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Cognitive Biases in Criminal Case Evaluation: A Review of the Research

• Review Article
• Open access
• Published: 23 June 2021
• Volume 37 , pages 101–122, ( 2022 )

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• Vanessa Meterko   ORCID: orcid.org/0000-0002-1207-8812 1 &
• Glinda Cooper 1  

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Psychological heuristics are an adaptive part of human cognition, helping us operate efficiently in a world full of complex stimuli. However, these mental shortcuts also have the potential to undermine the search for truth in a criminal investigation. We reviewed 30 social science research papers on cognitive biases in criminal case evaluations (i.e., integrating and drawing conclusions based on the totality of the evidence in a criminal case), 18 of which were based on police participants or an examination of police documents. Only two of these police participant studies were done in the USA, with the remainder conducted in various European countries. The studies provide supporting evidence that lay people and law enforcement professionals alike are vulnerable to confirmation bias, and there are other environmental, individual, and case-specific factors that may exacerbate this risk. Six studies described or evaluated the efficacy of intervention strategies, with varying evidence of success. Further research, particularly in the USA, is needed to evaluate different approaches to protect criminal investigations from cognitive biases.

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Introduction

Decades of research in cognitive and social psychology have taught us that there are limitations to human attention and decision-making abilities (see, for example, Gilovich et al. 2002 ). We cannot process all the stimuli that surround us on a daily basis, so instead we have adapted for efficiency by attuning to patterns and developing mental shortcuts or rules of thumb to help us effectively navigate our complex world. While this tendency to rely on heuristics and biases can serve us well by allowing us to make quick decisions with little cognitive effort, it also has the potential to inadvertently undermine accuracy and thus the fair administration of justice.

Cognitive bias is an umbrella term that refers to a variety of inadvertent but predictable mental tendencies which can impact perception, memory, reasoning, and behavior. Cognitive biases include phenomena like confirmation bias (e.g., Nickerson 1998 ), anchoring (e.g., Tversky & Kahneman 1974 ), hindsight bias (e.g., Fischhoff 1975 ), the availability heuristic (e.g., Tversky & Kahneman 1973 ), unconscious or implicit racial (or other identifying characteristics) bias (e.g., Greenwald et al.  1998 ; Staats et al. 2017 ), and others. In this context, the word “bias” does not imply an ethical issue (e.g., Dror 2020 ) but simply suggests a probable response pattern. Indeed, social scientists have demonstrated and discussed how even those who actively endorse egalitarian values harbor unconscious biases (e.g., Pearson et al.  2009 ; Richardson 2017 ) and how expertise, rather than insulating us from biases, can actually create them through learned selective attention or reliance on expectations based on past experiences (e.g., Dror 2020 ). Consequently, we recognize the potential for these human factors to negatively influence our criminal justice process.

In an effort to explore the role of cognitive biases in criminal investigations and prosecutions, we conducted a literature review to determine the scope of available research and strength of the findings. The questions guiding this exercise were as follows: (1) what topics have been researched so far and where are the gaps?; (2) what are the methodological strengths and limitations of this research?; and (3) what are the results, what do we know so far, and where should we go from here?

We searched PsycINFO for scholarly writing focused on cognitive biases in criminal investigations and prosecutions in December 2016 and again in January 2020. Footnote 1 We reviewed all results by title and then reviewed the subset of possibly-relevant titles by abstract, erring on the side of over-inclusivity. We repeated this process using the Social Sciences Full Text, PubMed, and Criminal Justice Abstracts with Full Text databases to identify additional papers. Finally, we manually reviewed the reference lists in the identified papers for any unique sources we may have missed in prior searches.

We sorted the articles into categories by the actor or action in the criminal investigation and prosecution process that they addressed, including physical evidence collection, witness evaluation, suspect evaluation, forensic analysis and testimony, police case evaluation (i.e., integrating and drawing conclusions based on the totality of the evidence), prosecutors, defense attorneys, judges, juries, and sentencing. Within each of these categories, we further sorted the articles into one of three types of sources: “primary data studies” describing experimental or observational studies that involved data collection or analysis, “intervention studies” that were solution-oriented and involved implementing some type of intervention or training to prevent or mitigate a phenomenon, and “secondary sources” (e.g., commentaries, letters, reviews, theoretical pieces, general book chapters) that discussed cognitive biases but did not present primary data.

To narrow the scope of this review, we did not include articles that focus solely on implicit racial bias or structural racial bias in the criminal legal system. The foundational and persistent problem of racial (particularly anti-Black) bias throughout our legal system—from policing to sentencing (e.g., Voigt et al. 2017 ; NYCLU 2011 ; Blair et al.  2004 ; Eberhardt et al.  2006 )—has been clearly demonstrated in laboratory experiments and analyses of real-world data and is well-documented in an ever-growing body of academic publications and policy reports (e.g., Correll et al.  2002 ; Chanin et al.  2018 ; Owens et al. 2017 ; Staats et al. 2017 ).

Scope of Available Research and Methodology

Cognitive biases in forensic science have received the most attention from researchers to date (for a review of these forensic science studies, see Cooper & Meterko 2019 ). The second most substantial amount of scholarship focused on case evaluation (i.e., integrating and drawing conclusions based on the totality of the evidence in a case). Ultimately, we found 43 scholarly sources that addressed various issues related to the evaluation of the totality of evidence in criminal cases: 25 primary data (non-intervention) studies, five intervention studies, and one additional paper that presented both primary data and interventions, and 12 secondary sources. For the remainder of this article, we focus solely on the primary data and intervention studies. One of the primary data studies (Fahsing & Ask 2013 ) described the development of materials that were used in two subsequent studies included in this review (Fahsing & Ask 2016 ; 2017 ), and thus, this materials-development paper is not reviewed further here. Table 1 presents an overview of the research participants and focus of the other 30 primary data and intervention studies included in our review.

One challenge in synthesizing this collection of research is the fact that these studies address different but adjacent concepts using a variety of measures and—in some instances—report mixed results. The heterogeneity of this research reveals the complex nature of human factors in criminal case evaluations.

Eighteen of the 30 papers (13 primary data and three intervention) included participants who were criminal justice professionals (e.g., police, judges) or analyzed actual police documents. An appendix provides a detailed summary of the methods and results of the 18 criminal justice participant (or document) studies. Fifteen papers were based on or presented additional separate analyses with student or lay participants. Recruiting professionals to participate in research is commendable as it is notoriously challenging but allows us to identify any differences between those with training and experience versus the general public, and to be more confident that conclusions will generalize to real-world behavior. Of course, representativeness (or not) must still be considered when making generalizations about police investigations.

Reported sample sizes ranged from a dozen to several hundred participants and must be taken into account when interpreting individual study results. Comparison or control groups and manipulation checks are also essential to accurately interpreting results; some studies incorporated these components in their designs while others did not.

Most studies used vignettes or case materials—both real and fictionalized—as stimuli. Some studies did not include enough information about stimulus or intervention materials to allow readers to critically interpret the results or replicate an intervention test. Future researchers would benefit from publishers making more detailed information available. Further, while the use of case vignettes is a practical way to study these complex scenarios, this approach may not completely mimic the pressures of a real criminal case, fully appreciate how the probative value of evidence can depend on context, or accurately reflect naturalistic decision-making.

Notably, only two of the criminal case evaluation studies using professional participants were conducted in the USA; all others were based in Europe (Austria, Netherlands, Norway, Sweden, and the UK). The differences between police training, operations, and the criminal justice systems writ large should be considered when applying lessons from these studies to the USA or elsewhere.

Finally, all of these papers were published relatively recently, within the past 15 years. This emerging body of research is clearly current, relevant, and has room to grow.

Research Findings

The primary data studies address a constellation of concepts that demonstrate how human factors can inadvertently undermine the seemingly objective and methodical process of a criminal investigation. To organize these concepts, we used a taxonomy originally developed to describe potential sources of bias in forensic science observations and conclusions as a guide (Dror 2017 ; Dror et al.  2017 ) and adapted it to this collection of case evaluation literature. Footnote 2 As in Dror’s taxonomy, the broad base of this organizing pyramid is “human nature,” and as the pyramid narrows to its peak, potential sources of bias become increasingly dependent on environmental, individual, and case-specific circumstances and characteristics (Fig.  1 ). Some authors in this collection address more than one of these research areas within the same paper through multiple manipulations or a series of studies (Table 1 ).

Organizational framework for case evaluation studies, adapted from Dror’s ( 2017 ) taxonomy of different sources of potential bias that may cognitively contaminate forensic observations and conclusions. The specific factors listed in this pyramid are those that were examined in the collection of studies in the present literature review

Human Nature

The “human nature” studies include those that demonstrate universal psychological phenomena and their underlying mechanisms in the context of a criminal case evaluation. Several studies focused on confirmation bias. Confirmation bias, sometimes colloquially referred to as “tunnel vision,” denotes selective seeking, recalling, weighting, and/or interpreting information in ways that support existing beliefs, expectations, or hypotheses, while simultaneously avoiding or minimizing inconsistent or contradictory information (Nickerson 1998 ; Findley 2012 ). Some authors in this collection of studies used other terms to describe this concept or elements of it, including “context effects,” the term used by Charman et al. ( 2015 ) to describe when “a preexisting belief affects the subsequent interpretation of evidence” (p. 214), and asymmetrical skepticism (Ask & Granhag 2007b ; Marksteiner et al.  2010 ).

Eight studies with law enforcement personnel (Ask & Granhag 2007b ; Ask et al.  2008 ; Charman et al.  2017 ; Ditrich 2015 ; Groenendaal & Helsloot 2015 ; Marksteiner et al. 2010 ; Rassin 2010 ; Wallace 2015 ) examined aspects of confirmation bias; one addressed the distinct but related phenomenon of groupthink (Kerstholt & Eikelboom 2007 ). The importance of this issue was demonstrated by a survey of an unspecified number of professional crime scene officers conducted by Ditrich ( 2015 ), asking for their opinions about the relative frequency and severity of various cognitive errors that could potentially negatively affect a criminal investigation; based on their experiences, respondents highlighted confirmation bias (as well as overestimating the validity of partial information and shifting the burden of proof to the suspect). The other studies within this group used experimental designs to assess police officers’ evaluation of evidence. Charman et al. ( 2017 ) reported that police officers’ initial beliefs about the innocence or guilt of a suspect in a fictional criminal case predicted their evaluation of subsequent ambiguous evidence, which in turn predicted their final beliefs about the suspect’s innocence or guilt. This is not the only study to demonstrate that, like the rest of us, police officers are susceptible to confirmation bias. Ask and colleagues ( 2008 ) found that police recruits discredited or supported the same exact evidence (“the viewing distance of 10 m makes the witness identification unreliable” versus “from 10 m one ought to see what a person looks like”) depending on whether it was consistent or inconsistent with their hypothesis of a suspect’s guilt. Ask and Granhag ( 2007b ) found that when experienced criminal investigators read a vignette that implied a suspect’s guilt (but left room for an alternative explanation), they rated subsequent guilt-consistent evidence as more credible and reliable than evidence that was inconsistent with their theory of guilt; similar results were seen in a study of police officers, district attorneys, and judges by Rassin ( 2010 ).

Marksteiner et al. ( 2010 ) investigated the motivational underpinnings of this type of asymmetrical skepticism among police trainees, asking whether it is driven by a desire to reconcile inconsistent information with prior beliefs or by the goal of case closure, and encountered mixed results. The group who initially hypothesized guilt reacted as expected, rating subsequent incriminating evidence as more reliable, but in the group whose initial hypothesis was innocence, there was no difference in the way that they rated additional consistent or inconsistent information. Wallace ( 2015 ) found that the order in which evidence was presented influenced guilt beliefs. When police officers encountered exculpatory evidence prior to inculpatory evidence, guilt belief scores decreased, suggesting their final decisions were influenced by their initial impressions. Kerstholt and Eikelboom ( 2007 ) describe how teams tend to converge on one interpretation, and once such an interpretation is adopted, individual members are less able to examine underlying assumptions critically. They asked independent crime analysts to evaluate a realistic criminal investigation with fresh eyes and found that they were demonstrably influenced when they were aware of the investigative team’s existing working hypothesis.

Studies in student and general populations examining confirmation bias and other aspects of human cognition (Ask et al. 2011b ; Charman et al.  2015 ; Eerland et al.  2012 ; Eerland & Rassin 2012 ; Greenspan & Surich 2016 ; O’Brien 2007 ; 2009 ; Price & Dahl 2014 ; Rassin et al.  2010 ; Simon et al.  2004 ; Wastell et al.  2012 ) reported similar patterns to those described above with police participants. O’Brien ( 2007 ; 2009 ) found that students who named a suspect early in a mock criminal investigation were biased towards confirming that person’s guilt as the investigation continued. O’Brien measured memory for hypothesis-consistent versus hypothesis-inconsistent information, interpretation of ambiguous evidence, participants’ decisions to select lines of inquiry into the suspect or an alternative, and ultimate opinions about guilt or innocence. In a novel virtual crime scene investigation, Wastell et al. ( 2012 ) found that all students (those who ultimately chose the predetermined “correct” suspect from the multiple available people of interest and those who chose incorrectly) sought more chosen-suspect-consistent information during the exercise. However, those who were ultimately unsuccessful (i.e., chose the wrong person) spent more time in a virtual workspace (a measure of the importance placed on potential evidence) after accessing confirmatory information. They also found that students who settled on a suspect early in the exercise—measured by prompts throughout the virtual investigation—were comparatively unsuccessful.

Other psychological phenomena such as recency effects (i.e., our ease of recalling information presented at the end of a list relative to information presented at the beginning or middle) and the feature positive effect (i.e., our tendency to generally attune to presence more than absence) were also examined in studies with student or general population participants. Price and Dahl ( 2014 ) explored evidence presentation order and found that under certain circumstances, evidence presented later in an investigation had a greater impact on student participant decision-making in a mock criminal investigation. Charman and colleagues also found order of evidence presentation influenced ratings of strength of evidence and likelihood of guilt in their 2015 study of evidence integration with student participants. These results appear to provide evidence against the presence of confirmation bias, but recency effects still demonstrate the influence of human factors as, arguably, the order in which one learns about various pieces of evidence -whether first or last- should not impact interpretation. Several research teams found that a positive eyewitness identification is seen as more credible than a failure to identify someone (Price & Dhal 2014 , p.147) and the presence of fingerprints—as opposed to a lack of fingerprints—is more readily remembered and used to make decisions about a criminal case (Eerland et al. 2012 ; Eerland & Rassin 2012 ), even though the absence of evidence can also be diagnostic. Other researchers highlighted our psychic discomfort with cognitive dissonance (Ask et al. 2011b ) and our tendency to reconcile ambiguity and artificially impose consistency in a criminal case by engaging in “ bidirectional coherence-based reasoning” (Simon et al. 2004 ; Greenspan & Surich 2016 ).

Environment and Culture

The three “environment and culture” studies with police personnel (Ask & Granhag 2007b ; Ask et al.  2011a ; Fahsing & Ask 2016 ) revealed the ways in which external factors can influence an investigation. For instance, type of training appears to impact the ability to generate a variety of relevant hypotheses and actions in an investigation. English and Norwegian investigators are trained and performed differently when faced with semi-fictitious crime vignettes (Fahsing & Ask 2016 ). Organizational culture can impact the integrity of an investigation as well. Ask and colleagues ( 2011a ) concluded that a focus on efficiency—as opposed to thoroughness—produces more cursory processing among police participants, which could be detrimental to the accurate assessment of evidence found later in an investigation. Ask and Granhag ( 2007b ) observed that induced time pressure influenced officers’ decision-making, creating a higher tendency to stick with initial beliefs and a lower tendency to be influenced by the evidence presented.

Individual Characteristics

Seven “individual characteristics” studies with police personnel (Ask & Granhag 2005 ; 2007a ; Dando & Ormerod 2017 ; Fahsing & Ask 2016 ; 2017 ; Kerstholt & Eikelboom 2007 ; Wallace  2015 ) plus two studies with student populations (Rassin 2010 , 2018a ) examined ways in which personal attributes can influence an investigation. Varying amounts of professional experience may matter when it comes to assessments of potential criminal cases and assumptions about guilt. For instance, police recruits appear to have a strong tendency toward criminal—as opposed to non-criminal—explanations for an ambiguous situation like a person’s disappearance (Fahsing & Ask 2017 ) and less experienced recruits show more suspicion than seasoned investigators (Wallace 2015 ). In a departure from the typical mock crime vignette method, Dando and Ormerod ( 2017 ) reviewed police decision logs (used for recording and justifying decisions made during serious crime investigations) and found that senior officers generated more hypotheses early in an investigation, and switched between considering different hypotheses both early and late in an investigation (suggesting a willingness to entertain alternative theories) compared with inexperienced investigators. An experimental study, however, found that professional crime analyst experience level (mean 7 months versus 7 years) was not related to case evaluation decisions and did not protect against knowledge of prior interpretations of the evidence influencing conclusions (Kerstholt & Eikelboom 2007 ).

Two studies examined differences in reasoning skills in relation to the evaluation of evidence. Fahsing and Ask ( 2017 ) found that police recruits’ deductive and inductive reasoning skills were not associated with performance on an investigative reasoning task. In contrast, in a study with undergraduate students, accuracy of decision-making regarding guilt or innocence in two case scenarios was associated with differences in logical reasoning abilities as measured by a test adapted from the Wason Card Selection Test (Rassin 2018a ).

Ask and Granhag ( 2005 ) found inconsistent results in a study of police officers’ dispositional need for cognitive closure and the effect on criminal investigations. Those with a high need for cognitive closure (measured with an established scale) were less likely to acknowledge inconsistencies in case materials when those materials contained a potential motive for the suspect, but were more likely to acknowledge inconsistencies when made aware of the possibility of an alternative perpetrator. In a replication study with undergraduate students, Ask & Granhag ( 2005 ) found that initial hypotheses significantly affected subsequent evidence interpretation, but found no interaction with individual need for cognitive closure. Students who were aware of an alternative suspect (compared with those aware of a potential motive for the prime suspect) were simply less likely to evaluate subsequent information as evidence supporting guilt.

In another study, when Ask and Granhag ( 2007a ) induced negative emotions in police officers and then asked them to make judgments about a criminal case, sad participants were better able to substantively process the consistency of evidence or lack thereof, whereas angry participants used heuristic processing.

Case-Specific

Four studies of police personnel (Ask et al. 2008 ; Fahsing & Ask 2016 ; 2017 ; Wallace 2015 ), one using police records (Dando & Omerod  2017 ), and three studies of student populations (Ask et al. 2011b ; O’Brien  2007 ; 2009 ; Rassin et al. 2010 ) examined “case-specific” and evidence-specific factors. In a study of police officers, Ask and colleagues ( 2008 ) showed that the perceived reliability of some types of evidence (DNA versus photographs versus witnesses) is more malleable than others; similar results pertaining to DNA versus witness evidence were found in a study of law students (Ask et al. 2011b ).

Fahsing and Ask ( 2016 ) found that police recruits who were presented with a scenario including a clear “tipping point” (an arrest) did not actually produce significantly fewer hypotheses than those who were not presented with a tipping point (though they acknowledge that the manipulation—one sentence embedded in a case file—may not have been an ecologically valid one). In a subsequent study with police recruits, the presence of a tipping point resulted in fewer generated hypotheses, but the difference was not statistically significant (Fahsing & Ask 2017 ).

Other studies using law students (Rassin et al. 2010 ) or undergraduate students (O’Brien 2007 ) examined the influence of crime severity on decision-making. Rassin et al. ( 2010 ) observed that the affinity for incriminating evidence increases with crime severity, but in one of O’Brien’s ( 2007 ) studies, crime severity did not have a demonstrable impact on confirmation bias.

Interventions

Taken together, this body of work demonstrates vulnerabilities in criminal investigations. Some researchers have suggested theoretically supported solutions to protect against these vulnerabilities, such as gathering facts rather than building a case (Wallace 2015 ) or institutionalizing the role of a “contrarian” in a criminal investigation (MacFarlane 2008 ). Few studies have tested and evaluated these potential remedies, however. Testing is an essential prerequisite to any advocacy for policy changes because theoretically sound interventions may not, in fact, have the intended effect when applied (e.g., see below for a description of O’Brien’s work testing multiple interventions with differing results).

Four studies have examined various intervention approaches with police departments or investigators (Groenendaal & Helsloot 2015 ; Jones et al.  2008 ; Rassin 2018b ; Salet & Terpstra 2014 ). Jones et al. ( 2008 ) created a tool that helped an experimental group of investigators produce higher quality reviews of a closed murder case than those working without the aid of the review tool. Their article provides an appendix  with “categories used in the review tool” (e.g., crime scene management, house-to-house enquiries, community involvement) but lacks a detailed description of the tool itself and the outcome measures. Importantly, the authors raise the possibility that a review tool like this may improve how officers think through a case because of the structure or content of the tool or it may succeed by simply slowing them down so they can think more critically and thoroughly. Another approach that shows promise in reducing tunnel vision is using a pen and paper tool to prompt investigators to consider how well the same evidence supports different hypotheses (Rassin 2018b ). In a study of actual case files, supplemented with interviews, Salet and Terpstra ( 2014 ) explored “contrarians” and found that there are real-world challenges to the position’s efficacy (e.g., personal desire to be a criminal investigator, desire for solidarity with colleagues) and considerable variability in the way contrarians approach their work, with some opting for closeness to an investigation and others opting for distance; individuals also embraced different roles (e.g., supervisor, devil’s advocate, focus on procedure). The researchers concluded that, in practice, these contrarians appear to have exerted subtle influence on investigations but there is no evidence of a radical change in case trajectory. Similarly, members of criminal investigation teams in the Netherlands reported that, in practice, designated devil’s advocates tend to provide sound advice but do not fundamentally change the course of investigations (Groenendaal & Helsloot  2015 ). Groenendaal and Helsloot describe the development and implementation of the Criminal Investigation Reinforcement Programme in the Netherlands, which was prompted by a national reckoning stemming from a widely publicized wrongful conviction. The program included new policies aimed at, among other things, reducing tunnel vision (including the use of devil’s advocates, structured decision-making around “hypotheses and scenarios,” and professionalized, permanent “Command Core Teams” dedicated to major crimes). This deliberate intervention provided an opportunity for researchers to interview investigators who were directly impacted by the new policies. Groenendaal and Helsloot conclude that the main effect of this intervention was an increased awareness about the potential problem of tunnel vision, and they focus on an unresolved a tension between “efficacy” (more convictions) and “precaution” (minimizing wrongful convictions). Their work underscores the importance of collecting criminal legal system data, as interviewees reported their experiences and impressions but could not report whether more correct convictions had been obtained or more wrongful convictions avoided.

Other studies have examined various intervention ideas with student populations (Haas et al.  2015 ; O’Brien 2007 ; 2009 ). Haas et al. ( 2015 ) found that using a checklist tool to evaluate evidence appears to improve students’ abductive reasoning and reduce confirmation bias. O’Brien ( 2007 ; 2009 ) found that orienting participants to being accountable for good process versus outcome had no impact, and that when participants expected to have to persuade someone of their hypothesis, this anticipation actually worsened bias. More promisingly, she discovered that participants who were asked to name a suspect early in an investigation, but were then told to consider how their selected suspect could be innocent and then generate counter-arguments, displayed less confirmation bias across a variety of measures (they looked the same as those who did not name a suspect early). But another approach—asking participants to generate two additional alternative suspects—was not effective (these participants showed the same amount of bias as those who identified just one suspect).

Zalman and Larson ( 2016 ) have observed “the failure of innocence movement advocates, activists, and scholars to view the entirety of police investigation as a potential source of wrongful convictions, as opposed to exploring arguably more discrete police processes (e.g., eyewitness identification, interrogation, handling informants)” (p.3). While the thorough examination of these discrete processes has led to a better understanding of risk factors and, ultimately, reforms in police practices (e.g., see the Department of Justice 2017 guidelines for best practices with eyewitnesses), a recent shift towards viewing wrongful convictions from a “sentinel events” Footnote 3 perspective advances the conversation around these criminal justice system failures (Doyle 2012 ; 2014 ; Rossmo & Pollock 2019 ).

This literature review has identified a body of research that lends support to this holistic perspective. The studies reviewed here address a constellation of concepts that demonstrate how the human element—including universal psychological tendencies, predictable responses to situational and organizational factors, personal factors, and characteristics of the crime itself—can unintentionally undermine truth-seeking in the complex evidence integration process. Some concepts are addressed by one study, some are addressed by several, and some studies explored multiple variables (e.g., demonstrating the existence of confirmation bias and measuring how level of professional experience plays a role).

Several contemporary studies have demonstrated the existence of confirmation bias in police officers within the context of criminal investigations. Other psychological phenomena have not been examined in police populations but have been examined in student or general populations using study materials designed to assess the interpretation of criminal case evidence and decision-making. This collection of studies also investigates the role of environmental factors that may be specific to a department or organization, characteristics of individual investigators, or of the specific case under review. At the environmental level, type of training and organizational customs were influential and are promising areas for further research as these factors are within the control of police departments and can be modified. With respect to individual characteristics, a better understanding of advantageous dispositional tendencies and what is gained by professional experience, as well as the unique risks of expertise, could lead to better recruitment and training methods. Case-specific factors are outside the control of investigators, but awareness of factors that pose a greater risk for bias could serve as an alert and future research could identify ways to use this information in practice (see also Rossmo & Pollock 2019 for an in-depth discussion of “risk recipes”).

Charman and colleagues ( 2017 ) present a particularly interesting illustration of the way in which a criminal case is not merely the sum of its parts. In this study, the researchers presented law enforcement officers with exonerating, incriminating, or neutral DNA or eyewitness evidence, collected initial beliefs about guilt, asked participants to evaluate a variety of other ambiguous evidence (alibi, composite sketch, handwriting comparison, and informant information that could be reasonably interpreted in different ways), and then provide a final rating of guilt. As hypothesized, the researchers found those who were primed with incriminating evidence at the beginning were more likely to believe the suspect guilty at the end. However, even those who initially received exonerating information and initially rated the likelihood of suspect guilt as relatively low ended up increasing their guilt rating after reviewing the other ambiguous evidence. It appears that the cumulative effect of ambiguous evidence tilted the scales towards guilt. This unexpected outcome underscores the value of understanding how the totality of evidence in a criminal case is evaluated, and has implications for the legal doctrine of “harmless error” rooted in assumptions of evidentiary independence (e.g., Hasel & Kassin 2009 ).

Consistently incorporating control groups into future study designs and including complete stimulus materials in future publications could build on this foundation. This would help future researchers fully interpret and replicate study results and would assist in determining what elements of intervention strategies work. Since the majority of these studies were conducted in Europe, it would be worthwhile to explore whether or not these results can be replicated in the USA, given the similarities and differences in our criminal justice systems and the variety of approaches used to select and train detectives across police departments. Finally, valuable future research will move beyond the demonstration of these human vulnerabilities and will design and test strategies to mitigate them in the complex real world. Footnote 4 Vignettes and mock-investigations are clever ways of studying criminal investigations, but it is worth remembering that these approaches cannot fully capture the dynamics of a real criminal investigation. Collaboration between academic researchers and criminal investigators could generate robust expansions of this work.

Evidence evaluation and synthesis in criminal investigations is, of course, just one part of a larger legal process. In addition to police, defense attorneys, prosecutors, and judges have powerful roles in determining case outcomes, especially in a system that is heavily reliant on plea bargaining. Critically addressing the potential influence of cognitive biases throughout this system, and promoting and implementing proven, practical protections against these tendencies will advance accuracy and justice.

We used the following search terms and Boolean Operators: (criminal OR justice OR police OR investigat* OR forensic* OR jury OR juries OR judge* OR conviction* OR prosecut* OR defense OR defender* OR attorn*) in any field (e.g., text, title) AND (“cognitive bias” OR “cognitive dissonance” OR “tunnel vision” OR “confirmation bias” OR “interpretive bias” OR “belief perseverance” OR “asymmetrical skepticism”) in any field (e.g., text, title).

As Dror ( 2017 ) notes, the development of this taxonomy began in a paper in 2009 (Dror 2009 ) and was further developed in a 2014 paper (Stoel et al. 2014 ), with additional sources of bias added subsequently (in Dror 2015 , and Zapf & Dror 2017 ).

According to the National Institute of Justice ( 2017 ), a sentinel event is a significant negative outcome that (1) signals underlying weaknesses in the system or process, (2) is likely the result of compound errors, and (3) may provide, if properly analyzed and addressed, important keys to strengthen the system and prevent future adverse outcomes.

As Snook and Cullen ( 2008 ) assert, “it is unrealistic to expect police officers to investigate all possible suspects, collect evidence on all of those suspects, explore all possible avenues concerning the circumstances surrounding a crime, search for disconfirming and confirming evidence of guilt for every suspect, and integrate all of this information” (p. 72). Dando and Ormerod ( 2017 ) illustrate this real-world complexity when they describe an investigation that was delayed because a call for tips led to a flood of false leads, suggesting that more information is not always better. Further, though it addresses procedural justice in street policing rather than evidence integration in a criminal investigation (and thus was not included in this review), Owens et al. ( 2018 ) provide an example of a field study, complete with published scripts. Recognizing the automated thinking and behavior that comes with job experience, these researchers tested an intervention to reduce the number of incidents resolved with arrests and use of force by implementing a training program aimed at encouraging beat officers to think more slowly and deliberately during routine encounters; they also assessed the cost of this intervention in the police department.

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Thank you to Dr. Karen Amendola (Police Foundation), Ms. Prahelika Gadtaula (Innocence Project), and Dr. Kim Rossmo (Texas State University) for their thoughtful reviews of earlier drafts.

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Appendix. Detailed Summary of 18 Studies with Police Participants or Source Materials

• a Homicide case vignette was the same as the others with this designation
• b Assault case vignette was the same as the others with this designation
• c Homicide case vignette was the same as the others with this designation
• d Missing person case vignettes were the same as others with this designation
• e The second study reported in this article used undergraduate student participants

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Meterko, V., Cooper, G. Cognitive Biases in Criminal Case Evaluation: A Review of the Research. J Police Crim Psych 37 , 101–122 (2022). https://doi.org/10.1007/s11896-020-09425-8

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Childhood Behavior and Adult Criminality: Cluster Analysis in a Prospective Study of African Americans

Hee-soon juon.

Department of Health, Behavior, and Society, Bloomberg School of Public Health, Johns Hopkins University, 624 North Broadway, Baltimore, MD 21205, USA

Elaine Eggleston Doherty

Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

Margaret E. Ensminger

Adult criminality has important roots in childhood. While many studies have established that multiple problem behaviors in childhood increase the likelihood of future crime and deviance, the current study extends this “established” relationship by asking three questions: (1) Do different combinations of childhood behavioral risk factors affect adult offending? (2) Do family risk factors affect adult offending above and beyond these combinations of risks?, and (3) Are there gender differences present with respect to these two questions? Gender-specific cluster analyses identified seven clusters of childhood behavioral patterns based on teacher ratings measured in first grade among an epidemiologically-defined cohort of African Americans. Multinomial logistic regression analyses were utilized to examine the relationship of cluster membership, family risks, and criminal arrests through age 32 for serious violent and property crimes. While some gender differences emerged, both males and females in the multiple problem cluster were more likely to have later arrests for serious crime. Females who were frequently punished as first graders were most likely to have later arrests for serious crimes, while males who were from mother-only families were at higher risk of having serious criminal arrests compared to those from mother-father families. Implications for prevention and intervention strategies are also discussed.

Introduction

Over the past 50 years, it has become a well-established finding in criminology that “the past is prologue” (see, e.g., Glueck and Glueck 1950 ). The empirical finding that childhood problem behavior is predictive of adult problem behavior has been documented in several longitudinal samples, regardless of time, place, sample characteristics, or specific measures of outcome. For instance, Robins (1978) studied four samples of individuals: white child-guidance clinic patients from the 1920s, young black men in the 1940s, and Vietnam veterans and matched non-veterans from the 1960s. The results on the continuity of antisocial behavior indicate that “adult antisocial behavior virtually requires childhood antisocial behavior” (p. 611, emphasis in original). Similarly, Tremblay et al. (2003) found that boys who were physically aggressive and oppositional in adolescence also had displayed oppositional and aggressive behaviors in childhood. This pattern emerges for officially-defined problem behavior as well. For instance, in both of the Philadelphia Birth Cohorts (1945 and 1958), a subject’s delinquency status was the most important predictor of adult criminality ( Wolfgang et al. 1972 ; Tracy and Kempf-Leonard 1996 ). Taken together, although the relationship between childhood and adult behavior is not perfect and change in behavior over time also occurs, continuity between childhood behavior and later behavior has become one of the few “knowns” in criminology (see also, Robins 1966 ; West and Farrington 1977 ; Loeber 1982 ; McCord 1983 ; White et al. 1990 ; Brook et al. 1992 ; Patterson et al. 1992 ; Sampson and Laub 1993 ; Lahey et al. 1999 ; Loeber et al. 2004 ).

A variety of childhood problem behaviors have repeatedly been targeted as risk factors for later deviance. However, there is a paucity of research which systematically investigates how various combinations of these behavioral indicators within the same individual predict offending outcomes into adulthood. Thus, the current study extends the “established” relationship between childhood problem behavior and later crime and deviance by asking three questions: (1) Do different combinations of childhood behavioral risk factors affect adult offending? (2) Do family interactions and/or resources, also well-established childhood risk factors of antisocial behavior, affect adult offending above and beyond these combinations of risks?, and (3) Are there gender differences present with respect to these two questions? To begin, we briefly address the literature regarding the key childhood behavioral and family risk factors that lead to crime and delinquency and the existing research on gender differences before discussing the analytical strategy of the study.

Childhood Risk Factors

Borrowing from public health and medicine, the risk-factor paradigm has become a popular approach to examining the longitudinal patterns of crime and delinquency ( Farrington 2000 ). Two key categories of developmental risk factors for crime and delinquency are individual characteristics such as childhood disruptive behavior (e.g. opposition, impulsivity, hyperactivity and aggression) and family characteristics (e.g. parental deviance, family type, parental rejection, parental discord, ineffective discipline, and poor supervision) ( Farrington 1989 , 1991 ; Hawkins et al. 1991 , 1998 ; Lipsey and Derzon, 1998 ; Reiss and Roth 1993 ; McCord 1994 ; Tremblay and Craig 1995 ; Rutter et al. 1998 ; Tremblay 2000 ).

Developmentally, childhood disruptive behaviors may indicate an underlying behavioral disposition that continues to manifest itself as disruptive behavior throughout childhood and as delinquent and criminal behavior into adolescence and adulthood. These disruptive behaviors may also initiate negative interactions with peers and authority figures in school and at home facilitating the continuation of these behaviors. The family also plays an important role. Those in families with rejecting parents or parents who impose harsh or inconsistent punishment are most likely to be delinquent ( Sampson and Laub 1993 ; Patterson 1995 ; Tremblay and Craig 1995 ; McCord and Ensminger 1997 ; Farrington and Loeber 1998 ; Loeber et al. 2004 ). Family resources such as family size and structure, family income and maternal education have also been related to later violence ( McCord 1994 ). In their reanalysis of the Glueck delinquents and nondelinquents, Laub and Sampson (1988) found that family process variables such as parental discipline practices and maternal attachment were the most important predictors of serious delinquency. In addition, they found that background factors such as residential mobility and family income had an indirect effect on serious delinquency through these family process variables.

Cumulative Risk

One sturdy finding from risk-factor research is that risk factors tend to cluster in the same individual and that those with multiple risk factors are the most negatively affected when compared to those with fewer risk factors ( Rutter 1979 ). Some recent examples include the longitudinal research from the Denver Youth Survey and the Pittsburgh Youth Survey which reveal an increase in the probability of violent, serious, and/or persistent offending as the number of risk factors a person experiences increases ( Huizinga et al. 2003 ; Loeber et al. 2003 ). Overall, the consensus seems to be that risk factors do not appear to function as independent entities separable from the web of influences in which they occur.

Moreover, a few studies have identified different risk combinations as key correlates of later problems as opposed to a mere sum of risk factors. For instance, several studies have shown that while shy behavior alone is protective, when it exists in combination with aggressive behavior, it is detrimental in its impact ( Kellam et al. 1983 ; Block et al. 1988 ; McCord 1988 ; Moskowitz and Schwartzman 1989 ; Pulkkinen and Tremblay 1992 ; Kerr et al. 1997 ). Using a person-oriented approach, Tremblay et al. (2003, p. 211) found that in their study of boys in Montreal, “the kindergarten boys most at risk of early delinquency were physically aggressive, but also non prosocial, hyperactive, not anxious, and not inattentive.” Similarly, Raine and colleagues (1996) found that a subgroup of Danish males who had both biological and social deficits had particularly high rates of criminal and violent behavior. Kerr et al. (1997) found that disruptive-withdrawn boys who had depressive symptoms in the Montreal Longitudinal-Experimental Study were at the greatest risk for delinquency. In the cohort used in the present study, earlier analyses using self reports of delinquency as the outcome found that boys rated as shy in first grade by their teachers were less likely to be delinquent at ages 16–17 than those rated as neither shy nor aggressive, while boys who were rated as aggressive or as both shy and aggressive were more likely to be delinquent ( Ensminger et al. 1983 ).

Taken together, these studies suggest that patterns or combinations of childhood risk factors as a whole need to be considered for understanding the development and continuation of antisocial behavior rather than examining each behavior as a separate entity or as a mere sum of risks. Therefore, this study adopts a person-oriented approach to investigate how different combinations of childhood problem behaviors affect later crime, independent of childhood family resources and family interactions.

Gender Differences

To extend the research further, this study examines the potential gender differences in both the clustering of the behavioral combinations and their effect on adult offending outcomes. In the past, much of our knowledge about patterns of crime has come from longitudinal studies of males only (e.g., Loeber and Dishion 1983 ; McCord 1983 ; Wolfgang et al. 1987 ; Farrington 1989 ; Magnusson and Bergman 1990 ; Sampson and Laub 1993 ). The research on gender and crime has grown dramatically over the past 15 years. However, the longitudinal research examining the relationship between early behavioral problems and later deviant behavior among African-American females is still somewhat scarce. While a number of studies have been successful in advancing our knowledge about the developmental patterns of female crime, these studies sample European or Canadian females who are predominantly white (see, White et al. 1990 ; Brook et al. 1992 ; Keenan et al. 1999 ; Cote et al. 2001 ; Moffitt et al. 2001 ; Broidy et al. 2003 ). Thus, many questions still remain as to the patterns of behavior in childhood and later crime for African–American females.

The existing research on gender differences and continuity of behavior over time is mixed. Several studies have found heterotypic continuity in both boys and girls such as childhood aggression predicts adolescent delinquency or adult criminality (see Lanctot and Le Blanc, 2002 : 131, for a review). However, a few researchers do not find similarity among the sexes such as Stattin and Magnusson (1989) who found a strong connection between teachers’ ratings of aggression at ages 10–13 and adult crime at age 26 for males but not for females. In addition, Broidy et al. (2003) examine the continuity of aggression and adolescent delinquency among six longitudinal samples and find that the continuity found in the boys over time does not consistently appear among girls. Also, in the earlier Woodlawn study, the continuity between first grade aggression and adolescent delinquency was present for males but not females ( Ensminger et al. 1983 ). Thus, given the inconsistency in findings across several samples and the fact that the vast majority of studies have examined white females, it is still an open question whether long-term behavioral continuity is similar for males and females in an African-American population.

In general, the purpose of this paper is to understand how childhood behavioral patterns are associated with young adult serious crime in an epidemiologically-defined cohort of African Americans followed prospectively from first grade to age 32. We identify homogeneous clusters of children based on early behavioral ratings using cluster analysis to examine whether some combinations of childhood behaviors are more detrimental for adult criminal outcomes than others. In addition, this study examines whether family resources and relationships contribute to serious adult arrests independent of these combinations of behavioral risks. Finally, this study examines the potential gender differences in these combinations of childhood behaviors and their relationship with serious adult crime.

Description of the Woodlawn Study

This prospective, longitudinal study consists of a cohort of 1242 children who began first grade in the nine public and three parochial schools in Woodlawn in 1966–67 and remained in a Woodlawn school during their first grade year. 1 Woodlawn is a socially disadvantaged, inner-city community on the South side of Chicago with high rates of delinquency and crime. In the mid-1960s, when this study began, almost all Woodlawn residents were African American. This fact provides a unique opportunity to test continuity in behavior over several decades among African Americans who have been an understudied population in longitudinal research. The cohort comprises males and females (51.2 and 48.8%, respectively) from a variety of economic backgrounds (e.g., working-class, iddle-class, and welfare families).

A distinguishing feature of this longitudinal study is the high rate of criminal activity both within the cohort and within the community where the cohort lived when the study was initiated. During the period from 1966 to 1972, when this cohort was in adolescence, Woodlawn had the highest rate of male juvenile delinquents of the 76 community areas of Chicago [33.5 per 100 males between the ages of 12 and 16] ( Council for Community Services 1975 ). The cohort members themselves display high rates of criminal activity with close to 50% of the males arrested for at least one serious crime between ages 17 and 32 (and 38% of the males arrested two or more times for serious crimes in adulthood). Moreover, this study is especially well-suited for examining gender differences since the females also display a high prevalence of crime. In the Woodlawn sample, 17% of the females had been arrested for at least one serious crime in adulthood and 7% of the females were arrested for two or more serious crimes.

During first grade, teachers were asked about each child’s classroom behavior; clinicians observed the children in standardized play situations; and mothers (or mother surrogates) were interviewed about their first grade child and the family. In 1975–1976, 10 years after the children had been in first grade (age 16–17), both mothers or mother surrogates and the adolescents were reinterviewed. At ages 32–34, the “children” were interviewed again. In addition, the Chicago Police and FBI records were searched for arrest information on the population. Criminal justice records for both the males and females included multiple names for many of the cohort members. In the follow-up of the “children,” family members and neighbors were queried as to where the cohort member might be located and if they (especially the women) had changed their names. In this paper, we rely on the information collected from mothers and teachers in first grade and the arrest records collected on cohort members from ages 17 to 32.

Criminal Arrests

We obtained arrest records for the Woodlawn population from the Chicago Police Department in 1985, and then updated these in 1992. In 1993, the FBI matched their records with the names from the Woodlawn cohort. These arrest records gave a history of each person’s contact with the criminal justice system and were cumulative, beginning when an individual was 17 (the age of majority in Illinois). The type of offense, the disposition, and the dates of arrest were coded for each crime. 2 For the purposes of this study, we divided the sample into four mutually exclusive groups: those with at least one serious violent crime, those with at least one serious property crime but no serious violent crime, those with only a non-serious crime, and those with no criminal arrests. 3 We use this categorization as opposed to a continuous crime count to safeguard against underestimating the actual arrest rate for each individual. We do not have complete incarceration information for the time period of ages 17–32. Therefore, a true lambda measure of offending cannot be calculated for a Poisson regression. Since the arrest counts in a Poisson regression would be an underestimate of the predicted offending rate, we used a nominal measure of serious offending which is less sensitive to incarceration stays.

As shown in Table 1 , 38% of the cohort had an official arrest record between the ages of 17 and 32 (56% of the males and 21% of the females). This is relatively high compared to other longitudinal samples due to the high-risk nature of this cohort. Piquero et al. (2003, p. 429) review the overall, gender-specific, and race-specific prevalence rates in several of the leading longitudinal studies that report official criminal career histories. They report a range of overall prevalence rates between 7 and 40% with higher percentages among males and African-Americans.

Descriptive statistics of criminal categorization

Among this cohort, males were more likely to be arrested for serious crimes than females and more likely to have an arrest for a serious violent crime as opposed to only a serious property crime. The majority of the males who were arrested for at least one serious violent crime were most likely to be arrested for crimes of injury against a person followed by robbery (87 and 30%, respectively). Similarly, the females with at least one serious violent arrest were most likely to commit these two crime types (89% arrested for injury against a person and 13% arrested for robbery).

Early Behavioral Responses in School for Clustering

Using cluster analysis, we examine six teacher ratings of first grade classroom behaviors that measure conceptually distinctive indicators of childhood behavior. In first grade, teachers in an interview rated each child in their classroom on five aspects of school adaptation, including aggressive behavior, shy behavior, problems with concentration (restlessness), underachievement, and immaturity. These behaviors had earlier been identified by teachers as important indicators of adaptation to school ( Kellam et al. 1975 ). In addition, the teacher graded each child’s general classroom conduct as unsatisfactory, fair, good, or excellent. All six of the classroom behavior measures were coded on a scale that ranged from 0 to 3 (see Appendix for a correlation matrix of these six variables).

Family Characteristics

The indicators of family resources and family interaction patterns were collected from the mothers or mother surrogates in a home interview at the time of first grade in 1967. The family interview included questions about the family itself such as income, family structure, occupation, residential mobility, social integration, and education; questions about the children such as mothers’ ratings of mental and physical health; and questions concerning the family’s child rearing practices, especially with regard to discipline and affection.

Mother’s education, welfare receipt, family size, and family type were included as measures of family resources in 1967. Mother’s education is a continuous measure of years completed in school and ranged from 0 to 18 years. Welfare receipt referred to whether the family was supported by welfare and was dichotomized as yes or no. Since mother’s education (0–11 years versus 12+ years) and receiving welfare were highly associated (χ 2 =64.40, P < 0.001), only mother’s education was included in the multivariate analyses. Family size is a continuous measure that indicates the number of children under 19-years-old at home and ranges from 1 to 15. Family type was based on the combinations of adults in the family of the first graders and included four types: mother and father present, mother alone, mother and other adults (not the father), and families with no mother present (mother absent).

In 1967, mothers were asked a series of questions about their and other adult family members’ interactions with their children ( family interactions ). In terms of family affection, mothers were asked: (1) how often did they play with or read to the child (1 = less often to 3 = everyday) and (2) how often did the child get taken out (0 = never to 4 = every week). In terms of discipline, mothers were asked: (1) how often the child was spanked (0 = never spanked to 5 = almost every day); and (2) how often the child got punished for misbehavior (1 = hardly ever to 4 = always). Factor analysis of these four items showed a two factor solution with 62.6% explained variance. First, the measures of family discipline includes spanking and getting punished ( r = 0.27, P < 0.001). The composite score of family discipline was constructed with a range of 1–9. Next, the measures of family affection include the number of times the child was played with or read to and the frequency that the child was taken out ( r = 0.19, P < 0.001). The composite score of family affection was constructed with a range of 1–7.

There are three stages of analyses. Initially, cluster analysis was used to identify coherent subgroups based upon the six ratings of early behavioral responses in school as described above. Children who are similar on a variety of behaviors are grouped together to form clusters of children with similar behavior patterns. This approach simultaneously combines several behaviors to uncover meaningful, integrative behavioral patterns. In the second phase, we examined the relationships of cluster memberships to first grade family resources and family interaction patterns. ANOVA and contingency tables were used to examine the overall relationship between cluster membership and early family resources and family interactions. We also examined how the clusters related to later serious crime. Finally, we estimated multivariate multinomial regressions to identify whether childhood behavior patterns and family characteristics related to later arrests for serious violent and property crimes during adulthood. We also checked whether multinomial estimates might violate the independence of irrelevant alternatives (IIA) assumption (see Long 1997 : 182–184). Generalized Hausman tests (with suest command) were performed using STATA 8.0. Under the IIA assumption, multinomial logit models assume that the odds for specific pairs of outcomes should not depend on other outcomes available. All of the analyses were based on ( N = 1198); 44 cases who had died by age 32 were excluded.

Description of Clusters

Cluster analysis was employed to assess whether coherent subgroups could be identified based upon the six ratings of early behavioral responses in school described above. Past research has shown gender differences in the prevalence of early behavioral ratings and in the prevalence of criminal arrests. Therefore, males and females were clustered and analyzed separately. Since cluster analysis is sensitive to outliers, we used the average squared Euclidean distance between observations to locate observations that fell outside a threshold distance of 0.5 from at least one other observation ( Bergman 1998 ). Seven boys and 15 girls did not fit any cluster and were left unclassified and placed in the residual group.

Ward’s method (1963), a hierarchical agglomerative clustering technique, assigned cases to initial clusters in the Sleipner Statistical package for pattern-oriented analysis ( Bergman and El-Khouri 1998 ). This method begins with observations as separate clusters and then gradually links them together based on their squared Euclidean distance from one another. This method is designed to optimize the variance between clusters ( Aldenderfer and Blashfield 1984 ). Based on the variance explained by solutions containing varying numbers of clusters, we decided on a seven-cluster solution, which explained about 50% of the total variance. While a higher cluster solution explains more variance, there are diminishing returns with increasing cluster solutions. Based on a sudden drop in the explained error sum of squares at the seven cluster solution, we chose a seven cluster solution ( Bergman 1998 ).

In the final step of the clustering process cases were reassigned by moving ill-fitting observations into better-fitting clusters so that homogeneous clusters were obtained ( Bergman and El-Khouri 1998 ). This is an iterative procedure; observations are reassigned iteratively until all observations are assigned to the best-fitting cluster. This procedure increased the explained error sum of squares to about 53% for both males and females. The seven-cluster solutions are shown in Table 2 for males and Table 3 for females.

Patterns of early behavioral problems at age 6–7 (Boys, n = 574)

Note . All scores are 0–3

1. No problems

2. Mild conduct problems

3. High shyness

4. Moderate problems but not shy or aggressive

5. Moderate aggressiveness

6. Multiple problems but not shy

7. Multiple problems

Patterns of early behavioral problems at age 6–7 (Girls, n = 624)

Pattern Analysis of Behavioral Problems Among First Grade Boys

For males, Cluster 1 (12.7%, n = 72) was characterized by the absence of problems; the means of all six indicators were less than 1 on a scale ranging from 0 to 3 with 0 indicating adapting or no problem (see Table 2 ). This cluster was labeled “no problems.” The second male cluster (40.6%, n = 230) was characterized by moderate conduct scores, with no other adaptation problems. This cluster was labeled “mild conduct problems.” Those in Cluster 3 (7.6%, n = 43) had high ratings on shy behavior as well as moderate ratings on underachievement and immaturity; this cluster was labeled “high shy behavior.” Cluster 4 (13.4%, n = 76) was characterized primarily by moderate problems in restlessness, underachievement, and immaturity, but not shy or aggressive behavior, and was labeled “moderate problems but not shy or aggressive.” Cluster 5 (10.9%, n = 62) included boys with moderately high conduct grades and aggressive ratings and was labeled “moderate aggressiveness.” Two multiple problem male clusters were identified (6, 7). Cluster 6 (7.9%, n = 45) was characterized by severe maladaptation on all behaviors except shy behavior and was labeled “multiple problems but not shy.” Cluster 7 (6.9%, n = 39) was characterized by severe maladaptation on all the behavioral problems. This subgroup was labeled “multiple problems.” The residual group, although moderate to high on all of the behavioral indicators, did not fit into any specific cluster. These seven males are excluded from the multivariate analyses.

Pattern Analysis of Behavioral Problems Among First Grade Girls

Table 3 displays the cluster solution for females. The first five clusters show similar patternings of behavior (but not similar prevalences) to those identified among the male sample. For instance, Cluster 1 (25.8%, n = 157) was labeled “no problems” and included girls with low scores on each of the behavioral dimensions. The girls in Cluster 2 (30.9%, n = 188) had moderate conduct scores yet no other behavioral problems and this cluster was labeled “mild conduct problems.” Cluster 3 (5.6%, n = 34) was characterized as a “high shy behavior” group which included those girls with high scores on shyness, underachievement, and immaturity. Cluster 4 (5.9%, n = 36) was characterized as having moderate problems in restlessness, underachievement, and immaturity but not shy or aggressive behavior. This cluster was labeled as “moderate problems but not shy or aggressive.” Again, similar to the males, Cluster 5 (12.3%, n = 75) was characterized by moderate aggressiveness and moderate conduct problems only and was labeled a “moderate aggressive” group. Unlike the male sample, no multiple problem but not shy group was identified in the female sample. The sixth cluster for the females (14.0%, n = 85) included girls with moderate levels of underachievement and was referred to as the “mild underachievement” group. Finally, as seen with the male subsample, Cluster 7 (5.6%, n = 34) was characterized by high maladaptation in first grade for all the behavioral responses. This group was labeled the “multiple problems” group. Fifteen girls did not fit any cluster and were left unclassified and placed in the residual group. These 15 females show moderate levels of each behavior but do not fit into any specific cluster and are excluded from the multivariate analyses.

Cluster Membership and Early Family Characteristics

Tables 4 and ​ and5 5 show the relationship of family resources and family interactions to cluster membership for males and females, respectively. As Table 4 indicates, the male no problem group (Cluster 1) had the most family resources, the most affection, and the lowest level of discipline/punishment. These families had the lowest percentage of welfare receipt, the highest level of maternal education, the lowest number of children in the family, the lowest percentage of mother alone families, the lowest level of discipline, and the highest level of affection. Males in the high shy behavior cluster (Cluster 3) had the lowest overall family resources during first grade as indicated by a high percentage of welfare receipt (55.8%), the lowest level of maternal education, and the highest number of children at home. They also had the lowest levels of affection (4.05 ± 1.55). With respect to discipline, the moderate aggressive group (Cluster 5) had the highest discipline scores, indicating the harshest and most frequent discipline practices (6.11 ± 1.74).

Family resources and family interaction by the cluster groups (Males = 567)

Family resources and family interaction by the cluster groups: (Females = 609)

6. Mild underachievement

There are some similarities and some differences for the females (see Table 5 ). Similar to the males, the no problem group (Cluster 1) had the most family resources with the lowest percentage of families on welfare and the highest levels of maternal education (22.9% on welfare and 11.2 ± 2.14 for maternal education). One difference from the male findings is that the multiple problems group (Cluster 7), not the high shy group, scored lowest on family resources in terms of receiving welfare in first grade (52.9%) and having mothers with low educational attainment (10.1 ± 2.16). In addition, for females, while the no problem group had the lowest discipline scores, again the multiple problem group had the highest scores on the discipline scale (4.71 ± 1.93 and 6.06 ± 1.85, respectively).

Cluster Differences on Criminal Outcome

Next we turn to how criminal arrests for serious violent and property crimes varied by cluster membership. Figure 1 shows that cluster membership is associated with criminal arrests for both males and females ( P < 0.05). For males, three of the seven clusters have rates of serious crimes that exceed 50%. For both males and females, the multiple problems group had the highest percentage of criminal participation; about 72% of males in the multiple problem cluster had an arrest for a serious crime compared to almost 31% in the no problem cluster, while about 29% of females in the multiple problem cluster had an arrest for a serious crime compared to about 12% in the no problem cluster and 9% in the high shyness cluster.

Serious criminal arrests and cluster membership by sex

Factors Associated with Criminal Outcome

To investigate any differences due to crime type, multinomial (polychotomous) logistic regressions were used to examine the characteristics associated with both three categories and four categories of criminal outcomes. The three categories included: (0 = no crime, 1 = non-serious crime, 2 = serious property or serious violent crime). The four categories were: (0 = no crime, 1 = non-serious crime, 2 = serious property crime only, 3 = serious violent crime). 4 In these analyses, family resources and family interactions were included as control variables to examine whether cluster membership affects serious criminal arrests, independent of these family influences. Since those with non-serious crime did not differ on any of the characteristics that we examined, their findings are not presented. 5

Any Serious Crime

Table 6 shows the results from the multiple regression analysis of cluster membership and serious crime. For males (right-hand column), the multiple problems cluster (Cluster 7) (OR = 4.84, 95% CI 1.93, 12.14), the multiple problems but not shy cluster (Cluster 6) (OR = 3.75, 95% CI 1.53, 9.19), the moderately aggressive group (Cluster 5) (OR = 2.84, 95% CI 1.29, 6.25), and the mild conduct problem group (Cluster 2) (OR = 2.18, 95% CI 1.17, 4.06) each have a significantly higher odds than the no problem cluster of being arrested for a serious crime. Those males in a mother alone family in first grade were about one and a half times more likely to be arrested for a serious crime by age 32 while the family interaction indicators were not associated with being arrested for a serious crime in adulthood for males.

Multiple multinomial regression of childhood behaviors, family resources and interaction, on serious criminal arrests: odds ratio and 95% confidence interval

For females (left-hand column), the multiple problems cluster (Cluster 7) (OR = 2.77, 95% CI 1.07, 7.19) and the moderate aggressiveness cluster (Cluster 5) (OR = 2.24, 95% CI 1.04, 4.80) each have a significantly higher odds than the no problem cluster of being arrested for a serious crime in adulthood. In addition, discipline in childhood was associated with later serious crime for the females; those who had frequent punishment and spanking had a higher odds of criminal arrests than those with less punishment and spanking (OR = 1.15, 95% CI 1.01, 1.31). None of the family resource variables in first grade were related to serious crimes for the females.

Serious Property and Violent Crime

Interestingly, from the crime-specific analysis displayed in Table 7 it becomes clear that the results on serious crime from Table 6 are driven by violent arrests. For both the males and females, cluster membership with one exception is unrelated to serious property offending when compared to no criminal arrests. The one exception is that the multiple problems but not shy cluster for males (Cluster 6) has a higher risk of a serious property arrest than the no problem category (OR = 4.54, 95% CI 1.17, 17.66). For females, those from larger families have a higher risk of a serious property arrest (OR = 1.18, 95% CI 1.03, 1.34). When the focus is on serious violent crimes, the same substantive conclusions emerge for both the males and the females that were reported in Table 6 . The same four male clusters (2, 5, 6, and 7) and two female clusters (5 and 7) have a significantly higher odds than the no problem cluster of being arrested for a serious violent crime. Moreover, the odds ratios in the crime-specific analysis are larger than those found with the combined serious property and violent crimes category. Finally, family type was associated with serious violent crime for males; those males from mother only families were more likely to be arrested for a serious violent crime compared to those from mother–father families (OR = 1.79, 95% CI, 1.15, 2.81). For females, family discipline practice is marginally associated with serious violent crimes (OR = 1.19, P = 0.058).

Discussion and Conclusions

This research study attempted to address several issues regarding the continuity of childhood problem behaviors and adult offending. First, we asked whether different combinations of childhood behaviors influence serious adult offending. The answer is not a straightforward one. We employed a person-oriented approach to analyze this continuity in behavior. In reality, specific behaviors rarely function as independent entities, thus, a person-oriented analyses rather than variable-oriented analyses seems most appropriate ( Meyer and Megargee 1977 ; Magnusson 1988 , 1996 ; Stattin and Magnusson 1989 , 1996 ; Pulkkinen and Tremblay 1992 ; Sorenson and Johnson 1996 ; Raine et al. 1996 ; Kerr et al. 1997 ; Flanagan et al. 2003 ).

In this analysis, a person-oriented approach proved to be beneficial in that, as opposed to arbitrarily assuming that behaviors operate independently as one might in a variable-oriented analysis, the clustering technique uncovered the combinations of behaviors that naturally occur within individuals. These analyses identified seven distinct and meaningful clusters of childhood behavior for both males and females. Moreover, these clusters were more similar to each other than distinct across gender. There were two minor differences between the male and female cluster analysis results. First, there was a distinct cluster (Cluster 6) for the males and females. The male Cluster 6 was a multiple problems group that had high scores in every behavior except shyness. The female Cluster 6 had moderate levels of underachievement but low levels of the other behaviors. The second difference was that, among the six similar clusters, the males always had higher means on the problems than the females and the prevalence in each group varied somewhat across gender.

While this consistency between males and females in the clusters indicates that the clusters are meaningful, the drawback is that not all possible combinations of behaviors emerged naturally, making it difficult to come to a straightforward conclusion regarding whether different behavior patterns affect serious adult offending. For instance, underachievement and restlessness did not occur at high levels in isolation. In fact, they did not occur without high levels of aggression and/or high levels of immaturity. Since these patterns did not occur, it is not relevant to speculate about their influence on serious adult offending.

The key finding with respect to the combinations identified for both genders is that the presence of aggression, even at moderate levels, is a key predictor of adult offending regardless of its combination with other behaviors. Thus, whether a person has moderate aggression and poor general conduct scores (the moderate aggressive group) or has these two behaviors in combination with immaturity, restlessness, and underachievement, the common predictive factor of adult serious offending for both males and females was the presence of aggression. This is consistent with research that finds aggression to be a key predictor of crime into adulthood (see Huesmann et al. 2002 ). Those with poor general conduct scores without high levels of aggression (mild conduct problems group) also were predictive of adult offending, although this was true only for males. Low conduct scores are generally given to those with misbehavior that is similar to aggressive behavior—not following teacher’s instructions, disruptive behavior, or difficulty with other children. This finding indicates that even a minimal indication of conduct problems is predictive of later serious offending among this population. Finally, these clusters predict serious violent adult offending when compared with non-offenders or non-serious offenders. In both the males and females, the same clusters that were found to predict serious offending overall predicted serious violent offending but did not consistently predict serious property offending.

One unexpected finding was the lack of a protective finding for shyness. In prior research on the Woodlawn cohort, shy behavior has been a protective behavior with those who are rated as shy but not aggressive as indicated by lower rates of both drug use and delinquency ( Kellam et al. 1983 ; Ensminger et al. 1983 ). However, in this person-oriented approach, the high shyness group for both genders was not protective of serious adult offending. In fact, for males, the high shyness group had close to 50% of its members with a serious criminal arrest. This discrepancy could be due to the fact that these individuals were not merely high in shyness but also high in their immaturity scores and in underachievement, and that they came from the most disadvantaged families. High levels of shyness do not appear to occur in isolation and thus, once a holistic approach is taken, the finding of shyness as a protective factor is no longer present. Another explanation for the inconsistency in these results is that the outcome of interest is serious adult offending as opposed to childhood or adolescent problem behaviors. Perhaps once the time frame is extended through young adulthood, childhood shyness no longer plays a role in predicting behavior. Or perhaps, shyness is protective of drug involvement or delinquency as opposed to serious violent and property offending.

With respect to the second question posed in this study, childhood family resources and interactions related differently to the outcomes for the males and the females. In the multivariate analyses, females who had more family discipline practices were less likely to be arrested for serious crimes. Whether this is a selection factor or a social causation factor is not possible to determine from these data, but it does indicate that early discipline practices may be an important indicator for girls. Family discipline was not related for males and family affection was not related to offending for either males or females.

Males from mother alone families in first grade were more likely to have a criminal arrest by age 32. Several studies have shown the long-term impact of family structure on adult outcomes ( Amato 1991 ; Nurco et al. 1996 ; Cherlin et al. 1998 ). Barrett and Turner (2005) show that single parent households differ in their socioeconomic status, family processes and stressful life events, and that all three of these contribute to the detrimental impact of having grown up in a single parent household. In our study, socioeconomic differences, family processes, and early childhood behavior patterns were controlled in the analyses and still there was an effect of being from a mother alone family. Early family structure may be a marker for a variety of family issues—socioeconomic status, family processes, family stress. While we have controlled for these somewhat in our analyses, family structure is still important.

Family factors are also related to the cluster assignments. Females in the multiple problems cluster came from families with fewer resources than those from the other groups. For males, those in the high shy group came from families with low resources and poor family interactions. Perhaps for these males, growing up in a family with fewer resources, harsh and frequent discipline, and lower affection inhibited their social relationships in early childhood, while for females it resulted in a variety of maladaptive behaviors. Regardless of the mechanism, the direct and indirect effect of childhood family factors on adult offending is clear and is consistent with previous research (see Sampson and Laub 1993 ; Huesmann et al. 2002 ).

Finally, the overall conclusion is that there are no major differences between males and females in either the combinations of risks or the continuity of behavior over time among this sample of African–Americans. That is, similar clusters emerged for both males and females and the developmental continuity between childhood clusters of behavior and serious adult crime was also apparent for both genders. This finding, while it concurs with some of the existing research is counter to those reported by others such as Broidy and her colleagues (2003) . Thus, it is important to try and reconcile our findings with those of Broidy et al.’s especially since they sampled six longitudinal samples from three countries and found no such clear relationship between aggression and offending among the female samples. There are two primary differences that may explain the discrepancy. The first is that Broidy et al. use a variable-centered approach that includes aggression with three other early behavioral problems into a multivariate model. While the evidence indicates that there are significant bivariate associations between early behaviors and later delinquency among boys and girls, these relationships are not sustained in the multivariate framework. In contrast, since we know risks tend to cluster in the same individuals, our approach is a person-centered approach which allows aggression to co-occur with other behavioral problems and reveals that those with multiple problems as well as any evidence of high levels of aggression or mild conduct problems predict later violence. The second difference is that we are examining a sample of African–American females while only one of the six sites in their study includes African–Americans with the majority of that sample being Caucasian (80%). Thus, the differences in findings could be due to the differences between using a person-centered approach versus a variable-oriented approach or to the nature of the samples used.

Overall, the evidence from this study indicates that no additional complexity in pathways to serious adult crime is necessary to accommodate female behavior patterns. Although family discipline emerged as a significant family predictor for females and family structure emerged as a family predictor for men in the multivariate analysis, the bivariate relationships between the clusters for both genders showed that those with more resources and affection had fewer behavioral problems. Taken together, a gender-neutral approach to continuity in behavior over time seems most appropriate, at least among high-risk African–American men and women. These results concur with the findings of similarity between genders among all-white populations as well (see Moffitt et al. 2001 ).

This gender-neutral approach extends to prevention policy as well. The results from the current study indicate that programs that are aimed at early aggressive behavior are focused on an attribute shown to be an important predictor of serious offending in early adulthood among both sexes (e.g., Kellam et al. 1994 ; Bierman et al. 2002 ). In addition, the potential importance of the family context is shown in these results, thus, multi-level prevention approaches which target aggression as well as family practices may be most beneficial.

Finally, the Woodlawn study has some unique study design features that can contribute to the existing knowledge from previous studies that have shown continuity in behavior among whites, males, adolescents, and/or populations with low rates of crime. First, there are few cohort studies that have followed an African–American community population from childhood to adulthood using a prospective, longitudinal design. Second, while many longitudinal studies of crime have included males only, this study includes both males and females. Third, the study population has relatively high rates of criminal arrests for serious crimes. Thus, in corroboration of and in addition to prior research, our results show developmental continuity of antisocial behavior across the life course among this unique study population of African–American males and females with high rates of offending who have been studied from first grade to age 32.

One limitation of the present study is its reliance on official records for the measure of crime. Official police records reflect police behavior as well as criminal behavior. It also has been argued that more extreme differences in race, class, and sex are found with official records as compared to self-reports ( Elliott and Ageton 1980 ). However, self-reports may also be biased by underreporting, over reporting, and problems of recall bias. In the Woodlawn study, we compared respondents self-reports of criminal activity with their criminal justice records from the Chicago police and from the FBI ( McCord and Ensminger 1995 ). There was a high correlation of official records and self reports. About 77% of respondents who had official arrest records self-reported committing crimes. Future research could investigate the issue of behavioral continuity using minor offenses and self-reported offending.

While one strength of the study is the nature of its population, it is also a limitation in that it is not clear to whom the results can be generalized. The cohort includes a very specific community population born in the 1960s. Whether the findings pertain to those who differ in year of birth or community of residence is not known and can only be evaluated in comparisons with other study populations. We are fortunate with the criminology literature in that there are more than a few longitudinal studies to which we can compare our findings. The general conclusion of behavioral continuity revealed among this cohort of African–American children from one neighborhood in Chicago is consistent with other studies from different time periods, in varying environments, and with a variety of populations.

The results from this study also point to several other important areas to be considered in future research. In this study we were able to investigate only a subset of possible childhood behaviors. Future research could evaluate whether combinations of other childhood behaviors affect adult offending. In addition, future research could investigate how adolescent variables such as peers and school factors mediate this relationship between child behavior patterns and adult offending. This research also does not include adult social variables such as marriage and employment or marital discord and job instability which have also been found to affect adult offending and may mediate the relationship between behavior clusters and adult offending (see Sampson and Laub 1993 ). Finally, future research should employ a person-centered approach to evaluate the continuity between childhood behaviors and adult behaviors.

Acknowledgments

This paper was supported by the National Institute of Drug Abuse (R01-DA06630, Margaret Ensminger, P.I.) and the Centers for Disease Control and Prevention (R49/CCR318627-02, Phillip Leaf, P.I.). We are grateful to the late Joan McCord for the comments and suggestions on an earlier draft of the manuscript as well as for the collection and coding of criminal records. Our gratitude also goes to the late Honorable Loretta Hall Morgan who helped obtain and code the FBI and Chicago Police Records. We wish to thank the Woodlawn Project Advisory Board for their continued support and cooperation of this research project during the past 30 years.

The correlation matrix for variables in the cluster analysis for males (below the diagonal) and females (above the diagonal)

1 The sample used in this study equals 1198 due to the exclusion of 44 cases who died prior to age 32.

2 These records were independently coded by Joan McCord, a criminologist, and Loretta Hall Morgan, a criminal court judge in Chicago.

3 Serious violent crimes include murder, manslaughter, rape, assault, battery, domestic assault, weapons charges, and kidnapping. Serious property crimes include arson, auto theft, possession of a stolen automobile, breaking and entering, burglary, theft, and attempted theft. Non-serious crimes include crimes of prostitution, traffic violations, crimes against order, business crimes, and alcohol or drug-related crimes.

4 Each of the multinomial logistic regressions were analyzed with and without the 44 dead cases revealing virtually identical results. The analyses excluding the deceased cases are presented.

5 Using the arrest counts (not accounting for incarceration time), negative binomial regression analyses indicate similar results for males and females with respect to significance and direction (data not shown). While there were a few differences in the results, the primary conclusions from the negative binomial analyses concur with the multinomial logit analyses reported here.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

Contributor Information

Hee-Soon Juon, Department of Health, Behavior, and Society, Bloomberg School of Public Health, Johns Hopkins University, 624 North Broadway, Baltimore, MD 21205, USA.

Elaine Eggleston Doherty, Department of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.

Margaret E. Ensminger, Department of Health, Behavior, and Society, Bloomberg School of Public Health, Johns Hopkins University, 624 North Broadway, Baltimore, MD 21205, USA.

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Criminal Behavior: A Psychological Approach Research Paper

Through many centuries, criminal behavior was mistakenly explained mostly with biological defects of the individual. It was believed, that abnormal actions, negatively affected society, were preferably conducted by those, who suffered from serious physical deviations in development. However, as the science was progressing, scholars have begun searching other possible causes of the formation of antisocial behavior and psychology began to actively explore this area. Nowadays, it is known, that incorrect parenting style, negative influence of environment, and formation of improper role models may significantly result inner conflicts and thus, led to the abnormal behavior. In this essay, we would further examine, how these aspects can influence the behavior of individuals and lead to criminal conduct, illustrating it with examples from biographies of famous criminals to show how concepts can be applied in reality. First of all, it is necessary to draw a distinction between biological and psychological factors, influencing the behavior of individual. Under biological factors we mean physical anomalies, such as neurotransmitter dysfunction, bradygenesis, and other problems of neural development, genetically predisposed or caused by trauma or injury. Under psychological factors we mean mental disorders, and deviation in education and mental development of the individual. However, despite the fact that the differences seem obvious, both areas are interconnected between deeply. As we know, the connection of nature and nature is rooted into every individual. As the course textbook states " everything psychological is simultaneously biological " (Meyers, 1999, Chapter 2, p.47). Thus, various theories, designed to explain the emerging and development of deviant behavior, combine both approaches. For example, theory of personality and crime by Hans J. Eysenck, British psychologist, states that each individual has innate hereditary predisposition towards asocial behavior, which discloses in certain circumstances. Thus, " criminal behavior is the result of an interaction between certain environmental conditions and features of the nervous system " (Bartol & Bartol, 2005). However, Eysenck does not say, that deviant behavior inborn, but may be caused by the compound of heredity and environment. It is not itself, or criminality that is innate; it is certain peculiarities of the central and autonomic nervous system that react with the environment, with upbringing, and many other environmental factors to increase the probability that a given person would act in a certain antisocial manner (Eysenck & Gudjonsson, 1989)

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Jack D . Muir

There are many theories that have been developed which attempt to explain the causes of criminal behaviour, including explanations of crime which focus on the individual – his or her thought process, biological factors, and psychological factors; and explanations of crime which focus on factors external to the individual – the surrounding environment, others who influence the individual, and society as a whole. These two areas of criminology are often seen to be in direct conflict with each other. Nowadays most criminologists agree that it isn't an either-or situation – internal factors and social factors both play a part in determining one's behaviour. This essay takes the same view and argues that both sides of criminology have their strengths and weaknesses in explaining crime, and the notion that only internal or external factors can be the cause for an individual being criminal is outdated. To demonstrate that neither explanations of crime are more convincing than the other, this essay will examine both biological theories and ecological theories and compare them against each other. In doing so, each theory and their origins will be explained, the weaknesses of each theory will be explored, and the crimes that each theory can explain will be stated. Biological theories are interested in the inherited genetics of the individual to determine their predisposition to antisocial behaviour (Hirschi & Gottfredson 1990, p. 414). Cesare Lombroso, dubbed " the father of modern criminology " , popularised biological positivism during the nineteenth century. His general theory proposed that 'the criminal was born, not made' – he believed that atavistic criminals (people who are biologically inferior) were a reversion of the human specimen, having physical features similar to that apes and early man – such a person could be identified by examining their appearance and noting any physical abnormalities or 'stigmata' (White, Haines & Asquith 2012, p. 49).

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In the long past biology was applied in explaining deviant or criminal behaviour, and theories were formulated based on biological make-up of the individual. Over the years there has been a shift from biology focused theories in explaining criminality to genetic, social, and psychological account of the matter. This literature also explored the implications of different theoretical explanation to criminality.

Pratyush Upreti , Dr. Nirmal Kanti Chakraborty

Several genetic research characterize at aiming the existence of genetic influence on criminal behaviour. Studies on twins also play a major role in determining the existence of such theories. Physical and mental disorders too contribute to such behaviors. Nonetheless researchers also prove the effect of environment on the criminal behavior. In contrary to it researchers also prove that not in all cases that the relevant environmental factors do influence the criminal behavior. The interaction of genetic-environment factors also contribute majorly to such criminal behavior. The relevancy of such biological and environmental testimony can play a major role while dealing with criminal cases.

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Despite major advances in understanding the biological basis of human behaviour, the most popular theories of criminal behaviour remain restricted to those that consider only learning and social environmental variables. All of these strictly environmental theories have difficulty explaining why neurological, hormonal, and other biological factors would be related to criminal behaviour, yet evidence for links between such biological factors and criminality has grown. This article puts forward a theory that takes account of biological as well as environmental factors, and predicts that variables such as age, gender and social status will be associated with offending probabilities. It is argued that male sex hormones operating on the human brain increase the probability of competitive/victimizing behaviour. This type of behaviour (or behavioural tendency) is hypothesized to exist along a continuum, with ‘crude’ (criminal) forms at one end and ‘sophisticated’ (commercial) forms at the o...

International Journal of Law and Psychiatry

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BULLETIN OF THE PENITENTIARY ASSOCIATION OF UKRAINE

Andrzej Kacprzak , Krzysztof Pękala

Our research project is framed as biosocial analyzes of the etiology of criminal behavior. This research can be characterized as a conceptually and methodologically innovative attempt to connect the research areas that so far have not been considered together in one project. The research project is based on three components: sociological, psychological, and biological. The main aims of the article are to initiate and facilitate debate concerning the interaction between factors belonging to a variety of different areas and provide an interdisciplinary approach to the study of criminal behavior etiology. A discussion over proveniences of criminal behavior is needed both to enrich consciousness and knowledge about conditions lying under criminal behavior and to reshape practical solutions in the field of social rehabilitation. The article also concerns our methodological and ethical reflections coming from our pilotage project involvement. Keywords: criminal behavior etiology; biosocial criminology; GxE; aggressive crime.

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Criminal Behavior Research Paper

The causes of criminal behavior have been studied by many psychologists and sociologies who tried to identify what pushes people to commit crimes. Some of the researchers claim that delinquent behavior can be explained by the set of following factors: income level, social environment, and cultural background. If the first two factors have much described and analyzed, the cultural context remains at the point of debate.

This research aims to find out whether the ethnic origin can predict the possible criminal behavior. The target population chosen for investigation is Asians with the particular emphasis being made on psychopathic juveniles.

Concept/Population A lot has been written about the criminal behavior of African Americans, but not a lot of research has been done on Asians. The vast availability of research on Blacks can be explained – African Americans commit the majority of the juvenile crimes, and this tendency made sociologists think about the roots of such trend. Asians, similar to other minority as well as majority groups, commit crimes which can be explained in the same manner as the rest. The assumption is that crimes committed by Asians are different from the crimes committed by other ethnic groups. The question arises – whether the cultural background can impact the predisposition to delinquent behavior. In the course of this research paper, I will try to show that even though Asians commit somewhat different crimes, the delinquent behavior is the result of social interactions and environment they live in rather than cultural peculiarities.

This topic is of high importance, especially taking into account the events of September 11, when all Asian Americans became the potential suspect in criminal acts. The issue of discrimination is closely connected to the criminal behavior of Asians. If the research will prove that the criminal behavior can be predicted based on the nationality, then it would not be discriminatory to say that Asian youth is more open for violence than, for example, whites. However, if the research will conclude with the note that criminal behavior is learned or observed and there is no link between violence and culture, then any claims that Asians are more probable to become criminals are based on prejudice and discriminatory attitude towards this ethnic group.

The investigation of the Asian juveniles psychopath can help to understand the psychology of criminal behavior better. The research will identify the factors that play a significant role in violent activities and the literature review of several journal articles will support the idea that the nationality does not impact the youth to be violent or non-violent. Social factors are much more critical in addressing the juvenile psychopath while the nationality might only modify the direction of violence.

(By the way, you can get help with writing a research paper on criminal behavior topics here )

Background Asians are a minority group in the United States and their living standard is not much different from African American’s. The vast majority of Asian American belongs to the lower level of the social ladder; they have limited opportunities for education and further career advancement. The American society puts a particular over-valued emphasis on money and financial well-being importance in life; however, the means to achieve the desired goal are not equally distributed. As a result, Asian Americans share the American dream but are not able to achieve it with legitimate means. It is one of the roots of deviant behavior among Asians.

Moreover, the Asians are under-represented in the federal, state and local authorities. Therefore, the Asian youth understands that there is nobody to protect them and fight the way for equal opportunities. The only way for youth out of the difficult position is violence and crime. Through violent acts, Asian youth can get money, respect of friends and find the place in life, even if this place is behind the line of the law. They do realize that it is not right, but they feel that opportunities are distributed unequally and change this distribution with the only known to them effective method – crime.

The problem is not limited to Asian youth violence, but Asian gangs have a negative impact on society in general. One of the points is that Asian gangs are open only for Asians. It means that the person who is not Asian can rarely become the member of the gang. This notion has a deeper meaning – if the gang is located in the district where a lot Asian live, the community will think about all Asians as criminals. In this way, the community itself will encourage the increase of violence.

What exactly is an Asian gang? Asian, in particular, Vietnamese and Cambodian gangs, have started in emerge in the 1980s and represented the security problem on the street of California. Today most of the gangs’ activities have been suppressed, but many delinquent young Asians dream about the renewal of previous power. The Asian gang is typically large – from 50 to 200 members. Unlike African American gangs, Asians were rarely involved in drive-by shootings. In additions, they do not use any characterization like tattoos or hand signs which are typical for other street formations. Female gang members were not accepted, and there were only a few exceptions to this rule (Duffy, 2004).

Asian psychopaths tend to commit organized crimes like auto thefts, extortions, home robberies, and murders. Unlike African Americans who acted spontaneously, Asians tend to plan their activities. It has been estimated that currently there as many as 14,000 Asian gang members in California alone (Duffy, 2004). Asian gangs are especially known for their home robberies. In a typical home robbery, gang members do not wait until resident leaves, instead, they prefer residents to be home. Residents are tied up, terrorized, beaten, robbed and very often killed. Can this violence be explained by the cultural background of Asian gangs or other social factors have evolved into such cruelty?

Literature Review Gene Warfare Sheldon, the author of the article Gene Warfare, talks about the movement known as “eugenics”. The major point of this social movement was identification and elimination of “bad seeds” from American society (Sheldon, 2000, p. 162). Asian Americans were part of the target group for elimination. The author has the aim to inform the reader about this fact, and it is obvious that Sheldon does not support this point of view. For example, while expressing the opinion on the initiative to focus educational campaigns on infants, she notes that it was very “alarming” (Sheldon, 2000, p. 162). According to studies of the early 1990s the children of American minorities, including African American and Asians, were smaller-brained, slower to mature, less sexually restrained and more aggressive. Fortunately, such conclusion was not supported by people and was not proved scientifically. The modern science notifies that the violent behavior is learned rather than received from parents. It is not a unique case when the murder has very calm parents not able to hurt anybody while their grown-up child has killed a person. Sheldon says that these beliefs were racist and now people are more “enlightened” to be able to tell what an assumption is and what a fact is.

Culture as Sameness The author of the article Culture as Sameness: Toward a Synthetic View of Provocation and Culture in the Criminal Law John Sing looks at the criminal behavior of minority from a different perspective. He notes that the criminal behavior is the result of social pressure – immigrants are forced to assimilate themselves into the American culture, and those who are not very successful are labeled as delinquent. The topic of the article is quite unusual – the author is talking about the cultural defense and describes the framework used by jurists to adjudicate the cultural claims. Sing notes that the decision of the court might be influenced by nationality of the person who has committed the crime. For example, if two similar crimes were committed by the white and Asian persons, the Asian is more likely to get the more strict punishment only because he is Asian and believed to be more violent.

Asians of Influence The article Asians of Influence is about the attractiveness of anti-Asian campaigns of American politicians. O’Sullivan notes that the American society has created the stereotype of Asians being more violent. The author also criticizes the racial profiling used at airports and believes that this practice is not effective and discriminatory in its essence. This is what American society thins about Asians “they are crafty, deceitful, villainous and half-crazed automatons manipulated by evil rulers” (O’Sullivan, 1999, p. 22). The society has no right to penalize all Asian Americans for the sins of some Asians (recalling the events of September 11). O’Sullivan concludes that the judgments of Asians as being more psychopathic than other nations are purely based on discrimination and prejudicial attitude towards this ethnic group.

Triads and Tongs Team Up to Prey on Asian Populace Hanson pays special attention to organized crimes by Asian gangs including Chinese and Vietnamese groupings. The author mentions the hard position of Asians in America in the 1960s when the immigration laws have almost prohibited the movements of Asians into the United States. In some cases, the Asians who have traveled to their native country to see relatives could not return to their families in the United States. This immigration restriction was based on the assumption that Asian were highly criminal, while at those times, indeed, there have been numerous Asian gangs, in California for example, and the government did not find a more effective tool to reduce the crime level. Unfortunately, the society did not realize that the nationality cannot be the predictor of violent behavior – crimes are committed by all nationalities, despite ethnic origin.

The Social Construction of a Hate Crime Epidemic Henry, the author of the article The Social Construction of a Hate Crime Epidemic, says that Asian-Americans as violent and delinquent as any other nation. He notes that culture has the impact on personality development, but not a single culture teaches youth to commit crimes. Moreover, Henry puts Asian-Americans in one line with sexual minorities, feminists, and disabled who often become the victims of hate crimes. Therefore, the author reverses the problem – Asians are not offenders but victims. Also, the author claims that the society labels Asians as more violent similar to the labeling of gays as immoral. It is like a defensive reaction of society which does not understand these groups. “Although definitions vary from state to state, “hate crime” generally means a crime against persons or property motivated in whole or in part by racial, ethnic, religious, gender, sexual orientation and other prejudices!” (Henry & Jacobs, 1996, p. 366). The reader feels that the author is highly concerned with the issue and does not accept the judgment that Asian youth is more psychopathic.

Genetic Differences and Human Identities The article by Erik Parens is the research in which the author makes a note that the fact that genetic differences exist should not be denied, regardless of how complicated the truth is to understand. Parens thinks that people with specific cultural backgrounds are more open to violence. However, he also adds that all of his assumptions (which are based on references to more than 50 research) are generalized and do not necessarily apply to all representatives of the ethnic groups, Asians for example. Genetics helps society to understand that each of us possesses unique characteristics and there is no such thing as the genetics of one ethnic group. The delinquent behavior should be addressed from a social perspective and if the society wants to reduce the number of crimes people should look around and answer the question if they are also guilty in the situation when one person has to steal to have food or has to kill to feel satisfied. The author does not justify criminals; he just tries to knock to people’s heart for every reader to understand that criminals are not born, they learn criminal behavior from the environment.

In conclusion, the research has failed to collect the evidence that Asians are more violent than other ethnic groups. Instead, the research proves that predisposition to violence cannot be explained regarding cultural background. Asian culture does not teach youth how to commit crimes, and Asian psychopaths are violent because of the same factors are other youth. Criminal behavior is the result of a set of different social and political pressures. For example, Asians are pushed to get involved in criminal activities because the society is unable to provide the adequate environment in which Asians can gain the financial benefits. Therefore, Asian youth realizes that the government will not help them in life and they see the criminal activity as the only mean to get money.

For many decades the society tries to find the ways to identify the causes of crimes and all of the attempts fail. Why? This is the topic of the further research, but so far the society is moving in the wrong direction – violent behavior cannot be explained regarding cultural background, as it is often done with Asian psychopaths. This research paper is crucial because it falsifies one of the social assumptions that Asians are more violent only because they are Asians. I am sure that in the nearest future the American people will find new, more efficient ways to address criminal behavior. The first step is to abandon labeling of ethnic groups as inherently violent and create the environment in which all people, despite their ethnic origin, will be given equal opportunities to succeed in life.

When people label one Asians as criminal psychopaths, they do not realize that the feelings of the dignity of millions of Asians are hurt. Yes, it is hard to deny that some Asians have committed terrible crimes (like the attack on September 11), but it is not a valid basis to perceive all Asians as violent. Such generalization is not accepted and should not be tolerated because many pieces of research do not prove it. Science has no evidence to show that Asians have some genes that make them more violent. Asian-American youth has the same everyday problems as all youth in the world, but they have to face many social pressures and not all of them can cope with hardships. The young people of any other ethnic origin would react in the same way.

References Duffy, M. (2004). Teen Gangs: A Global View. Westport: Greenwood Press. Hanson, G. (1997). Triads and Tongs Team Up to Prey on Asian Populace. Insight on the News, 13 (11), 18+. Henry, J. S. & Jacobs, J. B. (1996). The Social Construction of a Hate Crime Epidemic. Journal of Criminal Law and Criminology, 86 (2), 366-391. O’Sullivan, J. (1999). Asians of Influence. National Review, 51 (12), 22. Parens, E. (2004). Genetic Differences and Human Identities: On Why Talking about Behavioral Genetics Is Important and Difficult. The Hastings Center Report, 34 (1), 1+. Shelden, R. G. (2000). Gene Warfare. Social Justice, 27 (2), 162. Sing, J. J. (1999). Culture as Sameness: Toward a Synthetic View of Provocation and Culture in the Criminal Law. Yale Law Journal, 108 (7), 1845-1884.

*** As far as you know this criminal behavior research project sample is plagiarized. If you need an original research paper or proposal on this topic, you can hire professional writers online at this site: https://writemypaperhub.com/research-paper.html .