Teaching Statistical Principles Using Epidemiology

Stroup, Donna F.; Goodman, Richard A.; Cordell, Ralph L.; Scheaffer, Richard L.

doi:10.1198/0003130042791

Cited by 7 publications

(6 citation statements)

References 54 publications

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“…But the use of these terms is not necessarily a disadvantage. On the contrary, it has been suggested that epidemiology provides a useful context for teaching statistical principles and methods, both because its relevance to real-life problems motivates students, and because epidemiological analyses illustrate basic statistical principles and the role of statistics in distinguishing between association and causality [ 11 ].…”

Section: Learning and Teaching Potentialmentioning

confidence: 99%

WINPEPI updated: computer programs for epidemiologists, and their teaching potential

Abramson

2011

Epidemiol Perspect Innov

719

423

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BackgroundThe WINPEPI computer programs for epidemiologists are designed for use in practice and research in the health field and as learning or teaching aids. The programs are free, and can be downloaded from the Internet. Numerous additions have been made in recent years.ImplementationThere are now seven WINPEPI programs: DESCRIBE, for use in descriptive epidemiology; COMPARE2, for use in comparisons of two independent groups or samples; PAIRSetc, for use in comparisons of paired and other matched observations; LOGISTIC, for logistic regression analysis; POISSON, for Poisson regression analysis; WHATIS, a "ready reckoner" utility program; and ETCETERA, for miscellaneous other procedures. The programs now contain 122 modules, each of which provides a number, sometimes a large number, of statistical procedures. The programs are accompanied by a Finder that indicates which modules are appropriate for different purposes. The manuals explain the uses, limitations and applicability of the procedures, and furnish formulae and references.ConclusionsWINPEPI is a handy resource for a wide variety of statistical routines used by epidemiologists. Because of its ready availability, portability, ease of use, and versatility, WINPEPI has a considerable potential as a learning and teaching aid, both with respect to practical procedures in the planning and analysis of epidemiological studies, and with respect to important epidemiological concepts. It can also be used as an aid in the teaching of general basic statistics.

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Section: Learning and Teaching Potentialmentioning

confidence: 99%

WINPEPI updated: computer programs for epidemiologists, and their teaching potential

Abramson

2011

Epidemiol Perspect Innov

719

423

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“…Empirical evidence on strategies for improving quantitative literacy suggests that teaching practices that engage students in hands-on activities are more effective. These include asking substantive questions relevant to students' lives (Atkinson, Czaja, and Brewster 2006;Burdette and McLoughlin 2010;Lindner 2012;Stroup et al 2004), having students collaborate in groups 867996T SOXXX10.1177/0092055X19867996Teaching SociologyStojmenovska et al (Caulfield and Caroline 2006), write reflective learning journals (Denton 2018) or collect their own data (Strangfeld 2013), and using computers for working with data (Wilder 2009).…”

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confidence: 99%

Teaching Replication to Graduate Students

2019

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Replicating published studies promotes active learning of quantitative research skills. Drawing on experiences from a replication course, we provide practical tips and reflections for teachers who consider incorporating replication in their courses. We discuss teaching practices and challenges we encountered at three stages of a replication course: student recruitment, course structure and proceedings, and learning outcomes. We highlight that by engaging in replication, students learn from established scholarly work in a collaborative and reflective manner. Students not only improve their quantitative literacy but also learn more generally about the scientific method and the production of research.

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“…Moreover, epidemiology is multidisciplinary in nature: it includes unifying and organizing frameworks for conceptualizing, modeling, and explaining variability, as well as facilitating causal analysis and understanding (Ahrens, Krickeberg, & Pigeot, 2005) and generating plausible hypotheses (Stroup, Goodman, Cordell, & Scheaffer, 2004). Such models operate as intuitive theories, which Tenenbaum, Griffiths, & Kemp (2006, p. 309) define as:…”

Section: Epidemiological Concepts As Basis For Inductive Reasoning With Big Datamentioning

confidence: 99%

A Foundation for Inductive Reasoning in Harnessing the Potential of Big Data

Hassad

2020

SERJ

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Training programs for statisticians and data scientists in healthcare should give greater importance to fostering inductive reasoning toward developing a mindset for optimizing Big Data. This can complement the current predominant focus on the hypothetico-deductive reasoning model, and is theoretically supported by the constructivist philosophy and Gestalt theory. Big-Data analytics is primarily exploratory in nature, aimed at discovery and innovation, and this requires fluid or inductive reasoning, which can be facilitated by epidemiological concepts (taxonomic and causal) as intuitive theories. Pedagogical strategies such as problem-based learning (PBL) and cooperative learning can be effective in this regard. Empirical research is required to ascertain instructors’ and practitioners’ perceptions about the role of inductive reasoning in Big-Data analytics, what constitutes effective pedagogy, and how core epidemiological concepts interact with the evidence from Big Data to produce outcomes. Together these can support the development of guidelines for an effective integrated curriculum for the training of statisticians and data scientists First published February 2020 at Statistics Education Research Journal Archives

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Teaching Statistical Principles Using Epidemiology

Cited by 7 publications

References 54 publications

WINPEPI updated: computer programs for epidemiologists, and their teaching potential

WINPEPI updated: computer programs for epidemiologists, and their teaching potential

Teaching Replication to Graduate Students

A Foundation for Inductive Reasoning in Harnessing the Potential of Big Data

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