This post contains my analytic replication of Noah's analyses. Quotes are from the paper unless otherwise stated. My code is here: https://gist.github.com/Deleetdk/a5913f7...0adb9a1456

Replicated.

Replicated, except I get a median of 2.52. Typo perhaps.

Replicated.

Correlation replicated, CI differed. I get:

This is an analytic CI. The paper does not say which kind of CI is used, so I assumed it was an analytic.

Replicated. R output:

I get d = .69.

R output:

It was difficult to calculate a p value in r for the SMD. However, I think I managed to do it and got .13.

Correlation and lower CI replicated, upper CI did not. R output:

Replicated.

I get 1.63.

R:

I did not replicate the p value because the function I used rounded the number down to 0 (not your fault).

Replicated.

Replicated.

Replicated.

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I'd like to resolve these slight discrepancies but note that the results generally replicated.

Quote:"The mean terrorism threat level in the sample is 2.4, while the median is 2.5. "

Replicated.

Quote:"This variable ranges from 0 (Czech Republic) to 8.2 (France), with a mean of 3.1, and a median of 2.6."

Replicated, except I get a median of 2.52. Typo perhaps.

Quote:"21 countries (75%) in the sample sustained at least one military death in Iraq or Afghanistan; the mean number of military deaths sustained in Iraq or Afghanistan is 294, while the median is 11; 7 countries (25%) are part of the anti-ISIS military coalition."

Replicated.

Quote:"The correlation between terrorism threat level and percentage Muslim is r = .64 (p < 0.001; 95% CI = [.33, .95])"

Correlation replicated, CI differed. I get:

Code:

`> cor.test(d_main$terror[d_main$west], d_main$muslim15[d_main$west])`

Pearson's product-moment correlation

data: d_main$terror[d_main$west] and d_main$muslim15[d_main$west]

t = 4.2844, df = 26, p-value = 0.0002219

alternative hypothesis: true correlation is not equal to 0

95 percent confidence interval:

0.3555625 0.8196607

sample estimates:

cor

0.6433038

This is an analytic CI. The paper does not say which kind of CI is used, so I assumed it was an analytic.

Quote:"When percentage Muslim squared was included in a model of terrorism threat level alongside percentage Muslim it was not significant (p > 0.1), indicating minimal non-linearity."

Replicated. R output:

Code:

`Call:`

lm(formula = "terror ~ poly(muslim15, 1)", data = d_main, subset = d_main$west)

Residuals:

Min 1Q Median 3Q Max

-2.33148 -0.57178 0.03772 0.43508 1.85348

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 3.258 0.256 12.726 1.13e-12 ***

poly(muslim15, 1) 27.366 6.387 4.284 0.000222 ***

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Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8865 on 26 degrees of freedom

Multiple R-squared: 0.4138, Adjusted R-squared: 0.3913

F-statistic: 18.36 on 1 and 26 DF, p-value: 0.0002219

Call:

lm(formula = "terror ~ poly(muslim15, 2)", data = d_main, subset = d_main$west)

Residuals:

Min 1Q Median 3Q Max

-2.2881 -0.3663 -0.1067 0.6465 1.6725

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) -7.519 8.858 -0.849 0.404

poly(muslim15, 2)1 -315.886 282.091 -1.120 0.273

poly(muslim15, 2)2 -72.623 59.668 -1.217 0.235

Residual standard error: 0.8784 on 25 degrees of freedom

Multiple R-squared: 0.4466, Adjusted R-squared: 0.4024

F-statistic: 10.09 on 2 and 25 DF, p-value: 0.0006133

Quote:"The standardised difference in terrorism threat level by any military deaths in Iraq or Afghanistan is d = 0.67 (p > 0.05; 95% CI = [–0.20, 1.54])."

I get d = .69.

R output:

Code:

`d estimate: -0.6888248 (medium)`

95 percent confidence interval:

inf sup

-1.6418342 0.2641846

It was difficult to calculate a p value in r for the SMD. However, I think I managed to do it and got .13.

Quote:"The correlation between terrorism threat level and log of 1 + military deaths is r = .40 (p = 0.037; 95% CI = [.03, .77])"

Correlation and lower CI replicated, upper CI did not. R output:

Code:

`> cor.test(d_west$terror, d_west$deaths2)`

Pearson's product-moment correlation

data: d_west$terror and d_west$deaths2

t = 2.1992, df = 26, p-value = 0.03696

alternative hypothesis: true correlation is not equal to 0

95 percent confidence interval:

0.02693856 0.67010334

sample estimates:

cor

0.3960353

Quote:"When log of 1 + military deaths squared was included in a model of terrorism threat level alongside log of 1 + military deaths it was not significant (p > 0.1), indicating minimal non-linearity."

Replicated.

Quote:"The standardised difference in terrorism threat level by part of anti-ISIS military coalition is d = 1.34 (p < 0.001; 95% CI = [0.60, 2.08])."

I get 1.63.

R:

Code:

`Cohen's d`

d estimate: -1.632013 (large)

95 percent confidence interval:

inf sup

-2.6807130 -0.5833124

I did not replicate the p value because the function I used rounded the number down to 0 (not your fault).

Quote:"Table 1. Effects of Muslim percentage and military intervention in the Middle East on terrorism threat level among Western countries."

Replicated.

Quote:"Table 2. Effects of Muslim percentage and military intervention in the Middle East on terrorism threat level among all OECD countries."

Replicated.

Quote:"Table 3. Effects of Muslim percentage and military intervention in the Middle East on terrorism threat level among OECD countries located in Europe."

Replicated.

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I'd like to resolve these slight discrepancies but note that the results generally replicated.