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Boris S Gafurov - One of the best experts on this subject based on the ideXlab platform.
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Investigation of Single-Case Multiple-Baseline Randomization Tests of Trend and Variability
Educational Psychology Review, 2020Co-Authors: Joel R Levin, John M Ferron, Boris S GafurovAbstract:Previous simulation studies of Randomization Tests applied in single-case educational intervention research contexts have typically focused on A-to-B phase changes in means/levels. In the present simulation study, we report the results of two multiple-baseline investigations, one targeting between-phase changes in slopes/trends and the other targeting between-phase changes in variability. For each of these measures, we examine the comparative type I errors and powers of several Randomization Test procedures that have previously appeared in the literature. In so doing, we propose an alternative measure of variability that is more sensitive to detecting between-phase change than is the variance itself. We conclude by providing a summary table of recommended Randomization Test procedures for assessing different types of intervention-based effects associated with level, trend, and variability.
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comparison of Randomization Test procedures for single case multiple baseline designs
Developmental Neurorehabilitation, 2018Co-Authors: Joel R Levin, John M Ferron, Boris S GafurovAbstract:: In three simulation investigations, we examined the statistical properties of several different Randomization-Test procedures for analyzing the data from single-case multiple-baseline intervention studies. Two procedures (Wampold-Worsham and Revusky) are associated with single fixed intervention start points and three are associated with randomly determined intervention start points. Of the latter three, one (Koehler-Levin) is an existing procedure that has been previously examined and the other two (modified Revusky and restricted Marascuilo-Busk) are modifications and extensions of existing procedures. All five procedures were found to maintain their Type I error probabilities at acceptable levels. In most of the conditions investigated here, two of the random start-point procedures (Koehler-Levin and restricted Marascuilo-Busk) were more powerful than the others with respect to detecting immediate abrupt intervention effects. For designs in which it is not possible to include the same series lengths for all cases, either the modified Revusky or restricted Marascuilo-Busk procedure is recommended.
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additional comparisons of Randomization Test procedures for single case multiple baseline designs alternative effect types
Journal of School Psychology, 2017Co-Authors: Joel R Levin, John M Ferron, Boris S GafurovAbstract:A number of Randomization statistical procedures have been developed to analyze the results from single-case multiple-baseline intervention investigations. In a previous simulation study, comparisons of the various procedures revealed distinct differences among them in their ability to detect immediate abrupt intervention effects of moderate size, with some procedures (typically those with randomized intervention start points) exhibiting power that was both respectable and superior to other procedures (typically those with single fixed intervention start points). In Investigation 1 of the present follow-up simulation study, we found that when the same Randomization-Test procedures were applied to either delayed abrupt or immediate gradual intervention effects: (1) the powers of all of the procedures were severely diminished; and (2) in contrast to the previous study's results, the single fixed intervention start-point procedures generally outperformed those with randomized intervention start points. In Investigation 2 we additionally demonstrated that if researchers are able to successfully anticipate the specific alternative effect types, it is possible for them to formulate adjusted versions of the original Randomization-Test procedures that can recapture substantial proportions of the lost powers.
Joel R Levin - One of the best experts on this subject based on the ideXlab platform.
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Investigation of Single-Case Multiple-Baseline Randomization Tests of Trend and Variability
Educational Psychology Review, 2020Co-Authors: Joel R Levin, John M Ferron, Boris S GafurovAbstract:Previous simulation studies of Randomization Tests applied in single-case educational intervention research contexts have typically focused on A-to-B phase changes in means/levels. In the present simulation study, we report the results of two multiple-baseline investigations, one targeting between-phase changes in slopes/trends and the other targeting between-phase changes in variability. For each of these measures, we examine the comparative type I errors and powers of several Randomization Test procedures that have previously appeared in the literature. In so doing, we propose an alternative measure of variability that is more sensitive to detecting between-phase change than is the variance itself. We conclude by providing a summary table of recommended Randomization Test procedures for assessing different types of intervention-based effects associated with level, trend, and variability.
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comparison of Randomization Test procedures for single case multiple baseline designs
Developmental Neurorehabilitation, 2018Co-Authors: Joel R Levin, John M Ferron, Boris S GafurovAbstract:: In three simulation investigations, we examined the statistical properties of several different Randomization-Test procedures for analyzing the data from single-case multiple-baseline intervention studies. Two procedures (Wampold-Worsham and Revusky) are associated with single fixed intervention start points and three are associated with randomly determined intervention start points. Of the latter three, one (Koehler-Levin) is an existing procedure that has been previously examined and the other two (modified Revusky and restricted Marascuilo-Busk) are modifications and extensions of existing procedures. All five procedures were found to maintain their Type I error probabilities at acceptable levels. In most of the conditions investigated here, two of the random start-point procedures (Koehler-Levin and restricted Marascuilo-Busk) were more powerful than the others with respect to detecting immediate abrupt intervention effects. For designs in which it is not possible to include the same series lengths for all cases, either the modified Revusky or restricted Marascuilo-Busk procedure is recommended.
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additional comparisons of Randomization Test procedures for single case multiple baseline designs alternative effect types
Journal of School Psychology, 2017Co-Authors: Joel R Levin, John M Ferron, Boris S GafurovAbstract:A number of Randomization statistical procedures have been developed to analyze the results from single-case multiple-baseline intervention investigations. In a previous simulation study, comparisons of the various procedures revealed distinct differences among them in their ability to detect immediate abrupt intervention effects of moderate size, with some procedures (typically those with randomized intervention start points) exhibiting power that was both respectable and superior to other procedures (typically those with single fixed intervention start points). In Investigation 1 of the present follow-up simulation study, we found that when the same Randomization-Test procedures were applied to either delayed abrupt or immediate gradual intervention effects: (1) the powers of all of the procedures were severely diminished; and (2) in contrast to the previous study's results, the single fixed intervention start-point procedures generally outperformed those with randomized intervention start points. In Investigation 2 we additionally demonstrated that if researchers are able to successfully anticipate the specific alternative effect types, it is possible for them to formulate adjusted versions of the original Randomization-Test procedures that can recapture substantial proportions of the lost powers.
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extensions of a versatile Randomization Test for assessing single case intervention effects
Journal of School Psychology, 2011Co-Authors: Joel R Levin, Venessa F Lall, Thomas R KratochwillAbstract:Abstract The purpose of the present study was to investigate the statistical properties of two extensions of the Levin–Wampold (1999) single-case simultaneous start-point model's comparative effectiveness Randomization Test. The two extensions were (a) adapting the Test to situations where there are more than two different intervention conditions and (b) examining the Test's performance in classroom-based intervention situations, where the number of time periods (and associated outcome observations) is much smaller than in the contexts for which the Test was originally developed. Various Monte Carlo sampling situations were investigated, including from one to five participant blocks per condition and differing numbers of time periods, potential intervention start points, degrees of within-phase autocorrelation, and effect sizes. For all situations, it was found that the Type I error probability of the Randomization Test was maintained at an acceptable level. With a few notable exceptions, respectable power was observed only in situations where the numbers of observations and potential intervention start points were relatively large, effect sizes were large, and the degree of within-phase autocorrelation was relatively low. It was concluded that the comparative effectiveness Randomization Test, with its desirable internal validity and statistical-conclusion validity features, is a versatile analytic tool that can be incorporated into a variety of single-case school psychology intervention research situations.
Patrick Onghena - One of the best experts on this subject based on the ideXlab platform.
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Handling missing data in Randomization Tests for single-case experiments: A simulation study
Behavior Research Methods, 2020Co-Authors: Tamal Kumar De, Bart Michiels, René Tanious, Patrick OnghenaAbstract:Single-case experiments have become increasingly popular in psychological and educational research. However, the analysis of single-case data is often complicated by the frequent occurrence of missing or incomplete data. If missingness or incompleteness cannot be avoided, it becomes important to know which strategies are optimal, because the presence of missing data or inadequate data handling strategies may lead to experiments no longer “meeting standards” set by, for example, the What Works Clearinghouse. For the examination and comparison of strategies to handle missing data, we simulated complete datasets for ABAB phase designs, randomized block designs, and multiple-baseline designs. We introduced different levels of missingness in the simulated datasets by randomly deleting 10%, 30%, and 50% of the data. We evaluated the type I error rate and statistical power of a Randomization Test for the null hypothesis that there was no treatment effect under these different levels of missingness, using different strategies for handling missing data: (1) randomizing a missing-data marker and calculating all reference statistics only for the available data points, (2) estimating the missing data points by single imputation using the state space representation of a time series model, and (3) multiple imputation based on regressing the available data points on preceding and succeeding data points. The results are conclusive for the conditions simulated: The randomized-marker method outperforms the other two methods in terms of statistical power in a Randomization Test, while keeping the type I error rate under control.
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A Randomization Test wrapper for synthesizing single-case experiments using multilevel models: A Monte Carlo simulation study
Behavior Research Methods, 2019Co-Authors: Bart Michiels, René Tanious, Tamal Kumar De, Patrick OnghenaAbstract:Multilevel models (MLMs) have been proposed in single-case research, to synthesize data from a group of cases in a multiple-baseline design (MBD). A limitation of this approach is that MLMs require several statistical assumptions that are often violated in single-case research. In this article we propose a solution to this limitation by presenting a Randomization Test (RT) wrapper for MLMs that offers a nonparametric way to evaluate treatment effects, without making distributional assumptions or an assumption of random sampling. We present the rationale underlying the proposed technique and validate its performance (with respect to Type I error rate and power) as compared to parametric statistical inference in MLMs, in the context of evaluating the average treatment effect across cases in an MBD. We performed a simulation study that manipulated the numbers of cases and of observations per case in a dataset, the data variability between cases, the distributional characteristics of the data, the level of autocorrelation, and the size of the treatment effect in the data. The results showed that the power of the RT wrapper is superior to the power of parametric Tests based on F distributions for MBDs with fewer than five cases, and that the Type I error rate of the RT wrapper is controlled for bimodal data, whereas this is not the case for traditional MLMs.
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Randomization Tests for changing criterion designs
Behaviour Research and Therapy, 2019Co-Authors: Patrick Onghena, Tamal Kumar De, René Tanious, Bart MichielsAbstract:Abstract Randomization Tests for alternating treatments designs, multiple baseline designs, and withdrawal/reversal designs are well-established. Recent classifications, however, also mention the “changing criterion design” as a fourth important type of single-case experimental design. In this paper, we examine the potential of Randomization Tests for changing criterion designs. We focus on the rationale of the Randomization Test, the random assignment procedure, the choice of the Test statistic, and the calculation of Randomization Test p-values. Two examples using empirical data and an R computer program to perform the calculations are provided. We discuss the problems associated with conceptualizing the changing criterion design as a variant of the multiple baseline design, the potential of the range-bound changing criterion design, experimental control as an all-or-none phenomenon, the necessity of random assignment for the statistical-conclusion validity of the Randomization Test, and the use of Randomization Tests in nonrandomized designs.
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confidence intervals for single case effect size measures based on Randomization Test inversion
Behavior Research Methods, 2017Co-Authors: Bart Michiels, Mieke Heyvaert, Ann Meulders, Patrick OnghenaAbstract:In the current paper, we present a method to construct nonparametric confidence intervals (CIs) for single-case effect size measures in the context of various single-case designs. We use the relationship between a two-sided statistical hypothesis Test at significance level α and a 100 (1 – α) % two-sided CI to construct CIs for any effect size measure θ that contain all point null hypothesis θ values that cannot be rejected by the hypothesis Test at significance level α. This method of hypothesis Test inversion (HTI) can be employed using a Randomization Test as the statistical hypothesis Test in order to construct a nonparametric CI for θ. We will refer to this procedure as Randomization Test inversion (RTI). We illustrate RTI in a situation in which θ is the unstandardized and the standardized difference in means between two treatments in a completely randomized single-case design. Additionally, we demonstrate how RTI can be extended to other types of single-case designs. Finally, we discuss a few challenges for RTI as well as possibilities when using the method with other effect size measures, such as rank-based nonoverlap indices. Supplementary to this paper, we provide easy-to-use R code, which allows the user to construct nonparametric CIs according to the proposed method.
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Randomization Tests for restricted alternating treatments designs.
Behaviour Research and Therapy, 1994Co-Authors: Patrick Onghena, Eugene S. EdgingtonAbstract:Abstract Alternating Treatments Designs (ATD) with random assignment of the treatments to the measurement times provide very powerful single-case experiments. However, complete Randomization might cause too many consecutive administrations of the same treatment to occur in the design. In order to exclude these possibilities, an ATD with restricted Randomization can be used. In this article we provide a general rationale for the random assignment procedure in such a Restricted Alternating Treatments Design (RATD), and derive the corresponding Randomization Test. A software package for Randomization Tests in RATD, ATD and other single-case experimental designs [Van Damme & Onghena Single-case Randomization Tests, version 1.1, Department of Psychology, Katholieke Universiteit Leuven, Belgium] is discussed.
John M Ferron - One of the best experts on this subject based on the ideXlab platform.
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Investigation of Single-Case Multiple-Baseline Randomization Tests of Trend and Variability
Educational Psychology Review, 2020Co-Authors: Joel R Levin, John M Ferron, Boris S GafurovAbstract:Previous simulation studies of Randomization Tests applied in single-case educational intervention research contexts have typically focused on A-to-B phase changes in means/levels. In the present simulation study, we report the results of two multiple-baseline investigations, one targeting between-phase changes in slopes/trends and the other targeting between-phase changes in variability. For each of these measures, we examine the comparative type I errors and powers of several Randomization Test procedures that have previously appeared in the literature. In so doing, we propose an alternative measure of variability that is more sensitive to detecting between-phase change than is the variance itself. We conclude by providing a summary table of recommended Randomization Test procedures for assessing different types of intervention-based effects associated with level, trend, and variability.
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comparison of Randomization Test procedures for single case multiple baseline designs
Developmental Neurorehabilitation, 2018Co-Authors: Joel R Levin, John M Ferron, Boris S GafurovAbstract:: In three simulation investigations, we examined the statistical properties of several different Randomization-Test procedures for analyzing the data from single-case multiple-baseline intervention studies. Two procedures (Wampold-Worsham and Revusky) are associated with single fixed intervention start points and three are associated with randomly determined intervention start points. Of the latter three, one (Koehler-Levin) is an existing procedure that has been previously examined and the other two (modified Revusky and restricted Marascuilo-Busk) are modifications and extensions of existing procedures. All five procedures were found to maintain their Type I error probabilities at acceptable levels. In most of the conditions investigated here, two of the random start-point procedures (Koehler-Levin and restricted Marascuilo-Busk) were more powerful than the others with respect to detecting immediate abrupt intervention effects. For designs in which it is not possible to include the same series lengths for all cases, either the modified Revusky or restricted Marascuilo-Busk procedure is recommended.
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additional comparisons of Randomization Test procedures for single case multiple baseline designs alternative effect types
Journal of School Psychology, 2017Co-Authors: Joel R Levin, John M Ferron, Boris S GafurovAbstract:A number of Randomization statistical procedures have been developed to analyze the results from single-case multiple-baseline intervention investigations. In a previous simulation study, comparisons of the various procedures revealed distinct differences among them in their ability to detect immediate abrupt intervention effects of moderate size, with some procedures (typically those with randomized intervention start points) exhibiting power that was both respectable and superior to other procedures (typically those with single fixed intervention start points). In Investigation 1 of the present follow-up simulation study, we found that when the same Randomization-Test procedures were applied to either delayed abrupt or immediate gradual intervention effects: (1) the powers of all of the procedures were severely diminished; and (2) in contrast to the previous study's results, the single fixed intervention start-point procedures generally outperformed those with randomized intervention start points. In Investigation 2 we additionally demonstrated that if researchers are able to successfully anticipate the specific alternative effect types, it is possible for them to formulate adjusted versions of the original Randomization-Test procedures that can recapture substantial proportions of the lost powers.
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a Randomization Test sas iml program for making treatment effects inferences for extensions and variations of abab single case experimental designs
2012Co-Authors: John M FerronAbstract:While the evaluation of intervention effects in single-case research has relied on visual inspection of the data (Kazdin, 1980), the description of graphical forms are not considered an adequate substitute for statistical Tests (Edgington, 1980). Moreover, there are cases when graphical displays of data tend to be quite ambiguous and treatment effects are not easily appreciated (Ferron & Sentovich, 2002); in these cases, inferential statistics are often necessary to determine if a treatment effect exists. Randomization Tests are considered valid statistical Tests for determining the presence of a treatment effect in single-case experimental data (Edgington, 1980). In addition, significance Tests lead to a more informed and reflective statistical analysis (Thompson & Snyder, 1997). Although the statistical validity of Randomization Tests has been established, Randomization Tests for single-case data are not incorporated into readily available statistical software like SAS® and SPSS, making it difficult for researchers to implement Randomization Tests into their statistical analysis of data. The example provided for Onghena (1992) was used to illustrate a worked example of a Randomization Test where the use of random assignment of treatment to treatment times and the incorporation of Randomization into single-case reversal designs is explained and applied to statistical Testing. SAS/IML code for Randomization Tests for extensions and variations of ABAB single-case experimental designs is provided and discussed.
Xueguang Shao - One of the best experts on this subject based on the ideXlab platform.
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selecting significant genes by Randomization Test for cancer classification using gene expression data
Journal of Biomedical Informatics, 2013Co-Authors: Xueguang ShaoAbstract:Graphical abstractDisplay Omitted A new approach was developed to identify genes from gene expression data.A statistic is defined to evaluate the significance of the genes in the method.Informative genes are selected by the statistic for cancer classification.The method may provide an alternative for gene selection problem. Gene selection is an important task in bioinformatics studies, because the accuracy of cancer classification generally depends upon the genes that have biological relevance to the classifying problems. In this work, Randomization Test (RT) is used as a gene selection method for dealing with gene expression data. In the method, a statistic derived from the statistics of the regression coefficients in a series of partial least squares discriminant analysis (PLSDA) models is used to evaluate the significance of the genes. Informative genes are selected for classifying the four gene expression datasets of prostate cancer, lung cancer, leukemia and non-small cell lung cancer (NSCLC) and the rationality of the results is validated by multiple linear regression (MLR) modeling and principal component analysis (PCA). With the selected genes, satisfactory results can be obtained.
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a wavelength selection method based on Randomization Test for near infrared spectral analysis
Chemometrics and Intelligent Laboratory Systems, 2009Co-Authors: Heng Xu, Xueguang ShaoAbstract:Partial least squares (PLS) regression has been widely used in the analysis of near-infrared (NIR) spectroscopy. The informative wavelength selection can improve the predictive ability of the PLS models by reducing the bias introduced by the uninformative wavelength. A new method based on Randomization Test was proposed for wavelength selection in NIR spectral analysis. In the proposed method, a regular PLS model and a number of random PLS models are constructed at first. Then, with the regression coefficients of these models, a statistic, P, which is defined as the ratio of the number of the coefficients that are bigger than the corresponding coefficient in the regular model to the total number of the random models, is calculated for each variable. Therefore, the variables with very low P values will be the important ones for building a stable model, whereas the variables whose P value is bigger than a threshold can be eliminated. To validate the performance of the proposed method, it was applied to the PLS modeling of two NIR spectral data sets. Results show that the proposed method can effectively select the informative wavelength from the measured NIR spectra, and enhance the prediction ability of the PLS model.
