Abstract
This dataset contains information on 237 criminal cases litigated in Florida, United States, between 2005 and 2013. Ackermann, Goodman, Gilbert et al (2015) investigated whether, in this setting of Stand Your Ground applicability (Fla Stat. 776.012, Fla Stat. 776.031, Fla Stat. 776.032), there exist statistically significant race differences in conviction. Multiple predictor logistic regression was used to model outcome event of conviction (n=204, # events = 75) in relationship to the primary predictor defined as race of victim = White race (versus other) in the 204 cases with complete data. Selected covariates were included in model development to explore variations in this relationship by: race of the accused, specifics of the crime (e.g., did crime result in death) and type of legal proceeding (e.g., investigating agency). Ackermann, Goodman, Gilbert et al (2015) found that, compared to Non-White victim cases, cases in which the victim was White were statistically significantly more likely to result in conviction, overall and after adjustment for other statistically significant predictors (adjusted relative odds OR = 2.19, 95% CI = 1.13 – 4.20). These data are suitable for teaching purposes only and it is noted that these data are selected. It is appropriate for a first year applied data analysis course or equivalent. The data is mostly clean. However, the data requires some manipulations prior to modeling and there are some missing values. See below, Additional Information for Instructors.
| Study Design | Topic | Statistical Method | Statistical Method | Statistical Method |
|---|---|---|---|---|
| Retrospective Cohort | Stand Your Ground | Description | Basic Inference | Logistic Regression |
Contributor
The Stand Your Ground dataset was contributed by Ms. Nicole Ackermann, Staff Scientist, Division of Public Health Sciences, Washington University School of Medicine. Please refer to this resource as: Ackermann N. “Stand Your Ground”, TSHS Resources Portal (2025). Available at https://causeweb.org/tshs/?p=1834
Background
Violence and racial and cultural inequities in the American criminal justice system produce socially unjust public health and require additional research to inform new laws (and their implementation) that are “blind” to race and culture. In this regard and importantly, while advances in the social sciences have yielded promising frameworks for understanding race and culture, the incorporation of these frameworks in public health research designs is often limited. What is needed are more scientific investigations of human behaviors (e.g., violence or the litigation of violence) and their interrelationships with human identity indices (e.g., race and culture, etc.). Ideally, such studies would leverage common and accepted definitions of race and culture with the goal of informing future criminal justice policy development.
Ackermann, Goodman, Gilbert et al.’s (2015) study is an example of bridging social sciences advances in understanding race and culture with public health research addressing violence and equality in the criminal justice system. This dataset contains data from the 237 criminal cases in Florida, United States, between 2005 and 2015, that met the conditions for Stand Your Ground applicability in Florida. Most of the data were obtained from the publicly available Tampa Bay Times Stand Your Ground database. Additional data, as needed, was obtained from publicly available (online) court documents and news reports.
Objective
The goal of this investigation was to assess the relationship between event of conviction and race of the victim (White versus other), overall and after adjustment for other statistically significant correlates of conviction, in the setting of Stand Your Ground Law applicability in Florida, United States, between 2005 and 2013.
Subjects & Variables
| Subject | # Obs | # Var | Introduction | Data Dictionary |
|---|---|---|---|---|
| Stand Your Ground | 237 | 24 | Stand Your Ground Data Introduction | Stand Your Ground Data Dictionary |
Data Downloads
| Posting Date | Contributor (email) |
|---|---|
| 1/29/25 | Nicole Ackermann (nackermann@wustl.edu) |
| R | SAS | STATA | SPSS | Minitab | Excel |
|---|---|---|---|---|---|
| Stand Your Ground-R | Stand Your Ground-SAS | Stand Your Ground-Stata | Stand Your Ground-SPSS | Stand Your Ground-Minitab | Stand Your Ground-Excel |
Teaching Resources
last updated on 1/29/2025
| # | Name (link) | Posting Date | Author (email) | Type | Statistical Topic | Level | Keywords |
|---|---|---|---|---|---|---|---|
| – | – | – | – | – | – | – | – |