A Decade Review of Two Potential Analysis Altering Variables in Graph Construction

2020 ◽  
Author(s):  
Corey Peltier ◽  
Reem Muharib ◽  
April Haas ◽  
Art Dowdy
Keyword(s):  
Time Series ◽  
Developmental Disorders ◽  
Type I Error ◽  
Functional Relation ◽  
Autism Spectrum ◽  
Error Rates ◽  
Future Research ◽  
Type I ◽  
Axis Ratio ◽  
Functional Relations

Single-case research designs (SCRDs) are used to evaluate functional relations between an independent variable and dependent variable(s). When analyzing data related to autism spectrum disorder, SCRDs are frequently used. Namely, SCRDs allow for empirical evidence in support of practices that improve socially significant outcomes for individuals diagnosed with ASD. To determine a functional relation in SCRDs, a time-series graph is constructed and visual analysts evaluate data patterns. Preliminary evidence suggest that the approach used to scale the ordinate (i.e., y-axis) and the proportions of the x-axis length to y-axis height (i.e., data points per x- to y-axis ratio) impact visual analysts’ decisions regarding a functional relation and the magnitude of treatment effect, resulting in an increased likelihood of a Type I errors. The purpose for this systematic review was to evaluate all time-series graphs published in the last decade (i.e., 2010-2020) in four premier journals in the field of ASD: Journal of Autism and Developmental Disorders, Research in Autism Spectrum Disorders, Autism, and Focus on Autism and Other Developmental Disabilities. The systematic search yielded 348 articles including 2,675 graphs. We identified large variation across and within types of SCRDs for the standardized X:Y and DPPXYR. In addition, 73% of graphs were below a DPPXYR of 0.14, providing evidence of the Type I error rate. A majority of graphs used an appropriate ordinate scaling method that would not increase Type I error rates. Implications for future research and practice are provided.

scholarly journals Fit Indices for Measurement Invariance Tests in the Thurstonian IRT Model

2019 ◽  
Vol 44 (4) ◽  
pp. 282-295
Author(s):  
HyeSun Lee ◽  
Weldon Z. Smith
Keyword(s):  
Measurement Invariance ◽  
Type I Error ◽  
Error Rates ◽  
Forced Choice ◽  
Model Fit ◽  
Future Research ◽  
Type I ◽  
Fit Indices ◽  
Irt Model ◽  
The Impact

This study examined whether cutoffs in fit indices suggested for traditional formats with maximum likelihood estimators can be utilized to assess model fit and to test measurement invariance when a multiple group confirmatory factor analysis was employed for the Thurstonian item response theory (IRT) model. Regarding the performance of the evaluation criteria, detection of measurement non-invariance and Type I error rates were examined. The impact of measurement non-invariance on estimated scores in the Thurstonian IRT model was also examined through accuracy and efficiency in score estimation. The fit indices used for the evaluation of model fit performed well. Among six cutoffs for changes in model fit indices, only ΔCFI > .01 and ΔNCI > .02 detected metric non-invariance when the medium magnitude of non-invariance occurred and none of the cutoffs performed well to detect scalar non-invariance. Based on the generated sampling distributions of fit index differences, this study suggested ΔCFI > .001 and ΔNCI > .004 for scalar non-invariance and ΔCFI > .007 for metric non-invariance. Considering Type I error rate control and detection rates of measurement non-invariance, ΔCFI was recommended for measurement non-invariance tests for forced-choice format data. Challenges in measurement non-invariance tests in the Thurstonian IRT model were discussed along with the direction for future research to enhance the utility of forced-choice formats in test development for cross-cultural and international settings.

The Runs Test for Autocorrelated Errors: Unacceptable Properties

1996 ◽  
Vol 21 (4) ◽  
pp. 390-404 ◽  
Author(s):  
Bradley E. Huitema ◽  
Joseph W. McKean ◽  
Jinsheng Zhao
Keyword(s):  
Time Series ◽  
Regression Models ◽  
Degrees Of Freedom ◽  
Type I Error ◽  
Error Rates ◽  
Type I ◽  
Time Series Regression ◽  
Monte Carlo Investigation ◽  
Autocorrelated Errors ◽  
Asymmetrical Error

The runs test is frequently recommended as a method of testing for nonindependent errors in time-series regression models. A Monte Carlo investigation was carried out to evaluate the empirical properties of this test using (a) several intervention and nonintervention regression models, (b) sample sizes ranging from 12 to 100, (c) three levels of α, (d) directional and nondirectional tests, and (e) 19 levels of autocorrelation among the errors. The results indicate that the runs test yields markedly asymmetrical error rates in the two tails and that neither directional nor nondirectional tests are satisfactory with respect to Type I error, even when the ratio of degrees of freedom to sample size is as high as .98. It is recommended that the test generally not be employed in evaluating the independence of the errors in time-series regression models.

Covariance Structure Selection and Type I Error Rates in Split-Plot Designs

Methodology ◽  
2013 ◽  
Vol 9 (4) ◽  
pp. 129-136 ◽  
Author(s):  
Pablo Livacic-Rojas ◽  
Guillermo Vallejo ◽  
Paula Fernández ◽  
Ellián Tuero-Herrero
Keyword(s):  
Type I Error ◽  
Covariance Structure ◽  
Error Rates ◽  
Information Criteria ◽  
Covariance Structures ◽  
Future Research ◽  
Dispersion Matrix ◽  
Type I ◽  
Type I Error Rates ◽  
Split Plot

We examined the selection of covariance structures and the Type I error rates of the Criterion Selector Akaike’s (Akaike’s Information Criteria, AIC) and the Correctly Identified Model (CIM). Data were analyzed with a split-plot design through the Monte Carlo simulation method and SAS 9.1 statistical software. We manipulated the following variables: sample size, relation between group size and dispersion matrix size, type of dispersion matrix, and form of the distribution. Our findings suggest that AIC selects heterogeneous covariance structure more frequently than original covariance structure. Specifically, AIC mostly selected heterogeneous covariance structures and displayed slightly higher Type I error rates than the CIM. These were mostly associated with main and interaction effects for the ARH and RC structures and a marked tendency toward liberality. Future research needs to assess the power levels exhibited by covariance structure selectors.

Smoothing Facilitates the Detection of Coupled Responses in Psychophysiological Time Series

2000 ◽  
Vol 14 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Joni Kettunen ◽  
Niklas Ravaja ◽  
Liisa Keltikangas-Järvinen
Keyword(s):  
Time Series ◽  
Type I Error ◽  
Electrodermal Activity ◽  
Moving Average ◽  
Type I ◽  
Facial Electromyography ◽  
Response Characteristics ◽  
Smoothing Methods ◽  
Response Systems ◽  
Different Response

Abstract We examined the use of smoothing to enhance the detection of response coupling from the activity of different response systems. Three different types of moving average smoothers were applied to both simulated interbeat interval (IBI) and electrodermal activity (EDA) time series and to empirical IBI, EDA, and facial electromyography time series. The results indicated that progressive smoothing increased the efficiency of the detection of response coupling but did not increase the probability of Type I error. The power of the smoothing methods depended on the response characteristics. The benefits and use of the smoothing methods to extract information from psychophysiological time series are discussed.

Correction: “Influence of Selection Bias on the Test Decision – A Simulation Study”

2014 ◽  
Vol 53 (05) ◽  
pp. 343-343
Keyword(s):  
Selection Bias ◽  
Simulation Study ◽  
Error Rate ◽  
Type I Error ◽  
Block Size ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rate ◽  
Representation Error ◽  
Numeric Representation

We have to report marginal changes in the empirical type I error rates for the cut-offs 2/3 and 4/7 of Table 4, Table 5 and Table 6 of the paper “Influence of Selection Bias on the Test Decision – A Simulation Study” by M. Tamm, E. Cramer, L. N. Kennes, N. Heussen (Methods Inf Med 2012; 51: 138 –143). In a small number of cases the kind of representation of numeric values in SAS has resulted in wrong categorization due to a numeric representation error of differences. We corrected the simulation by using the round function of SAS in the calculation process with the same seeds as before. For Table 4 the value for the cut-off 2/3 changes from 0.180323 to 0.153494. For Table 5 the value for the cut-off 4/7 changes from 0.144729 to 0.139626 and the value for the cut-off 2/3 changes from 0.114885 to 0.101773. For Table 6 the value for the cut-off 4/7 changes from 0.125528 to 0.122144 and the value for the cut-off 2/3 changes from 0.099488 to 0.090828. The sentence on p. 141 “E.g. for block size 4 and q = 2/3 the type I error rate is 18% (Table 4).” has to be replaced by “E.g. for block size 4 and q = 2/3 the type I error rate is 15.3% (Table 4).”. There were only minor changes smaller than 0.03. These changes do not affect the interpretation of the results or our recommendations.

The Use of Theory of Linear Mixed-Effects Models to Detect Fraudulent Erasures at an Aggregate Level

2021 ◽  
pp. 001316442199489
Author(s):  
Luyao Peng ◽  
Sandip Sinharay
Keyword(s):  
Type I Error ◽  
Real Data ◽  
Mixed Effects ◽  
Error Rates ◽  
Mixed Effects Models ◽  
Type I ◽  
Aggregate Level ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Best Linear Unbiased

Wollack et al. (2015) suggested the erasure detection index (EDI) for detecting fraudulent erasures for individual examinees. Wollack and Eckerly (2017) and Sinharay (2018) extended the index of Wollack et al. (2015) to suggest three EDIs for detecting fraudulent erasures at the aggregate or group level. This article follows up on the research of Wollack and Eckerly (2017) and Sinharay (2018) and suggests a new aggregate-level EDI by incorporating the empirical best linear unbiased predictor from the literature of linear mixed-effects models (e.g., McCulloch et al., 2008). A simulation study shows that the new EDI has larger power than the indices of Wollack and Eckerly (2017) and Sinharay (2018). In addition, the new index has satisfactory Type I error rates. A real data example is also included.

Increasing the Number Sense Understanding of Preschool Students With ASD

2021 ◽  
pp. 027112142110061
Author(s):  
Bonnie L. Ingelin ◽  
Seyma Intepe-Tingir ◽  
Nanette C. Hammons
Keyword(s):  
Preschool Children ◽  
Number Sense ◽  
Functional Relation ◽  
Autism Spectrum ◽  
Future Research ◽  
Preschool Students ◽  
Children With Asd ◽  
Early Number ◽  
Mathematical Success ◽  
Number Talks

Teaching children with autism spectrum disorder (ASD) academic skills supports their future opportunities. For example, early number sense skills are predictive of future mathematical success for all children including children with ASD. Yet, research on foundational early childhood mathematics skills of children with ASD is limited. This study used an adapted version of Number Talks to increase the number sense skills of preschool children with ASD. Number Talks is a constructivist approach that was combined with systematic instruction (i.e., system of least prompts and modeling) in this study. A multiple probe across participants design established a functional relation between using an adapted version of Number Talks and the early number sense skills of preschool children with ASD. Findings suggest using an adapted version of Number Talks can increase the early number sense skills of preschool children with ASD. Implications for practice and future research are discussed.

scholarly journals Health-Related Quality of Life in Children with Developmental Disorders

2021 ◽  
Author(s):  
Marina M. Schoemaker ◽  
Suzanne Houwen
Keyword(s):  
Quality Of Life ◽  
Developmental Disorders ◽  
Developmental Coordination Disorder ◽  
Autism Spectrum ◽  
Future Research ◽  
Health Related Quality ◽  
Related Quality ◽  
Health Related ◽  
Lower Hrqol

Abstract Purpose of Review (1) To give an overview of what is currently known about health-related quality of life (HRQoL) in three common and co-occurring developmental disorders: attention deficit hyperactivity disorder (ADHD), autism spectrum disorders (ASD), and developmental coordination disorder (DCD), and (2) to provide directions for future research. Recent Findings HRQoL is compromised in all three developmental disorders, affecting various domains of HRQoL. However, some domains are more affected than others depending on the nature of the core deficits of the disorder. Overall, parents’ rate HRQoL of their children lower than the children themselves. Children with ASD and ADHD with co-occurring disorders have lower HRQoL compared to those with singular disorders. Future studies in DCD are needed to investigate the effect of co-occurring disorder in this population. Summary Children with developmental disorders have lower HRQoL than typically developing children. Future research should focus on the effects of co-occurring disorders on HRQoL and on protective factors that may increase HRQoL. HRQoL should be a part of clinical assessment, as it reveals the areas in life children are struggling with that could be targeted during intervention.

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