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Quill pens were before my time. Technology had replaced them with steel nibs well ahead of the start of my working life in the Bank in the late 1940s. Only three months later, however, technology made a much greater impact on the work in my branch, with the installation of electric accounting machines. These came to relieve us of the laborious system of writing ledgers and statements by hand. In fact they represented simply an improved method of carrying out the same duties with some saving of manpower. Mechanised accounting had begun to be used before the war, of course; but it was not until 1957 that we could claim its use in every branch of Lloyds Bank. Only three years later we were preparing the installation of a computer to service our largest branch, and to utilise a technological development of much greater importance.

IN THE COURSE of a recent lecture at the Royal Society of Arts, Mr Laurence Irving, Master of the Faculty of Royal Designers for Industry, touched on the subject of training for industrial design in the broader context of a serious division which, in his view, exists between art and industry. He put it that, while science requires ever higher standards of accuracy from industry, in art there is toleration and even encouragement of imperfect execution.

The purpose of this paper is to apply focal concerns theory as a theoretical explanation for police officer decision making during a traffic stop that results in a consent search. The study uses coefficients testing to better examine the issue of racial profiling through the use of a race-specific model.

The data for this study come from traffic stops conducted by the Louisville Police Department between January 1 and December 31, 2002.

The results show that the three components of focal concerns theory can explain police officer decision making for consent searches. Yet, the components of focal concerns theory play a greater role in stops of Caucasian male drivers.

The data for this study are cross-sectional and self-reported from police officers.

This paper shows the utility of applying focal concerns theory as a theoretical explanation for police officer decision making on consent searches and how the effects of focal concerns vary depending on driver race.

The findings based on focal concerns theory can provide an opportunity for police officers or departments to explain what factors impact the decision making during consent searches.

This is the first study (to the researchers’ knowledge) that examines the racial effects of focal concerns on traffic stop consent searchers using coefficients testing.

The purpose of this paper is to draw a better understanding of the potential impact of daylight in officer decision making. In order to this, the authors test the veil of darkness hypothesis, which theorizes that racial bias in traffic stops can be tested by controlling for the impact of daylight, while operating under the assumption that driver patterns remain constant across race.

Publicly available traffic-stop records from the Louisville Metro Police Department for January 2010–2019. The analysis includes both propensity score matching to examine the impact of daylight in similarly situated stops and coefficients testing to analyze how VOD may vary in citation-specific models.

The results show that using PSM following the VOD hypothesis does show evidence of racial bias, with Black drivers more likely to be stopped. Moreover, the effects of daylight significantly varied across citation-specific models.

The data are self-reported from the officer and do not contain information on the vehicle make or model.

This paper shows that utilizing PSM and coefficients testing provides for a better analysis following the VOD hypothesis and does a better job of understanding the impact of daylight and the officer decision-making on traffic stops.

Based on the quality of the data, the findings show that the use of VOD allows for the performance of more rigorous analyses of traffic stop data – giving police departments a better way to examine if racial profiling is evident.

This is the first study (to the researchers' knowledge) that applies the statistical analyses of PSM to the confines of the veil of darkness hypothesis.

The purpose of this paper is to compare two conflicting theoretical frameworks that predict or explain police decision making. Klinger's ecological theory proposes that an increased level of serious crimes in an area decreases the likelihood an officer will deal with order‐maintenance issues, while Fagan and Davies suggest an increase in low‐level disorder will increase order maintenance behavior of police officers.

Using a vignette research design, the authors examines factors that may contribute to police officers’ decision to make a traffic stop in four jurisdictions with varying levels of serious crime. Ordered logistic regression with robust standard errors was used in the analysis.

Analysis of the findings demonstrates that officers who work in higher crime areas are less likely to stop a vehicle, as described in the vignettes. Additional predictors of police decision to stop include vehicles driven by teenaged drivers and drivers who were speeding in a vehicle.

The current research is limited to an adequate but fairly small sample size (n=204), and research design that examines hypothetical scenarios of police decision making. Further data collection across different agencies with more officers and more variation in crime levels is necessary to extend the current findings.

This paper adds to the literature in two primary ways. First, it compares two competing theoretical claims to examine a highly discretionary form of police behavior and second, it uniquely uses a vignette research design to tap into an area of police behavior that is difficult to study (e.g. the decision not to stop).

Within the last 15 years, law enforcement agencies have increased their collection of data on vehicle stops. A variety of resource guides, research reports, and peer‐reviewed articles have outlined the methods used to collect these data and conduct analyses. This literature is spread across numerous publications and can be cumbersome to summarize for practical use by practitioners and academics. This article seeks to fill this gap by detailing the current best practices in vehicle stop data collection and analysis in state police agencies.

The article summarizes the data collection techniques used to assist in identifying racial/ethnic disparities in vehicle stops. Specifically, questions concerning why, when, how, and what data should be collected are addressed. The most common data analysis techniques for vehicle stops are offered, including an evaluation of common benchmarking techniques and their ability to measure at‐risk drivers. Vehicle stop outcome analyses are also discussed, including multivariate analyses and the outcome test. Within this summary, strengths and weaknesses of these techniques are explored.

In summarizing these approaches, a body of best practices in vehicle stop data collection and analysis is developed.

Racial profiling continues to be a contentious issue for law enforcement and the community. A considerable body of research has developed to assess the prevalence of racial profiling. This article offers social scientists and practitioners a comprehensive, succinct, peer‐reviewed summary of the best practices in vehicle stop data collection and analysis.