According to the 20 Dec 2018 Samuel Perry and Andrew Whitehead Huffington Post article "What 'Make America Great Again' And 'Merry Christmas' Have In Common":

Christian theology, identity or faithfulness have nothing to do with an insistence on saying "Merry Christmas." To be more precise, when we analyzed public polling data, we found that there was no correlation between being an evangelical Christian, believing in the biblical Nativity story, attending church, or participating in charitable giving and rejecting "Season's Greetings" for "Merry Christmas." [emphasis added]

The referenced data are from a December 2013 Public Religion Research Initiative survey. Item Q5 is the "Merry Christmas" item:

Do you think stores and businesses should greet their customers with 'Happy Holidays' or 'Seasons Greetings' instead of 'Merry Christmas' out of respect for people of different faiths, or not? (Q5)

Item Q6 is the biblical Nativity belief item:

Do you believe the story of Christmas -- that is, the Virgin birth, the angelic proclamation to the Shepherds, the Star of Bethlehem, and the Wise Men from the East -- is historically accurate, or is it a theological story to affirm faith in Jesus? (Q6)

Here is the crosstab for the "Merry Christmas" item and the Nativity item:

PRRI-1Contra the article, these variables are correlated: ignoring the don't knows and refusals, 57 percent of participants who believe that the gospel Nativity story is historically accurate preferred the "Merry Christmas" response ("No, should not"), but only 41 percent of participants who believe that the gospel Nativity story is a theological story preferred the "Merry Christmas" response.

Here is a logit regression using the gospel Nativity responses (gospel) to predict the Merry Christmas responses (merry), removing from the analysis the participants who were coded as don't know or refusal for at least one of the items:

PRRI-2The p-value for the logit regression is also p<0.001 in weighted analyses.

The gospel predictor still has a p-value under p=0.05 when including the demographic controls below in unweighted analyses and in weighted analyses:

PRRI-3The gospel predictor still has a p-value under p=0.05 when including the demographic controls and controls for GOP partisanship and self-reported ideology in unweighted analyses:

PRRI-4There are specifications in which the p-value for the gospel predictor is above p=0.05, such as in a weighted analysis including the above controls for demographics, partisanship, and ideology. But the gospel predictor not being robust to every possible specification, especially specifications that control for factors such as GOP partisanship and charitable giving that are plausibly influenced by religious belief, isn't the impression that I received from "...we found that there was no correlation between...believing in the biblical Nativity story...and rejecting 'Season's Greetings' for 'Merry Christmas'".

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Here is another passage from the article:

What does this tell us? Ultimately, drawing lines in the sand over whether people say "Merry Christmas" over "Happy Holidays" has virtually nothing to do with Christian faithfulness or orthodoxy.  It has everything to do with the cultural and political insecurity white conservatives feel.

I didn't see anything in the reported analysis that permits the inference that "It has everything to do with the cultural and political insecurity white conservatives feel". Whites and conservatives being more likely than non-Whites and non-conservatives to prefer "Merry Christmas" doesn't require that this preference is due to "the cultural and political insecurity white conservatives feel" any more than a non-White or non-conservative preference for "Happy Holidays" and "Seasons Greetings" can be attributed without additional information to the cultural and political insecurity that non-White non-conservatives feel.

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NOTES:

1. Code here. Data here. Data acknowledgment: PRRI Religion & Politics Tracking Poll, December 2013; Principal Investigators Robert P. Jones and Daniel Cox; Data were downloaded from the Association of Religion Data Archives, www.TheARDA.com [http://www.thearda.com/Archive/Files/Descriptions/PRRIRP1213.asp].

2. I had a Twitter discussion of the article and the data with co-author Samuel Perry, which can be accessed here.

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The Kearns et al. study "Why Do Some Terrorist Attacks Receive More Media Attention Than Others?" has been published in Justice Quarterly; the abstract indicates that "Controlling for target type, fatalities, and being arrested, attacks by Muslim perpetrators received, on average, 357% more coverage than other attacks". A prior Kearns et al. analysis was reported on in a 2017 Monkey Cage post and a paper posted at SSRN with a "last edited" date of 3/5/17 limited to "media coverage for terrorist attacks in the United States between 2011 and 2015" (p. 7 of the paper).

Data for the Kearns et al. study published in Justice Quarterly has been expanded to cover terrorist attacks from 2006 to 2015 (instead of 2011 to 2015) and now reports a model with a predictor for "Perpetrator and group unknown", with a p-value under 0.05 for the Muslim perpetrator predictor. Footnote 9 of Kearns et al. 2019 discusses selection of 2006 as the starting point:

Starting in 2006, an increasing percentage of Americans used the Internet as their main source of news [URL provided, but omitted in this quote]. Since the news sources used for this study include both print and online newspaper articles, we started our analysis in 2006. In years prior to 2006, we may see fewer articles overall since print was more common and is subject to space constraints (p. 8).

That reason to start the analysis in 2006 does not explain why the analysis in the Monkey Cage post and the 3/5/17 paper started in 2011, given that the news sources in these earlier reports of the study also included both print and online articles.

In this 3/28/17 post, I reported that the Muslim perpetrator predictor had a 0.622 p-value in my analysis predicting the number of articles of media coverage using the Kearns et al. 2011-2015 outcome variable coding, controlling for the number of persons killed in the attack and for whether the perpetrator was unknown.

Using the 2006-2015 dataset and code that Dr. Kearns sent me upon request, I ran my three-predictor model, limiting the analysis to events from 2011 to 2015:

Kearns1The above p-value for the Muslim perpetrator predictor differs from my 0.622 p-value from the prior post, although inferences are the same. There might be multiple reasons for the difference, but the 3/5/17 Kearns et al. paper reports a different number of articles for some events; for example, the Robert Dear event was coded as 204 articles in the paper and as 178 articles in the 2019 article, and the number of articles for the Syed Rizwan Farook / Tashfeen Malik event dropped from 179 to 152.

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The inference about the Muslim perpetrator predictor is more convincing using the 2006-2015 data from Kearns et al. 2019 than from the 2011-2015 data: the 2006-2015 data produce a 2.82 Muslim perpetrator predictor t-score using my three-predictor model above and a 4.20 t-score with a three-predictor model replacing the number killed in the event with a predictor for whether someone was killed in the event.

For what it's worth, along with higher-than-residual news coverage for events with Muslim perpetrators, the Kearns et al. data indicate that, compared to other events with a known perpetrator, events with Muslim perpetrators also have higher-than-residual numbers of deaths, numbers of logged wounded, and (at least at p=0.0766) likelihood of a death:

Kearns2Kearns3Kearns4---

NOTES

1. I could not find the 3/5/17 Kearns et al. paper online now, but I have a PDF copy from SSRN (SSRN-id2928138.pdf) that the above post references.

2. Stata code for my analyses:

gen PerpUnknown=0
replace PerpUnknown=1 if eventid==200601170007
replace PerpUnknown=1 if eventid==200606300004
replace PerpUnknown=1 if eventid==200607120007
replace PerpUnknown=1 if eventid==200705090002
replace PerpUnknown=1 if eventid==200706240004
replace PerpUnknown=1 if eventid==200710200003
replace PerpUnknown=1 if eventid==200710260003
replace PerpUnknown=1 if eventid==200802170007
replace PerpUnknown=1 if eventid==200803020012
replace PerpUnknown=1 if eventid==200803060004
replace PerpUnknown=1 if eventid==200804070005
replace PerpUnknown=1 if eventid==200804220011
replace PerpUnknown=1 if eventid==200806140008
replace PerpUnknown=1 if eventid==200807250030
replace PerpUnknown=1 if eventid==200903070010
replace PerpUnknown=1 if eventid==200909040003
replace PerpUnknown=1 if eventid==201007270013
replace PerpUnknown=1 if eventid==201011160004
replace PerpUnknown=1 if eventid==201101060018
replace PerpUnknown=1 if eventid==201102220009
replace PerpUnknown=1 if eventid==201104230010
replace PerpUnknown=1 if eventid==201105060004
replace PerpUnknown=1 if eventid==201109260012
replace PerpUnknown=1 if eventid==201110120003
replace PerpUnknown=1 if eventid==201205200024
replace PerpUnknown=1 if eventid==201205230034
replace PerpUnknown=1 if eventid==201208120012
replace PerpUnknown=1 if eventid==201301170006
replace PerpUnknown=1 if eventid==201302260036
replace PerpUnknown=1 if eventid==201304160051
replace PerpUnknown=1 if eventid==201304170041
replace PerpUnknown=1 if eventid==201304180010
replace PerpUnknown=1 if eventid==201307250065
replace PerpUnknown=1 if eventid==201308220053
replace PerpUnknown=1 if eventid==201403180089
replace PerpUnknown=1 if eventid==201403250090
replace PerpUnknown=1 if eventid==201406110089
replace PerpUnknown=1 if eventid==201410030065
replace PerpUnknown=1 if eventid==201410240071
replace PerpUnknown=1 if eventid==201411040087
replace PerpUnknown=1 if eventid==201502170127
replace PerpUnknown=1 if eventid==201502230104
replace PerpUnknown=1 if eventid==201503100045
replace PerpUnknown=1 if eventid==201506220069
replace PerpUnknown=1 if eventid==201506230056
replace PerpUnknown=1 if eventid==201506240051
replace PerpUnknown=1 if eventid==201506260046
replace PerpUnknown=1 if eventid==201507150077
replace PerpUnknown=1 if eventid==201507190097
replace PerpUnknown=1 if eventid==201508010105
replace PerpUnknown=1 if eventid==201508020114
replace PerpUnknown=1 if eventid==201508190040
replace PerpUnknown=1 if eventid==201509040048
replace PerpUnknown=1 if eventid==201509300082
replace PerpUnknown=1 if eventid==201512260016
tab PerpUnknown, mi
tab PerpUnknown PerpMuslim, mi
tab PerpUnknown PerpNonMuslim, mi
tab PerpUnknown PerpGroupUnknown, mi
nbreg TOTALARTICLES PerpMuslim numkilled PerpUnknown if eventid>=201101060018
nbreg TOTALARTICLES PerpMuslim numkilled PerpUnknown
gen kill0=0
replace kill0=1 if numkilled==0
tab numkilled kill0
nbreg TOTALARTICLES PerpMuslim kill0     PerpUnknown
ttest numkilled if PerpUnknown==0, by(PerpMuslim)
ttest numkilled                  , by(PerpMuslim)
ttest logwound  if PerpUnknown==0, by(PerpMuslim)
ttest logwound                   , by(PerpMuslim)
prtest kill0    if PerpUnknown==0, by(PerpMuslim)
prtest kill0                     , by(PerpMuslim)

3. Kearns et al. 2019 used a different "unknown" perpetrator measure than I did. My PerpUnknown predictor (in the above analysis and the prior post) coded in a dichotomous variable as 1 any perpetrator listed as "Unknown" in the Kearns et al. list. Kearns et al. 2019 has a dichotomous PerpGroupUnknown variable that differentiated between perpetrators in which the group of the perpetrator was known (such as for this case with an ID of 200807250030 in the Global Terrorism Database, in which the perpetrators were identified as Neo-Nazis) and perpetrators in which the group of the perpetrator was unknown (such as for this case with an ID of 200806140008 in the Global Terrorism Database, in which the perpetrator group was not identified). Kearns et al. 2019 footnote 17 indicates that "Even when the individual perpetrator is unknown, we often know the group responsible so 'perpetrator unknown' is not a theoretically sound category on its own, though we account for these incidents in robustness checks"; however, I'm not sure why "perpetrator unknown" is not a theoretically sound category on its own for the purpose of a control when predicting media coverage: if a perpetrator's name is not known, then there might be fewer news articles because there will be no follow-up articles that delve into the background of the perpetrator in a way that could be done if the perpetrator's name were known.

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