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Using Multivariate Statistics

Basics of qualitative research: Grounded theory procedures and techniques

世界経済・社会統計 = World development indicators

计量经济分析 = Econometric analysis

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Criminologists are often interested in examining interactive effects within a regression context. For example, “holding other relevant factors constant, is the effect of delinquent peers on one's own delinquent conduct the same for males and females?” or “is the effect of a given treatment program comparable between first-time and repeat offenders?” A frequent strategy in examining such interactive effects is to test for the difference between two regression coefficients across independent samples. That is, does b1= b2? Traditionally, criminologists have employed a t or z test for the difference between slopes in making these coefficient comparisons. While there is considerable consensus as to the appropriateness of this strategy, there has been some confusion in the criminological literature as to the correct estimator of the standard error of the difference, the standard deviation of the sampling distribution of coefficient differences, in the t or z formula. Criminologists have employed two different estimators of this standard deviation in their empirical work. In this note, we point out that one of these estimators is correct while the other is incorrect. The incorrect estimator biases one's hypothesis test in favor of rejecting the null hypothesis that b1= b2. Unfortunately, the use of this incorrect estimator of the standard error of the difference has been fairly widespread in criminology. We provide the formula for the correct statistical test and illustrate with two examples from the literature how the biased estimator can lead to incorrect conclusions.

Using the correct statistical test for the equality of regression coefficients

Criminal careers have long occupied the imaginations of criminologists. Since the 1986 publication of the National Academy of Sciences report on criminal careers and career criminals, a variety of theoretical, empirical, and policy issues have surfaced. Data on key criminal career dimensions of prevalence, frequency, specialization, and desistance have raised theoretical questions regarding the patterning of criminal activity over the life course. Recent research has identified important methodological issues, including the relationship between past and future criminal activity, and potential explanations for this relationship: state dependence and persistent heterogeneity. Advanced statistical techniques have been developed to address these challenges. Criminal career research has identified important policy issues such as individual prediction of offending frequency and career duration, and has shifted the focus toward the interplay between risk and protective factors.

The Criminal Career Paradigm

On the overlap between victimization and offending: A review of the literature

Worldwide, criminologists have long been interested in the longitudinal patterning of criminal activity. Recently, methodological and statistical advances have “caught up” with longitudinal data and have provided criminologists with a unique window within which to study, document, and understand developmental trajectories of criminal activity. One such technique, the trajectory procedure, allows researchers to study howcriminal activity changes over time in a group-based framework. This methodology is well suited for studying crime over the life course because there may be different groups of offenders, their offending trajectories may exhibit different shapes at different ages, and they may be differentially affected by distinct factors. This paper presents an overview of the trajectory methodology, outlines its strengths and weaknesses, and summarizes key conclusions of the well over 80 studies that have used this technique. It concludes by pointing to several future research directions.

Taking Stock of Developmental Trajectories of Criminal Activity over the Life Course

This paper builds on work by Nagin and Paternoster in which they contend that two recent developments in criminological theory, self-control and rational choice, have been explored separately rather than in conjunction with one another. In their analysis, Nagin and Paternoster found direct effects for variables from each of these theories and called for more research into simultaneous examination of the two. We build on their work by delineating a more highly specified model of rational offending, in which we observe that the research thus far has not examined the indirect effects of low self-control. We believe that this area is grossly underdeveloped and that such an examination is necessary for a more complete understanding of criminal offending. We advance three hypotheses concerning the integration of low self-control into a rational choice framework: (1) that low self-control will have both direct and indirect effects via situational characteristics on intentions to shoplift and drive drunk; (2) tha...

Specifying the direct and indirect effects of low self-control and situational factors in offenders' decision making: Toward a more complete model of rational offending

Alex R. Piquero

Papers

Using the correct statistical test for the equality of regression coefficients

The Criminal Career Paradigm

On the overlap between victimization and offending: A review of the literature

Taking Stock of Developmental Trajectories of Criminal Activity over the Life Course

Specifying the direct and indirect effects of low self-control and situational factors in offenders' decision making: Toward a more complete model of rational offending