排序方式： 共有18条查询结果，搜索用时 15 毫秒
1.
Yan Wang Eunsook Kim John M. Ferron Robert F. Dedrick Tony X. Tan Stephen Stark 《Educational and psychological measurement》2021,81(1):61
Factor mixture modeling (FMM) has been increasingly used to investigate unobserved population heterogeneity. This study examined the issue of covariate effects with FMM in the context of measurement invariance testing. Specifically, the impact of excluding and misspecifying covariate effects on measurement invariance testing and class enumeration was investigated via Monte Carlo simulations. Data were generated based on FMM models with (1) a zero covariate effect, (2) a covariate effect on the latent class variable, and (3) covariate effects on both the latent class variable and the factor. For each population model, different analysis models that excluded or misspecified covariate effects were fitted. Results highlighted the importance of including proper covariates in measurement invariance testing and evidenced the utility of a model comparison approach in searching for the correct specification of covariate effects and the level of measurement invariance. This approach was demonstrated using an empirical data set. Implications for methodological and applied research are discussed. 相似文献
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Educational Psychology Review - Previous simulation studies of randomization tests applied in single-case educational intervention research contexts have typically focused on A-to-B phase changes... 相似文献
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Ian Hembry Rommel Bunuan John M. Ferron Wim Van den Noortgate 《Journal of Experimental Education》2015,83(4):514-546
A multilevel logistic model for estimating a nonlinear trajectory in a multiple-baseline design is introduced. The model is applied to data from a real multiple-baseline design study to demonstrate interpretation of relevant parameters. A simple change-in-levels (ΔLevels) model and a model involving a quadratic function (Quadratic) for the nonlinear intervention phase data were also estimated. In addition, a simulation study was conducted to assess Markov chain Monte Carlo estimation of the logistic model and compare its trajectory recovery with use of the ΔLevels and Quadratic models. While most of the logistic model's parameter values were recovered well, trajectory recovery was very reasonable using the simpler Quadratic model. Results are discussed along with recommendations for practitioners and directions for future research. 相似文献
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Eunkyeng Baek S. Natasha Beretvas Wim Van den Noortgate John M. Ferron 《Journal of Experimental Education》2020,88(4):698-710
AbstractRecently, researchers have used multilevel models for estimating intervention effects in single-case experiments that include replications across participants (e.g., multiple baseline designs) or for combining results across multiple single-case studies. Researchers estimating these multilevel models have primarily relied on restricted maximum likelihood (REML) techniques, but Bayesian approaches have also been suggested. The purpose of this Monte Carlo simulation study was to examine the impact of estimation method (REML versus Bayesian with noninformative priors) on the estimation of treatment effects (relative bias, root mean square error) and on the inferences about those effects (interval coverage) for autocorrelated multiple-baseline data. Simulated conditions varied with regard to the number of participants, series length, and distribution of the variance within and across participants. REML and Bayesian estimation led to estimates of the fixed effects that showed little to no bias but that differentially impacted the inferences about the fixed effects and the estimates of the variances. Implications for applied researchers and methodologists are discussed. 相似文献
5.
This paper assesses the relative contribution of maternal, household, child, and poverty characteristics to the quality of the home environment. The sample consists of 1,887 children birth to 4 years old from the National Longitudinal Survey of Youth (NLSY). Ordinary least-squares regression is used to explore conceptually distinct aspects of children's poverty experiences. Poverty variables are found to have a statistically significant effect on the quality of the home environment, after controlling for the effects of other variables in the models. Statistically significant interactions among poverty variables are identified. A major finding is that improvements in family income have the strongest effects on the quality of the home environment for children who were born poor or lived much of their lives in poverty. The conclusion emphasizes implications for future research. 相似文献
6.
Linda M. Raffaele Mendez Howard M. Knoff John M. Ferron 《Psychology in the schools》2002,39(3):259-277
This study examined out‐of‐school suspensions (OSS) in a large, ethnically diverse school district using both quantitative and qualitative procedures. Pearson product moment correlations and semi‐partial correlations were used to identify those school‐level variables that showed the strongest relationships to the duplicated OSS rate among elementary schools (n = 97) and secondary schools (n = 45). Additionally, interviews were conducted with administrators and student support personnel from the 24 schools in the district with the highest suspension rates and 24 demographically matched schools with significantly lower suspension rates. The majority of these schools served a high percentage of children from low socioeconomic backgrounds. Although the correlational analyses indicated that student demographic variables (e.g., percentage of White students, percentage of Black students, percentage of students receiving free or reduced price lunch) were strongly related to a school's suspension rate, the school comparisons showed that not all schools serving a high percentage of children placed at risk have high suspension rates. Implications of the findings for school discipline reform are discussed. © 2002 Wiley Periodicals, Inc. 相似文献
7.
Mariola Moeyaert Maaike Ugille John M. Ferron S. Natasha Beretvas Wim Van den Noortgate 《Journal of Experimental Education》2016,84(3):473-509
The impact of misspecifying covariance matrices at the second and third levels of the three-level model is evaluated. Results indicate that ignoring existing covariance has no effect on the treatment effect estimate. In addition, the between-case variance estimates are unbiased when covariance is either modeled or ignored. If the research interest lies in the between-study variance estimate, including at least 30 studies is warranted. Modeling covariance does not result in less biased between-study variance estimates as the between-study covariance estimate is biased. When the research interest lies in the between-case covariance, the model including covariance results in unbiased between-case variance estimates. The three-level model appears to be less appropriate for estimating between-study variance if fewer than 30 studies are included. 相似文献
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Maaike Ugille Mariola Moeyaert S. Natasha Beretvas John M. Ferron Wim Van den Noortgate 《Journal of Experimental Education》2013,81(3):358-374
A multilevel meta-analysis can combine the results of several single-subject experimental design studies. However, the estimated effects are biased if the effect sizes are standardized and the number of measurement occasions is small. In this study, the authors investigated 4 approaches to correct for this bias. First, the standardized effect sizes are adjusted using Hedges’ small sample bias correction. Next, the within-subject standard deviation is estimated by a 2-level model per study or by using a regression model with the subjects identified using dummy predictor variables. The effect sizes are corrected using an iterative raw data parametric bootstrap procedure. The results indicate that the first and last approach succeed in reducing the bias of the fixed effects estimates. Given the difference in complexity, we recommend the first approach. 相似文献
10.
John Ferron Lynn Foster-Johnson Jeffrey D. Kromrey 《Journal of Experimental Education》2013,81(3):267-288
The authors used Monte Carlo methods to examine the Type I error rates for randomization tests applied to single-case data arising from ABAB designs involving random, systematic, or response-guided assignment of interventions. Six randomization tests were examined (permuting blocks of 1, 2, 3, or 5 observations, and randomly selecting intervention triplets so that each phase has at least 3 or 5 observations). When the design included randomization, the Type I error rate was controlled. When the design was systematic or guided by the absolute value of the slope, the tests permuting blocks tended to be liberal with positive autocorrelation, whereas those based on the random selection of intervention triplets tended to be conservative across levels of autocorrelation. 相似文献