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This textbook is an advanced introduction to quantitative methods for students in communication and allied social science disciplines that focuses on why and how to conduct research that contributes to social justice.

Today’s researchers are inspired by the potential for scholarship to make a difference for society, to push toward more just and equitable ends, and to engage in dialogue with members of the public so that they can make decisions about how to navigate the social, cultural, and political world equipped with accurate, fair, and up-to-date knowledge. This book illustrates the mechanics and the meaning behind quantitative research methods by illustrating each step in the research design process with research addressing questions of social justice. It provides practical guidance for researchers who wish to engage in the transformation of structures, practices, and understandings in society through community and civic engagement and policy formation. It contains step-by-step guidance in quantitative methods―from conceptualization through all the stages of execution of a study, including providing a detailed guide for statistical analysis―and demonstrates how researchers can engage with social justice issues in systematic, rigorous, ethical, and meaningful ways.


This text serves as a core or supplementary textbook for graduate and advanced undergraduate courses in research methods for communication and social sciences and fills a gap for a methods text that is responsive to the desire of scholars to conduct socially impactful research.

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Table of Contents

Chapter 1: Foundations and Definitions
Chapter 2: Ethical Considerations
Chapter 3: Operationalization and Otherness
Chapter 4: Sampling and Representation
Chapter 5: Soliciting Opinions Through Survey Research        
Chapter 6: Studying Responses Through Experimental Methods
Chapter 7: Examining Communication Content in Content Analysis Research
Chapter 8: Calculations and Complexity
Chapter 9: Statistical Analysis
Chapter 10: Mixed Methods
Chapter 11: Community Partnerships and Participatory Research
Chapter 12: Communicating Research for Publication, Policy, and the Public

"Research on issues of social justice could not be more timely, urgent, and fundamental. This volume provides clear and thoughtful guidance on how we can use and learn from social science research to utilize the power of numbers for social good. This volume takes the reader through every stage of the research process, including design, data collection, data analysis, and community-based scholarship. Undoubtedly this book will be foundational for scholars and students who want to use their research for social justice."

-Mary Beth Oliver, Bellisario Professor of Media Studies, Penn State University, and President Elect, International Communication Association

Meet the Authors

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Erica Scharrer (PhD, Syracuse University) is Professor and Chair of the Department of Communication at the University of Massachusetts Amherst. She is co-author of three additional books, including Media and the American Child, and editor of the Media Effects/Media Psychology volume of the International Encyclopedia of Media Studies. Her research on the topics of media content, opinions of media, media effects, and media literacy has appeared in a number of journals.

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Srividya Ramasubramanian (PhD, Penn State University) is Presidential Impact Fellow, Professor of Communication, Affiliated Professor of Women’s and Gender Studies, and Director of the Difficult Dialogues Project at Texas A&M University. Her research focuses on critical media effects, diversity, anti-racism, prejudice reduction, media literacy, and social justice and has been published in several journals, books, and popular press outlets. She is also Executive Director of Media Rise, a nonprofit for meaningful media for social justice.

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Video Tutorial of Chapter 9

Create a data file in SPSS

Dealing with Missing Values

Measures of Central Tendency

Measures of Variability

Data reduction and transformation: Recode

Data reduction and transformation: 
Creating an index & testing its reliability

Data reduction and transformation: Compute

Data reduction and transformation: Select Cases

Inferential Statistics: t-tests for difference

Inferential Statistics:
Analysis of variance(ANOVA) for difference test

Inferential Statistics:
Chi-Square Tests for Relationship

Inferential Statistics:
Pearson’s Correlation Tests for Relationship

Inferential Statistics:
Multiple Regression Tests for Relationship

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