The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.

https://www.tandfonline.com/doi/pdf/10.1080/00273171.2011.568786

An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

This article presents findings from a meta-analysis of 213 school-based, universal social and emotional learning (SEL) programs involving 270,034 kindergarten through high school students. Compared to controls, SEL participants demonstrated significantly improved social and emotional skills, attitudes, behavior, and academic performance that reflected an 11-percentile-point gain in achievement. School teaching staff successfully conducted SEL programs. The use of 4 recommended practices for developing skills and the presence of implementation problems moderated program outcomes. The findings add to the growing empirical evidence regarding the positive impact of SEL programs. Policy makers, educators, and the public can contribute to healthy development of children by supporting the incorporation of evidence-based SEL programming into standard educational practice.

http://explorefhp.com/wp-content/uploads/2013/03/Durlak-Weissberg-Child-Development-Meta-Analysis.pdf

The Impact of Enhancing Students’ Social and Emotional Learning: A Meta‐Analysis of School‐Based Universal Interventions

A vital step in the development of an equal partnership for minorities in the academic, social, and economic life of the United States involves moving away from assumptions of the linear model of cultural acquisition. In this article we review the literature on the psychological impact of being bicultural. Assimilation, acculturation, alternation, multicultural, and fusion models that have been used to describe the psychological processes, social experiences, and individual challenges and obstacles of being bicultural are reviewed and summarized for their contributions and implications for investigations of the psychological impact of biculturalism. Emphasis is given to the alternation model, which posits that an individual is able to gain competence within 2 cultures without losing his or her cultural identity or having to choose one culture over the other. Finally, a hypothetical model outlining the dimensions of bicultural competence is presented.

Psychological impact of biculturalism: Evidence and theory.

There is emerging agreement that sustainability challenges require new ways of knowledge production and decision-making. One key aspect of sustainability science, therefore, is the involvement of actors from outside academia into the research process in order to integrate the best available knowledge, reconcile values and preferences, as well as create ownership for problems and solution options. Transdisciplinary, community-based, interactive, or participatory research approaches are often suggested as appropriate means to meet both the requirements posed by real-world problems as well as the goals of sustainability science as a transformational scientific field. Dispersed literature on these approaches and a variety of empirical projects applying them make it difficult for interested researchers and practitioners to review and become familiar with key components and design principles of how to do transdisciplinary sustainability research. Starting from a conceptual model of an ideal–typical transdisciplinary research process, this article synthesizes and structures such a set of principles from various strands of the literature and empirical experiences. We then elaborate on them, looking at challenges and some coping strategies as experienced in transdisciplinary sustainability projects in Europe, North America, South America, Africa, and Asia. The article concludes with future research needed in order to further enhance the practice of transdisciplinary sustainability research.

/pdf/transdisciplinary-research-in-sustainability-science-4wi0tfk5i7.pdf

Transdisciplinary research in sustainability science: practice, principles, and challenges

This meta-analytic review of 148 studies on child and adolescent direct and indirect aggression examined the magnitude of gender differences, intercorrelations between forms, and associations with maladjustment. Results confirmed prior findings of gender differences (favoring boys) in direct aggression and trivial gender differences in indirect aggression. Results also indicated a substantial intercorrelation (r = .76) between these forms. Despite this high intercorrelation, the 2 forms showed unique associations with maladjustment: Direct aggression is more strongly related to externalizing problems, poor peer relations, and low prosocial behavior, and indirect aggression is related to internalizing problems and higher prosocial behavior. Moderation of these effect sizes by method of assessment, age, gender, and several additional variables were systematically investigated.

/pdf/direct-and-indirect-aggression-during-childhood-and-3e5s5b36dw.pdf

Direct and Indirect Aggression During Childhood and Adolescence: A Meta-Analytic Review of Gender Differences, Intercorrelations, and Relations to Maladjustment

List of Tables List of Figures Preface About the Authors Chapter 1: Introduction Observational Studies History and Development Randomized Experiments Why and When a Propensity Score Analysis Is Needed Computing Software Packages Plan of the Book Chapter 2: Counterfactual Framework and Assumptions Causality, Internal Validity, and Threats Counterfactuals and the Neyman-Rubin Counterfactual Framework The Ignorable Treatment Assignment Assumption The Stable Unit Treatment Value Assumption Methods for Estimating Treatment Effects The Underlying Logic of Statistical Inference Types of Treatment Effects Treatment Effect Heterogeneity Heckman's Econometric Model of Causality Conclusion Chapter 3: Conventional Methods for Data Balancing Why Is Data Balancing Necessary? A Heuristic Example Three Methods for Data Balancing Design of the Data Simulation Results of the Data Simulation Implications of the Data Simulation Key Issues Regarding the Application of OLS Regression Conclusion Chapter 4: Sample Selection and Related Models The Sample Selection Model Treatment Effect Model Overview of the Stata Programs and Main Features of treatreg Examples Conclusion Chapter 5: Propensity Score Matching and Related Models Overview The Problem of Dimensionality and the Properties of Propensity Scores Estimating Propensity Scores Matching Postmatching Analysis Propensity Score Matching With Multilevel Data Overview of the Stata and R Programs Examples Conclusion Chapter 6: Propensity Score Subclassification Overview The Overlap Assumption and Methods to Address Its Violation Structural Equation Modeling With Propensity Score Subclassification The Stratification-Multilevel Method Examples Conclusion Chapter 7: Propensity Score Weighting Overview Weighting Estimators Examples Conclusion Chapter 8: Matching Estimators Overview Methods of Matching Estimators Overview of the Stata Program nnmatch Examples Conclusion Chapter 9: Propensity Score Analysis With Nonparametric Regression Overview Methods of Propensity Score Analysis With Nonparametric Regression Overview of the Stata Programs psmatch2 and bootstrap Examples Conclusion Chapter 10: Propensity Score Analysis of Categorical or Continuous Treatments Overview Modeling Doses With a Single Scalar Balancing Score Estimated by an Ordered Logistic Regression Modeling Doses With Multiple Balancing Scores Estimated by a Multinomial Logit Model The Generalized Propensity Score Estimator Overview of the Stata gpscore Program Examples Conclusion Chapter 11: Selection Bias and Sensitivity Analysis Selection Bias: An Overview A Monte Carlo Study Comparing Corrective Models Rosenbaum's Sensitivity Analysis Overview of the Stata Program rbounds Examples Conclusion Chapter 12: Concluding Remarks Common Pitfalls in Observational Studies: A Checklist for Critical Review Approximating Experiments With Propensity Score Approaches Other Advances in Modeling Causality Directions for Future Development References Index

Propensity Score Analysis: Statistical Methods and Applications

Risk and Resilience in Childhood: An Ecological Perspective

In both the academic and popular press, the related concepts of risk, protection, and resilience have emerged as constructs for conceptualizing social and health problems. The idea of "risk" is ubiquitous in social work. In everyday use, the term conveys the notion that an individual, family, group, school, neighborhood, or organization is likely to experience a negative outcome. Although the ideas of protection and resilience conjure up images of extraordinary feats in overcoming adversity, they are elusive. In light of the growing salience of what some call the "risk-and-resilience" perspective, this special issue of Social Work Research highlights social work research that uses the concepts of risk, protection, and resilience. In this introductory article, the authors define key terms, discuss methodological issues, and explore implications for the profession. Key words: resilience; risk factors; protective factors; social work practice In both the academic and popular press, the related concepts of risk, protection, and resilience have emerged as constructs for conceptualizing social and health problems. The idea of "risk" is ubiquitous in social work. In everyday use, the term conveys the notion that an individual, family, group, school, neighborhood, or organization is likely to experience a negative outcome. Although the ideas of protection and resilience conjure up images of extraordinary feats in overcoming adversity, they are elusive. In light of the growing salience of what some call the "risk and resilience" perspective, the purpose of this issue is to highlight social work research that used the concepts of risk, protection, and resilience. This introductory article defines key terms, discusses methodological issues, and explores implications for the profession. RISK FACTORS Risk, technically, is a probability describing the likelihood of a future event, given a certain condition or set of conditions. If the prevalence of schizophrenia is 1 percent in the population but we know that somewhere between 10 percent and 15 percent of children with a parent who has schizophrenia are likely to develop schizophrenia, we can say that children of parents with schizophrenia are at higher risk than the rest of the population (Richters & Weintraub, 1990). We might even say that the risk of developing schizophrenia is 10 to 15 times higher than the rest of the population. Risk factors are markers, correlates, and--in a best-case scenario--causes. For example, although other factors are related to serious mental illnesses such as schizophrenia, parental psychopathology is thought to influence the likelihood of developing the disorder. How it does that--whether through genetic or environmental influences--is not yet fully understood. But the word "risk" denotes the fact that a group of people with a similar characteristic is more likely than others in the population at large to develop a problem (in this case, schizophrenia). Risk is a central concept in the field of public health, where it has been coupled with the concepts of covariation and prediction. That is, risk refers to the relative influence of a variable on some outcome such as a heart attack, stroke, or hip fracture. Risk factors may be individual characteristics (such as traits and dispositions), specific life experiences or events (such as the death of a parent), or contextual factors (such as neighborhood safety). Risk is defined probabilistically as any influence that increases the likelihood of the onset of a problem or maintains a problem state (Coie et al., 1993). It is applied also to influences that cause digression to a more serious state, as in relapse or the recurrence of symptoms after a period of remission (Kirby & Fraser, 1997). Nonspecific Risk Factors Some individual, familial, and extrafamilial factors appear to affect many disorders concomitantly and in that sense they are "nonspecific" risks. …

Risk, Protection, and Resilience: Toward a Conceptual Framework for Social Work Practice.

Risk and resilience in childhood

1. What is Intervention Research 2. Stages in Intervention Research 3. Step 1: Specify the Problem and Develop a Program Theory 4. Step 2: Create and Revise Program Materials 5. Step 3 & Step 4: From Refining Program Components to Testing Effectiveness 6. Step 5: Dissemination of Findings and Program Materials 7. Challenges in Intervention Research Appendix Glossary References

Mark W. Fraser

Papers

Propensity Score Analysis: Statistical Methods and Applications

Risk and Resilience in Childhood: An Ecological Perspective

Risk, Protection, and Resilience: Toward a Conceptual Framework for Social Work Practice.

Risk and resilience in childhood

Intervention Research: Developing Social Programs