Is it all relative? Proposing the use of marginal effects for meta-analysis of binary outcomes in criminology and criminal justice
Bibliographic Data
| ID | 6434613 |
|---|---|
| Authors | Kevin Petersen (0000-0001-9212-3726, corresponding author) |
| Year | 2025 |
| Volume | 101 |
| Pages | 102536-102536 |
| Publication date | 2025-10-15 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Criminal Justice (JOURNAL) |
| Journal identifiers | ISSN: 0047-2352 • E-ISSN: 1873-6203 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.jcrimjus.2025.102536 |
| OpenAlex | W4415184995 |
| Language | EN |
| Citations received | 1 |
| References cited | 51 |
Advanced Causal Inference Techniques · Meta-analysis and systematic reviews · Statistical Methods and Bayesian Inference
Cochrane Handbook for Systematic Reviews of Interventions
Some benefits of dichotomization in psychiatric and criminological research
The positive effects of cognitive–behavioral programs for offenders
Log Odds and the Interpretation of Logit Models
Using Effect Size—or Why the P Value Is Not Enough
Marginal Effects—Quantifying the Effect of Changes in Risk Factors in Logistic Regression Models
Using odds ratios as effect sizes for meta-analysis of dichotomous data
Ex-offender employment programs and recidivism
Interaction terms in logit and probit models
The Relative Incident Rate Ratio Effect Size for Count-Based Impact Evaluations
The Effects of Halfway Houses on Criminal Recidivism
A Meta-Analysis of Race and Sentencing Research
Estimation of Limited Dependent Variable Models With Dummy Endogenous Regressors
Best Practices for Estimating, Interpreting, and Presenting Nonlinear Interaction Effects
The effectiveness of restorative justice programs
Measuring effectiveness
Measures of effectiveness in medical research
Inconvenient truths about logistic regression and the remedy of marginal effects
Making the Most of Statistical Analyses
Comparing Logit and Probit Coefficients Across Groups
Using Predictions and Marginal Effects to Compare Groups in Regression Models for Binary Outcomes
On Group Comparisons With Logistic Regression Models
Logistic Regression
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |