The Experts below are selected from a list of 9489 Experts worldwide ranked by ideXlab platform
Dermot Feenan - One of the best experts on this subject based on the ideXlab platform.
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legal issues in acquiring information about illegal behaviour through Criminological Research
British Journal of Criminology, 2002Co-Authors: Dermot FeenanAbstract:physical circumstances, 'this has not been matched by comparable advances in our understanding of the people responsible for most of these offences. For example, we know very relatively little about how they make decisions on where and when to commit (or not to commit) particular kinds of crime . . (p. 121). Researching such decisions or related activities may entail Researchers obtaining information which the law regards as necessary to disclose to the police. This may arise in the United Kingdom under a range of legislation, including the Criminal Law Act 1967, Prevention of Terrorism (Temporary Provisions) Act 1989, Terrorism Act 2000, Police and Criminal Evidence Act 1984, Contempt of Court Act 1981 and Official Secrets Acts 1911 to 1983. The risk of information being sought by the police or courts is real. Attempts to compel disclosure of Researchers' records has been reported mainly in the United States (Knerr 1982; Marshall 1993), with at least two attempts in the UK (Lee 1993; Norton Taylor 2000). The fact that in the UK journalists have been targeted frequendy should not lull academic Researchers. The government seems increasingly to be using a raft of legislation to force disclosure or keep secret information to serve its own interests. Subpoenas in the US have targeted academic Research on petty crime by young people, sex Research through observation of a sadomasochistic video, study of police social ization patterns through participant observation, and study of behaviour and medical treatment of alleged sex-crime victims (Knerr 1982). Leo (1995) was compelled by court order to hand over his field notes on police interrogations of a felony subject. Scarce (1994,1995) was jailed for refusing to obey a court order to release the names of confi dential informants obtained through his field notes on environmental activism. It may also arise where the Research, primarily participant observation, leads the Researcher to witness offences such as in Research on drug dealing (Adler 1993) but also in Research where the offence is incidental to the primary purpose of the Research. For instance,
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legal issues in acquiring information about illegal behaviour through Criminological Research
Social Science Research Network, 2002Co-Authors: Dermot FeenanAbstract:One of the challenges in conducting Criminological Research involves addressing and understanding the law regarding protection and disclosure of sources and/or information in respect of offences or intentions to commit offences. This paper presents how this challenge has been addressed by Researchers in criminology and examines a range of issues involved. The paper sets out the legal position in the United Kingdom and offers a number of suggestions for addressing the legal and other issues arising.
Kevin M Beaver - One of the best experts on this subject based on the ideXlab platform.
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a biosocial analysis of the sources of missing data in Criminological Research
Journal of Criminal Justice, 2014Co-Authors: Joseph A Schwartz, Kevin M BeaverAbstract:Abstract Purpose Failing to deal with missing data patterns effectively may result in biased parameter estimates and ultimately may produce inaccurate results and conclusions. The vast majority of Criminological Research has addressed this issue with listwise deletion (LD) and multiple imputation (MI) techniques. Identifying the specific covariates that directly contribute to patterns of missingness is highly important in deciding which technique to use. One of the more surprising omissions from the identified list of covariates is the potential role of genetic influences in the development of missingness. Methods The current study addresses this gap in the literature by estimating genetic (A), shared environmental (C), and the nonshared environmental (E) influences on missingness across measures of delinquency and self-control within a longitudinal sample of twin and sibling pairs. Results The results indicated that genetic influences explain a significant portion of the variance in missing values related to both delinquency and self-control. Conclusions Current methodological techniques aimed at addressing missing data should be amended to take genetic influences into account. Such modifications and the implications of the findings for future Research are discussed.
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On the consequences of ignoring genetic influences in Criminological Research
Journal of Criminal Justice, 2014Co-Authors: J. C. Barnes, Kevin M Beaver, Brian B. Boutwell, Chris L. Gibson, John Paul WrightAbstract:article i nfo Available online xxxx Purpose: Many Criminological scholars explore the social causes of crime while giving little consideration to the possibility that genetic factors underlie the observed associations. Indeed, the standard social science method (SSSM) assumes genetic influences do not confound the association between X and Y. Yet, a nascent stream of evidence has questioned the validity of this approach by revealing many Criminological variables are at least partially affected by genetic influences. As a result, a substantial proportion of the literature may be misspecified duetouncontrolledgeneticfactors.Noefforthasbeenmade to directlyestimate theextenttowhichgeneticcon- founding has biased the associations presented in Criminological studies. Methods: The present study seeks to address this issue by drawing on simulated datasets. Results/Conclusions: Results suggest genetic confounding may account for a negligible portion of the relationship between X and Y when their correlation (ryx) is larger than the correlation between genetic factors and Y (i.e., ryx N ryg). Genetic confounding appears to be much more problematic when the correlation between X and Y is in the moderate-to-small range (e.g., ryx = .20) and the genetic effect is in the moderate-to-large range (e.g., ryg ≥ .30).
Raymond Paternoster - One of the best experts on this subject based on the ideXlab platform.
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missing data problems in Criminological Research
2010Co-Authors: Robert Brame, Michael G Turner, Raymond PaternosterAbstract:Missing data problems are a ubiquitous challenge for criminology and criminal justice Researchers (Brame and Paternoster 2003). Regardless of whether Researchers are working with survey data or data collected from official agency records (or other sources), they will inevitably have to confront data sets with gaps and holes. As a result, Researchers who design studies must take whatever steps that are feasibly and ethically possible to maximize the coverage and completeness of the data they will collect. Even with the best planning and implementation, however, nearly all studies will come up short. Records will be incomplete, targeted survey participants will not be located, some who are located will not participate, and some who participate will not participate fully. These common pitfalls result in a problem most Researchers face, in that we want to use the data sets we collect to form inferences about an entire target population – not just the subset of that population for whom valid data are available.
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missing data problems in Criminological Research two case studies
Journal of Quantitative Criminology, 2003Co-Authors: Robert Brame, Raymond PaternosterAbstract:This paper considers the problem of missing data in two circumstances commonly confronted by criminologists. In the first circumstance, there is missing data due to subject attrition—some cases drop out of a study. In this context, analysts are frequently interested in examining the association between an independent variable measured at time t(xt) and an outcome variable that is measured at time t + 1(yt + 1); the problem is that the outcome variable is only observed for those cases which do not drop out of the study. In the second circumstance there is missing data on an independent variable of interest for typical reasons (i.e., the respondent did not wish to answer a question or could not be located). In this case, Researchers are interested in estimating the association between the independent variable with missing data and an outcome variable that is fully observed. Criminologists often handle these two missing data problems by conducting analyses on the subsample of observations with complete data. In this paper, we explore this problem with two case studies and we then illustrate the use of methods that directly address the uncertainty produced by missing data.
Robert Brame - One of the best experts on this subject based on the ideXlab platform.
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missing data problems in Criminological Research
2010Co-Authors: Robert Brame, Michael G Turner, Raymond PaternosterAbstract:Missing data problems are a ubiquitous challenge for criminology and criminal justice Researchers (Brame and Paternoster 2003). Regardless of whether Researchers are working with survey data or data collected from official agency records (or other sources), they will inevitably have to confront data sets with gaps and holes. As a result, Researchers who design studies must take whatever steps that are feasibly and ethically possible to maximize the coverage and completeness of the data they will collect. Even with the best planning and implementation, however, nearly all studies will come up short. Records will be incomplete, targeted survey participants will not be located, some who are located will not participate, and some who participate will not participate fully. These common pitfalls result in a problem most Researchers face, in that we want to use the data sets we collect to form inferences about an entire target population – not just the subset of that population for whom valid data are available.
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missing data problems in Criminological Research two case studies
Journal of Quantitative Criminology, 2003Co-Authors: Robert Brame, Raymond PaternosterAbstract:This paper considers the problem of missing data in two circumstances commonly confronted by criminologists. In the first circumstance, there is missing data due to subject attrition—some cases drop out of a study. In this context, analysts are frequently interested in examining the association between an independent variable measured at time t(xt) and an outcome variable that is measured at time t + 1(yt + 1); the problem is that the outcome variable is only observed for those cases which do not drop out of the study. In the second circumstance there is missing data on an independent variable of interest for typical reasons (i.e., the respondent did not wish to answer a question or could not be located). In this case, Researchers are interested in estimating the association between the independent variable with missing data and an outcome variable that is fully observed. Criminologists often handle these two missing data problems by conducting analyses on the subsample of observations with complete data. In this paper, we explore this problem with two case studies and we then illustrate the use of methods that directly address the uncertainty produced by missing data.
Joshua D Angrist - One of the best experts on this subject based on the ideXlab platform.
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Instrumental variables methods in experimental Criminological Research: what, why and how
Journal of Experimental Criminology, 2006Co-Authors: Joshua D AngristAbstract:Quantitative criminology focuses on straightforward causal questions that are ideally addressed with randomized experiments. In practice, however, traditional randomized trials are difficult to implement in the untidy world of criminal justice. Even when randomized trials are implemented, not everyone is treated as intended and some control subjects may obtain experimental services. Treatments may also be more complicated than a simple yes/no coding can capture. This paper argues that the instrumental variables methods (IV) used by economists to solve omitted variables bias problems in observational studies also solve the major statistical problems that arise in imperfect Criminological experiments. In general, IV methods estimate causal effects on subjects who comply with a randomly assigned treatment. The use of IV in criminology is illustrated through a re-analysis of the Minneapolis domestic violence experiment. The results point to substantial selection bias in estimates using treatment delivered as the causal variable, and IV estimation generates deterrent effects of arrest that are about one-third larger than the corresponding intention-to-treat effects.
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instrumental variables methods in experimental Criminological Research what why and how
Journal of Experimental Criminology, 2006Co-Authors: Joshua D AngristAbstract:Quantitative criminology focuses on straightforward causal questions that are ideally addressed with randomized experiments. In practice, however, traditional randomized trials are difficult to implement in the untidy world of criminal justice. Even when randomized trials are implemented, not everyone is treated as intended and some control subjects may obtain experimental services. Treatments may also be more complicated than a simple yes/no coding can capture. This paper argues that the instrumental variables methods (IV) used by economists to solve omitted variables bias problems in observational studies also solve the major statistical problems that arise in imperfect Criminological experiments. In general, IV methods estimate the causal effect of treatment on subjects that are induced to comply with a treatment by virtue of the random assignment of intended treatment. The use of IV in criminology is illustrated through a re-analysis of the Minneapolis Domestic Violence Experiment.