Search results

In the evaluation of most interventions in criminal justice settings, evaluators have no control over assignment to treatment and control/comparison conditions, which means that the treated and comparison groups may have differences that lead to biased conclusions regarding treatment effectiveness. Propensity score analysis can be used to balance the differences in the groups, which can be used in a number of ways to reduce biased conclusions regarding effectiveness. A review of propensity scoring studies was conducted for this chapter, where the limited number of evaluations of criminal justice interventions using these methods was identified. Due to the small number of these studies, research was also reviewed if propensity scoring had been employed to evaluate interventions that are similar to those in criminal justice systems. These studies are used as examples to demonstrate how the methods can be used to evaluate criminal justice interventions, the different ways propensity scores can be used to analyse treatment and comparison group differences, and the strengths and limitations of this approach. It is concluded that, while not appropriate for all interventions/settings, propensity score analysis can be useful in criminal justice arenas, at least to investigate the comparability of treatment and comparison groups, with suspected non-comparability being a common weakness of traditional quasi-experimental studies and frequently cited limitation in terms of drawing efficacy conclusions from such evaluations.

The purpose of this paper is to illustrate how to examine the effectiveness of a pilot summer bridge program for elementary algebra using propensity scores. Typically, selection into treatment programs, such as summer bridge programs, is based on self-selection. Self-selection makes it very difficult to estimate the true treatment effect because the selection process itself often introduces a source of bias.

By using propensity scores, the authors can match students who participated in the summer bridge program with equivalent students who did not participate in the summer bridge program. By matching students in the treatment group to equivalent students who do not participate in the treatment, the authors can obtain an unbiased estimate of the treatment effect. The authors also describe a method to conduct a sensitivity analysis to estimate the amount of hidden bias generated from unobserved factors that would be needed to alter the inferences made from a propensity score matching analysis.

Findings suggest there is no significant difference in the pass rates of the subsequent intermediate algebra course for students who participated in the summer bridge program when compared to matched students who did not participate in the summer bridge program. Thus, students who participate in the summer bridge program fared no better or worse when compared to similar students who do not participate in the program. These findings also appear to be robust to hidden bias.

This study describes a unique way to estimate the causal effect of participating in a treatment program when there is self-selection into the treatment program.

This paper aims to introduce matching in propensity score analysis (PSA) as an alternative statistical approach for researchers looking to make causal inferences using intact groups.

An illustrative example demonstrated the varying results of analysis of variance, analysis of covariance and PSA on a heuristic data set. The three approaches were compared by results and violations of statistical assumptions.

Through the illustrative example, it is demonstrated how different statistical approaches can produce varied results. Only PSA mitigated pre-existing group differences without violating the assumption of independence.

This paper attempts to answer calls in the literature for more robust statistical methodologies to better inform human resource development practice and theory.

Without controlling for selection bias and the potential endogeneity of the treatment by using proper methods, the estimation of treatment effect could lead to biased or incorrect conclusions. However, these issues are not addressed adequately and properly in higher education research. This study reviews the essence of self-selection bias, treatment assignment endogeneity, and treatment effect estimation. We introduce three treatment effect estimators – propensity score matching analysis, doubly robust estimation (augmented inverse probability weighted approach), and endogenous treatment estimator (control-function approach) – and examine literature that applies these methods to research in higher education. We then use the three methods in a case study that estimates the effects of transfer student pre-enrollment debt on persistence and first year grades. The final discussion provides guidelines and recommendations for causal inference research studies that use such quasi-experimental methods.

This study examines the relationship between small-firm managers' propensity to participate in a growth-oriented training program and their subsequent program outcome in terms of strategic reorientation. From a policy perspective, this relates to the important question of what benefit would come from recruiting managers who are normally not easily recruitable for training programs.

A control group design including pre- and post-training surveys is used to assess the effects of a large-scale management training program. Accounting for selection bias, the difference-in-difference method, together with propensity score matching, was applied to assess average program effects. The matching-smoothing method was used to assess heterogeneity in program effects associated with participation propensity.

Overall, program participation associated positively with change in strategic orientations. This effect was especially pronounced for managers with either low or medium to high inclinations for program enrollment, while diminishing in the modest to medium range.

The findings have important practical implications for selection of target groups and recruitment strategies in relation to small-firm management training programs. From the results, recruitment strategies may effectively include managers with either high or low participation propensities, rather than aiming to “fill up” with managers with moderately low participation propensity.

Several extant studies have examined average treatment effects from small-firm training programs. Yet there has been a lack of examination of the extent to which participation propensity modifies the effect of training on outcomes. This study brings new knowledge of the direction and magnitude of such heterogeneous training effects.

The current study seeks to assess the predictive utility of personality, family violence, associations with criminal friends, peer rejection, parental attachment, and parental supervision as predictors of homicidal behaviour among a sample of 144 male recidivistic offenders.

This research project utilized a quasi‐experimental design with propensity score matching in order to minimize the effect of selection bias. Post‐matching binary logistic regression analysis was subsequently conducted in order to determine what factors predict homicidal behaviour.

Post‐matching regression results indicated that experience of family violence, psychoticism, and parental attachments were significant predictors of being a homicidal murderer.

The findings provide strong empirical support for the important role of early childhood experiences in the prediction of homicidal acts, along with the crucial role of personality (psychoticism). These findings provide additional support for Eysenck's theoretical indications regarding the role of psychoticism in the prediction of violent criminal behaviours.

In this chapter, we evaluate the employment effects of job-creation schemes (JCS) on the participating individuals in Germany. JCS are a major element of active labour market policy in Germany and are targeted at long-term unemployed and other hard-to-place individuals. Access to very informative administrative data of the Federal Employment Agency justifies the application of a matching estimator and allows us to account for individual (group-specific) and regional effect heterogeneity. We extend previous studies for Germany in four directions. First, we are able to evaluate the effects on regular (unsubsidised) employment. Second, we observe the outcomes of participants and non-participants for nearly three years after the programme starts and can therefore analyse medium-term effects. Third, we test the sensitivity of the results with respect to various decisions that have to be made during implementation of the matching estimator. Finally, we check if a possible occurrence of a specific form of ‘unobserved heterogeneity’ distorts our interpretation. The overall results are rather discouraging, since the employment effects are negative or insignificant for most of the analysed groups. One exception are long-term unemployed individuals who benefit from participation at the end of our observation period. Hence, one policy implication is to address the programmes to this problem group more closely.

This article aims to analyze the impact that the inclusion of students with disabilities has on the achievement of their schoolmates and to analyze the impact that this inclusion has on the achievement of the students with disabilities themselves.

The author begins investigating how the inclusion of students with disabilities in regular schools affects achievement of schoolmates. To answer this research question, the author explores the natural variation in time in the number of students with disabilities and use data from National Exam of Upper Secondary Education (Enem). Then, the author investigates how the inclusion affects achievement of the students with disabilities themselves and uses propensity score matching methodologies and, again, data from Enem.

The results show that an additional percentage point in the proportion of students with disabilities would reduce schoolmates' writing scores by a 0.0031 standard deviation. In other subjects, the author finds weak or none evidence of a significant peer effect. In addition, using Propensity Score Matching methodologies, the results show that the mean scores are up to 44% of a standard deviation, which is higher among students with disabilities enrolled in regular schools compared to those who are enrolled in special schools. In summary, the evaluation is that inclusion policies achieve the goal of improving the performance of students with disabilities but such policies have a small and adverse side effect.

For this reason, the present study proposes to fill this gap in the literature by analyzing the impact of the inclusion of students with disabilities on both groups. In addition, this paper contributes to the empirical literature of peer effect with an analysis of the peer effect of students with disabilities per competency. Finally, the article is important given the existence of few articles in Brazil on the topic of education and people with disabilities.

Land is an emotive issue for women in Kenya, majority of who still suffer the consequences of not having access to land, leading to economic insecurity. This paper aims at examining the effects of women access to land on household nutritional outcomes among smallholder farmers in Kenya.

The study uses primary data collected from a sample of 384 small-scale women farmers selected using multi-stage sampling technique. For data analysis, household nutritional outcomes were measured using Households Dietary Diversity Scores (HDDS) and Household Hunger Scale Scores (HHS). Stratification multilevel and matching-smoothing approach that controls for pre-treatment heterogeneity bias and treatment effect heterogeneity bias was used in estimating heterogeneous effects of women access to land.

The analysis reveals that women access to land has a significant positive effect on household nutritional outcomes. All households across all propensity scores strata benefited significantly but differently from women access to land in terms of nutritional outcomes.

Econometrically, propensity matching technique used in computing heterogeneity effects captures selection bias due to observable characteristics but it fails to capture selection bias due to unobservable factors. However, robust strategies were employed to ensure minimal estimation bias.

The paper provides insights on the determinants of women access to land and the influence women access to land has on household nutritional outcomes. In addition, by employing one of the conventional impact evaluation techniques, the paper contributes to knowledge by taking into accounts the heterogeneity in the effects of women access to land on household nutritional outcomes.

This paper aims to presents one of the first direct micro-econometric impact of competitive industries (based on revealed comparative advantage [RCA] between Pakistan and China) on household welfare in Pakistan using semi-parametric matching technique.

The study has also measured and identified the industrial competitiveness in both agricultural and non-agricultural (manufacturing) industries using RCA approach. RCA at the four-digit ISIC level are matched to household survey data (Pakistan Social and Living Standard Measurement) for 2013-2014 to represent the competitive industries in which the household’s higher earner is employed.

The findings of the study reveal that the China–Pakistan ex-post treatment effect (industrial competitiveness) provides welfare-improving effects. Furthermore, on this behalf, this study further assesses ex-ante treatment effects of recently signed China–Pakistan Economic Corridor (CPEC) on household welfare and suggests that CPEC would boost further trade liberalization and, therefore, would lead to industrial competitiveness and hence economic growth.

Paper contributes to two streams of literature. First, it measures and identifies the industrial competitiveness in both agricultural and non-agricultural industries using RCA approach; and second, it assesses the welfare of those households associated with these industries using semi-parametric propensity score matching technique.