Whenever an analyst confronts some new and perplexing crime trend or crime problem, that analyst should form hypotheses about its causes. Unfortunately, newly formed hypotheses will often be based on incomplete information. But the experiences, training, and background of an analyst will be relevant in the initial formulation of hypotheses.
However, the initial hypothesis should be clear, although you should not be wedded to it, and you should use data to objectively test it. It is important to expect all hypotheses to be altered or discarded once relevant data have been examined because no hypothesis is completely right. For this reason, it is often best to test multiple conflicting hypotheses. A set of hypotheses is a road map for analysis. Hypotheses suggest types of data to collect, how these data should be analyzed, and how to interpret analysis results (Eck and Clarke, 2005).
For example, if you were investigating carjacking episodes at particular service stations, you might begin with the question, “How many service stations are in problem locations?” Based on common sense and the 80/20 rule (Center for Problem-Oriented Policing, 2015), you would state the hypothesis that some service stations will have many carjackings, but most will have few or none. You would then test this hypothesis by listing the service stations in the city and counting the number of carjacking reports at each over the last 12 months.
If your hypothesis was supported, you might ask, “What is different about the service stations with many carjackings compared with the service stations with few or no carjackings?” The concept of risky facilities (Center for Problem-Oriented Policing, 2015) would help you form a set of three hypotheses:
- 1. Risky service stations have more customers.
- 2. Risky service stations have features that carjackers find attractive.
- 3. The management or staff in risky service stations either fail to control behaviors, allow loitering, or provoke carjacking.
These hypotheses can be tested by gathering data on the number of customers at high- and low-risk service stations, analyzing the number and rate of carjackings per customer or per automobile, observing the interactions of people at troublesome and trouble-free service stations, and interviewing management, staff, and customers. If your first hypothesis was contradicted by the data, and you found that there was no great difference in the number of carjackings across all service stations, then you might ask, “Why are so many service stations troublesome?” But, this suggests another hypothesis: it’s a perception problem; the city has about as many carjackings as other comparable cities. This hypothesis suggests that you will need data from comparable cities. If, after you collected the relevant data, you found that your city has an abnormally high number of problem service stations, you might ask, “What is common to most service stations in the city that produces a large number of carjackings?”
One hypothesis is that it is the way service stations are run in the city and the way customer behavior is regulated at service stations. Another hypothesis is that there is something about the nature of service station customers in this city. Testing each would require you to collect relevant data and assess the validity of the hypothesis.
You will note how the questions and hypotheses structure the analysis. As you test each hypothesis, no matter what the results of the testing, new, more specific questions will come to you. The objective is to start with broad questions and hypotheses and, through a pruning process, come to a set of highly focused questions that point to possible answers.
Hypotheses suggest the type of data to collect. In the carjackings at service stations example, the test of each hypothesis requires specific data. Sometimes, the same data can test multiple questions. In the end, when enough data are collected and all of the questions are answered, a strategy should present itself. Following our carjacking problem to a conclusion, the analyst may decide, based on the data gathered, that carjackings are most likely to occur at a small number of service stations, but only at certain hours of the night, and only when a small number of staff are on duty. Furthermore, observation may tell the analyst that carjackings occur at these service stations only near gas pumps where the lighting is very poor and when young people are allowed to loiter both inside and outside of the service station. The analyst’s recommended plan would include meeting with the city and the management of the problem service stations and strongly recommending stricter regulation of young customers, improvement of the lighting at all pumps, and that security staff be stationed outside the service stations.
Although this kind of problem-solving project may sound easy, it is usually more complex and difficult. But in action research, the analyst and others who constitute a team should persist until success is achieved. That typically means refining and improving proposed interventions in the light of what is learned from earlier experiences. The process is not necessarily completed once the assessment has been made. If the problem persists, or has changed its form, the analyst and the team may have to start over. This is indicated by “Assessment” in the SARA problem-solving process. Assessment may read to starting over with “Scanning” (see Figure 13.1).