The research question asked to find out the relationship between a police officer’s high education and their propensity to make decisions in situations requiring operational control. Specifically, uncertainty has been reported as to whether having a high education degree affects officers’ professional performance. On the one hand, it was argued that officers with higher education were more likely to seek non-lethal solutions for the offender. On the other hand, this was refuted, and it was pointed out that having a degree had no effect on operational decisions and, in fact, did not distinguish such an officer from others. This creates a dearth of valuable information in researching the potential impact — or lack thereof — of higher education on a police officer’s professional performance, which is the crucial question of this study.
The Null and Alternate Hypotheses
The null hypothesis postulates that the presence of higher education and intent to destroy perpetrators are not related as variables; that is, the intent variable is independent of the education variable. In contrast, the alternative hypothesis, as part of the research question, postulates that the education variable and the intent variable show a relationship; that is, the intent variable depends on the education variable.
- H0: intent IS independent of education
- H1: intent IS NOT independent of education
Independent and Dependent Variable
The independent variable for this study uses the higher education status of the police officer participating in the project as the independent variable. This nominal variable has two levels and corresponds to either whether the officer has a high education degree (associate, bachelor’s, master’s, and doctoral) or not. Manipulation of the independent variable must lead — or may not — to a change in the dynamics of the dependent factor. Thus, the intent-to-destroy variable is used as the dependent variable during operational crisis situations. This variable is also measured at two levels and is, in fact, a dichotomous categorical variable: responses must vary between “Yes” and “No.” Thus, both variables in this study are proposed to be nominal and dichotomous, measured by non-numeric values.
For the study under development, the proposed design should be quasi-experimental in that no random assignment of respondents between groups is proposed. In fact, no intervention is implied in the study, so there should be no control group; therefore, RCT as a research design is not appropriate. That said, the quasi-experimental design allows for the identification of potential causal relationships, which is the motivation for choosing this type. An additional reason for choosing this design is also the fact that quasi-experimental studies tend to be conducted in vivo, which significantly reduces the costs associated with conducting a project (McCombes, 2021). In terms of temporal distribution, the design is proposed to be cross-sectional, as the project does not look at changes in a parameter over time but examines trends prevalent at a particular point in time. Thus, the study being designed is quantitative, quasi-experimental, and cross-sectional.
The study is conducted within the United States, so the general population for the project is all police officers who perform their professional activities within the jurisdiction of the state. Since the study does not assume gender, age, or ethnic differentiation for respondents, the general population is also not stratified by these characteristics. The only factor of interest within the project is the status of higher education for the respondent. Thus, the average member of the general population is a police officer who works in the United States, has or does not have a high education degree, and is directly involved in operational tasks related to the possible elimination of a criminal.
Creating a Sample
One of the fundamental issues in sample creation is finding a minimum and sufficient sample size: a sample size that is too small contributes to distortion in the analysis, while an extremely large sample can make functional calculations difficult. In addition, large samples tend to turn minor differences into statistically significant patterns, which also creates limits to the reliability of the conclusions. A rule of thumb reports that the ideal sample size is 10% of the size of the general population, but a maximum of 1,000 respondents (Bullen, 2020). Turning to statistical data shows that the size of the general population, that is, the total number of police officers in the United States, is just over 696,000 (Duffin, 2021). Since 10% of this number significantly exceeds the sample size of 1,000 people, it is 1,000 people as the final sample size that will be needed for the study.
The sample creation design is based on probabilistic methods in which each member of the general population has an equal chance to participate. To do this, a link to participate in an anonymous online survey is sent to the work email of police department employees. In case not all officers use email, the link to participate is sent to the department head, asking them to invite subordinate officers to take the quick survey. In this case, a simple random sample is used, as each participant has a chance to be involved in the survey, and the final number of respondents is determined at random. Giving potential participants equal rights increases the reliability of the survey and minimizes distortion.
Conceptualizing and Operationalizing Variables
For this project, it is proposed to conceptualize and operationalize the variables of education and intent to destroy the enemy to eliminate any ambiguity of interpretation. From a conceptualization perspective, education literally refers to the presence or absence of a diploma of completed education. In terms of operationalization, it could be any of the existing levels of higher education, be it associate, bachelor’s, master’s, and doctoral. The conceptualization of the intent variable defines a police officer’s professional intent to destroy the enemy during operational missions. From an operationalization perspective, this implies killing the perpetrator when the officer has deemed it a necessary action.
Age boundaries will be used as the control variables recorded for this project. Since only able-bodied police officers who are not retired but have already graduated are considered, the age limits for this project are fixed at 21 to 60 years of age. It is recognized that police officer may be younger or older than this interval, but they are not invited to participate in this study because they are either still unable to graduate (<21) or have already retired (>60) and are not participating in professional activities, which would skew the findings of the study. No other control variables are suggested for use.
Measuring the Variables
To measure the education variable, the respondent will be asked to answer the question of whether or not they have a degree. The specific question on the questionnaire has the following wording: “Please indicate whether you have a high education degree, which includes any level, whether associate, bachelor’s, master’s, or doctoral.” Expected responses to this question are measured on a nominal dichotomous scale and include either “Yes” or “No.” For the dependent variable measuring intent to destroy the enemy, the following question would be used, “In operational missions, when you or your group encounters a threat, do you intend to destroy the perpetrator, which includes killing the perpetrator?” This variable is also measured on a nominal scale and includes two answers, either “Yes” or “No.”
One of the sensitive issues of this design is examining a police officer’s intent to kill a criminal, information that not all officers are willing to share openly because it may discredit their humanity. For this reason, filling out the questionnaire is completely anonymous — the respondent will be aware that no data about their identity, other than educational status, is collected or used for the project. In addition, each respondent will sign an informed consent form embedded in the online survey, informing the respondent of the aims and objectives of the study and consent to data processing. In case the respondent wants to delete their answers, the online survey also offers the use of a keyword that the respondent will write themselves when completing the survey — by the keyword, the line will be identified and immediately deleted at the request of the officer. It is also worth realizing that there is no guarantee that the officer will not lie when filling out the survey: the respondent may indicate the wrong educational status or choose the wrong intention that they really want. This is an uncontrollable error that occurs in surveys, and the only way to eliminate it is to expand the sample size so that it inhibits such biases.
Type of Statistical Analysis
The motivation for choosing a particular type of statistical analysis should be based on a thorough understanding of the nature of the variables. Both the dependent and independent variables are measured on a nominal scale and have a dichotomous distribution, which means the number of levels is two for each. For this reason, an appropriate test would be the Chi-Square Test or as Pearson’s Chi-Squared Test is also called. The Chi-Square Test is based on a non-parametric comparison of observed and expected values to determine whether the relationship between variables is significant or not. Since one of the assumptions of the Chi-Square Test is that the observations are independent, this test is well suited to the objectives of the project. The number of degrees of freedom for this case will be determined by the formula df = (r-1)(c-1), and given the levels of the variables, the final number of degrees of freedom will be (2-1)(2-1) = 1. The significance level for the test will be determined at the classical level, that is, alpha =.05.
At the 5% significance level, the critical Chi-Square is 3.84 for a two-way test (no direction between variables is implied), so the final Chi-Square will be compared to this value. If the calculated p-value is greater than.05, then there are sufficient conditions to accept the null hypothesis, that is, to postulate no relationship between the variables. On the contrary, if it appears that the calculated p-value for the data is below this value, then the null hypothesis must be rejected, and the conclusion that the variable of intention is dependent on the variable of education must be accepted.
Reliability and Validity
Measurement reliability determines that the questionnaire produces consistent and reproducible results over time. In other words, it indicates that even after some time, if the control variables are maintained within that general population, the result will persist. Construct validity, on the other hand, defines a measurement design in which the questionnaire actually measures what is implied, that is, educational status and intent to destroy the offender. The reliability and validity of the questionnaire will be realized by formulating unambiguous questions that cannot lead to alternative interpretations of the wording. As one additional verification measure, several focus groups with small sample sizes (>30) can be conducted to determine if the results are reproducible. In addition, the anonymity of the responses implies freedom of choice for respondents, which should minimize bias and thus increase face validity.
Since one of the foundations of a democratic society is the value of human life, including the criminal’s life, the state is interested in reducing the number of murders committed by police officers. For this reason, if the intent is found to be education-dependent, and officers with higher education do commit fewer murders, it would entail the need to promote higher education among police officers, including through payment benefits. Meanwhile, reducing homicide rates would entail expanding prison capacity because the number of living criminals would increase in the short term.
Bullen, P. B. (2020). How to choose a sample size (for the statistically challenged). Tool4Dev. Web.
McCombes, S. (2021). Research design | a step-by-step guide with examples. Scribbr. Web.
Duffin, E. (2021). Number of law enforcement officers U.S. 2004-2020. Statista. Web.