共查询到20条相似文献，搜索用时 47 毫秒
1.
This Monte Carlo simulation study compares methods to estimate the effects of programs with multiple versions when assignment of individuals to program version is not random. These methods use generalized propensity scores, which are predicted probabilities of receiving a particular level of the treatment conditional on covariates, to remove selection bias. The results indicate that inverse probability of treatment weighting (IPTW) removes the most bias, followed by optimal full matching (OFM), and marginal mean weighting through stratification (MMWTS). The study also compared standard error estimation with Taylor series linearization, bootstrapping and the jackknife across propensity score methods. With IPTW, these standard error estimation methods performed adequately, but standard errors estimates were biased in most conditions with OFM and MMWTS. 相似文献
2.
Multilevel Structural equation models are most often estimated from a frequentist framework via maximum likelihood. However, as shown in this article, frequentist results are not always accurate. Alternatively, one can apply a Bayesian approach using Markov chain Monte Carlo estimation methods. This simulation study compared estimation quality using Bayesian and frequentist approaches in the context of a multilevel latent covariate model. Continuous and dichotomous variables were examined because it is not yet known how different types of outcomes—most notably categorical—affect parameter recovery in this modeling context. Within the Bayesian estimation framework, the impact of diffuse, weakly informative, and informative prior distributions were compared. Findings indicated that Bayesian estimation may be used to overcome convergence problems and improve parameter estimate bias. Results highlight the differences in estimation quality between dichotomous and continuous variable models and the importance of prior distribution choice for cluster-level random effects. 相似文献
3.
Serge Herzog 《Research in higher education》2018,59(3):273-301
Using data from two freshmen cohorts at a public research university (N = 3730), this study examines the relationship between loan aid and second-year enrollment persistence. Applying a counterfactual analytical framework that relies on propensity score (PS) weighting and matching to address selection bias associated with treatment status, the study estimates that loan aid exerts a significant negative effect on persistence for students from low-income background (i.e., Pell eligible), and those taking up high amounts of loans in order to meet total cost of attendance, including students who exhausted the available amount of subsidized loan aid. However, no significant incremental effect associated with unsubsidized loan aid, net of subsidized loan aid, could be detected. The estimated effect of loan aid on persistence controls for first-year academic experience and takes into account 26 factors related to loan selection and persistence in order to match students with loan aid to a counterfactual case in covariate adjusted regression. Comparison with results from non-matched-sample analysis suggests selection bias may mask the negative effect of loans detected with matched-sample estimation. Validity of covariates determining the loan selection process and criteria for acceptable balance in the matched data are discussed, and implications for future research are addressed. 相似文献
4.
This paper estimates the impact of private education on the academic achievement of low-income students in Chile. To deal with selection bias, we use propensity score matching to compare the test scores of reduced-fee paying, low-income students in fee-charging private voucher schools to those of similar students in public schools and free private voucher schools. Our results reveal that students in fee-charging private voucher schools score slightly higher than students in public schools. The difference in standardized test scores is approximately 10 points, a test score gain of 0.2 standard deviations. We find no difference in the academic achievement of students in the fee-charging private voucher treatment group relative to their counterparts in free private voucher schools. 相似文献
5.
In applied research, such as with motivation theories, typically many variables are theoretically implied predictors of an outcome and several interactions are assumed (e.g., Watt, 2004). However, estimation problems that might arise when several interaction and/or quadratic effects are analyzed simultaneously have not been investigated because simulation studies on interaction effects in the structural equation modeling framework have mainly focused on small models that contain single interaction effects. In this article, we show that traditional approaches can provide estimates with low accuracy when complex models are estimated. We introduce an adaptive Bayesian lasso approach with spike-and-slab priors that overcomes this problem. Using a complex model in a simulation study, we show that the parameter estimates of the proposed approach are more accurate in situations with high multicollinearity or low reliability compared with a standard Bayesian lasso approach and typical frequentist approaches (i.e., unconstrained product indicator approach and latent moderated structures approach). 相似文献
6.
Daniel McNeish 《Structural equation modeling》2016,23(5):750-773
As Bayesian methods continue to grow in accessibility and popularity, more empirical studies are turning to Bayesian methods to model small sample data. Bayesian methods do not rely on asympotics, a property that can be a hindrance when employing frequentist methods in small sample contexts. Although Bayesian methods are better equipped to model data with small sample sizes, estimates are highly sensitive to the specification of the prior distribution. If this aspect is not heeded, Bayesian estimates can actually be worse than frequentist methods, especially if frequentist small sample corrections are utilized. We show with illustrative simulations and applied examples that relying on software defaults or diffuse priors with small samples can yield more biased estimates than frequentist methods. We discuss conditions that need to be met if researchers want to responsibly harness the advantages that Bayesian methods offer for small sample problems as well as leading small sample frequentist methods. 相似文献
7.
Marvin A. Titus 《Research in higher education》2007,48(4):487-521
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
8.
Research in regularization, as applied to structural equation modeling (SEM), remains in its infancy. Specifically, very little work has compared regularization approaches across both frequentist and Bayesian estimation. The purpose of this study was to address just that, demonstrating both similarity and distinction across estimation frameworks, while specifically highlighting more recent developments in Bayesian regularization. This is accomplished through the use of two empirical examples that demonstrate both ridge and lasso approaches across both frequentist and Bayesian estimation, along with detail regarding software implementation. We conclude with a discussion of future research, advocating for increased evaluation and synthesis across both Bayesian and frequentist frameworks. 相似文献
9.
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
10.
Patrick J. Curran Veronica T. Cole Daniel J. Bauer W. Andrew Rothenberg Andrea M. Hussong 《Structural equation modeling》2018,25(6):860-875
Although it is currently best practice to directly model latent factors whenever feasible, there remain many situations in which this approach is not tractable. Recent advances in covariate-informed factor score estimation can be used to provide manifest scores that are used in second-stage analysis, but these are currently understudied. Here we extend our prior work on factor score recovery to examine the use of factor score estimates as predictors both in the presence and absence of the same covariates that were used in score estimation. Results show that whereas the relation between the factor score estimates and the criterion are typically well recovered, substantial bias and increased variability is evident in the covariate effects themselves. Importantly, using covariate-informed factor score estimates substantially, and often wholly, mitigates these biases. We conclude with implications for future research and recommendations for the use of factor score estimates in practice. 相似文献
11.
《Journal of research on educational effectiveness》2013,6(4):361-379
Abstract Educators and policymakers are paying increased attention to the academic outcomes of students in the middle grades (i.e., Grades 6–8). One reform proposed to improve outcomes for these students is to replace middle schools (with Grade 6–8, 7–8, or 7–9 configurations) with K-8 schools. This longitudinal study evaluated the effects of continuously attending a K-8 school, rather than transitioning from an elementary school to a middle school, on Grade 8 reading and mathematics achievement. Drawing on nationally representative data from the Early Childhood Longitudinal Study–Kindergarten 1998 cohort (N = 8,237), the study used propensity score stratification to control for observable selection bias. Findings indicated that K-8 schools produce small, significant effects for reading (effect size = 0.15 or approximately 6–8 months of schooling), but nonsignificant effects for mathematics. Results were robust to several alternative specifications, including accounting for nesting of children within schools and using different approaches for propensity score matching. Findings provide conditional support for K-8 schools, highlight the need for cost-effectiveness research on this topic, and raise questions about the specific mechanisms for K-8 schools’ advantages. 相似文献
12.
Statistical methods involving high-dimensional testing, i.e., a large number of simultaneous tests, have become important in recent days. Applications include microarrays, fMRI images and signal processing. Information that can be obtained by treating them as connected tests leads to the concept of discovery rates as well as to the Bayesian approach to hypothesis tests. It gives us great pleasure to honour Herbert Robbins who introduced the Empirical Bayes technique in statistical inference, which connects the frequentist and the Bayesian approaches in this problem. 相似文献
13.
The purpose of this study is to provide guidance on a process for including latent class predictors in regression mixture models. We first examine the performance of current practice for using the 1-step and 3-step approaches where the direct covariate effect on the outcome is omitted. None of the approaches show adequate estimates of model parameters. Given that Step 1 of the 3-step approach shows adequate results in class enumeration, we suggest using an alternative approach: (a) decide the number of latent classes without predictors of latent classes, and (b) bring the latent class predictors into the model with the inclusion of hypothesized direct covariate effects. Our simulations show that this approach leads to good estimates for all model parameters. The proposed approach is demonstrated by using empirical data to examine the differential effects of family resources on students’ academic achievement outcome. Implications of the study are discussed. 相似文献
14.
This article presents relevant research on Bayesian methods and their major applications to modeling in an effort to lay out differences between the frequentist and Bayesian paradigms and to look at the practical implications of these differences. Before research is reviewed, basic tenets and methods of the Bayesian approach to modeling are presented and contrasted with basic estimation results from a frequentist perspective. It is argued that Bayesian methods have become a viable alternative to traditional maximum likelihood-based estimation techniques and may be the only solution for more complex psychometric data structures. Hence, neither the applied nor the theoretical measurement community can afford to neglect the exciting new possibilities that have opened up on the psychometric horizon. 相似文献
15.
Sylke V. Schnepf 《Higher Education Quarterly》2017,71(1):75-96
Dropping out of university is regularly discussed as a negative indicator. However, research on actual career trajectories of dropouts is virtually non‐existent. This study estimates the association between tertiary dropouts and career chances in 15 European countries. Using data from the 2011 Programme for the International Assessment of Adult Competencies (PIAAC), estimates are derived from the application of propensity score matching taking a variety of individual background characteristics including cognitive skills into account. Results indicate that individuals are likely to fare better in the labour market if they enrol in university and drop out than if they do not enrol at all. Policy makers need to revise the notion that dropping out is purely negative. 相似文献
16.
《The Journal of educational research》2012,105(5):400-405
ABSTRACTThe 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
17.
《教育实用测度》2013,26(3):231-244
For any testing program intended for licensure, certification, competency, or proficiency, the estimation of content relevant test scores for pass/fail decision making is necessary. This study compares number-correct scoring to empirical option weighting in the context of such tests. The study was conducted under two test design conditions, three test length conditions, and four passing score levels. Two criteria were used to evaluate the effectiveness of empirical option weighting versus number-correct scoring. Empirical option weighting typically produced slightly more reliable domain score estimates and more consistent pass/fail decisions than number-correct scoring, particularly in the lower half of the test score distribution. For many types of testing programs where the passing scores are established in the lower half of the test score distribution, the empirical option weighting method used in this study seems both appropriate and effective in improving the depend- ability of test scores and the consistency of pass/fail decisions. Test users, however, must weigh the effort required to use option weighting against the small gains obtained with this method. Other problems are discussed that may limit the usefulness of option weighting. 相似文献
18.
This paper uses a nationally representative sample from the 2004–09 Beginning Postsecondary Students (BPS) survey to examine the effect of education tax benefits on college completion. The paper employs a propensity score matching approach to correct for differences between eligible and ineligible students. Results suggest that tax benefits increase the likelihood of completing a college degree by 8 percentage points. The effect of tax benefits is largest for students who attended private and four-year institutions. 相似文献
19.
Coffman DL 《Structural equation modeling》2011,18(3):357-369
Mediation is usually assessed by a regression-based or structural equation modeling (SEM) approach that we will refer to as the classical approach. This approach relies on the assumption that there are no confounders that influence both the mediator, M, and the outcome, Y. This assumption holds if individuals are randomly assigned to levels of M but generally random assignment is not possible. We propose the use of propensity scores to help remove the selection bias that may result when individuals are not randomly assigned to levels of M. The propensity score is the probability that an individual receives a particular level of M. Results from a simulation study are presented to demonstrate this approach, referred to as Classical + Propensity Model (C+PM), confirming that the population parameters are recovered and that selection bias is successfully dealt with. Comparisons are made to the classical approach that does not include propensity scores. Propensity scores were estimated by a logistic regression model. If all confounders are included in the propensity model, then the C+PM is unbiased. If some, but not all, of the confounders are included in the propensity model, then the C+PM estimates are biased although not as severely as the classical approach (i.e. no propensity model is included). 相似文献
20.
Justin C. Ortagus 《Research in higher education》2018,59(8):1035-1058
This study examines the influence of first-year online enrollment on the long-term academic outcomes of postsecondary students. Using a nationally representative sample and propensity score weighting, I find that enrolling in some online courses is associated with lower odds of dropping out of college. Additional results reveal a positive relationship between enrolling in some online courses and sub-baccalaureate indicators of long-term academic success, such as earning an associate’s degree and transferring from a community college to a 4-year institution. 相似文献