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1.
Propensity score methods allow investigators to estimate causal treatment effects using observational or nonrandomized data. In this article we provide a practical illustration of the appropriate steps in conducting propensity score analyses. For illustrative purposes, we use a sample of current smokers who were discharged alive after being hospitalized with a diagnosis of acute myocardial infarction. The exposure of interest was receipt of smoking cessation counseling prior to hospital discharge and the outcome was mortality with 3 years of hospital discharge. We illustrate the following concepts: first, how to specify the propensity score model; second, how to match treated and untreated participants on the propensity score; third, how to compare the similarity of baseline characteristics between treated and untreated participants after stratifying on the propensity score, in a sample matched on the propensity score, or in a sample weighted by the inverse probability of treatment; fourth, how to estimate the effect of treatment on outcomes when using propensity score matching, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, or covariate adjustment using the propensity score. Finally, we compare the results of the propensity score analyses with those obtained using conventional regression adjustment.  相似文献   

2.
Propensity score methods allow investigators to estimate causal treatment effects using observational or nonrandomized data. In this article we provide a practical illustration of the appropriate steps in conducting propensity score analyses. For illustrative purposes, we use a sample of current smokers who were discharged alive after being hospitalized with a diagnosis of acute myocardial infarction. The exposure of interest was receipt of smoking cessation counseling prior to hospital discharge and the outcome was mortality with 3 years of hospital discharge. We illustrate the following concepts: first, how to specify the propensity score model; second, how to match treated and untreated participants on the propensity score; third, how to compare the similarity of baseline characteristics between treated and untreated participants after stratifying on the propensity score, in a sample matched on the propensity score, or in a sample weighted by the inverse probability of treatment; fourth, how to estimate the effect of treatment on outcomes when using propensity score matching, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, or covariate adjustment using the propensity score. Finally, we compare the results of the propensity score analyses with those obtained using conventional regression adjustment.  相似文献   

3.
Hong G 《心理学方法》2012,17(1):44-60
Propensity score matching and stratification enable researchers to make statistical adjustment for a large number of observed covariates in nonexperimental data. These methods have recently become popular in psychological research. Yet their applications to evaluations of multi-valued and multiple treatments are limited. The inverse-probability-of-treatment weighting method, though suitable for evaluating multi-valued and multiple treatments, often generates results that are not robust when only a portion of the population provides support for causal inference or when the functional form of the propensity score model is misspecified. The marginal mean weighting through stratification (MMW-S) method promises a viable nonparametric solution to these problems. By computing weights on the basis of stratified propensity scores, MMW-S adjustment equates the pretreatment composition of multiple treatment groups under the assumption that unmeasured covariates do not confound the treatment effects given the observed covariates. Analyzing data from a weighted sample, researchers can estimate a causal effect by computing the difference between the estimated average potential outcomes associated with alternative treatments within the analysis of variance framework. After providing an intuitive illustration of the theoretical rationale underlying the weighting method for causal inferences, the article demonstrates how to apply the MMW-S method to evaluations of treatments measured on a binary, ordinal, or nominal scale approximating a completely randomized experiment; to studies of multiple concurrent treatments approximating factorial randomized designs; and to moderated treatment effects approximating randomized block designs. The analytic procedure is illustrated with an evaluation of educational services for English language learners attending kindergarten in the United States.  相似文献   

4.
Mediation analysis uses measures of hypothesized mediating variables to test theory for how a treatment achieves effects on outcomes and to improve subsequent treatments by identifying the most efficient treatment components. Most current mediation analysis methods rely on untested distributional and functional form assumptions for valid conclusions, especially regarding the relation between the mediator and outcome variables. Propensity score methods offer an alternative whereby the propensity score is used to compare individuals in the treatment and control groups who would have had the same value of the mediator had they been assigned to the same treatment condition. This article describes the use of propensity score weighting for mediation with a focus on explicating the underlying assumptions. Propensity scores have the potential to offer an alternative estimation procedure for mediation analysis with alternative assumptions from those of standard mediation analysis. The methods are illustrated investigating the mediational effects of an intervention to improve sense of mastery to reduce depression using data from the Job Search Intervention Study (JOBS II). We find significant treatment effects for those individuals who would have improved sense of mastery when in the treatment condition but no effects for those who would not have improved sense of mastery under treatment.  相似文献   

5.
Confounding present in observational data impede community psychologists’ ability to draw causal inferences. This paper describes propensity score methods as a conceptually straightforward approach to drawing causal inferences from observational data. A step-by-step demonstration of three propensity score methods—weighting, matching, and subclassification—is presented in the context of an empirical examination of the causal effect of preschool experiences (Head Start vs. parental care) on reading development in kindergarten. Although the unadjusted population estimate indicated that children with parental care had substantially higher reading scores than children who attended Head Start, all propensity score adjustments reduce the size of this overall causal effect by more than half. The causal effect was also defined and estimated among children who attended Head Start. Results provide no evidence for improved reading if those children had instead received parental care. We carefully define different causal effects and discuss their respective policy implications, summarize advantages and limitations of each propensity score method, and provide SAS and R syntax so that community psychologists may conduct causal inference in their own research.  相似文献   

6.
Causal effect modeling with naturalistic rather than experimental data is challenging. In observational studies participants in different treatment conditions may also differ on pretreatment characteristics that influence outcomes. Propensity score methods can theoretically eliminate these confounds for all observed covariates, but accurate estimation of propensity scores is impeded by large numbers of covariates, uncertain functional forms for their associations with treatment selection, and other problems. This article demonstrates that boosting, a modern statistical technique, can overcome many of these obstacles. The authors illustrate this approach with a study of adolescent probationers in substance abuse treatment programs. Propensity score weights estimated using boosting eliminate most pretreatment group differences and substantially alter the apparent relative effects of adolescent substance abuse treatment.  相似文献   

7.
8.
Contemporary social‐scientific research seeks to identify specific causal mechanisms for outcomes of theoretical interest. Experiments that randomize populations to treatment and control conditions are the “gold standard” for causal inference. We identify, describe, and analyze the problem posed by transformative treatments. Such treatments radically change treated individuals in a way that creates a mismatch in populations, but this mismatch is not empirically detectable at the level of counterfactual dependence. In such cases, the identification of causal pathways is underdetermined in a previously unrecognized way. Moreover, if the treatment is indeed transformative it breaks the inferential structure of the experimental design. Transformative treatments are not curiosities or “corner cases,” but are plausible mechanisms in a large class of events of theoretical interest, particularly ones where deliberate randomization is impractical and quasi‐experimental designs are sought instead. They cast long‐running debates about treatment and selection effects in a new light, and raise new methodological challenges.  相似文献   

9.
This study measures the effect of regular worship attendance at age 17 on total years of schooling by age 25, using data from the National Longitudinal Survey of Youth 1997. Expanding on previous work, this study estimates differences in the impact of worship attendance by race and family income status using propensity score matching. Individuals who frequently attend religious services complete .69 more years of schooling than similar individuals who do not frequently attend services. There are significantly greater returns to attendance for low‐income youth and no significant difference in returns by religious affiliation. These findings suggest that religious observance provides greater benefits for low‐income individuals or perhaps provides resources high‐income individuals have access to elsewhere. Moreover, this study extends previous work by examining a more recent and nationally representative sample of youth and by using methods that allow for greater causal inference.  相似文献   

10.
Although randomized studies have high internal validity, generalizability of the estimated causal effect from randomized clinical trials to real-world clinical or educational practice may be limited. We consider the implication of randomized assignment to treatment, as compared with choice of preferred treatment as it occurs in real-world conditions. Compliance, engagement, or motivation may be better with a preferred treatment, and this can complicate the generalizability of results from randomized trials. The doubly randomized preference trial (DRPT) is a hybrid randomized and nonrandomized design that allows for estimation of the causal effect of randomization versus treatment preference. In the DRPT, individuals are first randomized to either randomized assignment or choice assignment. Those in the randomized assignment group are then randomized to treatment or control, and those in the choice group receive their preference of treatment versus control. Using the potential outcomes framework, we apply the algebra of conditional independence to show how the DRPT can be used to derive an unbiased estimate of the causal effect of randomization versus preference for each of the treatment and comparison conditions. Also, we show how these results can be implemented using full matching on the propensity score. The methodology is illustrated with a DRPT of introductory psychology students who were randomized to randomized assignment or preference of mathematics versus vocabulary training. We found a small to moderate benefit of preference versus randomization with respect to the mathematics outcome for those who received mathematics training.  相似文献   

11.
In the behavioral and social sciences, quasi-experimental and observational studies are used due to the difficulty achieving a random assignment. However, the estimation of differences between groups in observational studies frequently suffers from bias due to differences in the distributions of covariates. To estimate average treatment effects when the treatment variable is binary, Rosenbaum and Rubin (1983a) proposed adjustment methods for pretreatment variables using the propensity score. However, these studies were interested only in estimating the average causal effect and/or marginal means. In the behavioral and social sciences, a general estimation method is required to estimate parameters in multiple group structural equation modeling where the differences of covariates are adjusted. We show that a Horvitz–Thompson-type estimator, propensity score weighted M estimator (PWME) is consistent, even when we use estimated propensity scores, and the asymptotic variance of the PWME is shown to be less than that with true propensity scores. Furthermore, we show that the asymptotic distribution of the propensity score weighted statistic under a null hypothesis is a weighted sum of independent χ2 1 variables. We show the method can compare latent variable means with covariates adjusted using propensity scores, which was not feasible by previous methods. We also apply the proposed method for correlated longitudinal binary responses with informative dropout using data from the Longitudinal Study of Aging (LSOA). The results of a simulation study indicate that the proposed estimation method is more robust than the maximum likelihood (ML) estimation method, in that PWME does not require the knowledge of the relationships among dependent variables and covariates.  相似文献   

12.
Differential item functioning (DIF) analysis is important in terms of test fairness. While DIF analyses have mainly been conducted with manifest grouping variables, such as gender or race/ethnicity, it has been recently claimed that not only the grouping variables but also contextual variables pertaining to examinees should be considered in DIF analyses. This study adopted propensity scores to incorporate the contextual variables into the gender DIF analysis. In this study, propensity scores were used to control for the contextual variables that potentially affect the gender DIF. Subsequent DIF analyses with the Mantel-Haenszel (MH) procedure and the Logistic Regression (LR) model were run with the propensity score applied reference (males) and focal groups (females) through propensity score matching. The propensity score embedded MH model and LR model detected fewer number of gender DIF than the conventional MH and LR models. The propensity score embedded models, as a confirmatory approach in DIF analysis, could contribute to hypothesizing an inference on the potential cause of DIF. Also, salient advantages of propensity score embedded DIF analysis models are discussed.  相似文献   

13.
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.  相似文献   

14.
This article examines if deep‐seated psychological differences add to the explanation of attitudes toward immigration. We explore whether the Big Five personality traits matter for immigration attitudes beyond the traditional situational factors of economic and cultural threat and analyze how individuals with different personalities react when confronted with the same situational triggers. Using a Danish survey experiment, we show that different personality traits have different effects on opposition toward immigration. We find that Openness has an unconditional effect on attitudes toward immigration: scoring higher on this trait implies a greater willingness to admit immigrants. Moreover, individuals react differently to economic threat depending on their score on the traits Agreeableness and Conscientiousness. Specifically, individuals scoring low on Agreeableness and individuals scoring high on Conscientiousness are more sensitive to the skill level of immigrants. The results imply that personality is important for attitudes toward immigration, and in the conclusion, we further discuss how the observed conditional and unconditional effects of personality make sense theoretically.  相似文献   

15.
This study aims to reparameterize ordinary factors into between‐ and within‐person factor effects and utilize an array of the within‐person factor loadings as a latent profile which encapsulates all score responses of individuals in a population. To illustrate, the Woodcock–Johnson III (WJ‐III) tests of cognitive abilities were analysed and one between‐ and two within‐person factors were identified. The scoring patterns of individuals in the WJ‐III sample were interpreted according to the within‐person factor patterns. Regression analyses were performed to examine how much the within‐person factors accounted for the person scoring patterns and criterion variables. Finally, the importance and applications of the between‐ and within‐person factors are discussed.  相似文献   

16.
17.
Information about the structure of a causal system can come in the form of observational data—random samples of the system's autonomous behavior—or interventional data—samples conditioned on the particular values of one or more variables that have been experimentally manipulated. Here we study people's ability to infer causal structure from both observation and intervention, and to choose informative interventions on the basis of observational data. In three causal inference tasks, participants were to some degree capable of distinguishing between competing causal hypotheses on the basis of purely observational data. Performance improved substantially when participants were allowed to observe the effects of interventions that they performed on the systems. We develop computational models of how people infer causal structure from data and how they plan intervention experiments, based on the representational framework of causal graphical models and the inferential principles of optimal Bayesian decision‐making and maximizing expected information gain. These analyses suggest that people can make rational causal inferences, subject to psychologically reasonable representational assumptions and computationally reasonable processing constraints.  相似文献   

18.
Matching unfamiliar faces is known to be difficult. Here, we ask whether performance can be improved by asking viewers to work in pairs, a manipulation known to increase accuracy for low‐level visual discrimination tasks. Across four experiments we consistently find that face matching accuracy is higher for pairs of viewers than for individuals. This ‘pairs advantage’ is generally driven by adopting the response of the higher scoring partner. However, when the task becomes difficult, both partners' performance is improved by working in a pair. In two experiments, we find evidence that working in a pair can lead to subsequent improvements in individual performance, specifically for viewers whose accuracy is initially low. The pairs' technique therefore offers the opportunity for substantial improvements in face matching performance, along with an added training benefit.  相似文献   

19.
The increased proportion of juvenile court‐involved girls has spurred interest to implement and evaluate services to reduce girls’ system involvement. The purpose of this study was to examine the effectiveness of a family‐based intervention by using a dominant sequential mixed methods evaluation approach. First, we examined quantitative data using a quasi‐experimental design to determine whether the family‐based intervention reduced recidivism among court‐involved girls. Propensity score matching (PSM) was used to construct statistically equivalent groups to compare one‐year recidivism outcomes for girls who received the court‐run family‐based intervention (n = 181) to a group of girls on probation who did not receive the intervention (n = 803). Qualitative interviews (n = 39) were conducted to contextualize the quantitative findings and highlighted the circumstances that family‐focused interventions for court‐involved girls. Girls who received the program had slightly lower recidivism rates following the intervention. The qualitative findings contextualized the quasi‐experimental results by providing an explanation as to the girls’ family circumstances and insights into the mechanisms of the intervention. Results highlighted the importance of family‐focused interventions for juvenile justice‐involved girls. These findings have practical and policy implications for the use interventions—beyond the individual level—with adjudicated girls and offer suggestions for ways to improve their effectiveness using a community psychology lens. In addition, this paper includes a discussion of evaluating of juvenile court programming from a community psychology perspective including strengths, challenges, and considerations for future work in this area.  相似文献   

20.
We examine the influence of individuals’ propensity to morally disengage on a broad range of unethical organizational behaviors. First, we develop a parsimonious, adult‐oriented, valid, and reliable measure of an individual's propensity to morally disengage, and demonstrate the relationship between it and a number of theoretically relevant constructs in its nomological network. Then, in 4 additional studies spanning laboratory and field settings, we demonstrate the power of the propensity to moral disengage to predict multiple types of unethical organizational behavior. In these studies we demonstrate that the propensity to morally disengage predicts several outcomes (self‐reported unethical behavior, a decision to commit fraud, a self‐serving decision in the workplace, and supervisor‐ and coworker‐reported unethical work behaviors) beyond other established individual difference antecedents of unethical organizational behavior, as well as the most closely related extant measure of the construct. We conclude that scholars and practitioners seeking to understand a broad range of undesirable workplace behaviors can benefit from taking an individual's propensity to morally disengage into account. Implications for theory, research, and practice are discussed.  相似文献   

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