INTRODUCTION TO QUANTITATIVE RESEARCH IN COMMUNICATION

Prerequisites & course information

Requisites: Sophomore standing and satisfied Quantitative Reasoning (QR) A Course Designation: Core GenEd - Mathematics & Quantitative Reasoning Comm QR - Quantitative Reasoning B Breadth - Social Science Level - Intermediate L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Fall 2025 Learning Outcomes: 1. Manipulate quantitative information to create models, and/or devise solutions to problems using multi-step arguments, based on and supported by quantitative information Audience: Undergraduate 2. Evaluate models and arguments using quantitative information Audience: Undergraduate 3. Express and interpret in context, models, solutions, and/or arguments using verbal, numerical, graphical, algorithmic, computational or symbolic techniques Audience: Undergraduate 4. Become familiar with key issues in designing surveys, experiments, and content analyses Audience: Undergraduate 5. Develop clear, falsifiable hypotheses Audience: Undergraduate 6. Examine the validity and reliability of measures for an online survey to test your hypotheses Audience: Undergraduate 7. Understand the logic of various statistical tests, which statistics are appropriate to examine your data, including univariate distributions, bivariate relationships, and moderated relationships Audience: Undergraduate 8. Become competent in the use of Excel to conduct these analyses, and evaluate your hypotheses Audience: Undergraduate 9. Assess the limitations of your study and the implications for your conclusions Audience: Undergraduate Learning Outcomes: 1. Manipulate quantitative information to create models, and/or devise solutions to problems using multi-step arguments, based on and supported by quantitative information Audience: Undergraduate 2. Evaluate models and arguments using quantitative information Audience: Undergraduate 3. Express and interpret in context, models, solutions, and/or arguments using verbal, numerical, graphical, algorithmic, computational or symbolic techniques Audience: Undergraduate 4. Become familiar with key issues in designing surveys, experiments, and content analyses Audience: Undergraduate 5. Develop clear, falsifiable hypotheses Audience: Undergraduate 6. Examine the validity and reliability of measures for an online survey to test your hypotheses Audience: Undergraduate 7. Understand the logic of various statistical tests, which statistics are appropriate to examine your data, including univariate distributions, bivariate relationships, and moderated relationships Audience: Undergraduate 8. Become competent in the use of Excel to conduct these analyses, and evaluate your hypotheses Audience: Undergraduate 9. Assess the limitations of your study and the implications for your conclusions Audience: Undergraduate