Abstract
Objective
To evaluate the relationship between police cadets’ facial traits and their subsequent promotional success.
Methods
Using archival police academy photographs, we use a two-phase experiment to evaluate the impact of facial traits on future promotional success. First, respondents (n = 507) view randomly selected photographs of cadets (observations = 15,669) and evaluate them for facial traits and perceived leadership ability. Second, respondents are presented with random dyads of differentially promoted recruits, and choose one based on the highest perceived leadership ability. We compare those leadership evaluations to the subsequent promotional success of the cadets featured in the photographs (observations = 5739). We employ Bayesian multilevel modeling throughout both phases.
Results
Facial traits are the primary driver of subject perceptions of leadership ability, and those perceptions successfully predict promotional success later in the cadets’ careers. When selecting for leadership potential based on police cadet photographs, respondents predict correct promotional choices at levels well above chance as measured by an AUC score of .70. Further, respondents’ evaluations successfully discriminate both between no promotion and lieutenant promotion, and sergeant versus lieutenant promotions.
Conclusions
Promoting the most capable police officers is a critical feature of public service. Our findings cast a degree of doubt on the purportedly meritocratic foundations of police promotion and selection. Extra-legal information, such as facial features, predicts later promotional success.
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Data availability
Data and code available upon request. Because of personal identification concerns, no photographic material will be released.
Notes
As Gelman and Hill (2006) describe, survey data is often hierarchical in nature with a non-trivial likelihood of respondent group characteristics (e.g., age, gender, race) influencing their responses. Multilevel models help account for this variation, providing more accurate inferences. Further, while the amount of missing data in our dataset was minimal, multilevel models consider relationships between observations within respondents, making better use of the available data and providing more accurate estimates than generalized linear models (Enders, 2010). Moreover, survey data, as in our survey, often features multiple response categories such as Likert response scales. Multilevel models allow for more robust and flexible analysis of these data than a generalized linear model (Raudenbush & Bryk, 2002). Finally, upon plotting respondent varying intercepts we observed variation across respondents, some markedly so, further supporting this modeling decision. At a reviewer’s request, we further assessed our models without using multilevel modeling. When doing so, WAIC scores for all models increased substantially: Model 1’s WAIC score increased by 4,950.8, Model 2 by 4,744.6, Model 3 by 4,255.4, Model 4 by 4,287.9, and the kitchen-sink model by 4,609.7. These results also support our multilevel modeling decision.
We also estimated all models with uninformative (flat) priors. No substantive differences in results were noted for the two estimation processes.
The frequentist analog to a credible interval is a confidence interval, yet the two concepts are statistically different. In a frequentist paradigm, confidence intervals are based on repeated sampling theory. A 95% confidence interval indicates that 95 out of 100 replications of the same experiment will capture the fixed, but unknown, regression coefficient. A credible interval is interpreted as the probability that the population parameter is between the upper and lower bounds of the credibility interval, based on the available information (Kruschke, 2014; McElreath, 2020; Van de Schoot & Depaoli, 2014).
The widely applicable information criteria (WAIC) process compares models’ predictive capabilities by estimating the relative out-of-sample Kullback–Leibler (KL) divergence. WAIC is advantageous because it makes no assumptions about the shape of the posterior distribution and provides an approximation of the out-of-sample deviance that converges to the cross-validation approximation in a large sample. WAIC accomplishes this by taking the log-posterior-predictive-density and attaching a penalty proportional to the variance in the posterior predictions, thus controlling for model overfitting risk (McElreath, 2020).
We also plotted posterior predictive checks for the facial traits model. If a model is a good fit, then data synthesized from that model should be similar to the observed data. We drew five hundred simulated datasets from the posterior model parameters of the facial traits model and plotted them against the observed data. Appendix Fig. 3 visualizes mean and standard deviation posterior predictive checks for the model. The simulated data sets predict the observed data well.
Model checks confirm our model adequacy. We found minimal evidence of elevated correlation among study correlates. All correlations are well below the commonly employed 0.80 threshold (Berry et al., 1985). Post-estimation diagnostics show all models are free from impairing multicollinearity, with Variance Inflation Factors (VIF) for all variables in all models being below 2. Pairs plots confirmed multivariate normal distributions. Autocorrelation plots indicated that there is little to no autocorrelation present in the data. The Markov chains in all models converged, with Gelman-Rubin convergence diagnostics equaling 1.0, and an adequate number of effective samples observed (Kruschke, 2014).
We examined autocorrelation plots in all three models, and there is little or no autocorrelation in the data. All Markov chains in all models converged, with Gelman-Rubin convergence diagnostics equaling 1.0, and an adequate number of effective samples observed. Finally, posterior predictive checks predict the observed data well in all three models.
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Acknowledgements
We extend our thanks to Dr. Daniel Schiff (Purdue) for his help improving an early version of this paper and Dr. Carl Jenkinson (University of South Carolina) for his quaint friendship and helpful editing of a later draft.
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Appendix
Appendix
Fig. 2
Correlation matrix facial features
Fig. 3
Correlation matrix of personality factors
Fig. 4
Marginal effect plot. All promotional choices
Fig. 5
Marginal effect plot. No promotion vs lieutenant promotion choices
Fig. 6
Marginal effect plot. Sgt. Vs Lt. promotion choices
Photograph 7
Task 1. Question 1
Photograph 8
Task 1. Question 2
Photograph 9
Task 2
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Adams, I.T., Mourtgos, S.M., Simon, C.A. et al. If the face fits: predicting future promotions from police cadets’ facial traits. J Exp Criminol (2023). https://doi.org/10.1007/s11292-023-09554-0
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DOI: https://doi.org/10.1007/s11292-023-09554-0