research hypothesis examples criminal justice

The Importance of Research Methods in Criminal Justice

By Dr. Jarrod Sadulski   |  01/31/2024

Research methods in criminal justice enable researchers to address some of the most pressing issues that affect our society. The criminal justice system is always evolving. It shifts to meet the ever-changing trends in crime and technology.

Criminal justice research provides policymakers and criminal justice leaders with up-to-date and relevant insight to answer many of the challenges that they face. For example, criminal justice research can lead to new policies and even case laws that guide law enforcement officers daily. Policy analysis is used to solve problems in crime and criminology.

I once attended a large conference by the International Association of Chiefs of Police where police leaders from around the world examined criminal justice and criminology. Research formed the backbone of many of the presentations I attended.

These presentations addressed the most pressing problems in modern-day policing and enabled leaders to make informed decisions. Research can influence policy through practical application in criminology and criminal justice.

The Research Process

To obtain information that can shape policies and laws, effective criminal justice research methods are essential. The research process typically involves quantitative, qualitative, or mixed methods research that go through a peer review process to validate the researcher’s findings.

Once that validation occurs, the research is viewed as credible and is ready to be presented to policymakers. Research methods are commonly guided through a theoretical framework in criminal justice. For example, a criminal justice researcher studying police stress may wish to apply Agnew's General Strain Theory to guide the research.

A researcher who may wish to study if someone's upbringing and environment contribute to whether they engage in criminality as an adult may apply a Social Learning theoretical framework. If someone is studying crime mapping, they may wish to apply a Routine Activity theoretical framework.

Research Designs

Effective research begins with a quality research design to address a research problem. The design typically involves:

• A sample that represents a population
• Research questions in qualitative research or hypotheses in quantitative research
• A problem statement
• A purpose statement

A research design of high quality is also important in criminal justice research, and the research design should be detailed. For instance, it should explain how a researcher will collect data from the sample, how the data will be analyzed, and how the researcher’s conclusions should answer the research questions or hypotheses.

Access to participants is vital. That access typically begins with obtaining permission to recruit from an organization, then sending recruitment material to willing participants such as students who are interested in criminal justice and criminology.

Qualitative Methods

In qualitative research interviews, field research, questionnaires, participant observation, case studies, focus groups, and non-experimental methods are common. In data analysis, thematic analysis is commonly used, which involves developing themes obtained through participant data.

Saturation is an important part of this type of work that involves developing themes that occur through each participant's responses. Data between participants and existing literature are triangulated to ensure that there are the same findings among the data collected.

In multiple case studies research, triangulation is used among each case study to draw conclusions. Interviews are common in multiple case studies research.

In qualitative data surveys, open-ended questions are commonly used to collect data. One limitation of this type of research is that rigor in research may be more difficult to demonstrate due to the lack of experimental analysis.

Quantitative Research

Quantitative research tends to be more experimental and involve a scientific method. Data collection through quantitative research may be descriptive and may be collected through self-report surveys. Survey research is a common way to collect data in quantitative research.

Quantitative data analysis often involves experimental tests that recognize relationships between variables. For example, survey research may involve sending surveys to participants who can answer with either yes/no answers or with numerical values that can be analyzed. This analysis may occur through t-tests , an analysis of variance (ANOVA) , and other statistical analyses.

Secondary Data Analysis

Secondary data analysis involves using existing research in past research. For example, data may be collected from a published national crime victimization survey or other past survey research.

Secondary research can be helpful in answering a new problem. Social science research involves conducting research to develop information from a study into various social or societal issues.

Various methods can be used in criminological research. Properly designed research methods are an important part of criminal justice research and are explained in the study.

To ensure reliability and validity, another researcher should be able to follow the same data collection to address a research question and should come to the same conclusions. Critical thinking is an important part of content analysis.

Evaluation research can be used by decision-makers in criminal justice because it evaluates the merit and effectiveness of a program or policy. Evaluation research can help decision-makers to understand the effectiveness of policies.

The Role of Students

Students have an important role in serving as researchers. They can aid in policy analysis by addressing a current problem and developing findings that can be published. While completing coursework, students can learn the skills of a researcher and the process of the Institutional Review Board .

Students are also critical consumers of research. They will view a research topic with great interest and can provide useful feedback to academics when needed.

Dr. Jarrod Sadulski is an associate professor in the School of Security and Global Studies and has over two decades in the field of criminal justice. He holds a bachelor’s degree Criminal Justice from Thomas Edison State College, a master’s degree in criminal justice from American Military University, and a Ph.D. in criminal justice from Northcentral University.

His expertise includes training on countering human trafficking, maritime security, mitigating organized crime, and narcotics trafficking trends in Latin America. Jarrod has also testified to both the U.S. Congress and U.S. Senate on human trafficking and child exploitation. He has been recognized by the U.S. Senate as an expert in human trafficking.

Jarrod frequently conducts in-country research and consultant work in Central and South America on human trafficking and current trends in narcotics trafficking. Also, he has a background in business development. For more information on Jarrod and links to his social media and website, check out https://linktr.ee/jarrodsadulski .

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Crime Analysis for Problem Solvers in 60 Small Steps

Step 20: formulate hypotheses.

Whenever we confront some new and perplexing crime pattern we form hypotheses about its causes, often based on incomplete information. Experience and theory are good sources of hypotheses. You should (1) clearly state your hypotheses, (2) not be wedded to them, and (3) use data to objectively test them. 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 roadmap for analysis. Hypotheses suggest types of data to collect, how this data should be analyzed, and how to interpret analysis results. If you were investigating drinking-related assaults in bars you might begin with the question, "How many bars are problem locations?" Based on the 80-20 rule (Step 18), you would state the hypothesis that some bars will have many fights, but most will have few or none. You would then test this hypothesis by listing the licensed drinking places and counting the number of assault reports at each over the last 12 months.

If your hypothesis was supported, you might ask the question, "What is different about the bars with many fights compared to the bars with few assaults?" The concept of risky facilities (Step 28) would help you form a set of three hypotheses:

• Risky bars have more customers.
• Risky bars have features that attract assaulters.
• Bar staff in risky bars either fail to control behaviors, or provoke fights.

You can test these hypotheses by gathering data on the number of customers at high- and low-risk bars, analyzing the number and rate of assaults per customer, observing the interactions of people at troublesome and trouble-free bars, and interviewing staff and customers.

If your first hypothesis was contradicted by the data, and you found that there was no great difference in numbers of assaults across drinking establishments, then you might ask the question, why are so many bars troublesome? This suggests another hypothesis: It's a perception problem; the city has about as many bar assaults 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 bars, you might ask the question, "What is common to most bars in the city that produces a large number of assaults?" One hypothesis is that it is the way liquor licenses are dispensed and bars regulated. Another hypothesis is that there is something about the nature of bar customers in your city. Testing each would require you to collect relevant data and assess the validity of the hypothesis.

Notice how the questions and hypotheses structure the analysis. Test results - positive or negative - reveal new, more specific questions. 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 responses.

Hypotheses suggest the type of data to collect. In the bar assaults example, the test of each hypothesis requires specific data. Sometimes the same data can test multiple hypotheses (as is the case with choosing among the three alternative explanations for risky bars). Often a variety of data is required to select among alternative hypotheses (as is the case with the last set of hypotheses). The more specific your hypotheses, the more focused your data collection will be. This is why it is more important to have a clear hypothesis you personally dislike, than an unclear hypothesis you approve of, or worse, no hypothesis at all.

Paralysis by Analysis

The lack of explicit hypotheses can lead to "paralysis by analysis," collecting too much data, conducting too much analysis, and not coming to any useful conclusion.

Hypotheses can help direct the analysis of data. Every clear hypothesis suggests a pattern of data that you should be able to observe, if the hypothesis is correct. In the example above, the hypotheses derived from the concept of risky facilities can be tested using a simple analytical procedure. If a bar is a crime generator, then you should see a high number of assaults, a high number of customers, but a low assault rate (see Step 17). Failure to find this pattern suggests the hypothesis is wrong. So it is important to have a clear idea of what you should observe if your hypothesis is correct, and what you should observe if your hypothesis is wrong (see third column of the table). If you cannot do this, then this is an indicator that your hypothesis may be too vague.

Hypotheses help interpret the analysis results. Let's assume that the analysis of bar fights showed that a few bars had most fights, and observations of the high- and low-risk bars indicated that the security staff of the risky bars provoked fights. This immediately suggests a possible avenue for intervention. In short, the validity of a hypothesis must make a difference. That is, if the hypothesis is true you will take a different decision than if it is false. If you will make the same decision regardless of the test results, then the hypothesis and its test are irrelevant.

In summary, hypotheses are important for guiding analysis. To formulate hypotheses you need to ask important questions, then create simple and direct speculative answers to these questions. These answers are your hypotheses. These speculations must be bold enough that they could be wrong, and there must be a way of showing whether they are right or wrong. If possible, create competing hypotheses.

Hypothesis formation is a useful group exercise, as it allows participants with contrary views to put their perspectives on the table in a way that allows clear and objective tests. In this way, participants contributing invalid hypotheses make substantial contributions to the analysis of the problem. If each hypothesis is linked to a potential solution, the test of these hypotheses simultaneously directs attention to feasible responses and rules out ineffective approaches.

Questions, Hypotheses, and Tests

• Acknowledgements

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Criminal Justice

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Quantitative criminology.

The foundation of a sound quantitative criminology is a solid base of descriptive information. Descriptive inference in criminology turns out to be quite challenging. Criminal offending is covert activity, and exclusive reliance on official records leads to highly deficient inferences. Despite important challenges in descriptive analysis, researchers and policymakers still strive to reach a better understanding of the effects of interventions, policies, and life experiences on criminal behavior. (adsbygoogle = window.adsbygoogle || []).push({});

1. Introduction

2. quantitative data sources, 3. logical and inferential issues, 3.1. time horizon, 3.2. unit of analysis, 3.3. sampling, 3.4. target population, 3.5. concepts and variables, 3.6. descriptive and causal inference, 3.7. validity, 3.8. reliability, 3.9. relationship between reliability and validity, 3.10. estimates and estimators, 3.11. estimator properties: bias, efficiency, and consistency, 4. assessing evidence, 5. methods for descriptive inference, 5.1. measures of central tendency, 5.2. measures of dispersion, 5.3. criminal careers, 5.4. recidivism rates, 5.5. trajectories and developmental pathways, 6. analytic methods for causal inference, 6.1. independent variables and outcomes, 6.2. contingency tables, 6.3. measures of association, 6.4. chi-square, t tests, and analysis of variance, 6.5. linear regression, 6.6. regression for qualitative and counted outcomes, 6.7. structural equation models, 6.8. interrupted time series analysis, 6.9. models for hierarchical and panel data, 6.10. counterfactual reasoning and treatment effects, 6.11. randomized experiments, 6.12. natural experiments and instrumental variable estimators, 6.13. matching, 7. conclusion.

Since its inception as a field of scientific inquiry, criminology and criminal justice (CCJ) researchers have used quantitative data to describe and explain criminal behavior and social responses to criminal behavior. Although other types of data have been used to make important contributions to criminological thought, the analysis of quantitative data has always played an important role in the development of knowledge about crime. This research paper discusses the various types of quantitative data typically encountered by CCJ researchers. Then, some of the logical and inferential issues that arise when researchers work with quantitative data are described. Next, the research paper considers different analytic frameworks for evaluating evidence, testing hypotheses, and answering research questions. Finally, a discussion of the range of methodological approaches used by contemporary CCJ researchers is provided.

CCJ researchers commonly work with data collected for official recordkeeping by government or quasi-government agencies. Such data often include records of criminal events, offender and victim characteristics, and information about how cases are handled or disposed. Detailed information about crimes known to the police and crimes cleared by arrest are available in the UniformCrime Reports (UCR) and the National Incident Based Reporting System (NIBRS). In addition, for purposes of specific research projects, criminal justice agencies often make their administrative records available to criminologists—provided that appropriate steps are taken to protect individual identities. For example, the Bureau of Justice Statistics has conducted two major studies of recidivism rates for prisoners returning to the community in multiple states. Such projects require coordinated use of state correctional databases and access to criminal records, including arrests, convictions, and reincarceration.

More recently, researchers have also relied on information collected through direct interviews and surveys with various populations. In these surveys, respondents are asked about their involvement in offending activities, victimization experiences, background characteristics, perceptions, and life circumstances. Analyses from data collected through the National Crime Victimization Survey; the Arrestee Drug Abuse Monitoring program; the RAND inmate survey; the National Youth Survey; the National Longitudinal Survey of Youth; the Adolescent Health Study; Monitoring the Future (MTF); Research on Pathways to Desistance, and the Office of Juvenile Justice and Delinquency Prevention’s longitudinal youth studies in Rochester, New York, Pittsburgh, Pennsylvania, and Denver, Colorado, have all made important contributions to criminological thought and public policy.

Researchers have also attempted, in some studies, to collect detailed quantitative databases composed of information from both administrative and direct surveys on the same individuals. Among other findings, this research has consistently shown that most crime victimizations are not reported to the police and that most offending activities do not result in an arrest.

The analysis of quantitative crime-related data, like any other type of analysis, depends primarily on the question one is asking and the capabilities of the data available. This section briefly discusses some of the most prominent issues that crime researchers consider when analyzing quantitative data.

Regardless of the data source, research projects using quantitative data can generally be characterized as crosssectional or longitudinal. Cross-sectional studies examine individuals or populations at a single point in time, whereas longitudinal studies follow the same individuals or populations over a period of time. Among longitudinal studies, an important consideration is whether the data will be collected prospectively or retrospectively. In prospective studies, individuals are enrolled in the study and then followed to see what happens to them. In retrospective studies, individuals are enrolled in the study, and researchers then examine historical information about them. Some studies include both prospective and retrospective elements. For example, the Research on Pathways to Desistance study enrolled adolescent offenders in Phoenix, Arizona, and Philadelphia to see how these offenders adapt to the transition from adolescence to adulthood. In that sense, the study is prospective; however, historical information about the individuals included in the study is available and has been collected retrospectively as well.

In most studies, it is clear whether the project is crosssectional or longitudinal, but there are exceptions. For example, the MTF study repeatedly surveys nationally representative samples of high school seniors. This study can be viewed as cross-sectional because it does not survey the same individuals repeatedly, but it can also be viewed as longitudinal because the same methodology for drawing the sample and analyzing the data is repeated over time. Similar issues arise with UCR and NIBRS data. Often, specific studies using a repeated cross-sectional data source, such as MTF, UCR, or NIBRS, will tend to emphasize either crosssectional or longitudinal features of the data.

It is also useful to think about research projects in terms of the basic source of variation to be studied. For example, some studies focus on variation in crime between communities, whereas other studies examine variation in criminality between individual persons. Still other studies attempt to describe and explain variation in behavior over time for the same community or individual. In some studies, the unit of analysis is unambiguous, whereas in other instances, there may be multiple logical analysis units (e.g., multiple observations on the same person and multiple persons per community). These studies are generally referred to as hierarchical or multilevel analyses. An important issue arising in these analyses is lack of independence among observations belonging to a logical higher-order group. For example, individuals who live in the same community or who attend the same school are not likely to be truly independent of each other.

The list of all cases that are eligible to be included in a study is called the sampling frame. The sample included in the study will either be identical to the sampling frame or it will be a subset of the sampling frame. In some instances, the sampling frame is explicitly defined; at other times, the sampling frame is vague. Researchers generally describe the manner in which the sample was selected from the sampling frame in terms of probability or nonprobability sampling. In probability sampling, each case in the sampling frame has a known, non-zero probability of being selected for the sample. Samples selected in any other way are called nonprobability samples. The most basic form of probability sampling is simple random sampling, when each member of the sampling frame has an equal probability of being selected for the sample. More complicated forms of probability sampling, such as stratified random sampling, cluster sampling, and stratified multistage cluster sampling, are all commonly used in CCJ research.

The use of probability sampling allows researchers to make clear statements about the generalizability of their results. Although this is a desirable feature of probability samples, much CCJ research is based on nonprobability samples. The 1945 and 1958 Philadelphia birth cohort studies conducted by Marvin Wolfgang and his colleagues (Wolfgang, Figlio, & Sellin, 1972) focused on an entire population of individuals rather than a sample. Still, one can view the choice of the years 1945 and 1958 as a means of sampling. In fact, when populations are studied, there is almost always a way to conceive of them as nonprobability samples. In other studies, a researcher may survey all children in attendance at a school on a particular day. The resulting sample would be called a convenience or availability sample. Still other research projects rely on the purposive selection of certain numbers of people meeting particular criteria to ensure representation of people from different groups (i.e., males, females, blacks, whites, etc.). These samples are usually called quota samples.A key feature of nonprobability samples is that one is not able to make explicit probabilistic statements about quantities in the population based on what one observes in the sample. Nevertheless, nonprobability samples are quite useful and necessary for addressing many interesting research and policy questions that arise in CCJ research.

A key aspect of any scientific work is the identification of empirical regularities that transcend specific individuals, places, or times. Thus, the population to which the results of a study generalize is of considerable importance. In general, researchers tend to prefer studies that identify the target population and discuss how well the results are likely to generalize to that population. But the target population is sometimes ambiguous. If one studies all individuals in attendance at a particular school on a given day, one could argue that the sample is synonymous with the target population. The research community, however, is not likely to be interested in what is occurring at that individual school unless it somehow relates to what is occurring at other schools in other locations and at other times. This ambiguity means that one cannot make precise statements about the generalizability of the results to other settings. Thus, clear statements about the composition and boundaries of the target population are often the exception rather than the rule.

Scientific theories describe relationships between concepts. In this sense, concepts represent the key elements of a well-developed theory. Concepts are verbal cues or symbols that sometimes refer to simple or complicated sources of variation. Sex (male vs. female), for example, refers to a simple, objective source of variation, whereas the meaning of concepts such as delinquency or socioeconomic status is potentially quite complicated. Still, reference to concepts for purposes of theory and hypothesis development can be sufficient. For purposes of conducting empirical tests of theories and hypotheses, however, more rigor and specificity are required.

Variables are the language of actual empirical work. A researcher’s description of a variable explicitly defines how the concept in question is to be measured for purposes of an actual research project. An operational description or definition of a variable attends to how the variable was measured and what values the variable can take on. Variables such as sex and race are categorical, whereas variables such as age and income are quantitative. Categorical variables can be nominal (unordered categories) or ordinal (ordered categories, but the distance between categories is not well-defined). Quantitative variables can be interval (equal distance between categories) or ratio (existence of a true zero). Still another type of variable, of particular interest to criminologists, is a count of events. Event-count variables represent the number of times an event occurs within some period of time. One way to think of an event-count variable is to consider a two-category variable: Either an event occurs or does not occur within some small time interval. If one adds up the number of times an event occurs over many of these small time intervals, one gets a total count of events.

Some concepts are too broad to be measured effectively with a single variable. Socioeconomic status, for example, is often linked to a combination of at least three subordinate concepts: (1) educational attainment, (2) income, and (3) occupational prestige. Often, variables associated with closely related subordinate concepts can be combined into a scale or index that measures the conceptual variation of interest. There are different ways to form scales and indexes. Some are driven by mathematical decision rules based on correlations between the items comprising the scale or index, and others are based on conceptual considerations.

Still another important feature of any quantitative study is whether it emphasizes description or the identification of cause–effect relationships. Descriptive inference is a characterization or summary of important features of a population. For example, the main objective of the 1993 Bureau of Justice Statistics recidivism study was to estimate the percentage of offenders released from prison in 1993 who experienced subsequent involvement with the criminal justice system within 3 years of their release. No effort was made to explain variation in the recidivism rate; instead, the goal was pure description.

Causal inference is the process of distinguishing between a correlation or statistical association between two or more variables and a cause–effect relationship between those variables. In order for a variable x to be considered a cause of variable y, three criteria must be satisfied: (1) x precedes y in time, (2) x and y are statistically associated, and (3) the statistical association between x and y is not spurious (i.e., there is no other variable that can account for or explain the statistical association between x and y). It turns out that establishing the first two criteria is reasonably straightforward. Convincingly demonstrating nonspuriousness, however, is much more difficult. This issue is discussed in more detail in the “Analytic Methods for Causal Inference” section.

The word validity is often used in two broad contexts in CCJ research. It may be used to indicate whether (or to what extent) a specific measure is an accurate characterization of the concept being studied. For example, one might ask whether an IQ test is a valid measure of intelligence. The word validity is also used as a way of characterizing a study or particular methodological approach. In this case, the concern is whether the study or method is likely to faithfully present the world as it really operates or whether it will distort the phenomena under study in some important way. As an example of this usage, one might consider whether a study with a pretest outcome measurement followed by an intervention and then a posttest outcome measurement but no control group (a group that does not experience the intervention) is a valid study.

A number of different types of validity appear in the CCJ literature. A few common types are discussed here. Assessments of face validity are subjective judgments about whether a measurement or methodology is likely to yield accurate results. If a measure successfully predicts variation in a logically linked outcome, one can say that it rates high on criterion or predictive validity. For example, if one has a parole risk assessment instrument that is designed to predict likelihood of recidivism and the instrument, in fact, does do a good job of recidivism prediction, then one can say that it exhibits criterion validity. Measures with good construct validity are correlated with wellestablished indicators of the phenomenon in question. Such measures should also be independent of indicators that are not relevant to the phenomenon in question.

Studies with high internal validity take convincing steps to ensure that the logic of the study as applied to the individuals actually being studied is sound. External validity, on the other hand, refers to the generalizability of the study’s results to individuals other than those actually included in the study. Internal validity tends to be maximized when the researcher is able to exert a great deal of control over the study and the environment in which the study is conducted (i.e., a laboratory setting). Unfortunately, when the researcher exerts great control, the conditions of the study sometimes become more artificial and less realistic. This raises questions about how well the study results will generalize to other cases. To the extent that the researcher attempts to allow for more realistic study environments (and greater external validity), this will often lead to less control over the study, which produces threats to internal validity. Researchers desire studies that maximize both internal and external validity, but this is often difficult to achieve.

Reliability refers to the consistency, stability, or repeatability of results when a particular measurement procedure or instrument is used. Researchers aspire to the use of instruments and procedures that will produce consistent results (provided that the phenomena under study have not changed). There are different ways of assessing and quantifying reliability. One approach is to take a measurement at a particular point in time and then repeat that same measurement at a later point in time. The correlation between the two measurements is called test–retest reliability. Another approach is to conduct multiple measurements with some variation in the precise measurement method; for example, multiple questionnaires with variations in the wording of various items can be administered to the same individuals. The correlation between the various instruments is called parallel forms reliability.

In some instances, researchers need to code various pieces of information into quantitative research data. A concern often arises about whether the coding rules are written in such a way that multiple properly trained coders will reach the same coding decisions. Interrater reliability is considered to be high when there is a high correlation between the decisions of multiple coders who have reviewed the same information.

Reliability can also be assessed by examining correlations between multiple indicators of the same underlying concept. Assume, for example, that a researcher believes that a key influence on criminal behavior is an individual’s level of self-control. Because there is no single definitive measure of self-control, the researcher might measure many indicators and characteristics of individuals that he believes to be manifestations of one’s level of self-control (i.e., time spent on homework each day, grades in school, time spent watching television, etc.). One way of assessing the reliability of a scale or index that combines this information is to calculate the correlations between all of the indicators, which can then be used to calculate internal-consistency reliability. High levels of internal-consistency reliability imply that the various characteristics and indicators being studied are closely related to each other.

Measures or procedures for capturing measurements can be highly reliable but also invalid. It is possible, for example, to obtain consistent but wrong or misleading measurements. Measures or procedures can also be both unreliable and invalid. In general, however, if a measure is valid it must also, by definition, be reliable.

An estimate is a person’s guess about the value of some interesting quantity or parameter for a target population. Researchers obtain an estimate by applying a formula or estimator to observed data that can be used to develop inferences about the target population. The most straightforward case is when one studies observed data from a simple random sample drawn from a well-defined target population. The goal is to infer the value of a parameter or quantity in the population on the basis of what one observes in the sample. A researcher plugs the observed data into an estimator and then uses the estimator, or formula, to calculate an estimate of the quantity of interest in the population.

In the case of a probability sample drawn from a welldefined population, there is a true population parameter or quantity that researchers seek to estimate on the basis of what they see in the sample.An important issue is whether the estimator applied to the sample will—over the course of drawing many, many probability samples—on average lead to the correct inference about the population parameter. If the average of the parameter estimates is different from the true population parameter, one says that the estimator is biased.

Sometimes there are different unbiased estimators or formulas that could be used to estimate a population quantity. An important question is how to choose one estimator over another. Generally speaking, in this situation researchers would prefer the unbiased estimator that exhibits the least amount of variation in the estimates generated over many samples drawn from the same population. The estimator that exhibits the minimum amount of sample-to-sample variation in the estimates is the most efficient estimator. For example, the sample mean, the sample median, and the sample mode (see “Measures of Central Tendency” section) are both valid estimators for the population mean of a normally distributed variable. The sample mean, however, is a more efficient estimator than the sample median, which is itself more efficient than the sample mode.

In some circumstances, an unbiased estimator is not available. When this happens, researchers typically try to use a consistent estimator. A consistent estimator is biased in small samples, but the bias decreases as the size of the sample increases. Many commonly used estimators in the social sciences, such as logistic regression (discussed later in this research paper), are consistent rather than unbiased.

A statistical model is a description of a process that explains (or fails to explain) the distribution of the observed data. A problem that arises in quantitative CCJ research is how to consider the extent to which a particular statistical model is consistent with the observed data. This section describes several common frameworks for thinking about this correspondence.

4.1. Relative Frequency

In quantitative crime research, decisions about whether to reject or fail to reject a particular hypothesis are often of central importance. For example, a hypothesis may assert that there is no statistical association between two variables in the target population. A test of this hypothesis amounts to asking the following question: What is the probability of observing a statistical association at least as large (either in absolute value or in a single direction) as the one observed in this sample if the true statistical association in the target population is equal to zero? Put another way, assume that there is a target population in which the statistical association is truly equal to zero. If a researcher drew many simple random samples from that population and calculated the statistical association in each of those samples, he or she she would have a sampling distribution of the statistical association parameter estimates. This theoretical sampling distribution could be used to indicate what percentage of the time the statistical association would be at least as large as the association the researcher observed in the original random sample.

Generally speaking, if the percentage is sufficiently low (often, less than 5%), one would reject the hypothesis of no statistical association in the target population. A concern that arises in these kinds of tests is that the hypothesis to be tested is usually very specific (i.e., the statistical association in the target population is equal to zero). With a very large sample size it becomes quite likely that the so-called test statistic will lead a person to reject the hypothesis even if it is only slightly wrong. With a very small sample size, the test statistic is less likely to lead one to reject the hypothesis even if it is very wrong. With this in mind, it is important for researchers to remember that hypothesis tests based on the relative frequency approach are not tests of whether the statistical association in question is large or substantively meaningful. It is also important to keep in mind that the interpretation of statistical tests outside of the framework of well-defined target populations and probability samples is much more ambiguous and controversial.

4.2. Bayesian Methods

Researchers often find the relative frequency framework to be technically easy to use but conceptually difficult to interpret. In fact, researchers and policymakers are not necessarily so concerned with the truth or falsehood of a specific hypothesis (e.g., that a population parameter is equal to zero) as they are with the probability distribution of that parameter. For example, it might be of more interest to estimate the probability that a parameter is greater than zero rather than the probability that a sample test statistic could be as least as large as it is if the population parameter is equal to zero. Analysis conducted in the Bayesian tradition (named after the Rev. Thomas Bayes, who developed the well-known conditional probability theorem) places most of its emphasis on the estimation of the full probability distribution of the parameter(s) of interest. In general, Bayesian methods tend not to be as widely used as relative frequency (or frequentist) methods in CCJ research. This is probably due to the training received by most criminologists, which tends to underemphasize Bayesian analysis. Because Bayesian analyses can often be presented in terms that are easier for policy and lay audiences to understand, it is likely that Bayesian methods will become more prominent in the years ahead.

4.3. Parameter Estimation and Model Selection

CCJ researchers typically rely on quantitative criteria to estimate parameters and select statistical models. Common criteria for parameter estimation include least squares (LS) and maximum likelihood (ML). LS estimators minimize the sum of the squared deviations between the predicted and actual values of the outcome variable. ML estimators produce estimates that maximize the probability of the data looking the way they do. Provided the necessary assumptions are met, LS estimators are unbiased and exhibit minimum sampling variation (efficiency). ML estimators, on the other hand, are typically consistent, and they become efficient as the sample size grows (asymptotic efficiency).

Model selection involves the choice of one model from a comparison of two or more models (i.e., a model space). The most prominent model selection tools include F tests (selection based on explained variation) and likelihoodratio tests (selection based on likelihood comparisons). An important issue with these tests is that they typically require that one model be a special case of the other models in the model space. For these approaches, tests are therefore limited to comparisons of models that are closely related to each other. Increasingly, model selection problems require researchers to make comparisons between models that are not special cases of each other. In recent years, two more general model selection criteria have become more widely used: (1) the Akaike information criterion (AIC) and (2) the Bayesian information criterion (BIC). These criteria can be used to compare both nested and non-nested models provided the outcome data being used for the comparison are the same. Like F tests and likelihood-ratio tests, AIC and BIC penalize for the number of parameters being estimated. The logic for penalizing is that, all other things equal, we expect a model with more parameters to be more consistent with the observed data. In addition to penalizing for parameters, the BIC also penalizes for increasing sample size. This provides a counterweight to tests of statistical significance, such as the F test and the likelihood-ratio test, which are more likely to select more complicated models when the sample size is large. As modeling choices continue to proliferate, it seems likely that use of AIC and BIC will continue to increase.

This section briefly considers some descriptive parameters often studied in CCJ research. The first two subsections deal with parameters that are usually of interest to all social scientists. The final three subsections emphasize issues of particular importance for CCJ research.

Central tendency measures provide researchers with information about what is typical for the cases involved in a study for a particular variable. The mean or arithmetic average (i.e., the sum of the variable scores divided by the number of scores) is a common measure of central tendency for quantitative variables. The mean has an advantage in that each case’s numerical value has a direct effect on the estimate; thus, the mean uses all of the information in the scores to describe the “typical” case. A problem with the mean is that cases with extreme scores can cause the mean to be much higher or much lower than what is typical for the cases in the study. In situations where the mean is affected by extreme scores, researchers often prefer to use the median as a measure of central tendency. The median is the middle score of the distribution; half of the cases have scores above the median, and the other half have scores below the median. The median can also be viewed as the 50th percentile of the distribution. Unlike the mean, the median does not use all of the information in the data, but it is also not susceptible to the influence of extreme scores. For categorical variables, the mode (i.e., the most frequently occurring category) is often used as a measure of central tendency. For dichotomous or two-category variables, the most commonly used measure of central tendency is the proportion of cases in one of the categories.

In addition to summarizing what is typical for the cases in a study, researchers usually consider the amount of variation as well. Several common summaries of variation, or dispersion, are commonly reported in the literature. The most common measure of dispersion for quantitative variables is the variance and/or its square root, the standard deviation. Many interesting social science variables are either normally or approximately normally distributed (i.e., the distribution looks like a bell-shaped curve). In these types of distributions, approximately two thirds of the cases fall within 1 standard deviation of the mean, and about 95% of the cases fall within 2 standard deviations of the mean. Thus, for variables with a bell-shaped distribution, the standard deviation has a very clear interpretation. This is particularly important because sampling distributions are often assumed to have normal distributions. Thus, the standard error calculation that appears in much quantitative CCJ research is actually an estimate of the standard deviation of the sampling distribution. It can be used to form confidence intervals and other measures of uncertainty for parameter estimates in the relative frequency framework.

For qualitative or categorical variables, a common measure of dispersion is the diversity index, which measures the probability that cases come from different categories. Some CCJ researchers have used the diversity index to study offending specialization and ethnic–racial heterogeneity in communities and neighborhoods. A generalized version of the diversity index that adjusts for the number of categories is the index of qualitative variation, which indicates the extent to which individuals are clustered within the same category or distributed across multiple categories.

Over the past three to four decades, criminologists have developed the concept of the criminal career. According to researchers who study criminal career issues, within any given time period the population can be divided into two groups: (1) active offenders and (2) everyone else. The percentage of the population in the active offender category is the crime participation rate. Within that same time period, active offenders vary in several respects: (a) the number of offenses committed, (b) the seriousness of the offenses committed, and (c) the length of time the offender is actively involved in criminal activity. A key idea within the criminal career framework is that the causes of participation may not be the same as the causes of offense frequency, seriousness, or the length of time the offender is active.

There is an extensive body of research devoted to estimating these parameters for general and higher-risk populations, and more recent research has treated these criminal career dimensions as outcomes in their own right. For example, a large amount of research has been devoted to the study of offense frequency distributions. This literature shows that in both general and high-risk populations offense frequency distributions tend to be highly skewed, with most individuals exhibiting low frequencies and a relatively small number of individuals exhibiting high frequencies. Among the most prominent findings in the field came from Wolfgang et al.’s (1972) study of the 1945 Philadelphia male birth cohort, which showed that about 6% of the boys in the cohort were responsible for over 50% of the police contacts for the entire cohort.

A particularly important parameter for criminal justice policy is the rate at which individuals who have offended in the past commit new crimes in the future (the recidivism or reoffending rate). Recidivism rates are based on three key pieces of information: (1) the size of the population of prior offenders at risk to recidivate in the future, (2) the number of individuals who actually do reoffend by whatever measure is used (i.e., self-report of new criminal activity, rearrest, reconviction, return to prison), and (3) a known follow-up period or length of time that individuals will be followed. Recidivism is also sometimes studied in terms of the length of time that lapses between one’s entry into the population of offenders at risk to recidivate and the timing of one’s first recidivism incident.

With the advent of a large number of longitudinal studies of criminal and precriminal antisocial and aggressive behaviors, researchers have become increasingly interested in the developmental course of criminality as people age. To aid in the discovery of developmental trends and patterns, criminologists have turned to several types of statistical models that provide helpful lenses through which to view behavior change. The most prominent of these models are growth curve models, semiparametric trajectory models, and growth curve mixture models. These all assume that there is important variation in longitudinal patterns of offending. Some individuals begin offending early and continue at a sustained high rate of offending throughout their lives, whereas others who begin offending early seem to stop offending during adolescence and early adulthood. Some individuals avoid offending at all, whereas others offend in fairly unsystematic ways over time. Growth and trajectory models provide ways of summarizing and describing variation in the development of criminal behavior as individuals move through the life span.

The foundation of a sound quantitative criminology is a solid base of descriptive information. Descriptive inference in criminology turns out to be quite challenging. Criminal offending is covert activity, and exclusive reliance on official records leads to highly deficient inferences. Despite important challenges in descriptive analysis, researchers and policymakers still strive to reach a better understanding of the effects of interventions, policies, and life experiences on criminal behavior. Much of the CCJ literature is therefore focused on efforts to develop valid causal inferences. This section discusses some of the most prominent analytic methods used for studying cause and effect in CCJ research.

CCJ researchers typically distinguish between independent variables and dependent or outcome variables. In general, researchers conceive of dependent or outcome variables as variation that depends on the independent or predictor variables. Thus, independent variables explain variation in dependent or outcome variables. Sometimes researchers use stronger language, suggesting that independent variables cause variation in dependent variables. The burden of proof for use of the word cause is very high, however, and many researchers are careful to qualify their results if they do not think this burden of proof has been met.

Contingency tables are a useful way of presenting frequency distributions for two or three categorical variables at the same time. For example, if a person wanted to create a measure of offending participation (either someone offends in a particular time period or he or she does not) and then compare the distribution of that variable for individuals who are employed and those who are not employed, a contingency table could be constructed to display this information. Several measures of the strength of the statistical association (analogous to a correlation coefficient) have been designed for contingency tables. Although contingency tables are not often used for studying cause–effect relationships (except in randomized experiments), they are quite useful for exploratory data analysis and foundational work for more elaborate statistical models.

Researchers often want to summarize the strength of the statistical association between two variables. Correlation coefficients and other measures of association are used for this purpose. In general, measures of association are arrayed on a scale of – 1 to 1 or 0 to 1, where 0 usually represents no association at all and – 1 or 1 represents a perfect negative or positive association. Measures of association have been developed for categorical and quantitative variables. Some measures of association, such as the relative risk ratio and the odds ratio, are calibrated so that 1 implies no statistical association, whereas numbers close to zero and large positive numbers indicate strong association. Researchers often conduct tests of statistical significance to test the hypothesis of “no association” in the population.

CCJ researchers are able to draw on a wide variety of tools for conducting tests of statistical significance. In a contingency table setting, researchers often are interested in testing the hypothesis that two categorical variables are statistically independent. The chi-square test of independence is frequently used for this purpose. Sometimes, a researcher will want to test the hypothesis that the mean of a continuous variable is the same for two populations. The independent samples t test is most often used to conduct this test. In addition, researchers may need to test the hypothesis that the mean of a continuous variable remains the same at two time points. In this setting, the paired samples t test will most likely be used. Finally, if a researcher wants to test the hypothesis that a continuous variable has the same mean in three or more populations, then analysis of variance will be used. There are many statistical tests for many types of problems. Although these are among the most common applications, many others are available for more complicated situations.

Linear regression models are a class of statistical models summarizing the relationship between a quantitative or continuous outcome variable and one or more independent variables. Careful use of these models requires attention to a number of assumptions about the distribution of the outcome variable, the correctness of the model’s specification, and the independence of the observations in the analysis. If the assumptions underlying the model are valid, then the parameter estimates can provide useful information about the relationship between the independent variable or variables and the outcome variable.

Many outcome variables in CCJ are not continuous or do not meet some of the distributional assumptions required for linear regression. Statistical models for these variables, therefore, do not fit well into the linear regression framework. Examples of this problem include dichotomous and event-count outcomes. For dichotomous outcomes, researchers often estimate logistic or probit regression models; for counted outcomes, specialized models for event counts are usually estimated (i.e., binomial, Poisson, negative binomial).

CCJ researchers sometimes have well-developed ideas about the relationships between a complex system of independent and dependent variables. These ideas are usually based on theories or findings from previous empirical research. Structural equation models can be used to investigate whether the relationships between the variables in the system are in accord with the researcher’s predictions.

A time series analysis is based on the study of a particular cross-sectional unit (e.g., a community or city) over a sustained period of time. Over that period of time, the study takes repeated measurements of the phenomenon of interest (e.g., the number of gun homicides each month). Sometimes, an intervention occurs (e.g., the introduction of a new law restricting access to handguns) and the researcher has access to both the preintervention time series and the postintervention time series. These time series can be combined into a single interrupted time series analysis to study the effect of the intervention on the series. Researchers conducting interrupted time series analysis usually include both a series in which the intervention occurs and a series in which there is no intervention (a control series). If there is an apparent effect of the intervention in the interrupted time series analysis and the effect reflects a genuine causal effect, then there should be no corresponding change in the control series.

As discussed earlier (see the “Unit ofAnalysis” section), some data sets have more than one logical unit of analysis. For example, the National Longitudinal Survey of Youth follows the same individuals repeatedly over a sustained period of time (panel data). Other studies, such as the MTF study, sample schools and then sample multiple individuals within each school. A variety of modeling tools (i.e., fixed effect, random effect, hierarchical, and multilevel models) exist for working these kinds of data. An important feature of all of these tools is that they attend specifically to dependence within higher order units of analysis.

Increasingly, CCJ researchers are thinking about cause and effect in terms of counterfactual reasoning. Ultimately, this is an exercise in observing what actually occurs under a specific set of circumstances and then asking how things might have occurred differently if the circumstances had been different. The hypothetical aspect of the problem is a counterfactual, because it involves speculation about what might have occurred but actually did not occur. Counterfactual reasoning is particularly applicable to the problem of estimating treatment effects. For example, a researcher considers a group of people who received a particular treatment and observes their outcomes. What he would like to know (but cannot know for sure) is what outcomes these same people would have experienced if they had not received the treatment. The difference between the actual, observed outcome and the hypothetical outcome is the treatment effect. CCJ researchers usually look to the experience of a control group to estimate the hypothetical outcome.An important problemin CCJ research is the identification of appropriate control groups.

A randomized experiment is a study in which individuals are randomly assigned to treatment or control groups prior to treatment. They provide a useful framework for estimating valid counterfactuals because random assignment to treatment and control conditions ensures that the groups are statistically comparable to each other prior to treatment. Thus, the experience of the control group provides a very convincing answer to the question of what would happen to the treatment group if the treatment group did not receive treatment.

For a variety of reasons, randomized experiments are not possible in many instances, but sometimes conditions that closely approximate an experiment occur because of a key event or policy change. When researchers recognize these conditions, a natural experiment is possible—even when more conventional studies fail. Consider the problem of estimating the effect of police strength on crime rates. Estimating correlations and conventional regression models cannot help much with this problem. The critical ambiguity is that street crime almost certainly has an effect on police strength and that police strength almost certainly has some effect on street crime. Natural experiments can provide more convincing evidence.A recent study conducted in Washington, D.C., is illustrative (Klick & Tabarrok, 2005). It was based on the insight that changes in terror alert levels lead to meaningful changes in the presence of police on the street. The researchers examined what happened to crime rates when street-level police presence increased and decreased as terror alert levels changed. Researchers sometimes refer to natural experimentally based treatments as instrumental variable estimators, and they can provide a powerful method for estimating treatment effects when randomized experiments cannot be conducted.

Another approach to developing valid counterfactuals is to identify a group of cases that receive treatment and then identify another group of cases—the control group—that are similar to the treatment cases but do not receive treatment. To ensure that the treatment and control groups are similar, researchers match the groups on characteristics that are thought to be important. The direct matching approach guarantees that the treatment and control groups look alike on the matched characteristics.A problem is that the groups may look different from each other on characteristics that were not matched. Thus, in general, counterfactuals produced by the matching approach will not be as convincing as those produced by a randomized or natural experiment. However, in instances where experiments are not possible, direct matching designs can still provide convincing evidence about treatment effects. A generalization of the matching design involves matching on indexes based on combinations of variables. Propensity scores, which increasingly appear in the CCJ literature, are one such index. It can be shown that matching on a properly created index can lead to treatment and control groups that look like each other on many characteristics. It is likely that CCJ researchers will rely more and more heavily on matching designs and propensity scores to study treatment effects, in particular when randomized experiments are not possible.

Some aspects of quantitative CCJ research have remained relatively constant throughout the field’s history. Some CCJ research problems are very much like problems studied in other fields, and some are quite different, yet there has always been a major emphasis on description and learning about how much crime is occurring and what populations are at highest risk of criminal involvement and victimization. Other aspects, such as repeatedly and systematically following the same individuals over time and rigorously measuring the effects of changing policies, are more recent developments. CCJ is an interdisciplinary field that relies on insights from sociology, psychology, economics, political science, and statistics as well as its own rapidly emerging traditions. One thing is certain: Analytic methods in the field will continue to evolve. It is critical that quantitativeCCJ researchers monitor developments in their own field and stay well connected with developments in other allied fields to strengthen their efforts at descriptive and causal inference.

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• Open access
• Published: 22 September 2013

Five tests for a theory of the crime drop

• Graham Farrell 1  

Crime Science volume  2 , Article number:  5 ( 2013 ) Cite this article

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Many studies have sought to explain the major crime declines experienced in most advanced countries. Key hypotheses relate to: lead poisoning; abortion legalization; drug markets; demographics; policing numbers and strategies; imprisonment; strong economies; the death penalty; gun control; gun concealment; immigration; consumer confidence; the civilizing process, and; improved security. This paper outlines five tests that a hypothesis should pass to be considered further. It finds that fourteen of the fifteen hypotheses fail two or more tests. The security hypothesis appears to pass the tests, and thereby pave the way for further research.

Most advanced countries have experienced a major decline in street crime during the last two decades, but with some variation in the extent, timing, and crimes involved. This ‘crime drop’ was heralded in the United States where total violent crime fell over seventy percent between 1993 and 2011 (Truman and Planty 2012 ). Some property crimes in the US, particularly burglary and theft, have been falling since the early 1970s according to the National Crime Victimization Survey. Also in the mid-1990s, the UK began to experience dramatic declines across a wide range of property, personal and violent crimes, many falling by half or more (Office of National Statistics 2013 ). The sharp drops in Canada’s homicide and other crime has generally been remarkably similar to that in the US (Ouimet 2002a ), while New Zealand experienced rather similar declines in property crime (burglary, motor vehicle theft, and other theft) but not all personal crimes (Mayhew 2012 ). In this context, the crime drop in Australia seems delayed, beginning around 2001 where after motor vehicle theft led steep falls in burglary, theft and robbery but not some personal crimes (Mayhew 2012 ). There is considerable evidence, from the International Crime Victims Survey in particular, that most European countries have experienced significant crime drops (van Dijk et al. 2008 van Dijk 2008 van Dijk et al. 2012 ), tempered by some analysis of the variation (Aebi and Lande 2012 ), and the suggestion that Switzerland has not (Killias and Lanfranconi 2012 ). While recognising variation between and within countries, Tseloni et al. ( 2010 ) suggest the crime drop is more widespread than advanced countries and could perhaps be labeled as global. Knepper ( 2012 ) reviews the evidence from both sides and concludes there is a significant and widespread international crime drop with some variation in its nature.

There have been various efforts to try to explain the crime drop over the last two decades. Fifteen key hypotheses from the academic literature are summarized in Table  1 . This includes twelve identified in the reviews of Levitt ( 2004 ) and Blumstein and Rosenfeld ( 2008 ), and readers are referred to those studies for additional details. Three hypotheses are included that have gained ground since those reviews, and they are referenced further herein. Since crime drop research has expanded in recent years, it is possible that this is not an exhaustive list of hypotheses. However, it should be possible to apply the present study’s approach to other hypotheses.

This study sets out five tests that, it is proposed, a crime drop hypothesis must pass to be considered worthy of further scrutiny. The tests are proposed as necessary but not sufficient criteria to identify a viable theory of the crime drop.

The five tests

The proposed tests are:

The preliminary evidence test

Are there reasonable empirical grounds to consider the hypothesis, even if it is disputed?

The cross-national test

Can the hypothesis be applied to different countries (e.g. to Canada for hypotheses developed for the US)?

The prior crime increase test

Is the hypothesis compatible, or at least not in contradiction, with the fact that crime was previously generally increasing for several decades?

The phone theft and e-crimes test

Is the hypothesis compatible, or at least not in contradiction, with the fact that some crimes such as phone theft and e-crimes were increasing while many crime types were decreasing?

The varying trajectories test

Is the hypothesis compatible, or at least not in contradiction, with variation in the timing and trajectory of crime falls both between countries and between crime types?

Each test is described in more detail below as it is applied to the hypotheses shown in Table  1 .

Results and discussion

To pass this test, a hypothesis should not have been comprehensively falsified. The decision-making for this test uses the reviews by Levitt ( 2004 ) and Blumstein and Rosenfeld ( 2008 ) which are taken to be state-of-the-art works by leading scholars. For present purposes then, the hypotheses taken to have been falsified are increased capital punishment, newly implemented gun control laws, or laws allowing concealed weapons, and the strong economy, which were dismissed on the balance of evidence presented by Levitt ( 2004 ) as well as explicitly (or implicitly by exclusion) in the review of Blumstein and Rosenfeld ( 2008 ). Where either review determined that a hypothesis holds some empirical validity, then it passes the first test.

The civilizing process hypothesis is given a bye for this test as it is potentially credible despite the lack of evidence-based research. This is not without reservation, since as one of its proponents notes, it has problems

“not the least of which is whether such a theoretical perspective could be moved beyond the level of speculation and be subjected to more rigorous empirical tests.” (Eisner 2008 ; 312)

All additional hypotheses are taken to pass the test for present purposes even though some are disputed and others claim only a minor role including demographics (generally found to account for around 10 to 15 percent – see Fox 2000 ) and immigration (Stowell et al. 2009 claim it accounts for 6 percent of the crime drop). Blumstein and Rosenfeld are critical of the notion that changing demographics induced the crime drop, observing that in the US,

“during the sharp crime drop of the 1990s, age composition changes were trending in the wrong direction: the number of 18-year-olds in the U.S. population was increasing while crime rates were declining for other reasons.” (Blumstein and Rosenfeld 2008 ; 20).

Nevertheless, for present purposes both demographics and immigration are taken to retain some support and pass the test. Likewise, while Levitt ( 2004 ), Blumstein and Rosenfeld ( 2008 ) and others have largely discarded the notion that changed policing strategies induced the crime drop, it is held to pass the present test because it continues to garner some support, particularly that of Zimring ( 2012 ) though that study does not seem to identify a clear mechanism by which policing caused crime to fall in New York. Hence generous criteria are used here for this test, with the components summarized in Table  2 , and it serves primarily to separate the wheat from the chaff.

Franklin Zimring’s conversion to cross-national comparative research is worthy of recall:

“Closer inspection showed that the timing of the Canadian decline (1991–2000) fit perfectly with the timing of the declining in the United States (Zimring, 2006 :Chapter 5). The extraordinary similarity of these trends in breadth, magnitude, and timing suggested that whatever was driving the decline in the United States was also operating in Canada. … But … Canada in the 1990s didn’t increase its imprisonment, didn’t hire more police per 100,000 population, and didn’t have anything close to the economic boom we enjoyed south of the border.” (Zimring 2006 ; 619)

Marc Ouimet had made similar observations (Ouimet 2002 ), while van Dijk et al. ( 2008 ) and Rosenfeld and Messner ( 2009 ) note many European countries with far less imprisonment than, but similar crime drops to, the US.

Hence others have already noted that some hypotheses fail what is here termed the cross-national test. It is a credit to the pioneering nature of US crime drop research and the dearth of studies elsewhere, that most such hypotheses are US-focused. In addition to those noted by Zimring it includes gun control laws, concealed weapons laws, the death penalty, more police officers, better policing strategies, the abortion hypothesis (see also Kahane et al. 2008 for a UK study finding no effect), and the waning crack market. The last three of these hypotheses had passed the first test, but since other countries did not have the same increase in police officers, the same change in abortion law, or similarly extensive crack markets, as the United States, they do not pass the second.

In relation to the immigration hypothesis, for present purposes it is assumed that other countries had similar patterns of immigration to the US. While this may prove an incorrect assumption it is conservative insofar as it allows the immigration hypothesis to pass the second test. Likewise, it is simplest for present purposes to assume other countries had similarly increasing consumer confidence (Rosenfeld 2009 ), due to the absence of contrary evidence for the present study. An aging population seems common across industrialised nations and so demographics is assumed to pass this test for present purposes. Pinker ( 2011 ) exposition of the civilizing process hypothesis is arguably applicable to most advanced countries, and taken to be so for present purposes.

The two hypotheses that pass this test more compellingly are the childhood lead hypothesis (for which Nevin ( 2007 ; see also Reyes 2012 ) and the security hypothesis relating to car theft that has been empirically tested in Australia, England and Wales, the Netherlands, and the United States (Farrell et al. 2011a 2011b ; Fujita and Maxfield 2012 ; van Ours and Vollaard 2013 ).

Before the current spate of crime declines, it is fair to say that crime had increased rapidly over three decades or so in most advanced countries. A crime drop hypothesis need not necessarily explain why that is however, as it could be due to distinct factors, but it should not contradict the fact.

Although some of them already failed the preliminary evidence test, a key reason that gun control laws, the death penalty, concealed weapons laws, increased police numbers, and changed police strategies, were initially proposed is that they appeared to trigger change at about the right time (even those where the timing was subsequently found to be otherwise). The abortion hypothesis can also be assumed to pass this test as the timing of its effect is anticipated to coincide with the crime drop in the United States. Crack markets were also held to decline coincident with the fall in US crime. The civilizing process can be taken to pass this test because some explanation for prior crime increases is offered as due to the decline in the legitimacy of social institutions in the countercultures of the ‘swinging sixties’. Tests of the security hypothesis find that the spread of more and better vehicle security coincides with major declines in vehicle crime, and so it is taken to pass this test.

This leaves five hypotheses that are taken to fail this test. The US economy and consumer confidence were strong prior to the 1990s, the prison population rose earlier, immigration was increasing prior to 1990, and demographic change has been more gradual. Levitt ( 2004 ) empirically examines whether his conclusions could apply to the period before the crime drop, finding that

“Between 1973 and 1991, the incarceration rate more than tripled, rising from 96 to 313 inmates per 100,000 residents. By my estimates, that should have reduced violent crime and homicide by over 30 percent and property crime by more than 20 percent. Note that this predicted impact of incarceration is much larger than for the latter period.” (Levitt 2004 ; 184).

Levitt also finds that previous increases in police numbers should have reduced crime, as should the 1973 abortion legalization to some extent, with the crack market seemingly accounting for 16 percent of increased homicide and 8 percent of increased violence from 1973 to 1991. After some attempt to reconcile these findings it is argued that

“Thus, in contrast to the 1990s, the actual crime experience in the 1973–1991 period is not well explained by the set of factors analyzed in this paper…The real puzzle in my opinion, therefore, is not why crime fell in the 1990s, but why it did not start falling sooner.” (Levitt 2004 ; 186)

However this is not the real puzzle, because it can never be that crime fell sooner. For the present author it highlights the fact that much of the analysis does not pass what is here termed the prior crime increase test.

This third test identifies an issue relating to the childhood lead hypothesis, which is that it is really a hypothesis for why pre-1990s crime increased. Increased lead poisoning caused the crime increase of the 1960s to 1990s. Following that, the removal of lead from gasoline and other sources is proposed to have induced a fall in crime, presumably to pre-lead levels. In essence, it argues, lead poisoning generated more motivated offenders. So, while this is not a criticism, it suggests it is a theory of crime, and only by its absence does lead poisoning provide a theory of the crime drop. It seems to be unique in that respect in seemingly claiming to explain all major trends and variation in crime over the last half century or so. This may be a line of enquiry that opens the lead hypothesis up to further investigation. It also implies that routine activity theory is not the compelling explanation for the pre-1990s crime increases that Cohen and Felson ( 1979 ) and others have so convincingly argued.

The phone theft and E-crimes test

Some crime types increased during the crime drop. Most notable is the large increases in internet-related crime. This likely occurred too late to have induced the crime drop as a switch from street crime, and also involves different resources, skills and rewards. Phone theft, in contrast, is a street crime which increased when others were decreasing (Mayhew and Harrington 2001 ), and at the time of writing in 2013 is experiencing a resurgence due to expensive smart phones. More generally, theft of valuable electronic goods such as laptops and GPS-Satnavs have increased. Any explanation of the decline in other crime types must not contradict these facts.

Most hypotheses fail this test because they suggest that all types of crime should have decreased. This is because their focus is the number or the motivation of offenders. The demographic hypothesis suggests the relative number of offenders decreased, which suggests commensurate declines in rates of all crime types should occur. Others with this trait are the childhood lead hypothesis, the abortion hypothesis, the immigration hypothesis (perhaps to a lesser extent depending on the identified mechanism of change), and the waning crack epidemic if it is held to reduce the number of motivated offenders. In fact, all of the hypotheses except one appear incompatible with increases in some types of crime. The exception is the security hypothesis which is flexible in allowing opportunity for some crimes to increase at the same time as that for others was decreasing.

This test sounds similar to but differs significantly from the cross-national test. Whereas the cross-national test emphasised similarity between countries in the existence of a crime drop, this test reflects the sometimes considerable differences both between countries and between crime types within countries. Such differences can be in the timing (when does decline occur?) or trajectory (how fast is the decline?) for different crime types. Any theoretical explanation must be sufficiently flexible to account for the variation.

Some examples will clarify the justification for this test. The United States experienced major reductions in violence in the early 1990s but property crime (burglary and theft), according to the NCVS, had been declining since the early 1970s. The UK differs in experiencing more parallel dramatic drops in both violent and property crime. Australian property crime fell dramatically by 30–40 percent from 2001, and violent crime trends were mixed with a major decline in robberies alongside stable or increasing assaults (Mayhew 2012 ). New Zealand experienced falling property crime from the mid-1990s but violent crime was more stable or with slight increases (Mayhew 2012 ). When most crimes declines in a fashion similar to the US, car theft in Canada remained high through the 1990s then plummeted from the mid-2000s (Farrell and Brantingham 2013 ). Hence there are sometimes considerable differences between countries and within countries, and a hypothesis should not contradict those facts.

Most hypotheses fail this test because they suggest all types of crime should fall at the same time. For example, the childhood lead hypothesis fails to explain within-country variation both in terms of how violent and property crime differ in some countries but not others (e.g. they fall similarly in the UK but not NZ or the US), and in terms of how some crimes fall at different times than others (e.g. Canadian car theft compared to other Canadian crimes). If the childhood lead hypothesis is said to apply only to violent crime then it does not explain property crime falls. If it is said to explain both violent and property crime then it cannot account for why one but not the other falls in some instances. The trajectory of car theft in Canada is a good example here, because car theft fell far later than most crimes, falling only from the mid-2000s onwards, which could not be due to a decline in lead poisoning.

The security hypothesis does not contradict this test. Specifically with respect to car theft, improved car security was introduced at different times in different countries. This timing can also differ from the spread of security for other crime types. Similarly, differences in the trajectory of the car theft decline between places, and between cities or regions within a country, are probably explained at least in part by differences in affluence and the rate of purchasing of new cars, and hence the speed of penetration of new and better security.

The study findings are summarized in the matrix of Table  3 . A hypothesis either fails or passes each test, with a fail shown as a cross (‘x’) and a pass as a checkmark or tick (‘✔’).

One hypothesis fails all five tests, four fail four, seven fail three, and two fail two tests (Table  3 ). The security hypothesis appears to pass each test. It suggests that more and better security drove the crime drop. Triangulation from various data signatures provided strong supporting evidence for car theft in Australia, and even stronger for the UK (Farrell et al. 2011a 2011b ), similarly strong evidence for the Netherlands (van ours and Vollaard 2013 ), and supporting evidence for the US (Fujita and Maxfield 2012 ). Thus the security hypothesis passes the preliminary evidence and cross-national tests. The security hypothesis is crime specific. Car theft security improved and spread in the UK, for example, only shortly before phones became widely available to steal and the internet facilitated other crime types. Thus the hypothesis is compatible with the increases in phone theft and e-crimes, passing the third test. In the period before car security became more sophisticated and widespread, it was very easy to steal cars, and the number of car-related crime opportunities increased with car ownership (Wilkins 1967 ). Thus the security hypothesis is compatible with the existence of prior increases in crime, passing the fourth test. Car security was introduced and spread at different times in different countries, reflecting both market differences and purchasing rates for new cars, as well as differences in the timing of national legislation and other activities encouraging immobilizers. Thus the security hypothesis is compatible with variation in the timing and trajectory of the fall in car theft between countries, and variation between crime types within a country, and so passes the fifth test in relation to car crime. For example, Canada’s decline in car theft occurred after it experienced declines in many crime types, but this is consistent with a later introduction of mandatory electronic immobilizers.

Thus viewed, the security hypothesis passes all tests in relation to car theft. Its main limitation is a lack of evidence relating to other crime types, though it is conceivable that different security measures impacted various types of crime at different times. It is also conceivable that, since many crimes are inter-linked, that a version of the keystone crime hypothesis (Farrell et al. 2008 2011a ) occurred in some instances. Car theft plays a key role in facilitating many other types of crime and so its removal, like that of the keystone in an archway, causes those around it to tumble. In addition, the security hypothesis does not contradict other empirical evidence relating to the nature of the declines in crime such as the fact that falls in repeat victimization and crime at hot spots play important roles (Weisburd et al. 2004 Thorpe 2007 ; Britton et al. 2012 ). The steeper declines in more concentrated crime that these studies found is consistent with crime falling more in places (such as New York City) where it was previously at a higher rate.

The security hypothesis proposes that more and better security plays a key role in driving down different types of crime, and that specifically:

“1. Security improvements, including specific security devices, vary for different crimes but have been widely implemented.

Different security measures work in different ways to reduce the crimes to which they are applied: they increase actual or perceived risk to the offender; and/or they reduce actual or perceived reward for the offender; and/or they increase actual or perceived effort for the offender.

The different ways in which security measures work produce variations in expected changes in crime patterns associated with crime drops. These comprise expected security device crime change “signatures.”

The specific falls in crime produced by improvements in security alongside their associated diffusions of benefit (preventive effects spilling out beyond the operational range of measures) to other targets and methods of committing crime are not matched by equivalent displacement.” (Farrell et al. 2011a ; 152).

With respect to other crime types, there is mounting evidence that burglary declines in different countries coincide with the spread of more and better household security (van Dijk 2008 ; Tilley et al. 2011 ). Clarke and Newman outline some more widespread security improvements that have occurred for various types of crime (Clarke and Newman 2006 ). Businesses in particular have become more aware of crime and the cost effectiveness of security, and business improvement districts are suggested to have brought down crime (MacDonald et al. 2010 ). Note that the security hypothesis can be viewed as a component of the broader framework of crime opportunity theory (see Clarke 2012 ). Changes in other crime opportunities may pass the tests outlined here, and warrant further research. For example, the declining value of some once-targeted goods may play a role in the decline in some aspects of acquisitive crime. Video cassette recorders then DVD-players were drivers of burglary when they were valuable. It is also conceivable that shifts in urban form and traffic flows, including major changes such as growth in out-of-city shopping malls in the 1990s, may have changed the movement patterns of potential targets and offenders in ways that reduced street crime. a Tseloni et al. ( 2012 ) set out some steps towards a research agenda on crime opportunity theory including the security hypothesis.

Conclusions

This study offers five tests as a step in the direction of identifying a valid theory, or theories, of why many countries have experienced significant crime declines. An advantage of this approach is to allow some leeway in relation to instances where the direct evaluation evidence is disputed. Some of those disputes seem unlikely to be resolved in the near future and so the present study is useful in setting out additional assessment criteria. Thus, for example, whether Joyce’s ( 2011 ) critique of the abortion legalization hypotheses is sustained need not be critical here because the hypothesis cannot explain non-US crime falls, and cannot be reconciled with either increasing phone theft and e-crime or with variable crime drop trajectories across crimes and places.

The tests clarify particular aspects of some hypotheses. The demographics hypothesis retains an intuitive appeal for some commentators despite its impact being small at most, so its failure in other tests further clarifies its limitations. Demographic change suggests all types of crime would decline similarly in accordance with population change, with none increasing. Likewise, the childhood lead hypothesis offers no insight into why some types of crime increased when others were decreasing, why property crime fell alongside violence in the UK but had been falling for far longer than violence in the US, why there is significant variation in the crime drop between crime types in other countries, or why car theft in Canada fell only a decade or more after other crimes. And while the civilizing hypothesis is insightful when crime over the centuries is considered, its explanatory mechanism for dramatic recent crime drops seems weak and it lacks supporting evidence but, more importantly for present purposes, it also fails to explain why some crimes increased and why there is variation between crime types within countries.

Most hypotheses failed the phone theft and e-crime test as well as the variable trajectories test. Those hypotheses seem insufficiently nuanced to account for differences between crime types and places, particularly when crime increased or failed to decrease. This reflects their tendency to focus on the number or motivation of offenders. In contrast, the security hypothesis focuses on the number of suitable targets and capable guardians. Thus viewed, the routine activity theory provides a useful framework for comparing the hypotheses, even though it is not changes in legal routine activities that are considered as part of the security hypothesis.

The security hypothesis passes the five tests in relation to car theft. Repeated support from studies of car theft in Australia, England and Wales, the Netherlands, and the United States, suggest it might now be considered a theory rather than a hypothesis for that crime type. If so, on present evidence it also tends to eliminate rival hypotheses. This would suggest that while the fields of environmental criminology and crime science have been rather slow to address the crime drop, they offer a most promising perspective.

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Acknowledgements

This paper was presented at the International Symposium on Environmental Criminology and Crime Analysis (ECCA) at Temple University, 17 June 2013. It draws on work conducted over the last several years in collaboration with Nick Tilley, Andromachi Tseloni and Jen Mailley, and more recently with Louise Grove, Rebecca Thompson and Laura Garius, though they unfortunately cannot be blamed for mistakes herein. This includes research sponsored by the Economic and Social Research Council under grants RES-000-22-2386 and ES/K003771/1. I am indebted to colleagues at ICURS for input and encouragement of various kinds, and to an ongoing collaboration with the Police Services Division of the Ministry of Justice of British Columbia and the Royal Canadian Mounted Police ‘E-Division’. Thanks are also due to the students of my Spring 2013 graduate seminar at Simon Fraser University for a semester of enthralling discussions with only occasionally raised voices.

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Farrell, G. Five tests for a theory of the crime drop. Crime Sci 2 , 5 (2013). https://doi.org/10.1186/2193-7680-2-5

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Received : 24 June 2013

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DOI : https://doi.org/10.1186/2193-7680-2-5

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