contrasts(condition)

dark 0

light 1

> muhat exp(muhat)/(1+exp(muhat)) # Intercept + Slope

[1] 0.06913842

> muhat0 exp(muhat0)/(1+exp(muhat0)) # Intercept

[1] 0.1824255

stan_glm(response~condition,family=binomial())

#…

Estimates:

Median MAD_SD

(Intercept) -1.5 0.3

conditionlight -1.1 0.5

“It will be difficult to judge whether or not there is a clinically meaningful effect or, indeed, whether any compelling solanezumab-responsive subgroups emerge from post hoc analyses, as these subgroups likely will be identified on the basis of low p values and small effects. “

]]>Just fit using stan_glm() then, and the summary is what you’ll want to see, I think.

]]>Ugh! Please use display(), never summary()!

]]>light<-c(rep(1,6),rep(0,78-6))

dark<-c(rep(1,15),rep(0,78-15))

response<-c(light,dark)

condition contrasts(condition)

light

dark 0

light 1

## sanity check

length(response)

length(condition)

library(MASS)

> summary(glm(response~condition,family=binomial()))

Call:

glm(formula = response ~ condition, family = binomial())

Deviance Residuals:

Min 1Q Median 3Q Max

-0.6536 -0.6536 -0.4001 -0.4001 2.2649

Coefficients:

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

(Intercept) -1.4351 0.2873 -4.995 5.88e-07 ***

conditionlight -1.0498 0.5129 -2.047 0.0407 *

—

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

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 123.26 on 155 degrees of freedom

Residual deviance: 118.68 on 154 degrees of freedom

AIC: 122.68

Number of Fisher Scoring iterations: 5

]]>http://www.alzforum.org/news/conference-coverage/ctad-solanezumab-seen-nudge-ad-ever-so-slightly

…p values are probability statements about the randomness of the distributions of the outcomes, but are not measures of magnitude of effect. A less impressionistic and more nuanced approach to interpreting the clinical significance of solanezumab is to examine the effect sizes of the outcomes…

]]>In Study 5, participants were shown a list of Tea Party positions, one of which was “Strong opposition to President Barack Obama”–but actual support of Obama does not come up in any of the five studies, from what I can see.

]]>“In Study 1, two hundred and fifty-five participants identified as white (71.6%), 40 participants as Latino (11.2%), 23 as Asian (6.5%), 16 as black (4.5%), and 22 indicated another or mixed race (6.2%). Minorities showed less support for the Tea Party in the Dark Obama Prime condition (8%) as compared with the Light Obama Prime condition (19%), though this difference was not statistically significant (χ2 (1) = 2.72, p = .15).”

]]>Study 3: *White respondents assigned to the Income Gap Closing condition reported greater support for the Tea Party (M = 1.45) than did those participants assigned to the Income Gap Expanding condition (M = 1.23, t(215) = 2.10, p = .037, d = .29).*

We are talking about a difference of 1.45-1.23=.22 here where the dependent measure is a 7 point Likert scale. People think that model assumptions just don’t matter, but they do. You will repeatedly find that violating model assumptions can give you distorted conclusions, e.g., significance where none exists. Maybe Willer needs to read Kruschke’s book on how do this right right. Actually, I think R also allows for more appropriate models for Likert scales than t-tests.

This problem runs throughout the paper. All the experiments have this issue. In Study 4 the difference is 0.14. Study 5, 0.26. And so on.

This is a very general problem in the humanities and social sciences. People just ignore model assumptions and just plug data in, and look for the p-value.

Question: Is there a statistics department in Stanford? What about that political scientist, Jackman? In Willer’s place, I would go to the statisticians or hook up with Jackman and learn how to analyze data from him.

]]>BTW, it would not hurt the reader at all if a draft paper had page numbers.

* The observed power for these items was .465, .492, and .170, respectively, indicating that our sample size was insufficient to consistently find a significant effect of experimental manipulation on these measures.*

The paper computes observed power. Once you know the p-value, observed power has nothing new to offer. I have seen psychologists compute observed power too, I guess nobody got the memo that these two quantities are related. See Hoenig and Heisey on The Abuse of Power.

]]>I was referring to the notorious National Enquirer headline this past summer that claimed that Clinton had ballooned to 289 pounds. The National Enquirer is one of the most famous and longstanding sources of fake news in the United States.

]]>“Among the 101 participants of other races or ethnicities, by contrast, those who saw the lightened image of Obama were twice as likely to support the tea party as those who saw the darkened image. Because they had fewer subjects of color, Willer and his colleagues couldn’t rule out the possibility that this difference between the randomly assorted groups was due to chance.”

From the study itself: “Because our hypotheses concern white Americans’ responses to racial threats, our analyses in all studies focus on white participants(see Supplemental Material for analyses of minority respondents).” (I have not yet found a link to the supplemental material.)

]]>Andrew, when you say, make Clinton fatter, does this mean you think she is fat? US obesity stats would not classify her as fat.

]]>The book Superforecasing provides some concrete advice against what I think of as the certainty mindset.

]]>It is gratifying though that being a prof in Stanford or Harvard doesn’t mean one is not clueless. Maybe someone can do an mturk study as to whether profs from brand name unis question their own understanding more, or whether they are more “unskilled but unaware of it” than profs in non brand nam unis.

Andrew, a juicy bit you forgot to quote from the media report: “Their study, Willer said, is the first to demonstrate a causal link between Tea Party support and racial resentment.”

]]>and

"The predicted probability of voting for Obama increased by 18 percentage points in the light

condition among respondents with race IAT scores two standard deviations above the mean (high

anti-black implicit bias), compared to the dark condition"

http://pcl.stanford.edu/research/2010/iyengar-racial-candidate.pdf

]]>https://www.researchgate.net/publication/288888435_Bias_in_the_Flesh ]]>