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The Salience of Social Contextual Factors in Appraisals of Police Interactions with Citizens: A Randomized Factorial Experiment

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Abstract

Objectives

Prior research indicates that public assessments of the manner in which the police exercise their authority are a key antecedent of judgments about the legitimacy of the police. In this study, the importance of context in influencing people’s assessment of police wrongdoing is examined.

Methods

A randomized factorial experiment was used to test how respondents perceive and evaluate police–citizens interactions along a range of types of situations and encounters. 1,361 subjects were surveyed on factors hypothesized to be salient influences on how citizens perceive and evaluate citizen interactions with police. Subjects viewed videos of actual police–citizen encounters and were asked for their evaluations of these observed encounters. Contextual primes were used to focus subjects on particular aspects of the context within which the encounter occurs.

Results

Structural equation models revealed that social contextual framing factors, such as the climate of police–community relations and the legality of the stop that led to the encounter, influence citizen appraisals of police behavior with effects comparable in size to and even larger than demographic variables such as education, race, and income.

Conclusions

These results suggest that the understandings and perceptions that people bring to a situation are important determinants of their assessment of police fairness. The police can positively influence citizen interpretations of police actions by striving to create a climate of positive police–community relationships in cities.

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Notes

  1. Prior studies suggest the value of this type of vignette approach (Seron et al. 2006), but have used written vignettes of uncertain realism. We utilized actual videos of police interactions centered on the core issues in the study to supplement the vignettes and provide salient visual cues and contextual primes to stimulate respondents.

  2. KN has completed several studies to validate its methodology, including both accuracy and nonresponse bias. For example, Krosnick and Chang (2001) compared responses to a single survey on the 2000 presidential election using three different samples and methods: KN, Harris Interactive (HI), and a RDD survey conducted by the Ohio State University Center for Survey Research. They reported that the KN samples had higher completion rates, closer approximation to census estimates, and less skew on basic measures of opinion and voting behavior. They also show higher measurement reliability and “non-differentiation” of responses for Internet-enabled data collection compared to RDD. Skitka and Mullen (2002) and Skitka et al. (2002) show that on questions of civic values and actions, in particular, telephone survey responses were more often influenced by respondents’ motivations to present themselves in a positive light to interviewers compared to internet-enabled surveys. Benchmarking studies show that KN samples yield estimates for several health behaviors—current smoking, diabetes, ulcer, migraine headaches, and stroke—similar to the estimates in the National Health Interview Survey conducted by telephone annually by CDC. The average difference in the results is about 1 percentage point (Baker et al. 2003).

  3. The 15 U.S. cities were Baltimore, Boston, Chicago, Cleveland, Denver, Detroit, Houston, Los Angeles, Miami, New York, Philadelphia, Phoenix, San Diego, Seattle, and Washington, DC. Knowledge Networks completed between 85 (New York) and 98 (Baltimore) interviews in each city.

  4. The survey was fielded on March 27, 2008 to a sample of 2,183 Knowledge Networks panel members. To be included in this analysis, a respondent had to view all three videos (N = 1,361).

  5. These questions were randomly administered before or after the videos were viewed. 676 respondents completed the questionnaire after seeing the videos and 685 respondents completed the questionnaire before seeing the videos. To investigate whether the timing of the administration of the questionnaire impacted our findings, we split the sample and replicated the full sample analyses presented here. When the impact of the four framing effects dummy variables (city climate of police-citizen relations, stop context, officer history, and citizen history) on the “police were wrong/should be punished” outcome variable were estimated, the two split samples produced coefficients of similar direction, magnitude, and statistical significance (p < .05). As such, whether the respondent answered the questionnaire before or after the video did not substantively impact the findings presented in Tables 2, 3, and 4.

  6. OLS regressions were used to analyze the normally-distributed interval-level dependent variables “age” and “conservative” scale. Logistic regression models were used to examine the remaining dichotomous dependent variables: “White” (0 = non-white, 1 = white), “male” (0 = female, 1 = male), “income >$40 K” (0 = household income ≤$40,000 per year, 1 = household income >$40,000 per year), “college+” (0 = less education than a bachelor’s degree, 1 = bachelor’s degree or higher), and “victim” (0 = household member was a prior victim of crime, 1 = household member was not a prior victim of crime).

  7. Table 2 identifies only 6 statistically-significant (p < .05) differences across 168 hypothesis tests (3 videos * 8 contextual dummy variables per video * 7 outcome variables). This is lower than the 8.4 statistically-significant results that would be expected by chance alone for 168 hypothesis tests at a .05 level of statistical significance. We also ran similar OLS and Probit regression models for all observed variables that comprised the latent variables described above; these analyses yielded similar results that supported successful randomization. Further, we created an 81 cell variable representing the different combinations of framing conditions and social contextual issues and ran a series of Chi square tests to examine whether the observed distributions of cell counts were significantly different from the expected distribution of cells counts. We did not find any statistically significant differences between the observed and expected distributions, suggesting successful randomization by this measure.

  8. These videos showed real-life interactions between police officers and citizens. We acquired the three study videos from the internet and edited the footage down to 30 s that captured the key events briefly described in this section.

  9. We also recognized that our main effects models could be structured as hierarchical linear models to analyze multiple observations nested within a single subject. This analysis required restructuring the study dataset into a longitudinal panel design where each respondent views three consecutive videos for a total of 4,083 observations (3 videos * 1,361 respondents). In Stata 12.0, we used the XTSET command to declare that the data were structured in the longitudinal panel format and the XTREG command to execute hierarchical linear models to analyze the data. Video dummy variables and manipulated social context dummy variables were used to measure the main effects of viewing the different police-citizen interaction videos and the randomly-allocated framing effects, respectively. Interactive dummy variables were constructed (video dummies * manipulated social context dummies) and entered into the model to determine whether the observed framing effects were consistent across the three videos. These results were congruent with the main effects findings presented here and are available upon request from the authors.

  10. Since they are not directly measured, latent variables do not have intrinsic variances. For the factor loadings in Table 3, the variances of the latent variables were constrained to equal “1” for ease of interpretation (StataCorp 2011, p. 254). These coefficients can be interpreted as correlations that range from “0” to “1”.

  11. Unfortunately, small numbers of the 1,361 respondents did not answer all of the study questions for each video viewed and for the questionnaire. We handled missing responses through listwise case deletions. As Table 2 presents, the number of respondents included in our analyses ranged from 1,232 (90.5 % of 1,361) to 1,246 (91.5 %). We used t-tests to examine whether there were any statistically-significant differences between included and excluded cases for the covariates used in the SEM analyses presented in Tables 2, 3 and 4. Our simple comparisons did not reveal any statistically-significant differences between the missing cases and the included cases. This suggests that the data were missing at random and, as such, listwise case deletions were an appropriate method to address the very modest missing data problem.

  12. The CFI assesses the fit of a user-specified solution in relation to a more restricted, nested baseline model in which the covariances among all input indicators are fixed to zero or no relationship among variables is posited (Brown 2006, p. 84). The CFI coefficient value ranges from 0 to 1.00 with values greater than 0.90 indicating a reasonably good fit of the hypothesized model (Hu and Bentler 1999). RMSEA takes the error of population approximation and degrees of freedom into account and measures the lack of fit of the hypothesized model to the population covariance matrix. SRMR is estimated in a similar to RMSEA but does not penalize model complexity. As a general rule of thumb, SRMR and RMSEA results of 0.05 or less indicates a close approximate fit of the model (Hu and Bentler 1999).

  13. The sex effects were statistically significant in Videos 2 and 3 and in the expected direction with males expressing more favorable attitudes toward police.

  14. Arguably the effect of Education should be estimated without controlling for income, since presumably part of the effect of Education is mediated through Income. Dropping Income as a control only results in the effect of Education being reduced—with the effect dropping to −0.78 (SE = .028, p < .05). In our sample, levels of Education are very modestly correlated with levels of Income (r = .28). In reduced regression models of the effects of Education and Income on respondent assessment of police behavior, we find that both are statistically significant predictors of the outcome variable in all three videos. Controlling for Income, increasing levels of Education are associated with more negative perceptions of observed police behavior. This suggests that more educated people are more critical appraisers of police behavior independent of how much money they earn. Clearly, future research in this area should seek to better disentangle these complicated effects.

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Authors and Affiliations

  1. Rutgers University, Newark, NJ, USA

    Anthony A. Braga

  2. John F. Kennedy School of Government, Harvard University, 79 John F. Kennedy Street, Cambridge, MA, 02138, USA

    Anthony A. Braga & Christopher Winship

  3. Yale University, New Haven, CT, USA

    Tom R. Tyler & Tracey L. Meares

  4. Columbia University, New York, NY, USA

    Jeffrey Fagan

Authors
  1. Anthony A. Braga
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  2. Christopher Winship
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  3. Tom R. Tyler
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  4. Jeffrey Fagan
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Correspondence to Anthony A. Braga.

Appendix

Appendix

See Tables 5 and 6.

Table 5 OLS and logistic regressions to test randomization of framing effects for selected respondent characteristics, N = 1,361
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Table 6 Factor loadings for endogenous variables comprising latent variables in SEMs
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Braga, A.A., Winship, C., Tyler, T.R. et al. The Salience of Social Contextual Factors in Appraisals of Police Interactions with Citizens: A Randomized Factorial Experiment. J Quant Criminol 30, 599–627 (2014). https://doi.org/10.1007/s10940-014-9216-7

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