Introduction
The early diagnosis of Autism in children is a crucial part of the preventive strategies in healthcare. Moreover, the appropriate screening and analysis tests assist clinicians and psychologists in defining the proper and efficient educational and recovery treatment strategies for children. In this case, they obtain an opportunity to participate in social groups, conduct physical and mental activities, and feel integrated into society.
Nonetheless, the primary challenge for clinicians is to select the high quality and precise tool to analyze and diagnose children, as the unreliable or invalid test outcomes may cause more harm to children’s health then benefits. Therefore, the selection of the GARS-3 test for early autism diagnosis should offer new perspectives and findings, whether the statistical methods improve treatment methodologies and accurately define the disease among young patients.
Methodology
The sample size for the ongoing study is based on children who have and do not have autism. The test group is compounded with children aged from 36 to 60 months. All of them are attending pre-school facilities or participate in any educational program regarding their age. Such a selection is grounded to the GARS-3 description, which indicates that the test is efficient when applied in social and/or educational environment to catch all peculiarities in behavior, activities, and situation perspectives.
As a result, the GARS-3 will be referred as the statistical tool to analyze the collected during observations and study data to determine, whether this tool allows clinicians and psychologist differentiate between children with and without autism, define any distortions or complications of the disease, and indicate behavioral and mental peculiarities, which describe autism among children. In return, it is expected that the use of the GARS-3 will assist in early diagnosis and show statistically significant contribution to already existing tools and methods.
Literature Review
Cardon, T., Wangsgard, N., & Dobson, N. (2019). Video modeling using classroom peers as models to increase social communication skills in children with ASD in an Integrated preschool. Education and Treatment of Children, 42(4), 515-536. Web.
The article examines how the Video Modeling (VM) treatment approach affects the behavior of children with ASD. The study discusses how the VM can be introduced and integrated into the evidence-based practices used by clinicians and educators to increase social and communication patterns among young patients with ASD. The sample size of the experiment is a small group of six children aged from 39 to 52 months.
The authors indicate that the validity and reliability of the tests and sessions during the experiment are granted through the standardized and unified procedure of children’s responses registration. The research does not indicate specific type of validity check so that it is suggested that authors use predictive validity by measuring questionnaires outcomes. The study indicates that the validity was over 80%, while all responses were registered with 99% accuracy. Based on such information, the statistical outcomes can be marked as valid and precise due to the accuracy of observers and personnel, who worked with children.
Nonetheless, the study provides limited evidence on statistical calculations, and which methods were utilized to obtain results. In this instance, it can be suggested that validity checks may contain biases or subjective interpretation, as no additional statistical tests were used to avoid Error Type 1 and 2. As a result, the positive results from the experiment indicate that children have changed their behavior and mimic social models represented via video and recordings. On the contrary, more analysis is needed to prove that obtained outcomes are grounded to the practical efficiency and can be used for further treatment of ASD.
Hebert, E., Seifen-Adkins, T., & Gross, A. (2017). The DATA model for teaching preschoolers with autism. The Behavior Analyst, 40(2), pp.549-551. Web.
The article analyzes the contribution of the Data Model as the primary treatment tool for children’s ASD. The article discusses the issues of the model implementation and how the specific guidelines and recommendations may assist clinicians in improving their practice with the Data Model. Nonetheless, the article indicates that the Data Model has a high level of sensitivity and reflection on patients’ health and behavior changes, which are crucial when working with children. Moreover, the model delivers high accuracy in reliability and validity. On the contrary, the article does little to explain the statistical issues and differentiations within the model.
In this case, the suggestions about the Data Model requires more evidence to decide whether it should be utilized as the treatment tool in the early stages of ASD. As a result, the study only represents the model without any examples of successful integration. Such outcomes are unacceptable and require the exploration of literature devoted to the Data Model development and analysis.
Karren, B. (2016). A test review: Gilliam, J. E. (2014). Gilliam autism rating scale–Third edition (GARS-3). Journal of Psychoeducational Assessment, 35(3), 342-346. Web.
The study depicts the evaluation of GARS-3 validity and efficiency in ASD treatment. The authors indicate that the validity checks are based on the Cronbach’s alpha to ensure that data consistency is preserved. What is more, the research applies a test-retest approach to check validity and reliability indexes during the experiment. The obtained results show.94 and.93 within two testing groups, meaning that the GARS-3 outcomes are credible to use in further studies.
Moreover, the study selected Intraclass correlation coefficients (ICC) to increase statistical data consistency and reliability. The utilized approach ensures that interval data is measured and interpreted according to relationships between variables in all subscales. The ICC’s results also proved that GARS-3 deliverables are acceptable and have evidence in practical implementation. In return, little is provided on statistical outcomes of the validity and reliability measurements, meaning that ongoing recreation of experiment should be tested.
The obtained results from statistical tests reveal that GARS-3 is an easy and useful tool to determine ASD. Moreover, the data suggest that the third edition reflects the changes within the field and utilizes the required tools and measurements to become the psychometrical instrument for diagnosing and investigating autism. In this case, the review and analysis of the GARS-3 test deliver complete data on the validity and reliability of the test and its practical use for psychometrics.
Lordo, N. D., Bertolin, M., L. Sudikoff, E., Keith, C., Braddock, B., & Kaufman, D. (2016). Parents perceive improvements in socio-emotional functioning in adolescents with ASD following social skills treatment. Journal of Autism and Developmental Disorders, 47(1), 203-214. Web.
The study reveals the impacts of social behavior changes within children with ASD. What is more, the study provides several models to investigate the issue through the socio-emotional perspective. The authors indicate that the validity of results is ensured through the PEERS test to identify whether the discovered correlations have an impact on the children with ASD and their behavior. Moreover, the study conducted internal validity tests through the test-retest approach. The undertaken analysis confirmed the reliability and credibility of statistical outcomes. In this case, it can be admitted that the represented findings in socio-emotional change among children with ASD are confirmed and can be utilized for further studies on the topic.
Pfeiffer, B., Piller, A., Slugg, L., & Shiu, C. (2018). Brief report: Reliability of the participation and sensory environment questionnaire: Home scales. Journal of Autism and Developmental Disorders, 48(7), 2567-2576. Web.
The research investigates the impact of Sensory Environment on children with and without autism. The authors created the Home Scales (P-SEQ) questionnaire for parents to define the effects of the sensory environment on behavior and daily activities of children. The validity and reliability of the test are addressed through the entire study due to the importance of accurate and precise research outcomes. What is more, the authors discuss the validity and explain the used tools to ensure that the further recreation of the questionnaire and statistical test deliver identical results.
Data validity is reached through the Cronbach’s alpha test and the test-retest approach. The scholars offered parents to retake the questionnaire to investigate whether the outcomes will be changed. The reported evidence suggests that validity is at 0.94, meaning a high level of data consistency. Moreover, the researchers addressed reliability through the deletion of children’s data. Such a method showed low differentiation in outcomes, ensuring that tests and questions are developed correctly and preserve the credibility of results. The statistical findings from the conducted study showed that the P-SEQ differentiates between children with and without autism. Further, the authors confirm that the obtained primary data visually indicated differences between both groups of young patients.
Rossi, M., Vladescu, J., Reeve, K., & Gross, A. (2017). Teaching safety responding to children with autism spectrum disorder. Education and Treatment of Children, 40(2), 187-208. Web.
The selected article addresses the validity of statistical findings through the introduction of additional variables to identify the correlations between patients with and without ASD. What is more, the study suggests that the social validity of the selected diagnosis tool is high due to the double-check of findings. The social validity is measured through the Treatment Acceptability Rating. Further, the experimental method recreated the conclusions of the first test, confirming the acceptable level of validity and reliability of the obtained results.
From this perspective, it can be stated that the discovered statistical results confirmed GARS-3 efficiency and its impact on the parents’ and children’s social behavior. Moreover, the perceived relations between the BST approach and children with ASD safety measures. The utilization of GARS-3 as a determining tool and BST as a treatment tool is supported by positive responses from all experiment participants. In this case, the study proves the reliability of GARS-3 tool; however, more accurate evidence is needed to compare statistical outcomes.
Schwartz, I. S., Ashmun, J., McBride, B., Scott, C., & Sandall, S. R. (2017). The DATA model for teaching preschoolers with autism. Washington, DC: Paul H. Brookes Publishing Company.
The book delivers step-by-step guidelines for professionals on how to establish statistically grounded treatment models in working with children who have ASD. What is more, the book provides practical recommendations and templates on how to construct the data models, analyze them, test for reliability and validity, and interpret their outcomes. In this case, the book has significance for the study of children with ASD behavior, early treatment, and change intervention.
The vital aspect of the book is sections devoted to the measurements, validity, and test-retest approach. The authors admit that the healthcare field has numerous tools and models to conduct the statistical analysis of the results; however, only a few support high-quality validity and reliability. In this case, the authors reveal that the validity should be incorporated in the early stages of the research. Such action will guarantee that outcomes from the study will pass validation and reliability tests.
From this perspective, the book highlights that several types of data models should be used to address the children’s ASD disorder. Moreover, the authors highly recommend integrating statistical tools in screening, observations, and treatment to increase control over the entire process. In return, they admit that such a decision may assist clinicians in the timely diagnosis of ASD and the selection of preventive and treatment methodologies. Nonetheless, the book provides limited information on how the validity should be checked when the preventive tool is selected. As a result, it can be used as a theoretical guideline to understand how the Data Model supports research and observations of patients with ASD, especially on the early stage of disease development.
Conclusion
The review and analysis of articles devoted to the GARS-3 implication in the early diagnosis of autism among children reveal that the tool is highly useful in defining behavioral and mental deviations. Moreover, the selected studies confirmed that the test has a significant statistical contribution to the early diagnosis, as the reliability and validity of results remain on the high level. On the contrary, the literature analysis indicates that GARS-3 is usually applied in the social environment, where children interact with other children and adults. Such an environment limits the GARS-3 practicability, as there is a concern that the change of study settings may mitigate the efficiency of the test so that additional studies will be needed.