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      If you want to develop an effective autism training, ask autistic students to help you

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Autistic university students face stigma. Online trainings have been used to improve explicit autism stigma (social distance) and knowledge among university students in different countries. However, autistic university students have not typically been involved in developing such trainings. We developed two autism trainings: a participatory training (developed in collaboration with autistic university students) and a non-participatory training. We evaluated these trainings with undergraduate students in the United States and Lebanon. A pilot study revealed improvements in implicit biases (measured with an Implicit Association Test) and knowledge following both trainings, but no clear benefit of the participatory training in particular. Feedback revealed that participants found the Implicit Association Test tedious, suggesting that it might have dampened effects by boring participants. To increase engagement, we removed the Implicit Association Test and conducted a cross-university training comparison which revealed evidence that the participatory training was more effective than the non-participatory training at improving autism knowledge, explicit stigma, and attitudes toward inclusion. Autistic co-authors coded participant feedback and identified three key themes to guide future training development and adaptation: an (inter)personal element, accessibility, and clarity of information. These studies provide empirical support for the oft-cited, but rarely directed tested, benefits of involving autistic people in research about autism.

          Lay abstract

          Autistic university students are often left out because people do not understand autism. We wanted to help people understand autism. Most autism trainings are not made by autistic people. Autistic people know what it is like to be autistic. So autistic people may be the best teachers when it comes to teaching about autism. Autistic students and non-autistic professors made an autism training. The students made videos for the training. They also helped make questions to see what people learned from the trainings. Professors who are not autistic made a training on their own. Students in New York City tried out the trainings. After they answered questions, they did either the training the autistic students helped make or the training made by only professors. Then, they answered questions again. We learned from the students how to make our trainings better. Then, students from two universities in the United States and one university in Lebanon did our trainings and questions. Both trainings made hidden feelings about autism better. The training autistic students helped make taught students more than the training professors made on their own. The autistic-led training also helped students accept autism more. These studies show that autistic students can make autism research and trainings better. At the end of this article, autistic students share their ideas for how to make autism trainings even better in the future.

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          Most cited references62

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          Effect size, confidence interval and statistical significance: a practical guide for biologists.

          Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.
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            Conceptualizing Stigma

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              Discovering Statistics Using SPSS

              Andy Field (2009)
              <p>Written in his vivid and entertaining style, Andy Field provides students with everything they need to understand, use and report statistics—at every level—in the <b>Third Edition</b> of <b>Discovering Statistics Using SPSS</b>. Retaining the strong pedagogy from previous editions, he makes statistics meaningful by including playful examples from everyday student life (among other places), creating a gateway into the often intimidating world of statistics. In the process, he presents an opportunity for students to ground their knowledge of statistics through the use of SPSS.<br><br></p>
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Autism
                Autism
                SAGE Publications
                1362-3613
                1461-7005
                September 02 2021
                : 136236132110410
                Affiliations
                [1 ]College of Staten Island, City University of New York, USA
                [2 ]The Graduate Center, CUNY, USA
                [3 ]Clemson University, USA
                [4 ]American University of Beirut, Lebanon
                [5 ]Case Western Reserve University, USA
                [6 ]University of Georgia, USA
                [7 ]McNeese State University, USA
                [8 ]Borough of Manhattan Community College, CUNY, USA
                Article
                10.1177/13623613211041006
                34472359
                d4567b9d-3f44-4d93-8679-a2cba61194ec
                © 2021

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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