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Journal ArticleDOI: 10.1080/01639625.2019.1673924

Why Monitoring Doesn’t Always Matter: The Interaction of Personal Propensity with Physical and Psychological Parental Presence in a Situational Explanation of Adolescent Offending

04 Mar 2021-Deviant Behavior (Routledge)-Vol. 42, Iss: 3, pp 329-352
Abstract: Parental monitoring is often shown to have a negative relationship with crime involvement. However, research often ignores both the mechanism by which these relationships occur and the conditions u...

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Book ChapterDOI: 10.1007/978-3-030-46194-2_1
Beth Hardie1Institutions (1)
01 Jan 2020-
Abstract: This chapter describes the problems and solutions that justify the need for appropriate analysis of the convergence of people in environments to explain action. Thus, it justifies the research approach and methods that are described and explained in this volume.

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2 Citations


Journal ArticleDOI: 10.1177/0734016820949641
Helmut Hirtenlehner1, Sonja Schulz2Institutions (2)
Abstract: Research on differential deterrability suggests increasingly that the size of a potential sanction risk effect is conditional on characteristics of the person and properties of the setting. Whether...

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2 Citations


Open accessJournal ArticleDOI: 10.1007/S40865-021-00161-Z
Georg Kessler1, Jost Reinecke1Institutions (1)
01 Jun 2021-
Abstract: According to the Developmental Ecological Action Model (DEA) of the situational action theory (SAT), changes in crime rates over the life-course are explained through personal (moral) maturation and socio-ecological selection. This assumption is empirically tested by comparing results for the conditioning effect of the principle of moral correspondence (as an essential part of SAT’s perception-choice process) on crime rates for the transition from adolescence to adulthood. Comparing two waves of a German longitudinal study (CrimoC, 17 and 26 years old, n = 1738), a series of logistic and multinomial logistic regressions and ensuing estimated transition probabilities capture the cross-sectional but also developmental processes involved. Additionally, the CrimoC study offers a differential analysis of offending scales, separating offenses into youth and adult crimes. The principle’s conditioning effect on crime could be replicated at both times. We can observe a general trend of individual transitions, which correspond to predicted personal maturation and socio-ecological selection. The transitions correlate with the expected reduction in crime rates over time. Males and females show comparable results. The separation into different offending scales yielded tentative insights. We found stability in the mechanisms leading to crime as proffered by SAT and DEA across time. Personal (moral) maturation and socio-ecological selection are likely to be the driving forces behind reducing crime in adulthood. Future research needs to explain in detail how life-course events influence these factors. Considering adult crimes in the analysis is a promising endeavor that warrants further investigation.

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Topics: Life course approach (51%)

1 Citations



Journal ArticleDOI: 10.1007/S10940-021-09503-Y
Abstract: The goal of the study is to address several voids in the literature investigating Situational Action Theory (SAT) by: (1) testing the role of parenting as a precursor to individual moral rules and self-control; (2) investigating the interplay between personal moral rules, self-control, and rules of school settings; and (3) exploring the generalizability of these theoretical links across western and nonwestern country clusters. Using unique data from the third iteration of the International Self-Reported Delinquency Survey (ISRD-3) of 28 different countries, this investigation puts SAT to the test in school settings by estimating a series of two-level mixed effect negative binomial linear regression models to assess the effects of SAT-relevant individual and school-level predictors on offending. The results suggest a moderately strong performance of SAT in western and nonwestern countries. Specifically, parenting acts as a partial precursor to individual moral rules and self-control. Furthermore, some SAT-relevant predictors are equally potent as crime predictors across western and nonwestern countries. However, the effects of moral rules significantly differ across western and nonwestern country clusters. Moral rules also appear to interact significantly with self-control and a number of contextual factors in their effects on delinquency. The results of the study point to the cultural generalizability of SAT but warrant further investigation as some differences in the findings across clusters exist.

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Topics: Generalizability theory (51%)

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Open accessJournal ArticleDOI: 10.1037/0022-3514.51.6.1173
Abstract: In this article, we attempt to distinguish between the properties of moderator and mediator variables at a number of levels. First, we seek to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating, both conceptually and strategically, the many ways in which moderators and mediators differ. We then go beyond this largely pedagogical function and delineate the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena, including control and stress, attitudes, and personality traits. We also provide a specific compendium of analytic procedures appropriate for making the most effective use of the moderator and mediator distinction, both separately and in terms of a broader causal system that includes both moderators and mediators.

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Topics: Moderation (56%), Sobel test (51%), Moderated mediation (51%)

73,170 Citations


Open accessBook
01 Jan 1975-
Abstract: Contents: Preface. Introduction. Bivariate Correlation and Regression. Multiple Regression/Correlation With Two or More Independent Variables. Data Visualization, Exploration, and Assumption Checking: Diagnosing and Solving Regression Problems I. Data-Analytic Strategies Using Multiple Regression/Correlation. Quantitative Scales, Curvilinear Relationships, and Transformations. Interactions Among Continuous Variables. Categorical or Nominal Independent Variables. Interactions With Categorical Variables. Outliers and Multicollinearity: Diagnosing and Solving Regression Problems II. Missing Data. Multiple Regression/Correlation and Causal Models. Alternative Regression Models: Logistic, Poisson Regression, and the Generalized Linear Model. Random Coefficient Regression and Multilevel Models. Longitudinal Regression Methods. Multiple Dependent Variables: Set Correlation. Appendices: The Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elements. Determination of the Inverse Matrix and Applications Thereof.

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Topics: Segmented regression (73%), Regression diagnostic (70%), Local regression (68%) ... show more

29,559 Citations


Open accessBook ChapterDOI: 10.4324/9781439817803-12
Abstract: In this paper we present a "routine activity approach" for analyzing crime rate trends and cycles. Rather than emphasizing the characteristics of offenders, with this approach we concentrate upon the circumstances in which they carry out predatory criminal acts. Most criminal acts require convergence in space and time of likely offenders, suitable targets and the absence of capable guardians against crime. Human ecological theory facilitates an investigation into the way in which social structure produces this convergence, hence allowing illegal activities to feed upon the legal activities of everyday life. In particular, we hypothesize that the dispersion of activities away from households and families increases the opportunity for crime and thus generates higher crime rates. A variety of data is presented in support of the hypothesis, which helps explain crime rate trends in the United States 1947-1974 as a byproduct of changes in such variables as labor force participation and single-adult households.

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Topics: Crime displacement (66%), Crime science (66%), Crime prevention (65%) ... show more

6,928 Citations


MonographDOI: 10.1002/0471249688
Abstract: Preface. 1. Introduction: Distributions and Inference for Categorical Data. 1.1 Categorical Response Data. 1.2 Distributions for Categorical Data. 1.3 Statistical Inference for Categorical Data. 1.4 Statistical Inference for Binomial Parameters. 1.5 Statistical Inference for Multinomial Parameters. Notes. Problems. 2. Describing Contingency Tables. 2.1 Probability Structure for Contingency Tables. 2.2 Comparing Two Proportions. 2.3 Partial Association in Stratified 2 x 2 Tables. 2.4 Extensions for I x J Tables. Notes. Problems. 3. Inference for Contingency Tables. 3.1 Confidence Intervals for Association Parameters. 3.2 Testing Independence in Two Way Contingency Tables. 3.3 Following Up Chi Squared Tests. 3.4 Two Way Tables with Ordered Classifications. 3.5 Small Sample Tests of Independence. 3.6 Small Sample Confidence Intervals for 2 x 2 Tables . 3.7 Extensions for Multiway Tables and Nontabulated Responses. Notes. Problems. 4. Introduction to Generalized Linear Models. 4.1 Generalized Linear Model. 4.2 Generalized Linear Models for Binary Data. 4.3 Generalized Linear Models for Counts. 4.4 Moments and Likelihood for Generalized Linear Models . 4.5 Inference for Generalized Linear Models. 4.6 Fitting Generalized Linear Models. 4.7 Quasi likelihood and Generalized Linear Models . 4.8 Generalized Additive Models . Notes. Problems. 5. Logistic Regression. 5.1 Interpreting Parameters in Logistic Regression. 5.2 Inference for Logistic Regression. 5.3 Logit Models with Categorical Predictors. 5.4 Multiple Logistic Regression. 5.5 Fitting Logistic Regression Models. Notes. Problems. 6. Building and Applying Logistic Regression Models. 6.1 Strategies in Model Selection. 6.2 Logistic Regression Diagnostics. 6.3 Inference About Conditional Associations in 2 x 2 x K Tables. 6.4 Using Models to Improve Inferential Power. 6.5 Sample Size and Power Considerations . 6.6 Probit and Complementary Log Log Models . 6.7 Conditional Logistic Regression and Exact Distributions . Notes. Problems. 7. Logit Models for Multinomial Responses. 7.1 Nominal Responses: Baseline Category Logit Models. 7.2 Ordinal Responses: Cumulative Logit Models. 7.3 Ordinal Responses: Cumulative Link Models. 7.4 Alternative Models for Ordinal Responses . 7.5 Testing Conditional Independence in I x J x K Tables . 7.6 Discrete Choice Multinomial Logit Models . Notes. Problems. 8. Loglinear Models for Contingency Tables. 8.1 Loglinear Models for Two Way Tables. 8.2 Loglinear Models for Independence and Interaction in Three Way Tables. 8.3 Inference for Loglinear Models. 8.4 Loglinear Models for Higher Dimensions. 8.5 The Loglinear Logit Model Connection. 8.6 Loglinear Model Fitting: Likelihood Equations and Asymptotic Distributions . 8.7 Loglinear Model Fitting: Iterative Methods and their Application . Notes. Problems. 9. Building and Extending Loglinear/Logit Models. 9.1 Association Graphs and Collapsibility. 9.2 Model Selection and Comparison. 9.3 Diagnostics for Checking Models. 9.4 Modeling Ordinal Associations. 9.5 Association Models . 9.6 Association Models, Correlation Models, and Correspondence Analysis . 9.7 Poisson Regression for Rates. 9.8 Empty Cells and Sparseness in Modeling Contingency Tables. Notes. Problems. 10. Models for Matched Pairs. 10.1 Comparing Dependent Proportions. 10.2 Conditional Logistic Regression for Binary Matched Pairs. 10.3 Marginal Models for Square Contingency Tables. 10.4 Symmetry, Quasi symmetry, and Quasiindependence. 10.5 Measuring Agreement Between Observers. 10.6 Bradley Terry Model for Paired Preferences. 10.7 Marginal Models and Quasi symmetry Models for Matched Sets . Notes. Problems. 11. Analyzing Repeated Categorical Response Data. 11.1 Comparing Marginal Distributions: Multiple Responses. 11.2 Marginal Modeling: Maximum Likelihood Approach. 11.3 Marginal Modeling: Generalized Estimating Equations Approach. 11.4 Quasi likelihood and Its GEE Multivariate Extension: Details . 11.5 Markov Chains: Transitional Modeling. Notes. Problems. 12. Random Effects: Generalized Linear Mixed Models for Categorical Responses. 12.1 Random Effects Modeling of Clustered Categorical Data. 12.2 Binary Responses: Logistic Normal Model. 12.3 Examples of Random Effects Models for Binary Data. 12.4 Random Effects Models for Multinomial Data. 12.5 Multivariate Random Effects Models for Binary Data. 12.6 GLMM Fitting, Inference, and Prediction. Notes. Problems. 13. Other Mixture Models for Categorical Data . 13.1 Latent Class Models. 13.2 Nonparametric Random Effects Models. 13.3 Beta Binomial Models. 13.4 Negative Binomial Regression. 13.5 Poisson Regression with Random Effects. Notes. Problems. 14. Asymptotic Theory for Parametric Models. 14.1 Delta Method. 14.2 Asymptotic Distributions of Estimators of Model Parameters and Cell Probabilities. 14.3 Asymptotic Distributions of Residuals and Goodnessof Fit Statistics. 14.4 Asymptotic Distributions for Logit/Loglinear Models. Notes. Problems. 15. Alternative Estimation Theory for Parametric Models. 15.1 Weighted Least Squares for Categorical Data. 15.2 Bayesian Inference for Categorical Data. 15.3 Other Methods of Estimation. Notes. Problems. 16. Historical Tour of Categorical Data Analysis . 16.1 Pearson Yule Association Controversy. 16.2 R. A. Fisher s Contributions. 16.3 Logistic Regression. 16.4 Multiway Contingency Tables and Loglinear Models. 16.5 Recent and Future? Developments. Appendix A. Using Computer Software to Analyze Categorical Data. A.1 Software for Categorical Data Analysis. A.2 Examples of SAS Code by Chapter. Appendix B. Chi Squared Distribution Values. References. Examples Index. Author Index. Subject Index. Sections marked with an asterisk are less important for an overview.

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Topics: Categorical variable (64%), Quasi-likelihood (61%), Marginal model (61%) ... show more

4,319 Citations


Journal ArticleDOI: 10.1037/0033-2909.114.2.376
Gary H. McClelland1, Charles M. JuddInstitutions (1)
Abstract: Although interaction effects are frequently found in experimental studies, field researchers report considerable difficulty in finding theorized moderator effects. Previous discussions of this discrepancy have considered responsible factors including differences in measurement error and use of nonlinear scales. In this article we demonstrate that the differential efficiency of experimental and field tests of interactions is also attributable to the differential residual variances of such interactions once the component main effects have been partialed out. We derive an expression for this residual variance in terms of the joint distribution of the component variables and explore how properties of the distribution affect the efficiency of tests of moderator effects. We show that tests of interactions in field studies will often have less than 20% of the efficiency of optimal experimental tests, and we discuss implications for the design of field studies.

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3,010 Citations