Published June 16, 2020 | Version v1
Dataset Open

Data from: When policy and psychology meet: mitigating the consequences of bias in schools

  • 1. University of California, Berkeley

Description

Harsh exclusionary discipline predicts major negative life outcomes, including adult incarceration and unemployment. This breeds racial inequality, because Black students are disproportionately at risk for this type of discipline. Can a combination of policy and psychological interventions reduce this kind of discipline and mitigate this inequality? Two preregistered experiments (Nexperiment1 = 246 teachers; Nexperiment2 = 243 teachers) used an established paradigm to systematically test integration of two and then three policy and psychological interventions to mitigate the consequences of bias (troublemaker-labeling and pattern-perception) on discipline (discipline-severity). Results indicate the integrated interventions can curb teachers' troublemaker-labeling and pattern-prediction toward Black students who misbehave in a hypothetical paradigm. In turn, integration of the three components reduced racial inequality in teachers' discipline decisions. This research informs scientific theory, public policy, and interventions.

Notes

This is the dataset for Study 1. Also attached is the R-markdown with the coding/programing script used to analyze the data through R-programming.

Missing values are marked as NA. R-script is also attached.

 

Measures 

 

All questions were asked on a scale of 1 ("Not at all") to 5 ("Extremely"). Following each misbehavior, teachers were asked the following questions: 

  • • How severe was Darnell's behavior?
  • • To what extent is Darnell hindering you from maintaining order in the class?
  • • How irritating is Darnell? 
  • • How severely should Darnell be disciplined? 

Like in previous research, the responses to the first three questions were aggregated into a measure called "feeling troubled" (Okonofua et al., 2015). After the two misbehaviors, teachers were also asked the following questions:

  • • How likely is it that you would say that Darnell is a troublemaker?
  • • To what extent do you think Darnell's behavior is indicative of a pattern?
  • • How likely is it that you will be able to build a strong relationship with Darnell? 
  • • To what extent do you think Darnell is a danger to other students? 

Funding provided by: Google's Computer Science Education Research team*
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Funding provided by: The Tides Foundation*
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Funding provided by: Character Lab*
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Files

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