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1.
Until recently, public funding for preschool education had been growing rapidly over a decade with most state programs providing one year of preschool for four year olds. Fewer three year olds are enrolled in preschool. To investigate the importance of enrollment duration, this study is the first to estimate long-term dosage effects of years of preschool. We use data from a cohort of 1500 students in the Chicago Longitudinal Study who enrolled in the Chicago Public Schools in the mid-1980s. Many of these students participated in a high-quality preschool program called Child-Parent Centers (CPC) for one or two years. To address selection with multiple treatments, we employ inverse propensity score weighting. Relative to children who attended one year of CPC preschool, the two-year group is significantly less likely to receive special education or be abused or neglected or to commit crimes. The findings provide support for the long-term benefits of greater exposure to preschool.  相似文献   

2.
Most research in the area of higher education is plagued by the problem of endogeneity or self-selection bias. Unlike ordinary least squares (OLS) regression, propensity score matching addresses the issue of self-selection bias and allows for a decomposition of treatment effects on outcomes. Using panel data from a national survey of bachelor’s degree recipients, this approach is illustrated via an analysis of the effect of receiving a master’s degree, in various program areas, on wage earning outcomes. The results of this study reveal that substantial self-selection bias is undetected when using OLS regression techniques. This article also shows that, unlike OLS regression, propensity score matching allows for estimates of the average treatment effect, average treatment on the treated effect, and the average treatment on the untreated effect on student outcomes such as wage earnings.  相似文献   

3.
Propensity score (PS) adjustments have become popular methods used to improve estimates of treatment effects in quasi-experiments. Although researchers continue to develop PS methods, other procedures can also be effective in reducing selection bias. One of these uses clustering to create balanced groups. However, the success of this new method depends on its efficacy compared to that of the existing methods. Therefore, this comparative study used experimental and nonexperimental data to examine bias reduction, case retention, and covariate balance in the clustering method, PS subclassification, and PS weighting. In general, results suggest that the cluster-based methods reduced at least as much bias as the PS methods. Under certain conditions, the PS methods reduced more bias than the cluster-based method, and under other conditions the cluster-based methods were more advantageous. Although all methods were equally effective in retaining cases and balancing covariates, other data-specific conditions may likely favor the use of a cluster-based approach.  相似文献   

4.
ABSTRACT

Educational researchers frequently study the impact of treatments or interventions on educational outcomes. However, when observational or quasiexperimental data are used for such investigations, selection bias can adversely impact researchers’ abilities to make causal inferences about treatment effects. One way to deal with selection bias is to use propensity score methods. The authors introduce educational researchers to the general principles underlying propensity score methods, describe 2 practical applications of these methods, and discuss their limitations.  相似文献   

5.
Propensity score matching (PSM) has become a popular approach for research studies when randomization is infeasible. However, there are significant differences in the effectiveness of selection bias reduction among the existing PSM methods and, therefore, it is challenging for researchers to select an appropriate matching method. This current study compares four commonly used PSM methods for reducing selection bias on observational data from which the treatment effects are intended to be assessed. The selection bias, standardized bias and percent bias reduction are evaluated for each of the PSM methods using empirical data drawn from the national Education Longitudinal Study of 2002. The results of the current study provide empirical evidence and helpful information for researchers to select effective PSM methods for their research studies.  相似文献   

6.
Because random assignment is not possible in observational studies, estimates of treatment effects might be biased due to selection on observable and unobservable variables. To strengthen causal inference in longitudinal observational studies of multiple treatments, we present 4 latent growth models for propensity score matched groups, and evaluate their performance with a Monte Carlo simulation study. We found that the 4 models performed similarly with respect to model fit, bias of parameter estimates, Type I error, and power to test the treatment effect. To demonstrate a multigroup latent growth model with dummy treatment indicators, we estimated the effect of students changing schools during elementary school years on their reading and mathematics achievement, using data from the Early Childhood Longitudinal Study Kindergarten Cohort.  相似文献   

7.
ABSTRACT

Randomized experiments are considered the gold standard for causal inference because they can provide unbiased estimates of treatment effects for the experimental participants. However, researchers and policymakers are often interested in using a specific experiment to inform decisions about other target populations. In education research, increasing attention is being paid to the potential lack of generalizability of randomized experiments because the experimental participants may be unrepresentative of the target population of interest. This article examines whether generalization may be assisted by statistical methods that adjust for observed differences between the experimental participants and members of a target population. The methods examined include approaches that reweight the experimental data so that participants more closely resemble the target population and methods that utilize models of the outcome. Two simulation studies and one empirical analysis investigate and compare the methods’ performance. One simulation uses purely simulated data while the other utilizes data from an evaluation of a school-based dropout prevention program. Our simulations suggest that machine learning methods outperform regression-based methods when the required structural (ignorability) assumptions are satisfied. When these assumptions are violated, all of the methods examined perform poorly. Our empirical analysis uses data from a multisite experiment to assess how well results from a given site predict impacts in other sites. Using a variety of extrapolation methods, predicted effects for each site are compared to actual benchmarks. Flexible modeling approaches perform best, although linear regression is not far behind. Taken together, these results suggest that flexible modeling techniques can aid generalization while underscoring the fact that even state-of-the-art statistical techniques still rely on strong assumptions.  相似文献   

8.
Error indices (bias, standard error of estimation, and root mean squared error) obtained on different measurement scales under different test-termination rules in computerized adaptive testing (CAT) were examined. Four ability estimation methods (maximum likelihood estimation, weighted likelihood estimation, expected a posterior, and maximum a posterior), three measurement scales (θ, number-correct score, and ACT score), and three test-termination rules (fixed length, fixed standard error, and target information) were studied for a real and a generated item pool. The findings indicated that the amount and direction of bias, standard error of estimation, and root mean squared error obtained under different ability estimation methods were influenced both by scale transformations and by test-termination rules in a CAT environment. The implications of these effects for testing programs are discussed.  相似文献   

9.
Estimation of the direct effect of an exposure on an outcome requires adjustment for confounders of the exposure–outcome and mediator–outcome relationships. When some of the latter confounders have been affected by the exposure, then standard regression adjustment is prone to possibly severe bias. The use of inverse probability weighting under so-called marginal structural models has recently been suggested as a solution in the psychological literature. In this article, we show how progress can alternatively be made via G-estimation. We show that this estimation method can be easily embedded within the structural equation modeling framework and could in particular be used for estimating direct effects in the presence of latent variables. Moreover, by avoiding inverse probability weighting, it accommodates the typical problem of unstable weights in the alternative estimation approaches based on marginal structural models. We illustrate the approach both by simulations and by the analysis of a longitudinal study in individiduals who ended a romantic relationship. In this example we explore whether the effect of attachment anxiety during the relationship on mental distress 2 years after the breakup is mediated by rumination or not.  相似文献   

10.
Robust maximum likelihood (ML) and categorical diagonally weighted least squares (cat-DWLS) estimation have both been proposed for use with categorized and nonnormally distributed data. This study compares results from the 2 methods in terms of parameter estimate and standard error bias, power, and Type I error control, with unadjusted ML and WLS estimation methods included for purposes of comparison. Conditions manipulated include model misspecification, level of asymmetry, level and categorization, sample size, and type and size of the model. Results indicate that cat-DWLS estimation method results in the least parameter estimate and standard error bias under the majority of conditions studied. Cat-DWLS parameter estimates and standard errors were generally the least affected by model misspecification of the estimation methods studied. Robust ML also performed well, yielding relatively unbiased parameter estimates and standard errors. However, both cat-DWLS and robust ML resulted in low power under conditions of high data asymmetry, small sample sizes, and mild model misspecification. For more optimal conditions, power for these estimators was adequate.  相似文献   

11.
Propensity score (PS) analysis aims to reduce bias in treatment effect estimates obtained from observational studies, which may occur due to non-random differences between treated and untreated groups with respect to covariates related to the outcome. We demonstrate how to use structural equation modeling (SEM) for PS analysis to remove selection bias due to latent covariates and estimate treatment effects on latent outcomes. Following the discussion of the design and analysis stages of PS analysis with SEM, an example is presented which uses the Mplus software to analyze data from the 1999 School and Staffing Survey (SASS) and 2000 Teacher Follow-up Survey (TFS) to estimate the effects teacher’s participation in a network of teachers on the teacher’s perception of workload manageability.  相似文献   

12.
ABSTRACT

This paper estimates the relative effectiveness of private and public primary schools in Kenya using data from 4,433 Grade 6 schoolchildren. Using ordinary least squares as a baseline model, we use the proportion of private schools in a district as an instrument in a Heckman two-stage correction framework, as well as propensity score matching models to correct for selection bias. There is a positive private school effect across all models. In the corrected models, we find that private school pupils outperform their public school counterparts by between .24 and .52 standard deviations.  相似文献   

13.
利用中国社会综合调查2008年数据,对高等教育与收入分配之间的关系进行了系统考察.通过基础的OLS回归发现,接受高等教育可以有效提高个体收入,且提高幅度远大于整体教育以及义务教育等阶段;通过分位数回归发现,高等教育在不同收入分位点上的影响存在着显著的差异,对于中低收入群体体现出比高收入群体更高的回报率,起到了缩小收入差距的作用;倾向得分匹配的结果显示,普通OLS估计方法对于高等教育回报率估计产生微弱的向下偏误,结合OB分解可以得出结论,即高等教育与家庭背景等个人特征因素对于收入差距的贡献是相互抵消的,接受高等教育很大程度上弥补了家庭背景、社会关系造成的收入差异,成为打破阶层锁定、改变命运的有效途径.本文的分析结果,为高等教育的发展、高等教育作用的发挥以及缩小收入差距政策的制定提供了经验证据.  相似文献   

14.
ABSTRACT

Increased access to algebra was a focal point of the National Mathematics Advisory Panel's 2008 report on improving mathematics learning in the United States. Past research found positive effects for early access to algebra, but the focus on average effects may mask important variation across student subgroups. The author addresses whether these positive effects hold up when the analysis is expanded to examine effect heterogeneity. Using a nationally representative sample of eighth-grade students in 1988, the author examined sensitivity of findings to methods for selection bias adjustment, heterogeneity across the propensity to take algebra in Grade 8, and across schools. The findings support past research regarding positive benefits to Grade 8 algebra and are consistent with policies that increase access to algebra in middle school.  相似文献   

15.
Abstract: In observational studies, selection bias will be completely removed only if the selection mechanism is ignorable, namely, all confounders of treatment selection and potential outcomes are reliably measured. Ideally, well-grounded substantive theories about the selection process and outcome-generating model are used to generate the sample of covariates. However, covariate selection is more heuristic in actual practice. Using two empirical data sets in a simulation study, we investigate four research questions about bias reduction when the selection mechanism is not known but many covariates are measured: (1) How important is the conceptual heterogeneity of the covariate domains in the data set? (2) How important is the number of covariates assessing each domain? (3) What are the joint effects of this conceptual heterogeneity and of the number of covariates per domain? (4) What happens to bias reduction when the set of covariates is deliberately impoverished by removing the covariates most responsible for selection bias, thus ensuring a slightly smaller but still heterogeneous set of covariates? The results indicate: (1) increasingly more bias is reduced as the number of covariate domains and the number of covariates per domain increase, though the rate of bias reduction is diminishing in each case; (2) sampling covariates from multiple heterogeneous covariate domains is more important than choosing many measures from fewer domains; (3) the most heterogeneous set of covariate domains removes almost all of the selection bias when at least five covariates are assessed in each domain; and (4) omitting the most crucial covariates generally replicates the pattern of results due to the number of domains and the number of covariates per domain, but the amount of bias reduction is less than when all variables are included and will surely not satisfy all consumers of causal research.  相似文献   

16.
The integration of modern methods for causal inference with latent class analysis (LCA) allows social, behavioral, and health researchers to address important questions about the determinants of latent class membership. In this article, 2 propensity score techniques, matching and inverse propensity weighting, are demonstrated for conducting causal inference in LCA. The different causal questions that can be addressed with these techniques are carefully delineated. An empirical analysis based on data from the National Longitudinal Survey of Youth 1979 is presented, where college enrollment is examined as the exposure (i.e., treatment) variable and its causal effect on adult substance use latent class membership is estimated. A step-by-step procedure for conducting causal inference in LCA, including multiple imputation of missing data on the confounders, exposure variable, and multivariate outcome, is included. Sample syntax for carrying out the analysis using SAS and R is given in an appendix.  相似文献   

17.
Abstract

Despite the overwhelming focus on the overall average treatment effect in the methodological and statistical literature, in many cases the efficacy of an educational program or intervention might vary based on unit background characteristics. The identification of subgroups for which an educational intervention is particularly effective or, on the other hand, has no effect or is possibly harmful, may have important practical implications, especially in terms of allocation of resources. We propose a five-step approach using propensity score matching and regression trees to identify subgroups with heterogeneous treatment effects in observational studies. Results of two Monte Carlo simulation studies demonstrate that the proposed approach can accurately identify heterogeneous subgroups while maintaining Type I error rate. In a case study with Early Childhood Longitudinal Study-Kindergarten cohort data, we find that the effect of exposure to special education services on fifth-grade mathematics achievement varies based on kindergarten mathematics achievement and student gender.  相似文献   

18.
Abstract

Training abroad is an important avenue for promoting the specialized development of faculty and improving international accomplishments. On the basis of the data from the 2014 Faculty Survey in China, this paper applies the technique of propensity score matching to control for the self-selection bias in the sample, so as to quantitatively evaluate the economic benefits of training abroad for faculty. The study finds that training abroad presents significant economic returns, while the potential benefits of training abroad for faculty members who have not yet pursued training abroad are higher than the benefits for faculty members who have pursued training abroad; a nonlinear relationship exists between the period of time for which a faculty member pursues training abroad and the economic benefits thereof, with the order of the economic benefits of training abroad from least to most for different time periods being: 1?year, less than one half year, more than one half year to less than 1?year, and more than 1?year. The conclusions of the study provide an empirical basis for the selection of pathways for the professional development of faculty in the future, as well as the design and refinement of training abroad programs, et cetera.  相似文献   

19.
ABSTRACT

The Advanced Placement (AP) program is an educational program that permits high school students to take introductory college-level courses and receive college credit by passing a standardized end-of-course exam. Data were obtained from a statewide database of 2 high school graduating cohorts (N = 90,044). We used a series of propensity score analyses and marginal mean weighting through stratification to examine the impact of the AP program on students' academic achievement as measured by ACT scores. Results indicate that merely enrolling in an AP course produces very little benefit for students. Students who take and pass the AP exam, however, obtain higher ACT scores, even after controlling for a wide variety of academic, socioeconomic, and demographic variables. The authors conclude the article by discussing aspects of the AP program that remain unanswered.  相似文献   

20.
Often program administrators are interested in knowing how students benefit from participation in programs compared to students who do not participate. Such comparisons may be sullied by the fact that participants self-select into programs, resulting in differences between groups prior to programming. By controlling for researcher-identified–self-selection variables, propensity score matching enables researchers to create comparable matched groups. However, when employing propensity score matching, researchers are faced with a plethora of matching options. In the current study, we compared the quantity and quality of matches obtained when applying common matching techniques to real data. The methods produced matches of varying quantity and quality. Moreover, group comparisons on the outcome led to different conclusions depending on the matching method employed.  相似文献   

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