This course is the second in a two-part scale development course series on scale development. Part one of the series focused on the general theories and methods used in the scale development process. This second portion focuses on the analytic methods used to statistically test, refine, and validate scale data. These methods include Cronbach’s alpha, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and basic structural equation models testing for predictive validity.

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This course will teach participants how to use the programming language Julia to load, clean, plot, and analyze social-science data. Julia is a newer programming language with a focus on high-performance scientific computing, and allows efficient manipulation of large datasets. The course will cover the basics of loading tabular data; cleaning, filtering, and joining that data; calculating descriptive statistics; and estimating statistical models.

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Descriptions in textbooks about classic approaches to coding qualitative data rarely coincide perfectly with the demands imposed by time and resources in the real world of practice. This short course reviews three approaches to coding qualitative data. The first two approaches - a purely inductive approach and an approach that combines deductively and inductively derived codes are more familiar than the third approach. The third approach is when a coding scheme is developed by mixed analysis that links qualitative and quantitative data on related constructs. Advantages and disadvantages of each approach are identified, as well as suggestions about how the coding process can be adapted to be less time intensive. In an interactive component, participants will experiment with different approaches to coding. The course will be useful to researchers engaged in qualitative research projects, as well as those who are interested in using an approach with mixed methods.

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This course will teach participants how to use the programming language Julia to load, clean, plot, and analyze social-science data. Julia is a newer programming language with a focus on high-performance scientific computing, and allows efficient manipulation of large datasets. The course will cover the basics of loading tabular data; cleaning, filtering, and joining that data; calculating descriptive statistics; and estimating statistical models.

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OVCR Leadership and the Office of Federal Affairs will provide updates on the recent executive communications and actions from the new Presidential administration that could potentially affect research operations at UNC.

Speakers: Vice Chancellor Penny Gordon-Larsen, Senior Associate Vice Chancellor Andy Johns, and Director of the Office of Federal Affairs Kelly Dockham

There will be a dedicated Q&A session for participants to ask questions, and questions should be submitted in advance to ensure all are covered. You can submit questions by email to This email address is being protected from spambots. You need JavaScript enabled to view it..

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This online training session will provide an introduction to qualitative research methods. Topics covered will include differences between quantitative and qualitative research, qualitative research methodologies, strategies for qualitative data collection, and methods of qualitative data analysis. Participants will have the opportunity to ask questions and share experiences conducting qualitative research.

Presenters:
MaryBeth Grewe, MPH
Program Manager, Qualitative Research Service
Research Specialist, Patient and Community Engagement in Research (PaCER) Program
NC TraCS Institute

Simone Frank, MPH
Senior Project Manager, Patient and Community Engagement in Research (PaCER) Program
Research Specialist, Qualitative Research Service
NC TraCS Institute

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The NC TraCS Biostatistics Seminar Series is appropriate for those with a basic familiarity with statistics. These seminars introduce topics not often included in STATS 101. In this seminar Jeff Laux, PhD, will discuss statistical models for binary outcomes (e.g., died / did not die), covering how they work and how to understand the results of such analyses.

Please note: This event will not be recorded. Materials such as slides or handouts will be shared with documented attendees only. Attendance may be determined through Zoom meeting attendance reports.

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Improving Symptom Control in Pediatric Cancer Patients With SSPedi and SPARK

This NIH Collaboratory Rethinking Clinical Trials Grand Rounds features:

Lillian Sung, MD, PhD
Canada Research Chair in Pediatric Oncology Supportive Care
Division of Haematology/Oncology
Chief Clinical Data Scientist
The Hospital for Sick Children
Ontario, Canada

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