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1.
Small samples are common in growth models due to financial and logistical difficulties of following people longitudinally. For similar reasons, longitudinal studies often contain missing data. Though full information maximum likelihood (FIML) is popular to accommodate missing data, the limited number of studies in this area have found that FIML tends to perform poorly with small-sample growth models. This report demonstrates that the fault lies not with how FIML accommodates missingness but rather with maximum likelihood estimation itself. We discuss how the less popular restricted likelihood form of FIML, along with small-sample-appropriate methods, yields trustworthy estimates for growth models with small samples and missing data. That is, previously reported small sample issues with FIML are attributable to finite sample bias of maximum likelihood estimation not direct likelihood. Estimation issues pertinent to joint multiple imputation and predictive mean matching are also included and discussed.  相似文献   

2.
Using Monte Carlo simulations, this research examined the performance of four missing data methods in SEM under different multivariate distributional conditions. The effects of four independent variables (sample size, missing proportion, distribution shape, and factor loading magnitude) were investigated on six outcome variables: convergence rate, parameter estimate bias, MSE of parameter estimates, standard error coverage, model rejection rate, and model goodness of fit—RMSEA. A three-factor CFA model was used. Findings indicated that FIML outperformed the other methods in MCAR, and MI should be used to increase the plausibility of MAR. SRPI was not comparable to the other three methods in either MCAR or MAR.  相似文献   

3.
Marginal likelihood-based methods are commonly used in factor analysis for ordinal data. To obtain the maximum marginal likelihood estimator, the full information maximum likelihood (FIML) estimator uses the (adaptive) Gauss–Hermite quadrature or stochastic approximation. However, the computational burden increases rapidly as the number of factors increases, which renders FIML impractical for large factor models. Another limitation of the marginal likelihood-based approach is that it does not allow inference on the factors. In this study, we propose a hierarchical likelihood approach using the Laplace approximation that remains computationally efficient in large models. We also proposed confidence intervals for factors, which maintains the level of confidence as the sample size increases. The simulation study shows that the proposed approach generally works well.  相似文献   

4.
A well-known ad-hoc approach to conducting structural equation modeling with missing data is to obtain a saturated maximum likelihood (ML) estimate of the population covariance matrix and then to use this estimate in the complete data ML fitting function to obtain parameter estimates. This 2-stage (TS) approach is appealing because it minimizes a familiar function while being only marginally less efficient than the full information ML (FIML) approach. Additional advantages of the TS approach include that it allows for easy incorporation of auxiliary variables and that it is more stable in smaller samples. The main disadvantage is that the standard errors and test statistics provided by the complete data routine will not be correct. Empirical approaches to finding the right corrections for the TS approach have failed to provide unequivocal solutions. In this article, correct standard errors and test statistics for the TS approach with missing completely at random and missing at random normally distributed data are developed and studied. The new TS approach performs well in all conditions, is only marginally less efficient than the FIML approach (and is sometimes more efficient), and has good coverage. Additionally, the residual-based TS statistic outperforms the FIML test statistic in smaller samples. The TS method is thus a viable alternative to FIML, especially in small samples, and its further study is encouraged.  相似文献   

5.
Myriad approaches for handling missing data exist in the literature. However, few studies have investigated the tenability and utility of these approaches when used with intensive longitudinal data. In this study, we compare and illustrate two multiple imputation (MI) approaches for coping with missingness in fitting multivariate time-series models under different missing data mechanisms. They include a full MI approach, in which all dependent variables and covariates are imputed simultaneously, and a partial MI approach, in which missing covariates are imputed with MI, whereas missingness in the dependent variables is handled via full information maximum likelihood estimation. We found that under correctly specified models, partial MI produces the best overall estimation results. We discuss the strengths and limitations of the two MI approaches, and demonstrate their use with an empirical data set in which children’s influences on parental conflicts are modeled as covariates over the course of 15 days (Schermerhorn, Chow, & Cummings, 2010).  相似文献   

6.
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   

7.
《教育实用测度》2013,26(3):237-256
This study evaluated two methods for establishing weights for test plans for certification examinations. One method required a panel of experts to provide holistic judgments indicating the percentage of test questions to allocate to each content category. Weights were first obtained from individual panel members, discussed by the entire panel, and then finalized by group consensus. The other method derived weights using a statistical model. The model included ratings of task frequency and task criticality provided by a large sample of practitioners as well as information from a panel of experts concerning the linkages between specific tasks and the knowledge and skills required to perform those tasks. The study was replicated for four medical imaging specialties in the field of radiologic technology. The weights for the two methods exhibited moderate to high levels of agreement for sections of the test plans comprised of specific imaging procedures. However, there was much less agreement for those sections of the test plans that addressed more general topics. Possible reasons for the observed pattern of agreement and disagreement are considered.  相似文献   

8.
In structural equation modeling software, either limited-information (bivariate proportions) or full-information item parameter estimation routines could be used for the 2-parameter item response theory (IRT) model. Limited-information methods assume the continuous variable underlying an item response is normally distributed. For skewed and platykurtic latent variable distributions, 3 methods were compared in Mplus: limited information, full information integrating over a normal distribution, and full information integrating over the known underlying distribution. Interfactor correlation estimates were similar for all 3 estimation methods. For the platykurtic distribution, estimation method made little difference for the item parameter estimates. When the latent variable was negatively skewed, for the most discriminating easy or difficult items, limited-information estimates of both parameters were considerably biased. Full-information estimates obtained by marginalizing over a normal distribution were somewhat biased. Full-information estimates obtained by integrating over the true latent distribution were essentially unbiased. For the a parameters, standard errors were larger for the limited-information estimates when the bias was positive but smaller when the bias was negative. For the d parameters, standard errors were larger for the limited-information estimates of the easiest, most discriminating items. Otherwise, they were generally similar for the limited- and full-information estimates. Sample size did not substantially impact the differences between the estimation methods; limited information did not gain an advantage for smaller samples.  相似文献   

9.
Several structural equation modeling (SEM) strategies were developed for assessing measurement invariance (MI) across groups relaxing the assumptions of strict MI to partial, approximate, and partial approximate MI. Nonetheless, applied researchers still do not know if and under what conditions these strategies might provide results that allow for valid comparisons across groups in large-scale comparative surveys. We perform a comprehensive Monte Carlo simulation study to assess the conditions under which various SEM methods are appropriate to estimate latent means and path coefficients and their differences across groups. We find that while SEM path coefficients are relatively robust to violations of full MI and can be rather effectively recovered, recovering latent means and their group rankings might be difficult. Our results suggest that, contrary to some previous recommendations, partial invariance may rather effectively recover both path coefficients and latent means even when the majority of items are noninvariant. Although it is more difficult to recover latent means using approximate and partial approximate MI methods, it is possible under specific conditions and using appropriate models. These models also have the advantage of providing accurate standard errors. Alignment is recommended for recovering latent means in cases where there are only a few noninvariant parameters across groups.  相似文献   

10.
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   

11.
The purpose of this article is to examine the use of sample weights in the latent variable modeling context. A sample weight is the inverse of the probability that the unit in question was sampled and is used to obtain unbiased estimates of population parameters when units have unequal probabilities of inclusion in a sample. Although sample weights are discussed at length in survey research literature, virtually no discussion of sample weights can be found in the latent variable modeling literature. This article examines sample weights in latent variable models applied to the case where a simple random sample is drawn from a population containing a mixture of strata. A bootstrap simulation study is used to compare raw and normalized sample weights to conditions where weights are ignored. The results show that ignoring weights can lead to serious bias in latent variable model parameters and that this bias is mitigated by the incorporation of sample weights. Standard errors appear to be underestimated when sample weights are applied. Results on goodness‐of‐fit statistics demonstrate the advantages of utilizing sample weights.  相似文献   

12.
When analyzing incomplete data, is it better to use multiple imputation (MI) or full information maximum likelihood (ML)? In large samples ML is clearly better, but in small samples ML’s usefulness has been limited because ML commonly uses normal test statistics and confidence intervals that require large samples. We propose small-sample t-based ML confidence intervals that have good coverage and are shorter than t-based confidence intervals under MI. We also show that ML point estimates are less biased and more efficient than MI point estimates in small samples of bivariate normal data. With our new confidence intervals, ML should be preferred over MI, even in small samples, whenever both options are available.  相似文献   

13.
Missing data is endemic in much educational research. However, practices such as step-wise regression common in the educational research literature have been shown to be dangerous when significant data are missing, and multiple imputation (MI) is generally recommended by statisticians. In this paper, we provide a review of these advances and their implications for educational research. We illustrate the issues with an educational, longitudinal survey in which missing data was significant, but for which we were able to collect much of these missing data through subsequent data collection. We thus compare methods, that is, step-wise regression (basically ignoring the missing data) and MI models, with the model from the actual enhanced sample. The value of MI is discussed and the risks involved in ignoring missing data are considered. Implications for research practice are discussed.  相似文献   

14.
Sample attrition increases the risk of statistical bias and hinders the ability to plausibly estimate causal effects when patterns of nonresponse are correlated with key variables of interest. Drawing on leverage-salience theory and other work in the behavioral psychology field, we empirically capture the impact of distinct motivational appeals on the survey response rates of elementary education teachers in a large urban school district in the northeastern United States. During spring 2017, teachers were randomized to receive one of six motivational appeals and were rerandomized to receive a different appeal each subsequent week, conditional on not having completed the survey. We observe the results on four different margins that range in their time intensity (open email and click, start, and complete survey). We find that extrinsic rewards improved teacher response across all four margins, and the social norm of reciprocity substantially improved teacher response along margins of lower time intensity. As researchers continue to conduct multitreatment arm studies and large-scale evaluations that can suffer from serious issues of sample attrition, this work highlights the contribution of message framing in survey response.  相似文献   

15.
The mutual exclusivity bias in children's word learning   总被引：2，自引：0，他引：2  
Nearly every recent account of children's word learning has addressed the claim that children are biased to construct mutually exclusive extensions, that is, that they are disposed to keep the set of referents of one word from overlapping with those of others. Three basic positions have been taken--that children have the bias when they first start to learn words, that they never have it, and that they acquire it during early childhood. A review of diary and test evidence as well as the results of four experiments provide strong support for this last view and indicate that the bias develops in the months following the second birthday but does not gain full strength or become accessible to consciousness until sometime after the third birthday. Several studies also show that, after this point, it can still be counteracted by information in input or by a strong belief that something belongs to the extension of a particular word. The full body of evidence is compatible with the view that mutual exclusivity is the default option in children's and adults' procedures for integrating the extensions of new and old words. We present several arguments for the adaptive value of this kind of bias.  相似文献   

16.
本文基于1990-2008年的省际面板数据,采用面板协整和面板误差修正模型的计量方法,就我国滞后的城镇化对居民消费的影响效应进行了深入的实证分析,研究结果表明:长期来看,滞后的城镇化尽管也促进了居民消费增长,但是,城镇化对我国居民消费的扩大效应没有完全发挥,使得消费对城镇化的弹性下降0.041677;短期来看,滞后的城镇化促进了居民消费、改变了居民的消费习惯,并且使其自动平衡自身的消费支出和提高了居民消费需求对财政预算支出的弹性.  相似文献   

17.
With the increasing use of international survey data especially in cross-cultural and multinational studies, establishing measurement invariance (MI) across a large number of groups in a study is essential. Testing MI over many groups is methodologically challenging, however. We identified 5 methods for MI testing across many groups (multiple group confirmatory factor analysis, multilevel confirmatory factor analysis, multilevel factor mixture modeling, Bayesian approximate MI testing, and alignment optimization) and explicated the similarities and differences of these approaches in terms of their conceptual models and statistical procedures. A Monte Carlo study was conducted to investigate the efficacy of the 5 methods in detecting measurement noninvariance across many groups using various fit criteria. Generally, the 5 methods showed reasonable performance in identifying the level of invariance if an appropriate fit criterion was used (e.g., Bayesian information criteron with multilevel factor mixture modeling). Finally, general guidelines in selecting an appropriate method are provided.  相似文献   

18.
This article examines the effects of work intensity on adolescent mental health, academic achievement, and behavioral adjustment. Questionnaire data were collected yearly from an initial panel of 1,000 randomly selected ninth graders (14-15 years old). Consistent with other studies, students who worked at higher intensity engaged in more alcohol use. The methodological strengths of this research (a representative panel studied prospectively over a 4-year period with minimal attrition and an analysis incorporating key control and lagged variables) provide strong evidence that adolescent work fosters alcohol use. The contention that work of high intensity has deleterious effects on mental health, academic achievement, and 2 other indicators of behavioral adjustment did not withstand our stringent tests. However, high school seniors who worked at moderate intensity (1-20 hours per week) had higher grades than both nonworkers and students who worked more hours per week.  相似文献   

19.
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   

20.
Over the past decade and a half, methodologists working with structural equation modeling (SEM) have developed approaches for accommodating multilevel data. These approaches are particularly helpful when modeling data that come from complex sampling designs. However, most data sets that are associated with complex sampling designs also include observation weights, and methods to incorporate these sampling weights into multilevel SEM analyses have not been addressed. This article investigates the use of different weighting techniques and finds, through a simulation study, that the use of an effective sample size weight provides unbiased estimates of key parameters and their sampling variances. Also, a popular normalization technique of scaling weights to reflect the actual sample size is shown to produce negatively biased sampling variance estimates, as well as negatively biased within-group variance parameter estimates in the small group size case.  相似文献   

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