are subtle indications that even among climate “skeptics” a penny has dropped. Ardent “skeptics” suddenly recognize the need to address their own fringe. This is best illustrated by the moves of Mr. Andrew Bolt, a right-wing blogger and Murdoch columnist, who commands a large audience in Australia despite his high-profile conviction for racial vilification.

  Mr. Bolt has referred to me variously as a global warming evangelist or smearer. Despite those obvious failings, Mr. Bolt publicly distanced himself from the “Galileo Movement.” The Galileo Movement is an Australian climate-denial outfit that variously reminds me of Monty Python and Fox News.

  Although initially listed as one of their “advisors”, together with other practicing scientists such as  Australia’s most famous shock jock, Mr. Bolt discovered that the Movement’s views about climate science comprise an anti-Semitic conspiracy theory involving a “cabal” of bankers who strive to dominate the world via carbon trading (or something like that, I apologize if I have not penetrated the full nuances of this theory).

  If even Mr. Bolt is concerned about anti-Semitic conspiracy theories, then we have arrived at a Sister Souljah moment for climate denial.

My inbox has become a kaleidoscopic staging post of human diversity. A few requests are noteworthy for tutorial reasons:

First, a world-renowned expert on the peer-review process asked me to release the names of the people who reviewed my paper. I will leave it up to the commenters below to explain why I couldn’t possibly conform to that request.

Another, more modest, request was as follows:

“It is my understanding that there were two and possibly three different forms of the survey sent out to Blog Sites:

Survey ID=HKMKNF_991e2415

Survey ID=HKMKNG_ee191483

Survey ID=HKMKNI_9a13984

Are you able to confirm that 3 different instruments were used in your survey and, if that is the case, could you provide me with the different copies and indicate the reason they were used.”

I laud the stirring dedication to investigative Googling. Alas, this highly relevant detective work is far from perfect.

If I am not mistaken, I can indeed confirm that there were 4—not 3—versions of the survey (unless that was the number of my birth certificates, I am never quite sure, so many numbers to keep track of… Mr. McIntyre’s dog misplaced an email under a pastrami sandwich a mere 8.925307759554336³ days ago, and I have grown at least one tail and several new horns over the last few days, all of which are frightfully independent and hard to keep track of).

Versiongate!

Finally this new friend from Conspirania is getting some legs.

About time, too, I was getting lonely.

Astute readers will have noted that if the Survey ID’s from above are vertically concatenated and then viewed backwards at 33 rpm, they read “Mitt Romney was born in North Korea.”

To understand the relevance of Mr Romney’s place of birth requires a secret code word. This code word, provided below, ought to be committed to memory before burning this post.

So here it is, the secret code. Read it backwards:  gnicnalabretnuoc.

Translations are available in any textbook for Methodology 101.

The public response to my forthcoming paper in Psychological Science, entitled “NASA faked the moon landing—Therefore (Climate) Science is a Hoax: An Anatomy of the Motivated Rejection of Science,” has provided a perfect real-life illustration of the very cognitive processes at the center of my research.

In fact, the cascading eruption of allegations and theories about the paper and myself have illustrated the impoverished epistemology of climate denial better than any mountain of data could have done.

It is helpful to analyze some of the theories that have sprung up in response to my paper.

First out of the gate was the accusation that I might not have contacted the 5 “skeptic” bloggers, none of whom posted links to my survey. Astute readers might wonder why I would mention this in the Method section, if I hadn’t contacted anyone.

In an exercise more reminiscent of juvenile hyperventilation than adult cognitive control, several individuals jumped to the conclusion that I must be guilty of academic misconduct because no skeptic blogger could recall having been contacted by me. And of course, those bloggers know more about my research, or that in any other scientific discipline, than myself or any of my scientific colleagues.

This theory, alas, is now in terminal decline. First, one individual recovered his search skills after launching wild accusations against me and found that he had been contacted not once but twice.

Oops.

We now also know that two of the people who were contacted even replied to my assistant’s query.

Oops. Oops.

Let’s move on quickly. There must be another gourd somewhere.

And thus, as sure as night follows day, the second theory was born, arising like Phoenix from the ashes of the first one. The second theory revolves around the dates of certain events: It turns out that I gave a talk at Monash University in Melbourne, during which I alluded to these data briefly, after having done a very rough preliminary analysis. This event occurred a few days after Mr. McIntyre had been contacted with a request to post a link.

Oh how nefarious! I reported data only 3 days after contacting a blogger to collect data!

Never mind that the first theory claimed I never contacted anyone. That’s sooooo 2011. Let’s move on to the next conspiracy.

Only 3 days and I reported data from 1100 subjects. The travesty of it!

I wish this theory well, and I suspect much more analysis of dates, involving multi-colored Gantt charts, will be performed once the identity of the other 4 bloggers will (hopefully—I am working on it) have become public in the near future.

Reality-based readers may now note that it doesn’t matter whether 3, 30, or 666 days elapsed between Mr McIntyre ignoring an email and me giving a talk about data gathered from other blogs.

You know, it’s like this: when a link isn’t posted on a blog, then that blog could not have contributed data, however long one waits. But don’t let me stop anyone staring at that shiny object, it’s been approximately 666 + 45 days since Mr McIntyre ignored my email, and the cube root of 666+45  is, after all, 8.925307759554336.

On that pesky issue of reality: Mr. McIntyre was contacted twice, as he himself acknowledge, and the date of first contact would have actually given him ample time to direct his readers to my survey, for timely inclusion in my Monash talk.

This leaves us with at least two further theories. Both are still in their infancy and it may be advisable to let them grow a little more.

I will therefore tread lightly and speak softly to provide them both with the nurturing environment they deserve.

One theory involves the breathtaking discovery that there were different versions of my survey posted at different blogs.

Versiongate!

It’s a trick!

This theory is quite meritorious but has received way too little attention to date. I will therefore explore its laudable aspect in due course in one of my next posts, once it has gained more prominence and once more nonsense has reverberated around the denialist echo chambers. There is no point in pricking a balloon before it has been fully inflated with pompous self-importance.

The final theory involves the participants from Area 51, who apparently were on vacation from the grassy knoll: Warmists framing the survey, pretending to act like skeptics to make deniers look like nutters! Or something like that.

The data are invalid!

I call this the Daedalus theory and I sincerely hope it gets wings because it has such promise.

Let’s not interfere with it before it really takes off. Let’s wait till it gets a little closer to the sun.

It has come to my attention that one of the individuals who initially denied—yes, folks, that’s the correct word, look it up in a dictionary—having received an invitation to post a link to my survey on the rejection of science on his blog, has now found that email.

This is laudable, if entirely unsurprising, and I bear no grudge against that person for having had such trouble finding a message from two years ago among mountains of other correspondence—anyone who has ever had to respond to frivolous FOI requests can share that pain.

Should any others want to continue searching their correspondence, it might be helpful to know that my assistant has just re-read old correspondence from some time ago (e.g., from Thu, 23 Sep 2010 08:38:33 -0400) with considerable amusement in light of the frivolous accusations flying about the internet that we may not have contacted those blogs with a request to post a link.

One of the many tourist attractions in southern Western Australia is the town of Mt Barker, famous for being the gateway to the Porongorups. The Porongurups offer multiple recreational opportunities, among them some multi-pitch granite slab climbs (250 m, Rock Gibraltar) that I highly recommend because they make the climate wars look boring. Mt. Barker also features nice wines, and perhaps most famous of all, it is the home of the world’s best free-range eggs.

There are lots of eggs left in my fridge.

I recently published a paper on the motivated rejection of science that is forthcoming in Psychological Science. The abstract of the paper is as follows:

Although nearly all domain experts agree that human CO2 emissions are altering the world’s climate, segments of the public remain unconvinced by the scientific evidence. Internet blogs have become a vocal platform for climate denial, and bloggers have taken a prominent and influential role in questioning climate science. We report a survey (N > 1100) of climate blog users to identify the variables underlying acceptance and rejection of climate science. Paralleling previous work, we find that endorsement of a laissez-faire conception of free-market economics predicts rejection of climate science (r @ .80 between latent constructs). Endorsement of the free market also predicted the rejection of other established scientific findings, such as the facts that HIV causes AIDS and that smoking causes lung cancer. We additionally show that endorsement of a cluster of conspiracy theories (e.g., that the CIA killed Martin-Luther King or that NASA faked the moon landing) predicts rejection of climate science as well as the rejection of other scientific findings, above and beyond endorsement of laissez-faire free markets. This provides empirical confirmation of previous suggestions that conspiracist ideation contributes to the rejection of science. Acceptance of science, by contrast, was strongly associated with the perception of a consensus among scientists.

Perhaps unsurprisingly, this paper has caused a considerable media response and a flurry of activity  on the internet. I have also received a fair amount of correspondence, so much in fact that I have been unable to keep up with it. I apologize to those who have not received a reply to recent messages, and I hope this post covers some of the issues raised.

 In a somewhat ironic twist, given that the paper addressed conspiracist ideation, much attention has focused on the source of participants, which were “Visitors to climate blogs voluntarily completed an online questionnaire between August and October 2010 (N = 1377). Links were posted on 8 blogs (with a pro-science science stance but with a diverse audience); a further 5 “skeptic” (or “skeptic”-leaning) blogs were approached but none posted the link.”

To clarify, this means that participants were recruited from those blogs that posted the link—not those that did not. One might therefore presume that attention would focus on those blogs that provided entry points to the survey, not those that did not, because it is entirely unclear how the latter might contribute to the results of the survey. For example, the website of the British RSPCA also did not post a link to the survey, and neither did the Australian Woolworths website, so how might their non-involvement affect the results? I am keen to hear about potential mechanisms, perhaps we have overlooked something.

However, attention has primarily focused on those non-participating blogs and their identity. I have been inundated with requests to release their identities, and I have thus far declined to comply with those requests because I believe that a presumption of privacy should apply to my correspondence with potential participants in research.

Unlike some of the people who have been emailing me, my work is subject to ethical guidelines and is subject to approval by my University’s ethics committee—as is the work of any other behavioral scientist in Australia and elsewhere. It is therefore not solely my decision whether or not to reveal the identity of people who were approached on the presumption of privacy.

Because this issue is likely subject to different opinions, I have therefore approached the Australian Psychological Society and my University’s Human Research Ethics Committee to provide guidance on this decision.

There is an obvious asymmetry of potential harm here: If I release the names but it turns out to have been unethical, this cannot be undone. If I decline to release the names, as I have done to date, and it turns out that this was unnecessary, then no harm is done if release of the names is delayed by a few days.

I am therefore awaiting guidance on this issue.

In the meantime, I understand that there is a list on the internet of individuals who have declared that they were never contacted. As we are awaiting the decision about release of the names, just a matter of general principle, there can be no harm if those folks were to again check their inboxes (and outboxes) very carefully for correspondence from my assistant at UWA in August and September 2010. I know how difficult it is to locate individual emails among thousands received in a year, and a double check may therefore be quite prudent. (Who knows, it might even prevent some overly trigger-happy and creative people from floating a conspiracy theory about how I just made up the fact of having contacted those blogs, similar to the way NASA faked the moon landing.)

There are other issues that have been raised in connection with the paper, including some interesting points regarding the statistics, and they are worthy of further commentary in the near future. As it happens I am attending a conference at the moment with one of my co-authors, which ties us up for most of the time but which also provides an opportunity for discussion that is likely to lead to further posts in the not-too-distant future.

By any traditional measure, Australia’s graziers and pastoralists have made remarkable achievements in a highly variable climate and a difficult global marketplace. Australian demand for meat and milk remains high and steady, and our exports are strong and growing. Animal agriculture isn’t going away anytime soon. At the same time, livestock production is an important contributor to the global warming, albeit one of many.

In my work with rural communities and industries, I still hear a lot of confusion on carbon and climate matters. In amongst the genuine questions is an assortment of factoids that downplay agriculture’s role in climate change.

Factoids are what myths become when repeated so often they are accepted as fact. Factoids are a worry. In a rapidly changing climate, one would think the more sensible way ahead for livestock producers is to empower themselves with the science needed to craft solutions that work for them as much as the public good. As the world economy shifts into low-carbon gear it is the carbon savvy who will profit the most.

A recent article on these pages sought to counter campaigns to (as the author sees it) ‘demonize’ cattle for the methane they belch and encourage vegetarianism. Much of what that article says lends the subject much-needed perspective and the author raises some good questions. In the pursuit of balance, however, she might have overstepped the mark somewhat.

Before we go any further, let’s back up a bit and look at the big picture: methane is an important player in global warming. That’s a fact. Atmospheric levels of methane are now at their highest for at least 650,000 years, rising by about 160 per cent since 1750, according to the IPCC. To date, methane is thought to have caused around 20 per cent of observed global warming.

Now, it’s certainly true that there are a plethora of methane sources, some natural, some not. The former includes wetlands, wild ruminant animals, termites, and even the oceans. Wahlquist points out that termites up the Top End emit a hell of a lot of methane.

Sure, OK, so what?

Surely the issue is about what we can control and not what we can’t? Most methane emissions today—more than 70 per cent—are our doing. Anthropogenic sources include leaky gas wells and pipes, coal mining, rice paddies, landfills, and deliberate burning of biomass (forests, savannahs, crop residues, etc.). Then there is our livestock. Estimates vary, but around 20 per cent of human-caused methane is directly attributable to animal production.

Ah, but what of all those wild beasts? Well, it’s thought that there are around 75 million wild ruminants roaming the planet today. Doubtless there were millions more in the past, perhaps scores of millions, but it’s hard to see their numbers ever reaching the more than three billion cattle and sheep grazing today. This represents a veritable explosion in the size of the planetary herd since pre-industrial times. Of course, the clearing and burning of vegetation for pasture is itself a major source of methane and other greenhouse gases.

So, any attempt to characterize livestock and grazing as natural (and, by implication, good) doesn’t wash, especially on a continent like Australia that never knew ruminants like these. That doesn’t mean that it’s a wholly bad situation. After all, billions of people, including many of the world’s poor, benefit from livestock. Indeed, the boom in livestock production shows little sign of diminishing.  As demand, particularly in East Asia, really takes off the world will have to manage almost five-and-a-half billion cattle and sheep by mid-century. 

But wait, methane lasts only about twelve years in the air, broken down (so to speak) by a combination of atmospheric chemistry and sunlight. So, if methane exits the sky so quickly, what’s the problem?

While methane is a short-lived compound, it’s not as if its sources have gone away and it will all magically disappear in twelve years time.  In fact, over the long term, the concentration of methane in the air shows a nett growth and it’s growing still. The suggestion that booming global stock numbers have played little or no part in this is a lot to swallow.

A popular myth has it that cattle and sheep are essentially carbon neutral. Cows eat carbon in the grass, the reasoning goes, which is then returned to the atmosphere when they poo, burp, fart, die, etc., only to be sequestered in pasture growth later. It makes perfect sense until you grasp the power of methane as a greenhouse gas.

It’s still a carbon-based molecule, but the methane burped out of a cow is considerably more powerful global warming agent than carbon dioxide. Molecule for molecule, over a given century, methane outdoes carbon dioxide by 25 to 1 in the warming stakes. (Over a twenty-year period methane’s ‘global warming potential’ rises to 72.).

Often cited is a 2009 CSIRO report for the Queensland Government suggesting that hundreds of millions of tonnes of carbon—much more than all our livestock emit—could be soaked up in Australia’s grasslands. The fine print, however, reveals that these humungous figures refer to what is theoretically possible, not what is practical. That figure is bound to be much, much lower. 

It’s true that some microbial bugs in the soil gobble up methane. But this sink has never been shown to come even close to outweighing methane emissions from livestock.

Just as with transport or energy use, greenhouse gas reduction strategies are needed all along the supply chain. As things stand, direct emissions from agriculture are not liable for the carbon price, but landholders are unique in Australia’s emissions trading scheme in that they can, if they choose, create carbon credits to sell on the new market.

More efficient production is certainly part of the solution: healthier animals tend to put on weight quicker and emit less. (This could lead to a kind of ‘rebound effect’ if improved production spells higher stock numbers overall—something worth watching closely.) Changes in feed and other factors can reduce emissions substantially. And, where possible, converting manure into energy (as is being done in some piggeries and dairies now) saves fossil fuels and cuts methane emissions. Of course, setting aside land for bushland restoration or tree crops is a tried and true method of locking up carbon.

As for vegetarianism: it’s a highly unlikely prospect for most, so a bit of a (excuse the pun) red herring. There is, however, every reason to think the meat and livestock industries in affluent countries are smart enough to profitably adapt to more moderate consumption here while satisfying the growing needs of the developing world.

This then is the tricky dilemma we’ve inherited: to reduce the carbon hoofprint of the livestock sector, in a world demanding more and more, without losing the benefits—especially to those most in need. As problems go, it’s a biggy, but there’s no shying away from it. Australian farmers are good at doing more with less and the world needs them now more than ever. It’s one thing to be proud of agriculture’s achievements, but quite another to be over-sensitive to legitimate concerns. Unless farmers squarely face up to the problems, they’ll find it hard to make the most of the solutions.