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Session Type: Paper Session
With the exponential growth in the area of mixed methods research, the development of new tools for data collection and analysis is exciting and timely. In this session, researchers share dynamic information about the development of new measurements for assessing forms of capital and bias, as well as new analytical frameworks for artificial intelligence and machine learning.
Using Mixed-Methods Research to Develop and Validate an Instrument for Measuring Different Forms of Capital Among K–12 Teachers - Yael Grinshtain, Tel-Hai College; Alexander Zibenberg, Tel-Hai College; Audrey Addi-Raccah, Tel Aviv University
Designing a Measurement for Bias: The Impact Artist Identity Knowledge on Viewers' Aesthetic Emotions - Alysha Meloche, Villanova University
Best of Both Worlds: Affordances of Mixing Machine Learning and Qualitative Content Analysis - Reagan Curtis, West Virginia University; Abhik Roy; Michelle Richards-Babb, West Virginia University; Carinna F. Ferguson, West Virginia University
Machine-Driven Literature Classification: A Computer-Code-Free Software to Cultivate Equitable Access to Data Science Tools - Manuel S. Gonzalez Canche, University of Pennsylvania