In a perfect world, having more science knowledge would predict agreeing with scientific consensus on an issue. However, a lot of research in science communication (and other fields) have shown that this is not the case. In fact, the more people know about a topic, the more polarized they become. 

However, this may not be true for EVERYONE. That is, I suppose that people who have the most reason to disagree with scientific consensus will definitely find ways to do so; but, for those who don't care (e.g. political moderates, religious moderates), could we see a relationship between knowledge and acceptance of science information?

I was playing around with some data this morning from a project I am involved with in collaboration with Dan Kahan, Katie Carpenter, Laura Helft, Howard Hughes Medical Institute (HHMI), and Annenberg Public Policy Center (including my amazing fellow postdocs),  that examines the relationships between science curiosity, evolution belief, and engagement with documentaries on evolution. This data was collected from a nationally representative panel by YouGov.

To get people's stance on human evolution we ask (near the end of the survey):
True or False: Human beings, as we know them today, developed from an earlier species of animals.

About 38% of our sample (N=2,500) answer "false" on this item, which is pretty consistent with other nationally-representative polls asking about evolution. For instance, a 2007 Gallup/USA Today poll finds that 44% of people polled say evolution is either probably or definitely false. A 2011 Fox News Poll finds that 45% of people polled say that the biblical account of the origin of human life is most likely true (compared to 21% support for the theory of evolution as outlined by Darwin). And a 2014 poll by PRRI/AAR Religion, Values, & Climate Change Survey find that 41% of people polled either mostly or completely disagree that evolution is the best explanation for the origin of human life on earth.

Importantly, all of these items specifically ask about the origins of human life. Recent research suggests that people are more likely to endorse evolution when thinking about the origin of animal and plant life. 

The people most likely to be motivated to disagree with scientific consensus on evolution are those who are religious. Evolution is often contrasted with biblical accounts of the origins of human life such as creationism and/or intelligent design. For those who interpret their religious text literally, it is difficult to reconcile the theory of evolution with, for example, the account in Genesis. "

Thus, we wouldn't expect people who have strong religious beliefs to support the theory of evolution, no matter how much science knowledge they have.

Those who are not religious, on the other hand, have no reason to doubt the scientific consensus. So we wouldn't necessarily expect a correlation between science knowledge and acceptance of evolution there, either. 

Where a relationship between science knowledge and evolution may exist is in the middle---those who are somewhat religious, but are not so committed to a literal biblical interpretation that they would reject scientific evidence. But on the other hand, they are not so quick to automatically accept scientific consensus as those who are not religious would be, because they have an alternative hypothesis that is supported by their religious in-groups.  Thus, it is possible that we may see a relationship between accepting evolution and science knowledge here.

In our study, we used Dan's Ordinary Science Intelligence scale as a measurement of science knowledge. We also asked several items related to religiosity, such as how frequently people engage in prayer, how often they attend church services, whether they consider themselves "born-again", and how important their religion is to their daily lives. I combined these items into a index of "religiosity" using item response theory, which--when calculating scores of religiosity--takes into consideration that answering with higher values for some items (e.g., religious importance) are likely to indicate a higher level of religiosity than answering with higher values on other items (e.g., being "born again"). 

Then, I conducted logistic regression analyses in R predicting acceptance of human evolution (e.g., answering "true" to our evolution item) with ordinary science intelligence and religiosity as predictors. 

The regression revealed an interaction effect of religiosity and ordinary science intelligence. To graph this, I grouped religiosity into four categories: people who were in the bottom 25 percentile on religiosity, 25 to 50th percentile, 50th to 75th percentile, and 99th percentile (top 25%).

The raw data points are "jittered" so that you can more easily see the density of responses around particular points. The lines represent the predicted probability of accepting human evolution contingent on your level of science intelligence. The percentiles, as I said above, represent people's religiosity. 

You can see from the figure that science knowledge seems to predict acceptance of human evolution for people who are in the 25th to 50th percentile and for people who are in the 50th to 75th percentile. People who are in the lowest level of religiosity basically accept human evolution across most levels of science intelligence (discounting the small clump of participants who are really low on science knowledge).  For people who are the highest in religiosity, there is no relationship between science knowledge and accepting evolution. 

So, this data seems to show that knowledge *can* predict acceptance of evolution, but only for people who are not completely motivated to ignore scientific consensus, but have some reason to doubt it.

For the past year (almost), I've been working with colleagues at the Annenberg Public Policy Center to examine how Pope Francis's encyclical, Laudato si: On Care of Our Common Home,  may have influenced the U.S. public in terms of their climate change beliefs. 

We used nationally-representative survey data (with an over sample of Catholic respondents) and had several waves of data collection. However, when we began looking at the data, we were frustrated: It didn't seem like anyone was changing their beliefs at all. 

To operationalize climate change beliefs, we asked three basic questions (these questions are very similar to what is asked by other organizations that are measuring climate change attitudes, like Pew):

• position: Whether there is solid evidence that the average temperature on earth has been getting warmer over the past few decades, and if so is it because of human causes or natural fluctuations;
• seriousness: How serious of an issue confronting the nation is climate change (on a scale of 1 = Not at all serious to 4 = Very serious); and
• consensus: Which is most accurate about what most scientists believe: (a) most scientists believe that global warming is occurring; (b) most scientists believe that global warming is not occurring, or (c) most scientists are divided about whether or not global warming is occurring. 

We standardized responses on these items and then created an averaged index and used this as our measure of climate change beliefs.

However, this scale is not normally-distributed. In fact, there is a huge ceiling effect: many individuals choose the strongest rating on all three of these climate change options. For instance, let's look at a table showing the frequency of participants (from our pre/post encyclical release panel, n = 602) among each level of position by each level of consensus.

I've bolded the number in the bottom right corner. Note that is NOT a total of the columns and rows. That is the number of participants who answer with the highest value on both the scientific consensus item and on the position item. It is important to note that these are the values for the items BEFORE the encyclical has been released. If we anticipate that the encyclical would have a positive  effect, how are we going to measure that when a huge portion of the sample has hit the ceiling before the encyclical has been released?

To be fair, when most people are curious about whether the Pope influenced people's beliefs on climate change, they are not interested in those who are already climate concerned. Instead, we may want to see, for example, what climate change skeptics might think after they hear what the Pope has to say. However, it is likely that the Pope's messages will also influence those who already believe in climate change.

So how useful is our measure of climate change beliefs and did we really capture people's climate change concern at the higher ends of the scale?  Moreover, is this why it appears that no one is changing their beliefs (when we look at the overall numbers, not broken down by previous beliefs or ideology). 

To answer these questions, I used item response theory to examine our scale. 

I chose to use item response theory to examine our scale as it allows us to examine how informative the proposed scale is at each level of the latent variable (also called "theta"). While some scales, like this one, have high inter-item reliability (Cronbach's alpha), they may be informative for only a portion of the scores along theta. For instance,  item response theory shows that the Cognitive Reflection Task is only informative for people who score above the mean on the scale. 

Eventually, I would like to learn how to run IRT in R, but for now, I'm using XCalibre. In XCalibre, I ran a polytomous Generalized Rating Scale Model (GRSM: Samejima, 1969; 1996), which is the most appropriate model to use when item responses can be characterized as ordered categorical responses, such as those for a Likert scale. 

XCalibre provides output for both classical statistics as well as IRT parameters.

As predicted, our inter-item reliability is very high, Cronbach's alpha = .96. But this does not mean that our scale is informative across all levels of our latent variable (i.e., ccib, which can be conceptualized as climate change issue beliefs, climate change attitudes, or climate change concern).

Test Information Function
XCalibre provides the test information scores at every level of the latent variable theta. Then, the Test Information Function, is a graphical representation of how much information the test provides at each level of theta. You can see from the figure below that the information from our scale is skewed toward the lower scores, we don't get nearly as much information from people who are scoring at the top of our scale.

Category Response Functions
We can also look at the category response functions for each of the items that are used in our scale. These can be interpreted similarly to multinomial logistic function graphs. That is, we can see what the likelihood of each response is at each level of climate change concern. 

Here is the category response function for the position item. As you can see, once you get above 0, the most likely response is 4 (climate change is human-caused). Between 0 and -1 on climate change concern, the most likely response is 3 (climate change is due to natural fluctuations), between -1 and -2 it is 2 (we don't know enough about whether global warming is occurring) and from -2 to the end, the most likely response is 1 (climate change is not happening). In other words, this item is great at differentiating people's climate change position below the mean on climate change concern, but not that great above the mean. 

We see similar results with the seriousness item.

Like with the position item, once people are above about 0.5 on climate change concern, they become most likely to say that climate change is a very serious issue confronting the nation. So, we don't have any discrimination among participants who score above 1 on the scale.

Same with Consensus. Indeed, recent studies have aimed to examine whether telling people that there is consensus on climate change will increase their beliefs about climate change, but from our data, it appears that a lot of people already believe that there is consensus on climate change. Basically, everyone who scores above -0.5 on the latent variable is most likely to agree that scientists agree that global warming is happening and is human-caused.

So, while our scale has a strong inter-item reliability, it really could have used some items that help distinguish between the upper ends of the climate change scale. Otherwise, we can't expect to have room for people who start off as climate change concerned to increase their concern contingent on hearing about the encyclical and/or Pope Francis's views.

Again, this may not be too problematic given that we are most interested in the influence of the pope's messages on climate change skeptics; however this is likely the cause for some of the results that we have shown in our studies under review (e.g., people who are climate change concerned are most likely to maintain their climate change beliefs than to increase or decrease them after hearing about the Pope's messages).