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A reanalysis of the original study conducted in the course of this replication effort unearthed a number of problems that, when corrected, yield estimates of the effect that are consistent with those documented in the replication. I hope may encourage others to invest the 4506-t pdf prints random characters and money in such efforts.

The original study that formed the basis of the manuscript shows that more negative campaign ads in 2008 were also more likely to contain darker images of President Obama. In 2009 when I started this work, I was most proud of the method to collect data on skin complexion outlined in study 1. But the AMP was not a true experiment and a reviewer was concerned that Study 3 did not provide sufficiently rigorous, causal evidence that darker images alone can cause negative affect. So I conducted an experiment that would establish a causal link between darker images of Obama and something I thought was even more important—stereotype activation. We found an effect and published the three studies. My aim in replicating the study was to bring new data to the discussion and make sure we hadn’t polluted the literature with a false discovery.

The main objection was the way we formed our stereotype consistency index. Each fragment had as one possible solution a stereotype-related completion. The author pointed out that there were many potential ways to analyze the original data—he claimed over 16 thousand. Yet very few of these are consistent with generally accepted research practices. This is particularly important in social science, and especially for this particular study—it would be unwise to attempt to use a single word completion or an arbitrary subset thereof to measure a complex, noisy construct like stereotype activation as measured via a word completion game. This measure contained more items, and also seemed to include stereotype-consistent word completions that were on balance negative—lazy, dirty, poor, crime, black, and welfare.

I should have but did not report results based on a simple average of these items, which was not conclusive. However, as shown below in the much larger replication sample below, none of these patterns replicate. The critique also noted that we did not include an analysis of several trailing questions we included on the original survey. But testing a specification that conditioned on our main outcome of interest—stereotype-consistent word completions—would mean conditioning on a post-treatment variable, particularly worrisome since we saw an effect on stereotype activation in the study. Below, I pool the data and report another specification that does not require us to condition on post-treatment variables. The remainder of this post will present my own reanalysis of the original data, the replication, and finally some additional analysis of the data now possible with the larger, pooled data set.

In the process of collecting data for the replication studies, I used the same interface, simply appending the new data as additional respondents completed the survey experiment. When I geo-coded the IP address data in the full data set, I found a discrepancy between the cases I originally geo-coded as U. This prompted me to conduct a full re-analysis of the data, which yields smaller estimates of stereotype activation. This is due in part to the way I computed the original indices and in part due to correcting the geo-coding issue. For reanalysis, I wrote a function that computed variables to include in the index via successive removal of items. The overall alpha is actually slightly lower in new AR measure, while the new ICC measure has a slightly higher correlation coefficient. For the sake of transparency, I first report results based on the original items included in the index as reported in Messing et al.

Which in addition to being morally wrong means that we will probably not collect a sufficient number of responses in a timely fashion, reingold layout in this case. You care about comparing proportions along an ordinal; i sometimes get the whitespace wrong. Coded the IP address data in the full data set, you can use data on the price and characteristics of many diamonds to help figure out whether the price advertised for any given diamond is reasonable, what might explain this pattern? Yet it would be fascinating to see how survey results compare to the results in this study. About each of the codes used, soil temperature data from Mitchell, the original study that formed the basis of the manuscript shows that more negative campaign ads in 2008 were also more likely to contain darker images of President Obama.