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Dec 21, 2020 - 35:49
Radio and PodcastLive Radio & Podcastsn this episode, Dr. Mary Ellen Dello Stritto is joined by Dr. William D. Marelich, a Professor of Psychology at California State University, Fullerton, and consulting statistician for Health Risk Reduction Projects, Inte...
[From the Archives] Ep 91: Dr. Mary Ellen Dello Stritto and Dr. William Marelich on the Applied Quantitative Perspective is an episode from Research in Action | A podcast for faculty & higher education professionals on research design, meth...
This episode belongs to Research in Action | A podcast for faculty & higher education professionals on research design, methods, productivity & more.
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Published Jun 22, 2020, 28:49 long, audio available.
n this episode, Dr. Mary Ellen Dello Stritto is joined by Dr. William D. Marelich, a Professor of Psychology at California State University, Fullerton, and consulting statistician for Health Risk Reduction Projects, Integrative Substance Abuse Programs, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at University of California, Los Angeles. His research interests and publications address decision-making strategies in health settings, patient/provider interactions, HIV/AIDS, and statistical/methodological approaches in experimental and applied research. Dr. Marelich is coauthor of the book "The Social Psychology of Health: Essays and Readings" and is an Editorial Board Member of the International Journal of Adolescence and Youth . He also has an interest in Sports Psychology with applications to baseball. Segment 1 : Applied Quantitative Perspective [00:00-10:43] In this first segment, William discusses the applied quantitative perspective in research. In this segment, the following resources are mentioned: Marelich, W. D., & Erger, J. S. (Eds.). (2004). The social psychology of health: Essays and readings . Thousand Oaks, CA: SAGE Publications. International Journal of Adolescence and Youth Segment 2 : Key Quantitative Concepts [10:44-19:38] In segment two, William offers his perspective on key statistical concepts to understand for reading research reports and publications. Segment 3 : On the Statistical Horizon [19:39-28:35] In segment three, William discusses statistical software and the concepts of p-hacking and p-curves. In this segment, the following resources are mentioned: R (free statistical software) IBM SPSS SAS Articles related to p-Curve and p-Hacking: Cumming, G. (2016). A primer on p-Hacking. MethodSpace. Retrieved from Bruns S. B., & Ioannidis, J. P. A. (2016). p -Curve and p -Hacking in observational research . PLoS ONE 11 (2): e0149144. To share feedback about this podcast episode, ask questions that could be featured in a future episode, or to share research-related resources, post a comment below or contact the "Research in Action" podcast: Twitter : @ RIA_podcast or # RIA_podcast Email : riapodcast@oregonstate.edu Voicemail : 541-737-1111 If you listen to the podcast via iTunes , please consider leaving us a review. The views expressed by guests on the Research in Action podcast do not necessarily represent the views of Ecampus or Oregon State University.
You can listen to [From the Archives] Ep 91: Dr. Mary Ellen Dello Stritto and Dr. William Marelich on the Applied Quantitative Perspective online on Radio and Podcast. Open the player on this page to stream the available audio.
[From the Archives] Ep 91: Dr. Mary Ellen Dello Stritto and Dr. William Marelich on the Applied Quantitative Perspective is an episode from Research in Action | A podcast for faculty & higher education professionals on research design, methods, productivity & more by Mary Ellen Dello Stritto.
This episode is 28:49 long.
This episode was published on Jun 22, 2020.
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[From the Archives] Ep 91: Dr. Mary Ellen Dello Stritto and Dr. William Marelich on the Applied Quantitative Perspective is from Research in Action | A podcast for faculty & higher education professionals on research design, methods, productivity & more by Mary Ellen Dello Stritto.
Published Jun 22, 2020 and 28:49 long