Abstract
In the wake of recent high profile incidents of police misconduct, the issue of police integrity has become of heightened interest for practitioners, academics, and society. One of the continuing challenges, though, has been how to best measure misconduct/integrity. This chapter examines the properties of the latent constructs of police integrity using data collected by Klockars et al. (The measurement of police integrity, National Institute of Justice, Washington, DC, pp. 65–70, 1997) from thirty police departments throughout the United States. To bolster our understanding of the measurement of police integrity, we develop valid and reliable latent constructs of five of the dimensions measured, while, at the same time, considering potential inter-relationships between the constructs. We use latent trait models to reassess the validity of the measures, estimate the correlation between the various sub-constructs of police integrity, and examine measurement invariance of police integrity. Our analyses confirm that the Klockars et al. (The measurement of police integrity, National Institute of Justice, Washington, DC, pp. 65–70, 1997) methodology is a valid way to measure police integrity; however, we find there are several dimensions of police integrity that, while related, are distinct of one another. Specific results and implications for future research are discussed.
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Notes
- 1.
While we would have liked to include all seven dimensions, the two dimensions related to the discipline a person should receive and the discipline a person would receive for engaging in this behavior presented unique problems. Specifically, the results from the model indicated serious violation of the assumptions with the model, which suggests the need for additional work to develop the constructs being measured. These problems stem from differences between organizations, whereby some organizations have the ability—and willingness—to use some types of disciplinary procedures that are not available or widely employed in others.
- 2.
There are scholars who now suggest that the use of single-item indicators is appropriate in certain situations (e.g., relevancy and adequacy of the measure; Fuchs and Diamantopoulos 2009).
- 3.
Because we do not know who the agencies are or what their policies were, we are not sure that all vignettes depict something against department policy. However, because many departments prohibit the actions in all of these scenarios, and most agencies prohibit all of the most serious scenarios, we make the assumption that this is a violation of the policy in the respondent’s agency.
- 4.
The first parameter is the b parameter, called difficulty parameter, estimates the location on Θ (the abscissa) where a respondent is more likely to endorse one response category. The second parameter is the a parameter, called the discrimination parameter, which is the slope of the curve at the location of the b parameter. This parameter allows for understanding how well an item can identify a person with a certain level of the latent trait. The steeper the slope, the more discriminating the item. The final parameter is the c parameter, called the pseudo-chance parameter, which estimates uncertainty in respondents’ answers. In other words, some respondents have a non-zero probability of endorsing a category, regardless of their level on Θ.
- 5.
Thresholds refer to the location where the curve from one response option (e.g., strongly disagree) cross over with the response curve for another response option (e.g., disagree). If L equals the number of response options. There are always L-1 thresholds.
- 6.
Some scholars are reticent to use dichotomous items due to the loss of information (Brown 2006); however, here the respondents’ answers suggested a natural dichotomy in the data. The reason for this is unclear, but may have something to do with the ambiguity of the items and differences in department policy that are not captured here.
- 7.
Technically the maximum tolerance is derived Q3 = −1/(L − 1), where L is the number of items. This would yield a maximum Q3 value of |0.10|, but de Ayala (2009) suggests that using |.20| value reduces the chances of making a Type I error, especially with large samples.
- 8.
Recall that these coefficients can be interpreted like z-scores and therefore a 0 indicates an average level of the trait.
- 9.
This measure is analogous to the reliability coefficients in classical test theory.
- 10.
One thing that is important to note, while we are referring to these latent traits as police integrity, the measures are not on the same scale. In other words, we cannot directly compare the results from one construct to another without putting them both on the same metric.
- 11.
While most of the time this method is used with a temporal break in between, it is consistent with logic that different methods should exert the same level of correlation.
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Maskály, J., Donner, C.M., Chen, T. (2019). Improving the Measurement of Police Integrity: An Application of LTM to the Klockars et al. (1997) Scales. In: Kutnjak Ivković, S., Haberfeld, M.R. (eds) Exploring Police Integrity. Springer, Cham. https://doi.org/10.1007/978-3-030-29065-8_13
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