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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.

Among the research studies related to the relevance between religious belief and mental health, most of them highlight people with religious belief who tend to obtain mental comforting more easily. However, the research studies mentioned above were cross-sectional studies, and they only verified that religious beliefs and mental health are relevant, but they did not prove their cause-and-effect relationship. That is, they do not identify “due to people's religious beliefs, they have healthier mind” or “due to people's healthier minds, they have religious beliefs.” Therefore, the study aims to explore the benefit evaluation of religious belief affecting mental health.

The study uses propensity score matching (PSM) and treatment effect (ATT) to carry out the causal inference between religious beliefs and mental health. First, the propensity score (PS) is calculated from the impact factors that affect people's religious belief before establishing counterfactual analysis based on the PS to analyze the effect of religious beliefs to further understand the difference of mental health index between people with religious belief and without it, and confirm the cause-and-effect relationship between them.

Religious beliefs and participation are ubiquitous within and across populations. The associates between religious participation and health are considerably in great magnitude. Most of the research in the past related to religious beliefs and mental health only verified that religious beliefs and mental health are relevant but not proved its cause-and-effect relationship. This paper aimed to explore the causal relationship between religious belief and mental health. The experimental results showed religious belief has treatment effect toward “daily functioning,” “feeling affect,” “spirituality” and “mental health.” On a whole, religious belief can promote mental health.

In academic and practical circles, there are a lot of research studies exploring the relationship between religious belief and mental health. However, there is no research investigating the cause-and-effect relationship between religious belief and mental health. It also causes some questioning toward the relevant research studies. Therefore, the outcome of this study not only can clarify the legitimacy, importance, and practicality on the researches in the past but also provide the practical support for psychology and counseling.

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.

This study aims at developing an instrument to measure the latent trait propensity to drop out in face-to-face higher education.

Based on Tinto's student integration theory, a 27-item scale was created to measure student propensity to drop out of undergraduate programs. Item response theory was used to evaluate the psychometric analysis of the items. Furthermore, different methodologies were used to evaluate and provide evidence for content validity, response process validity, internal structure validity and criterion-related validity.

With the support of specialists in the construct, the interpretation of the scores for the use of the scale was defined in four levels of propensity: high, moderate, low and very low.

The latent trait propensity to drop out in face-to-face higher education allows the inclusion of new items and aspects in the instrument. Thus, it can be adapted to distance education.

The students' propensity to drop out score can be useful for researchers and administration units in colleges and universities in the planning of permanent institutional actions and programs to take preventive measures.

Minimize dropout in order to raise the educational level of the population and make better use of the resources invested in education.

This study points out when, why and how propensity to drop out can be measured and how scores can be interpreted in the context of the problem.

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.

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.

The purpose of this paper was to assess the effects of quality coffee production on climate change adaptation using household surveys and interview data gathered from coffee farmers in Yirgacheffe, southern Ethiopia.

A sample of 352 households, stratified into conventional coffee farmers 232 (66%) and specialty coffee producers 120(34%), was used. The propensity score model for participating in quality coffee production was estimated using 14 covariates, and the impact of quality coffee production on adaptation to climate change adaptation was examined. The results are augmented with qualitative data collected through focus group discussions and key informant interviews held with randomly selected smallholder farmers. A telecoupling theoretical perspective was used to understand the link between coffee farmers’ adaptation practices and the demand for quality coffee, as coffee is a global commodity.

The PSM analysis reveals that quality coffee production positively influences climate change adaptation. This implies that conventional coffee producers would have performed better in adaptation to climate change if they had participated in quality coffee production. The results of group discussions also confirm the positive effects of quality coffee production on adaptation to climate change, which also suggests a positive spillover effects for sustainable coffee farm management.

This study suggests enhancing quality coffee production is essential if a more sustainable and climate change resilient coffee livelihood is envisioned.

Though many studies are available on adaptation to climate change in general, this study is one of the few studies focusing on the effects of quality coffee production on climate change adaptation by smallholders in one of the least developed countries, Ethiopia. This study provides a better understanding of the importance of adaptation strategies specific to coffee production, which in turn help develop a more resilient coffee sector, as coffee production is one of the most sensitive activity to climate change.

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.

The purpose of this paper is to determine the impact of microcredit on smallholder poultry production and its subsequent role on domestic protein and food supply.

Cross-sectional farm-level data from 61 farmers with at least two years of microcredit access and 39 farmers without microcredit access in the Dormaa Municipality of Ghana collected in 2016 via semi-structured questionnaire were used. Using the propensity score matching, PSM, and data envelopment analyses approaches, the authors analysed the propensity of farmers’ taking microcredit and its effect on beneficiaries’ technical efficiency, productivity, profitability and domestic production of chicken and eggs, farm performance. The authors addressed selection biases with the PSM and answered the research question of whether farmers with microcredit access perform better than non-microcredit farmers.

Farmers with high years of education, farming experience, technology and machinery as well as micro-savings and female farmers are more likely to take microcredit whereas large farm size reduces farmers’ propensity to take microcredit. Furthermore, farms with microcredit access were more technically efficient, productive and profitable than they would have been in the absence of microcredit.

The paper can be useful to policymakers and microcredit institutions since it provides evidence of microeconomic impacts of microcredit on agricultural production and the determinants of farmers’ participation in microcredit.

The study helps to understand how access to credit can improve smallholders’ technology adoption, production efficiency and productivity and output thereby enhancing domestic food supply.

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.