importance of quantitative research on communication

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Unit 8: Research Design.

35 Quantitative Methods in Communication Research

Quantitative methods in communication research.

In communication research, both quantitative and qualitative methods are essential for understanding different aspects of communication processes and effects. Here’s how quant methods can be applied:

• Collecting data on communication patterns, relationship satisfaction, or conflict resolution strategies among different groups.
• Collecting numerical data on audience demographics, media consumption habits, or attitudes towards specific communication messages.
• Testing hypotheses about the effects of specific communication behaviors (e.g., eye contact, tone of voice) on relationship outcomes.
• Testing the effects of different communication strategies or messages on audience behavior or perception.
• Quantifying the frequency and types of communication behaviors in recorded interactions (e.g., supportive vs. critical comments)
• Quantifying the frequency of certain themes, words, or images in media content to identify patterns or trends.
• Statistical Analysis :  Using statistical tools to analyze data from surveys or experiments, such as correlation or regression analysis to explore relationships between variables.

Research Designs Outlined in JARS-Quant

The chapter in the APA handbook you read provided a detailed breakdown of reporting standards for various quantitative research designs within the framework of JARS-Quant. Here are the main types:

• Random Assignment: This involves randomly assigning participants to different experimental conditions, which enhances the internal validity of the study by minimizing confounding variables. The JARs chapter emphasizes reporting the unit of randomization, procedures for generating assignments, and masking provisions.
• Nonrandom Assignment: This method, used when random assignment is not feasible, involves assigning participants to conditions based on predetermined criteria. The JARs chapter recommends reporting the unit of assignment, assignment rules, and procedures to minimize selection bias (e.g., matching, propensity score matching).
• Trial Registration (e.g., on ClinicalTrials.gov)
• Site-Specific Considerations for Multisite Trials
• Detailed Description of the Standard Treatment (if applicable)
• Data Safety and Monitoring Board and Stopping Rules (if used)
• Rationale for Follow-Up Period (if applicable)
• Nonexperimental Designs: Also known as observational, correlational, or natural history studies, these designs examine naturally occurring relationships between variables without any manipulation. The JARs chapter emphasizes describing the design, participant selection, sampling methods (e.g., prospective, retrospective, case-control), data sources, and potential limitations.
• Longitudinal Studies: These involve repeated observations of the same individuals over time. The JARs chapter stresses reporting recruitment and retention methods, attrition rates, handling of missing data, contextual changes during the study, and changes in instrumentation.
• N -of-1 Studies: Focusing on a single individual as the unit of study, these designs often involve multiple phases and manipulations. The JARs chapter recommends describing the design type (e.g., withdrawal-reversal), phases, randomization (if used), sequence completed by each participant, and raw data for outcomes.
• Replication Studies: These aim to reproduce the findings of previous research. JARS-Quant underscores the need to specify the type of replication (e.g., direct, approximate, conceptual), compare the replication with the original study (participants, procedures, measures), report results using the original study’s analytic methods, and state the rules for determining replication success.

JARS-Quant emphasizes comprehensive and transparent reporting of quantitative research, ensuring readers can understand, evaluate, and potentially replicate the study. Remember that Figure 3.1 in the JARs chapter provides a helpful flowchart to guide researchers in selecting the appropriate JARS-Quant tables for their specific research design.

Communication Research in Real Life Copyright © 2023 by Kate Magsamen-Conrad. All Rights Reserved.

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Reader's guide

Entries a-z, subject index.

• Quantitative Research, Purpose of
• By: Nancy A. Burrell & Clare Gross
• In: The SAGE Encyclopedia of Communication Research Methods
• Chapter DOI: https:// doi. org/10.4135/9781483381411.n476
• Subject: Communication and Media Studies , Sociology
• Show page numbers Hide page numbers

The purpose of quantitative research is to generate knowledge and create understanding about the social world. Quantitative research is used by social scientists, including communication researchers, to observe phenomena or occurrences affecting individuals. Social scientists are concerned with the study of people. Quantitative research is a way to learn about a particular group of people, known as a sample population. Using scientific inquiry, quantitative research relies on data that are observed or measured to examine questions about the sample population.

The results of quantitative research specify an explanation into what is and is not important, or influencing, a particular population. Quantitative research also provides answers to questions about the frequency of a phenomenon, or the magnitude to which the phenomenon affects the sample population. Furthermore, when conducted proficiently, quantitative research allows information about a sample population to be generalized to a larger population. Quantitative research is used to create an awareness of truths about the social world. [Page 1378] The following sections address how quantitative research accomplishes these purposes by describing how it compares and contrasts from other types of inquiries, discussing key components of quantitative research, and providing examples of quantitative research.

Different Forms of Inquiry

Quantitative research uses scientific inquiry to focus on a particular problem affecting the sample population. To understand the purpose of quantitative research, it is important to look at the philosophy behind its development. Social scientists differ in approaches to the study of the social world. As communication developed as a discipline, some researchers held that knowledge could be observed and objectively measured; others believed knowledge was more subjective, needing to be interpreted. Differences in the view of knowledge and its relation to the social world created different forms of study, or methodologies. A methodology describes how researchers study, collect, an analyze data, and how researchers provide answers to the questions they are investigating. Quantitative research was developed from a perspective known as positivism . The view of positivism was that researchers could objectively study a sample population and verify or refute claims. Through the analysis of data, the perspective of positivism held that research could create an unbiased understanding of the data and its universal impact. Today, the positivist position has expanded, with researchers conscious that data collection and analysis in the social sciences can never be completely objective or proven beyond a doubt. Still, quantitative research strives to minimize potential bias from the researcher or in the process of data collection itself to present a clearer understanding of a phenomenon.

In addition to quantitative research, three other methodological perspectives developed in communication research: qualitative, rhetorical, and critical. Quantitative and qualitative research are focused on participants or human subjects, whereas rhetorical and critical research are expert or text centered. For example, qualitative research uses data to more descriptively characterize the impact or reality of a focused sample population. Rhetorical research provides reflective understandings about culture through the analysis of text, images, and artifacts, rather than an analysis of human subjects. Critical research uses a similar form of data collection as rhetorical, but differs in how it describes the outcome of data analysis. Specifically, critical research describes the moral implications of findings, and argues for a universal ethical change in society by pointing out groups or practices that are disadvantaged or unfair.

The Differences Between Quantitative and Qualitative Research

Quantitative and qualitative research both focus on understanding phenomena about participants, and this similarity often leads to a comparison of the two methodologies. Examining how quantitative and qualitative research differ provides a clearer picture of the purpose of quantitative research. Namely, quantitative and qualitative researchers contrast how they approach issues of study and in the data analysis they employ. Qualitative research is driven by broad questions with the goal of gaining an understanding of how problems affect the reality of participants. Assessing how a smaller sample population feels, qualitative methodologies commonly employ interviews, or direct explanations from participants that are interpreted by a researcher. The role of the researcher is more subjective than in quantitative research, making judgments or pointing out themes of importance to the sample population. Because the size of the sample population is smaller in qualitative research, the findings of the research cannot be generalized to a larger population. Qualitative researchers generalize to that specific group of participants rather than a large population.

In contrast, quantitative research focuses on a narrower problem affecting a sample population. Examining a narrower aspect of a problem makes it easier to try and find an answer about factors that influence each other. Quantitative researchers try to measure aspects of a problem to understand the relation to other variables. To do so, quantitative researchers gather data in a way that is quantifiable. Primarily, four main methods of data collection are utilized: surveys, experiments, field research, and public data with open access. [Page 1379] Surveys commonly measure the frequency with which the sample population experiences the phenomenon, using scales to open-ended questions to assess communicative traits or aspects belonging to participants or their relationships. For example, if examining aggression in college students’ romantic relationships is the area of interest to a researcher, surveys could be used during sampling to reach a large number of college students, having them report on how frequently and how intensely they have experienced verbal and physical aggression. Experiments manipulate conditions to see how participants respond under different conditions, making observations about how someone might be likely to act when the conditions naturally occur. Consider the previous example of studying aggression in college students’ dating relationships. From a different vantage, researchers could approach the problem using an experiment; romantic dyads that reported high verbal aggression in their relationships could be presented with different stressful hypothetical situations, and researchers could analyze under what conditions verbal aggression was exhibited.

Field research allows for a natural observation, recording how a group of people normally behaves. Researchers interested in studying physical aggression among middle-school students could observe their behaviors at recess. Using public data that is freely accessible allows researchers to assess frequencies similar to surveys, and also analyze how a group of people responded to an event, statement, or other type of stimulus. For instance, looking at how the public responds to an instance of hate speech, researchers could thematically analyze the content on a public blog. Despite the type of method of data collection used, quantitative research is united by a focus of answering a specific question to find out more about a sample population and come to an understanding of how a larger population also experiences the phenomenon similarly.

Generating Concepts and Supporting Theories

The formation of concepts and support of theories are the outcomes generated by quantitative research. Concepts and theories can be thought of as building blocks that allow researchers to understand and establish truths about the social world. A concept is simply understood as something that commonly occurs among a certain population. A theory is more complex, and describes how concepts influence each other. Theories are based on evidence resulting from quantitative research, and they suggest how and under what conditions phenomena work, change, or affect a population. Crucial to the understanding of theories is the knowledge that theories are not 100% proven. Social science is based on observation, and by its nature cannot be completely objective, as some studies can be in the field of hard science. However, theories are based on accumulated evidence, and an accurate theory allows researchers to make predictions about how issues impact a sample population.

An example of a concept and a theory will help to clarify how they are supported by quantitative research. A widely supported theory generated by the discipline of communication is uncertainty reduction theory (URT). Summarizing the many premises and predictive tenants of URT, the theory states that individuals in interpersonal relationships find uncertainty uncomfortable, and behave in ways to reduce uncertainty. While several concepts are involved in this theory, one of the most important concepts is uncertainty. Referring back to the definition of a concept, uncertainty is the uncomfortable tension that arises from a lack of information, and in the case of URT, uncertainty is assessed among different populations. A common population studied by URT centers on individuals in new relationships, particularly in romantic relationships. Examining the concept of uncertainty, or discomfort caused by a lack of information, allows researchers to make predictions of how individuals will behave in different situations to reduce uncertainty. Over time, the accumulated evidence generated by research expands the accuracy of URT to predict how the concept of uncertainty affects cognitive processing, behaviors, and interactions in the area of interpersonal communication.

In addition, accurate theories can be expanded, or applied to different populations; in the case of URT, it has also been studied among individuals in established relationships. Theories may also generate research in other areas, potentially leading to the development of new theories. For instance, in the case of URT, research examined under what conditions individuals might want to maintain [Page 1380] uncertainty, even though it is uncomfortable. This led to the development of uncertainty management theory (UMT), examining sample populations facing health challenges, which reacted to the concept of uncertainty differently. Rather than reducing uncertainty, some individuals from this population reveled that maintaining the uncertainty was preferable to knowing the extent of their conditions. As a result of research on the concept of uncertainty, theory development creates a larger understanding of the significance of this concept and how individuals react to it in different situations.

Deciding When to Use Quantitative Research

While multiple methodological perspectives are useful in helping to develop theories such as URT, there are reasons why researchers would utilize or avoid quantitative research. As mentioned previously, quantitative research allows for a generalization to a larger, more universal, population. This means that under certain conditions, the research findings of quantitative research can be applied to a population outside of the sample. Quantitative research usually analyzes data through the use of statistics, or a mathematical representation of the data, which can be placed into formulas and used for comparison of significance and to make predictions. Statistics strive to create an unbiased understanding of the data. Correct statistical analysis enables the researcher to make generalizations of how a larger population will have a similar reaction as the sample population. However, generalizations are not infallible and must be made with caution. A degree of error is always present, as the difference in size between the sample and actual population, and the way the study was conducted, affects the accuracy of generalizations.

In deciding if quantitative research is the right type of methodology to use, researchers must go back to the goals of their research. Particularly, quantitative research should be selected if it fits the claims the researchers are trying to make. For example, if researchers want to understand how victims of dating violence feel about their experiences, they would likely want to gather narrative evidence through in-depth interviews. Research can benefit from mixed methodologies, using quantitative research to understand how variables influence each other, and including questions to explore thematic areas in detail. Although researchers tend to develop a methodological preference, it is important to avoid thinking of one methodology as superior to the others. Quantitative methodologies have several strengths regarding social science research, but they must be conducted carefully and researchers must be cautious when deriving generalizations.

Nancy A. Burrell and Clare Gross

See also Qualitative Data ; Statistical Power Analysis ; Variables, Defining

Further Readings

Balnaves, M., & Caputi, Peter. (2001). Introduction to quantitative research methods. Thousand Oaks, CA: Sage.

Knobloch, L. K. (2008). Uncertainty reduction theory: Communicating under conditions of ambiguity. In L. A. Baxter & D. O. Braithwaite (Eds.), Engaging theories in interpersonal communication: Multiple perspectives (pp. 133–144). Thousand Oaks, CA: Sage.

Singleton, R. A., Jr., & Straits, B. C. (2005). Approaches to social research (4th ed.). New York, NY: Oxford University Press.

• Qualitative Data
• Quantitative Research, Steps for
• Authoring: Telling a Research Story
• Body Image and Eating Disorders
• Hypothesis Formulation
• Methodology, Selection of
• Program Assessment
• Research Ideas, Sources of
• Research Project, Planning of
• Research Question Formulation
• Research Topic, Definition of
• Research, Inspiration for
• Social Media: Blogs, Microblogs, and Twitter
• Testability
• Acknowledging the Contribution of Others
• Activism and Social Justice
• Anonymous Source of Data
• Authorship Bias
• Authorship Credit
• Confidentiality and Anonymity of Participants
• Conflict of Interest in Research
• Controversial Experiments
• Copyright Issues in Research
• Cultural Sensitivity in Research
• Data Security
• Debriefing of Participants
• Deception in Research
• Ethical Issues, International Research
• Ethics Codes and Guidelines
• Fraudulent and Misleading Data
• Funding Research
• Health Care Disparities
• Human Subjects, Treatment of
• Informed Consent
• Institutional Review Board
• Organizational Ethics
• Peer Review
• Plagiarism, Self-
• Privacy of Information
• Privacy of Participants
• Public Behavior, Recording of
• Reliability, Unitizing
• Research Ethics and Social Values
• Researcher-Participant Relationships
• Social Implications of Research
• Archive Searching for Research
• Bibliographic Research
• Databases, Academic
• Foundation and Government Research Collections
• Library Research
• Literature Review, The
• Literature Reviews, Foundational
• Literature Reviews, Resources for
• Literature Reviews, Strategies for
• Literature Sources, Skeptical and Critical Stance Toward
• Literature, Determining Quality of
• Literature, Determining Relevance of
• Meta-Analysis
• Publications, Scholarly
• Search Engines for Literature Search
• Vote Counting Literature Review Methods
• Abstract or Executive Summary
• Academic Journals
• Alternative Conference Presentation Formats
• American Psychological Association (APA) Style
• Archiving Data
• Blogs and Research
• Chicago Style
• Citations to Research
• Evidence-Based Policy Making
• Invited Publication
• Limitations of Research
• Modern Language Association (MLA) Style
• Narrative Literature Review
• New Media Analysis
• News Media, Writing for
• Panel Presentations and Discussion
• Pay to Review and/or Publish
• Peer Reviewed Publication
• Poster Presentation of Research
• Primary Data Analysis
• Publication Style Guides
• Publication, Politics of
• Publications, Open-Access
• Publishing a Book
• Publishing a Journal Article
• Research Report, Organization of
• Research Reports, Objective
• Research Reports, Subjective
• Scholarship of Teaching and Learning
• Secondary Data
• Submission of Research to a Convention
• Submission of Research to a Journal
• Title of Manuscript, Selection of
• Visual Images as Data Within Qualitative Research
• Writer’s Block
• Writing a Discussion Section
• Writing a Literature Review
• Writing a Methods Section
• Writing a Results Section
• Writing Process, The
• Coding of Data
• Content Analysis, Definition of
• Content Analysis, Process of
• Content Analysis: Advantages and Disadvantages
• Conversation Analysis
• Critical Analysis
• Discourse Analysis
• Interaction Analysis, Quantitative
• Intercoder Reliability
• Intercoder Reliability Coefficients, Comparison of
• Intercoder Reliability Standards: Reproducibility
• Intercoder Reliability Standards: Stability
• Intercoder Reliability Techniques: Cohen’s Kappa
• Intercoder Reliability Techniques: Fleiss System
• Intercoder Reliability Techniques: Holsti Method
• Intercoder Reliability Techniques: Krippendorf Alpha
• Intercoder Reliability Techniques: Percent Agreement
• Intercoder Reliability Techniques: Scott’s Pi
• Metrics for Analysis, Selection of
• Narrative Analysis
• Observational Research Methods
• Observational Research, Advantages and Disadvantages
• Observer Reliability
• Rhetorical and Dramatism Analysis
• Unobtrusive Analysis
• Association of Internet Researchers (AoIR)
• Computer-Mediated Communication (CMC)
• Internet as Cultural Context
• Internet Research and Ethical Decision Making
• Internet Research, Privacy of Participants
• Online and Offline Data, Comparison of
• Online Communities
• Online Data, Collection and Interpretation of
• Online Data, Documentation of
• Online Data, Hacking of
• Online Interviews
• Online Social Worlds
• Social Networks, Online
• Correspondence Analysis
• Cutoff Scores
• Data Cleaning
• Data Reduction
• Data Trimming
• Facial Affect Coding System
• Factor Analysis
• Factor Analysis-Oblique Rotation
• Factor Analysis: Confirmatory
• Factor Analysis: Evolutionary
• Factor Analysis: Exploratory
• Factor Analysis: Internal Consistency
• Factor Analysis: Parallelism Test
• Factor Analysis: Rotated Matrix
• Factor Analysis: Varimax Rotation
• Implicit Measures
• Measurement Levels
• Measurement Levels, Interval
• Measurement Levels, Nominal/Categorical
• Measurement Levels, Ordinal
• Measurement Levels, Ratio
• Observational Measurement: Face Features
• Observational Measurement: Proxemics and Touch
• Observational Measurement: Vocal Qualities
• Organizational Identification
• Outlier Analysis
• Physiological Measurement
• Physiological Measurement: Blood Pressure
• Physiological Measurement: Genital Blood Volume
• Physiological Measurement: Heart Rate
• Physiological Measurement: Pupillary Response
• Physiological Measurement: Skin Conductance
• Reaction Time
• Reliability of Measurement
• Reliability, Cronbach’s Alpha
• Reliability, Knuder-Richardson
• Reliability, Split-half
• Scales, Forced Choice
• Scales, Likert Statement
• Scales, Open-Ended
• Scales, Rank Order
• Scales, Semantic Differential
• Scales, True/False
• Scaling, Guttman
• Standard Score
• Time Series Notation
• Validity, Concurrent
• Validity, Construct
• Validity, Face and Content
• Validity, Halo Effect
• Validity, Measurement of
• Validity, Predictive
• Variables, Conceptualization
• Variables, Operationalization
• Z Transformation
• Confederates
• Generalization
• Imagined Interactions
• Interviewees
• Matched Groups
• Matched Individuals
• Random Assignment of Participants
• Respondents
• Response Style
• Treatment Groups
• Vulnerable Groups
• Experience Sampling Method
• Sample Versus Population
• Sampling Decisions
• Sampling Frames
• Sampling, Internet
• Sampling, Methodological Issues in
• Sampling, Multistage
• Sampling, Nonprobability
• Sampling, Probability
• Sampling, Special Population
• Opinion Polling
• Sampling, Random
• Survey Instructions
• Survey Questions, Writing and Phrasing of
• Survey Response Rates
• Survey Wording
• Survey: Contrast Questions
• Survey: Demographic Questions
• Survey: Dichotomous Questions
• Survey: Filter Questions
• Survey: Follow-up Questions
• Survey: Leading Questions
• Survey: Multiple-Choice Questions
• Survey: Negative-Wording Questions
• Survey: Open-Ended Questions
• Survey: Questionnaire
• Survey: Sampling Issues
• Survey: Structural Questions
• Surveys, Advantages and Disadvantages of
• Surveys, Using Others’
• Under-represented Group
• Alternative News Media
• Analytic Induction
• Archival Analysis
• Artifact Selection
• Autoethnography
• Axial Coding
• Burkean Analysis
• Close Reading
• Coding, Fixed
• Coding, Flexible
• Computer-Assisted Qualitative Data Analysis Software (CAQDAS)
• Covert Observation
• Critical Ethnography
• Critical Incident Method
• Critical Race Theory
• Cultural Studies and Communication
• Demand Characteristics
• Ethnographic Interview
• Ethnography
• Ethnomethodology
• Fantasy Theme Analysis
• Feminist Analysis
• Field Notes
• First Wave Feminism
• Fisher Narrative Paradigm
• Focus Groups
• Frame Analysis
• Garfinkling
• Gender-Specific Language
• Grounded Theory
• Hermeneutics
• Historical Analysis
• Informant Interview
• Interaction Analysis, Qualitative
• Interpretative Research
• Interviews for Data Gathering
• Interviews, Recording and Transcribing
• Marxist Analysis
• Meta-ethnography
• Metaphor Analysis
• Narrative Interviewing
• Naturalistic Observation
• Negative Case Analysis
• Neo-Aristotelian Method
• New Media and Participant Observation
• Participant Observer
• Pentadic Analysis
• Performance Research
• Phenomenological Traditions
• Poetic Analysis
• Postcolonial Analysis
• Power in Language
• Pronomial Use-Solidarity
• Psychoanalytic Approaches to Rhetoric
• Public Memory
• Queer Methods
• Queer Theory
• Researcher-Participant Relationships in Observational Research
• Respondent Interviews
• Rhetoric as Epistemic
• Rhetoric, Aristotle’s: Ethos
• Rhetoric, Aristotle’s: Logos
• Rhetoric, Aristotle’s: Pathos
• Rhetoric, Isocrates’
• Rhetorical Artifact
• Rhetorical Method
• Rhetorical Theory
• Second Wave Feminism
• Snowball Subject Recruitment
• Social Constructionism
• Social Network Analysis
• Spontaneous Decision Making
• Symbolic Interactionism
• Terministic Screens
• Textual Analysis
• Thematic Analysis
• Theoretical Traditions
• Third-Wave Feminism
• Transcription Systems
• Triangulation
• Turning Point Analysis
• Unobtrusive Measurement
• Visual Materials, Analysis of
• t -Test, Independent Samples
• t -Test, One Sample
• t -Test, Paired Samples
• Analysis of Covariance (ANCOVA)
• Analysis of Ranks
• Analysis of Variance (ANOVA)
• Bonferroni Correction
• Decomposing Sums of Squares
• Eta Squared
• Factorial Analysis of Variance
• McNemar Test
• One-Tailed Test
• One-Way Analysis of Variance
• Post Hoc Tests
• Post Hoc Tests: Duncan Multiple Range Test
• Post Hoc Tests: Least Significant Difference
• Post Hoc Tests: Scheffe Test
• Post Hoc Tests: Student-Newman-Keuls Test
• Post Hoc Tests: Tukey Honestly Significance Difference Test
• Repeated Measures
• Between-Subjects Design
• Blocking Variable
• Control Groups
• Counterbalancing
• Cross-Sectional Design
• Degrees of Freedom
• Delayed Measurement
• Ex Post Facto Designs
• Experimental Manipulation
• Experiments and Experimental Design
• External Validity
• Extraneous Variables, Control of
• Factor, Crossed
• Factor, Fixed
• Factor, Nested
• Factor, Random
• Factorial Designs
• False Negative
• False Positive
• Field Experiments
• Hierarchical Model
• Individual Difference
• Internal Validity
• Laboratory Experiments
• Latin Square Design
• Longitudinal Design
• Manipulation Check
• Measures of Variability
• Median Split of Sample
• Mixed Level Design
• Multitrial Design
• Null Hypothesis
• One-Group Pretest–Posttest Design
• Orthogonality
• Overidentified Model
• Pilot Study
• Population/Sample
• Power Curves
• Quasi-Experimental Design
• Random Assignment
• Replication
• Research Proposal
• Sampling Theory
• Sampling, Determining Size
• Solomon Four-Group Design
• Stimulus Pre-test
• Two-Group Pretest–Posttest Design
• Two-Group Random Assignment Pretest–Posttest Design
• Variables, Control
• Variables, Dependent
• Variables, Independent
• Variables, Latent
• Variables, Marker
• Variables, Mediating Types
• Variables, Moderating Types
• Within-Subjects Design
• Analysis of Residuals
• Bivariate Statistics
• Bootstrapping
• Confidence Interval
• Conjoint Analysis
• Contrast Analysis
• Correlation, Pearson
• Correlation, Point-Biserial
• Correlation, Spearman
• Covariance/Variance Matrix
• Cramér’s V
• Discriminant Analysis
• Kendall’s Tau
• Kruskal-Wallis Test
• Linear Regression
• Linear Versus Nonlinear Relationships
• Multicollinearity
• Multiple Regression
• Multiple Regression: Block Analysis
• Multiple Regression: Covariates in Multiple Regression
• Multiple Regression: Multiple R
• Multiple Regression: Standardized Regression Coefficient
• Partial Correlation
• Phi Coefficient
• Semi-Partial r
• Simple Bivariate Correlation
• Categorization
• Cluster Analysis
• Data Transformation
• Errors of Measurement
• Errors of Measurement: Attenuation
• Errors of Measurement: Ceiling and Floor Effects
• Errors of Measurement: Dichotomization of a Continuous Variable
• Errors of Measurement: Range Restriction
• Errors of Measurement: Regression Toward the Mean
• Frequency Distributions
• Heterogeneity of Variance
• Heteroskedasticity
• Homogeneity of Variance
• Hypothesis Testing, Logic of
• Intraclass Correlation
• Mean, Arithmetic
• Mean, Geometric
• Mean, Harmonic
• Measures of Central Tendency
• Mortality in Sample
• Normal Curve Distribution
• Relationships Between Variables
• Sensitivity Analysis
• Significance Test
• Simple Descriptive Statistics
• Standard Deviation and Variance
• Standard Error
• Standard Error, Mean
• Statistical Power Analysis
• Type I error
• Type II error
• Univariate Statistics
• Variables, Categorical
• Variables, Continuous
• Variables, Defining
• Variables, Interaction of
• Autoregressive, Integrative, Moving Average (ARIMA) Models
• Binomial Effect Size Display
• Cloze Procedure
• Cross Validation
• Cross-Lagged Panel Analysis
• Curvilinear Relationship
• Effect Sizes
• Hierarchical Linear Modeling
• Lag Sequential Analysis
• Log-Linear Analysis
• Logistic Analysis
• Margin of Error
• Markov Analysis
• Maximum Likelihood Estimation
• Meta-Analysis: Estimation of Average Effect
• Meta-Analysis: Fixed Effects Analysis
• Meta-Analysis: Literature Search Issues
• Meta-Analysis: Model Testing
• Meta-Analysis: Random Effects Analysis
• Meta-Analysis: Statistical Conversion to Common Metric
• Multivariate Analysis of Variance (MANOVA)
• Multivariate Statistics
• Ordinary Least Squares
• Path Analysis
• Probit Analysis
• Structural Equation Modeling
• Time-Series Analysis
• Acculturation
• African American Communication and Culture
• Agenda Setting
• Applied Communication
• Argumentation Theory
• Asian/Pacific American Communication Studies
• Bad News, Communication of
• Basic Course in Communication
• Business Communication
• Communication and Aging Research
• Communication and Culture
• Communication and Evolution
• Communication and Future Studies
• Communication and Human Biology
• Communication and Technology
• Communication Apprehension
• Communication Assessment
• Communication Competence
• Communication Education
• Communication Ethics
• Communication History
• Communication Privacy Management Theory
• Communication Skills
• Communication Theory
• Conflict, Mediation, and Negotiation
• Corporate Communication
• Crisis Communication
• Cross-Cultural Communication
• Cyberchondria
• Dark Side of Communication
• Debate and Forensics
• Development of Communication in Children
• Digital Media and Race
• Digital Natives
• Dime Dating
• Disability and Communication
• Distance Learning
• Educational Technology
• Emergency Communication
• Empathic Listening
• English as a Second Language
• Environmental Communication
• Family Communication
• Feminist Communication Studies
• Film Studies
• Financial Communication
• Freedom of Expression
• Game Studies
• Gender and Communication
• GLBT Communication Studies
• GLBT Social Media
• Group Communication
• Health Communication
• Health Literacy
• Human-Computer Interaction
• Instructional Communication
• Intercultural Communication
• Intergenerational Communication
• Intergroup Communication
• International Communication
• International Film
• Interpersonal Communication
• Intrapersonal Communication
• Language and Social Interaction
• Latino Communication
• Legal Communication
• Managerial Communication
• Mass Communication
• Massive Multiplayer Online Games
• Massive Open Online Courses
• Media and Technology Studies
• Media Diffusion
• Media Effects Research
• Media Literacy
• Message Production
• Multiplatform Journalism
• Native American or Indigenous Peoples Communication
• Nonverbal Communication
• Organizational Communication
• Parasocial Communication
• Patient-Centered Communication
• Peace Studies
• Performance Studies
• Personal Relationship Studies
• Philosophy of Communication
• Political Communication
• Political Debates
• Political Economy of Media
• Popular Communication
• Pornography and Research
• Public Address
• Public Relations
• Reality Television
• Relational Dialectics Theory
• Religious Communication
• Rhetorical Genre
• Risk Communication
• Robotic Communication
• Science Communication
• Selective Exposure
• Service Learning
• Small Group Communication
• Social Cognition
• Social Network Systems
• Social Presence
• Social Relationships
• Spirituality and Communication
• Sports Communication
• Strategic Communication
• Structuration Theory
• Training and Development in Organizations
• Video Games
• Visual Communication Studies
• Wartime Communication
• Academic Journal Structure
• Citation Analyses
• Communication Journals
• Interdisciplinary Journals
• Professional Communication Organizations (NCA, ICA, Central, etc.)

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Communicating Results of Quantitative Research

• First Online: 01 October 2023

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• Jane E. Miller 4  

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In this chapter, I show how to apply expository writing techniques and principles for writing about numbers to communicate effectively about the results of quantitative research. Using examples from the biomedical literature, I demonstrate how to write a clear narrative with numbers as evidence, introducing the question, describing individual facts and patterns, and maintaining a focus on the topic and context at hand. The chapter starts with basic principles for writing about numbers including specifying the context and several dimensions of units. It then discusses how to choose and design complementary tools (prose, tables, charts, and maps) to communicate results, with guidance about how to make exhibits self-contained and how to organize numbers in those exhibits to match the associated narrative description. Next, the chapter introduces principles for comparing two or more numbers, including specifying direction, magnitude, and statistical significance, and how to summarize complex patterns. Those principles are demonstrated for presenting results of both bivariate and multivariate analyses, with examples of how to coordinate tables or charts with prose. The chapter ends by emphasizing the importance of conveying both the substantive and statistical significance of numeric findings.

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Miller, J.E. (2023). Communicating Results of Quantitative Research. In: Jagadeesh, G., Balakumar, P., Senatore, F. (eds) The Quintessence of Basic and Clinical Research and Scientific Publishing. Springer, Singapore. https://doi.org/10.1007/978-981-99-1284-1_45

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Guide to Communication Research Methodologies: Quantitative, Qualitative and Rhetorical Research

Overview of Communication

Communication research methods, quantitative research, qualitative research, rhetorical research, mixed methodology.

Students interested in earning a graduate degree in communication should have at least some interest in understanding communication theories and/or conducting communication research. As students advance from undergraduate to graduate programs, an interesting change takes place — the student is no longer just a repository for knowledge. Rather, the student is expected to learn while also creating knowledge. This new knowledge is largely generated through the development and completion of research in communication studies. Before exploring the different methodologies used to conduct communication research, it is important to have a foundational understanding of the field of communication.

Defining communication is much harder than it sounds. Indeed, scholars have argued about the topic for years, typically differing on the following topics:

• Breadth : How many behaviors and actions should or should not be considered communication.
• Intentionality : Whether the definition includes an intention to communicate.
• Success : Whether someone was able to effectively communicate a message, or merely attempted to without it being received or understood.

However, most definitions discuss five main components, which include: sender, receiver, context/environment, medium, and message. Broadly speaking, communication research examines these components, asking questions about each of them and seeking to answer those questions.

As students seek to answer their own questions, they follow an approach similar to most other researchers. This approach proceeds in five steps: conceptualize, plan and design, implement a methodology, analyze and interpret, reconceptualize.

• Conceptualize : In the conceptualization process, students develop their area of interest and determine if their specific questions and hypotheses are worth investigating. If the research has already been completed, or there is no practical reason to research the topic, students may need to find a different research topic.
• Plan and Design : During planning and design students will select their methods of evaluation and decide how they plan to define their variables in a measurable way.
• Implement a Methodology : When implementing a methodology, students collect the data and information they require. They may, for example, have decided to conduct a survey study. This is the step when they would use their survey to collect data. If students chose to conduct a rhetorical criticism, this is when they would analyze their text.
• Analyze and Interpret : As students analyze and interpret their data or evidence, they transform the raw findings into meaningful insights. If they chose to conduct interviews, this would be the point in the process where they would evaluate the results of the interviews to find meaning as it relates to the communication phenomena of interest.
• Reconceptualize : During reconceptualization, students ask how their findings speak to a larger body of research — studies related to theirs that have already been completed and research they should execute in the future to continue answering new questions.

This final step is crucial, and speaks to an important tenet of communication research: All research contributes to a better overall understanding of communication and moves the field forward by enabling the development of new theories.

In the field of communication, there are three main research methodologies: quantitative, qualitative, and rhetorical. As communication students progress in their careers, they will likely find themselves using one of these far more often than the others.

Quantitative research seeks to establish knowledge through the use of numbers and measurement. Within the overarching area of quantitative research, there are a variety of different methodologies. The most commonly used methodologies are experiments, surveys, content analysis, and meta-analysis. To better understand these research methods, you can explore the following examples:

Experiments : Experiments are an empirical form of research that enable the researcher to study communication in a controlled environment. For example, a researcher might know that there are typical responses people use when they are interrupted during a conversation. However, it might be unknown as to how frequency of interruption provokes those different responses (e.g., do communicators use different responses when interrupted once every 10 minutes versus once per minute?). An experiment would allow a researcher to create these two environments to test a hypothesis or answer a specific research question. As you can imagine, it would be very time consuming — and probably impossible — to view this and measure it in the real world. For that reason, an experiment would be perfect for this research inquiry.

Surveys : Surveys are often used to collect information from large groups of people using scales that have been tested for validity and reliability. A researcher might be curious about how a supervisor sharing personal information with his or her subordinate affects way the subordinate perceives his or her supervisor. The researcher could create a survey where respondents answer questions about a) the information their supervisors self-disclose and b) their perceptions of their supervisors. The data collected about these two variables could offer interesting insights about this communication. As you would guess, an experiment would not work in this case because the researcher needs to assess a real relationship and they need insight into the mind of the respondent.

Content Analysis : Content analysis is used to count the number of occurrences of a phenomenon within a source of media (e.g., books, magazines, commercials, movies, etc.). For example, a researcher might be interested in finding out if people of certain races are underrepresented on television. They might explore this area of research by counting the number of times people of different races appear in prime time television and comparing that to the actual proportions in society.

Meta-Analysis : In this technique, a researcher takes a collection of quantitative studies and analyzes the data as a whole to get a better understanding of a communication phenomenon. For example, a researcher might be curious about how video games affect aggression. This researcher might find that many studies have been done on the topic, sometimes with conflicting results. In their meta-analysis, they could analyze the existing statistics as a whole to get a better understanding of the relationship between the two variables.

Qualitative research is interested in exploring subjects’ perceptions and understandings as they relate to communication. Imagine two researchers who want to understand student perceptions of the basic communication course at a university. The first researcher, a quantitative researcher, might measure absences to understand student perception. The second researcher, a qualitative researcher, might interview students to find out what they like and dislike about a course. The former is based on hard numbers, while the latter is based on human experience and perception.

Qualitative researchers employ a variety of different methodologies. Some of the most popular are interviews, focus groups, and participant observation. To better understand these research methods, you can explore the following examples:

Interviews : This typically consists of a researcher having a discussion with a participant based on questions developed by the researcher. For example, a researcher might be interested in how parents exert power over the lives of their children while the children are away at college. The researcher could spend time having conversations with college students about this topic, transcribe the conversations and then seek to find themes across the different discussions.

Focus Groups : A researcher using this method gathers a group of people with intimate knowledge of a communication phenomenon. For example, if a researcher wanted to understand the experience of couples who are childless by choice, he or she might choose to run a series of focus groups. This format is helpful because it allows participants to build on one another’s experiences, remembering information they may otherwise have forgotten. Focus groups also tend to produce useful information at a higher rate than interviews. That said, some issues are too sensitive for focus groups and lend themselves better to interviews.

Participant Observation : As the name indicates, this method involves the researcher watching participants in their natural environment. In some cases, the participants may not know they are being studied, as the researcher fully immerses his or herself as a member of the environment. To illustrate participant observation, imagine a researcher curious about how humor is used in healthcare. This researcher might immerse his or herself in a long-term care facility to observe how humor is used by healthcare workers interacting with patients.

Rhetorical research (or rhetorical criticism) is a form of textual analysis wherein the researcher systematically analyzes, interprets, and critiques the persuasive power of messages within a text. This takes on many forms, but all of them involve similar steps: selecting a text, choosing a rhetorical method, analyzing the text, and writing the criticism.

To illustrate, a researcher could be interested in how mass media portrays “good degrees” to prospective college students. To understand this communication, a rhetorical researcher could take 30 articles on the topic from the last year and write a rhetorical essay about the criteria used and the core message argued by the media.

Likewise, a researcher could be interested in how women in management roles are portrayed in television. They could select a group of popular shows and analyze that as the text. This might result in a rhetorical essay about the behaviors displayed by these women and what the text says about women in management roles.

As a final example, one might be interested in how persuasion is used by the president during the White House Correspondent’s Dinner. A researcher could select several recent presidents and write a rhetorical essay about their speeches and how they employed persuasion during their delivery.

Taking a mixed methods approach results in a research study that uses two or more techniques discussed above. Often, researchers will pair two methods together in the same study examining the same phenomenon. Other times, researchers will use qualitative methods to develop quantitative research, such as a researcher who uses a focus group to discuss the validity of a survey before it is finalized.

The benefit of mixed methods is that it offers a richer picture of a communication phenomenon by gathering data and information in multiple ways. If we explore some of the earlier examples, we can see how mixed methods might result in a better understanding of the communication being studied.

Example 1 : In surveys, we discussed a researcher interested in understanding how a supervisor sharing personal information with his or her subordinate affects the way the subordinate perceives his or her supervisor. While a survey could give us some insight into this communication, we could also add interviews with subordinates. Exploring their experiences intimately could give us a better understanding of how they navigate self-disclosure in a relationship based on power differences.

Example 2 : In content analysis, we discussed measuring representation of different races during prime time television. While we can count the appearances of members of different races and compare that to the composition of the general population, that doesn’t tell us anything about their portrayal. Adding rhetorical criticism, we could talk about how underrepresented groups are portrayed in either a positive or negative light, supporting or defying commonly held stereotypes.

Example 3 : In interviews, we saw a researcher who explored how power could be exerted by parents over their college-age children who are away at school. After determining the tactics used by parents, this interview study could have a phase two. In this phase, the researcher could develop scales to measure each tactic and then use those scales to understand how the tactics affect other communication constructs. One could argue, for example, that student anxiety would increase as a parent exerts greater power over that student. A researcher could conduct a hierarchical regression to see how each power tactic effects the levels of stress experienced by a student.

As you can see, each methodology has its own merits, and they often work well together. As students advance in their study of communication, it is worthwhile to learn various research methods. This allows them to study their interests in greater depth and breadth. Ultimately, they will be able to assemble stronger research studies and answer their questions about communication more effectively.

Note : For more information about research in the field of communication, check out our Guide to Communication Research and Scholarship .

Chapter 16: Communication Research

Quantitative methods, steps for doing quantitative research.

Quantitative methods represent the steps of using the Scientific Method of research.

• Decide on a focus of study based primarily on your interests. What do you want to discover or answer?
• Develop a research question(s) to keep your research focused.
• Develop a hypothesis(es). A hypothesis states how a researcher believes the subjects under study will or will not communicate based on certain variables. For example, you may have a research question that asks, “Does the gender of a student impact the number of times a college professor calls on his/her students?” From this, you might form two hypotheses: “Instructors call on female students less often then male students.” and “Instructors call on students of their same sex.”
• Collect data in order to test hypotheses. In our example, you might observe various college classrooms in order to count which students professors call on more frequently.
• Analyze the data by processing the numbers using statistical programs like SPSS that allow quantitative researchers to detect patterns in communication phenomena. Analyzing data in our example would help us determine if there are any significant differences in the ways in which college professors call on various students.
• Interpret the data to determine if patterns are significant enough to make broad claims about how humans communicate? Simply because professors call on certain students a few more times than other students may or may not indicate communicative patterns of significance.
• Share the results with others. Through the sharing of research we continue to learn more about the patterns and rules that guide the ways we communicate.

The term quantitative refers to research in which we can quantify, or count, communication phenomena . Quantitative methodologies draw heavily from research methods in the physical sciences explore human communication phenomena through the collection and analysis of numerical data. Let’s look at a simple example. What if we wanted to see how public speaking textbooks represent diversity in their photographs and examples. One thing we could do is quantify these to come to conclusions about these representations. For quantitative research, we must determine which communicative acts to count? How do we go about counting them? Is there any human communicative behavior that would return a 100% response rate like the effects of gravity in the physical sciences? What can we learn by counting acts of human communication?

Suppose you want to determine what communicative actions illicit negative responses from your professors. How would you go about researching this? What data would you count? In what ways would you count them? Who would you study? How would you know if you discovered anything of significance that would tell us something important about this? These are tough questions for researchers to answer, particularly in light of the fact that, unlike laws in the physical sciences, human communication is varied and unpredictable.

Nevertheless, there are several quantitative methods researchers use to study communication in order to reveal patterns that help us predict and control our communication. Think about polls that provide feedback for politicians. While people do not all think the same, this type of research provides patterns of thought to politicians who can use this information to make policy decisions that impact our lives. Let’s look at a few of the more frequent quantitative methods of communication research.

Types of Quantitative Methods

There are many ways researchers can quantify human communication. Not all communication is easily quantified, but much of what we know about human communication comes from quantitative research.

• Experimental Research is the most well-established quantitative methodology in both the physical and social sciences. This approach uses the principles of research in the physical sciences to conduct experiments that explore human behavior. Researchers choose whether they will conduct their experiments in lab settings or real-world settings. Experimental research generally includes a control group (the group where variables are not altered) and the experimental group(s) (the group in which variables are altered). The groups are then carefully monitored to see if they enact different reactions to different variables.

To determine if students were more motivated to learn by participating in a classroom game versus attending a classroom lecture, the researchers designed an experiment. They wanted to test the hypothesis that students would actually be more motivated to learn from the game. Their next question was, “do students actually learn more by participating in games?” In order to find out the answers to these questions they conducted the following experiment. In a number of classes instructors were asked to proceed with their normal lecture over certain content (control group), and in a number of other classes, instructors used a game that was developed to teach the same content (experimental group). The students were issued a test at the end of the semester to see which group did better in retaining information, and to find out which method most motivated students to want to learn the material. It was determined that students were more motivated to learn by participating in the game, which proved the hypothesis. The other thing that stood out was that students who participated in the game actually remembered more of the content at the end of the semester than those who listened to a lecture. You might have hypothesized these conclusions yourself, but until research is done, our assumptions are just that (Hunt, Lippert & Paynton).

Case In Point

Quantitative methods in action.

Wendy S. Zabada-Ford (2003) conducted survey research of 253 customers to determine their expectations and experiences with physicians, dentists, mechanics, and hairstylists. Her article, “Research Communication Practices of Professional Service Providers: Predicting Customer Satisfaction and Loyalty” researched the perceptions of customers’ personalized service as related to their expectations in order to predict their satisfaction with the actual service they received. In this study, the goal was to be able to predict the behavior of customers based on their expectations before entering a service-provider context.

Michael T. Stephenson’s (2003) article, “Examining Adolescents’ Responses to Anti-marijuana PSAs” examined how adolescents respond to anti-marijuana public service announcements in the U.S. On the surface, this study may fit into the “understanding” part of the continuum of intended outcomes. However, this research can be used to alter and change messages, such as PSAs, to produce behavioral change in the culture. In this case, the change would be to either keep adolescents from smoking marijuana, or to get them to stop this behavior if they are currently engaged in it.

• Survey Research is used to ask people a number of questions about particular topics. Surveys can be online, mailed, handed out, or conducted in interview format. After researchers have collected survey data, they represent participants’ responses in numerical form using tables, graphs, charts, and/or percentages. On our campus, anonymous survey research was done to determine the drinking and drug habits of our students. This research demonstrated that the percentage of students who frequently use alcohol or drugs is actually much lower than what most students think. The results of this research are now used to educate students that not everyone engages in heavy drinking or drug use, and to encourage students to more closely align their behaviors with what actually occurs on campus, not with what students perceive happens on campus. It is important to remember that there is a possibility that people do not always tell the truth when they answer survey questions. We won’t go into great detail here due to time, but there are sophisticated statistical analyses that can account for this to develop an accurate representation of survey responses.

• Content Analysis . Researchers use content analysis to count the number of occurrences of their particular focus of inquiry. Communication researchers often conduct content analyses of movies, commercials, television shows, magazines, etc., to count the number of occurrences of particular phenomena in these contexts to explore potential effects. Harmon, for example, used content analysis in order to demonstrate how the portrayal of blackness had changed within Black Entertainment Television (BET). She did this by observing the five most frequently played films from the time the cable network was being run by a black owner, to the five most frequently played films after being sold to white­-owned Viacom, Inc. She found that the portrayal, context and power of the black man changes when a white man versus a black man is defining it. Content analysis is extremely effective for demonstrating patterns and trends in various communication contexts. If you would like to do a simple content analysis, count the number of times different people are represented in photos in your textbooks. Are there more men than women? Are there more caucasians represented than other groups? What do the numbers tell you about how we represent different people?
• Meta-Analysis . Do you ever get frustrated when you hear about one research project that says a particular food is good for your health, and then some time later, you hear about another research project that says the opposite? Meta-analysis analyzes existing statistics found in a collection of quantitative research to demonstrate patterns in a particular line of research over time. Meta-analysis is research that seeks to combine the results of a series of past studies to see if their results are similar, or to determine if they show us any new information when they are looked at in totality. The article, Impact of Narratives on Persuasion in Health Communication: A Meta-Analysis examined past research regarding narratives and their persuasiveness in health care settings. The meta-analysis revealed that in-person and video narratives had the most persuasive impacts while written narratives had the least (Shen, Sheer, Li).

Outcomes of Quantitative Methodologies

Because it is unlikely that Communication research will yield 100% certainty regarding communicative behavior, why do Communication researchers use quantitative approaches? First, the broader U.S. culture values the ideals of quantitative science as a means of learning about and representing our world. To this end, many Communication researchers emulate research methodologies of the physical sciences to study human communication phenomena. Second, you’ll recall that researchers have certain theoretical and methodological preferences that motivate their research choices. Those who understand the world from an Empirical Laws and/or Human Rules Paradigm tend to favor research methods that test communicative laws and rules in quantitative ways.

Even though Communication research cannot produce results with 100% accuracy, quantitative research demonstrates patterns of human communication. In fact, many of your own interactions are based on a loose system of quantifying behavior. Think about how you and your classmates sit in your classrooms. Most students sit in the same seats every class meeting, even if there is not assigned seating. In this context, it would be easy for you to count how many students sit in the same seat, and what percentage of the time they do this. You probably already recognize this pattern without having to do a formal study. However, if you wanted to truly demonstrate that students communicatively manifest territoriality to their peers, it would be relatively simple to conduct a quantitative study of this phenomenon. After completing your research, you could report that X% of students sat in particular seats X% of times. This research would not only provide us with an understanding of a particular communicative pattern of students, it would also give us the ability to predict, to a certain degree, their future behaviors surrounding space issues in the classroom.

Quantitative research is also valuable for helping us determine similarities and/or differences among groups of people or communicative events. Representative examples of research in the areas of gender and communication (Berger; Slater), culture and communication (McCann, Ota, Giles, & Caraker; Hylmo & Buzzanell), as well as ethnicity and communication (Jiang Bresnahan, Ohashi, Nebashi, Wen Ying, Shearman; Murray-Johnson) use quantitative methodologies to determine trends and patterns of communicative behavior for various groups. While these trends and patterns cannot be applied to all people, in all contexts, at all times, they help us understand what variables play a role in influencing the ways we communicate.

While quantitative methods can show us numerical patterns, what about our personal lived experiences? How do we go about researching them, and what can they tell us about the ways we communicate? Qualitative methods have been established to get at the “essence” of our lived experiences, as we subjectively understand them.

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Quantitative Research in Communication

• By: Mike Allen , Scott Titsworth & Stephen K. Hunt
• Publisher: SAGE Publications, Inc.
• Publication year: 2009
• Online pub date: January 18, 2013
• Discipline: Media, Communication & Cultural Studies
• Subject: Communication Research Methods (general) , Quantitative/Statistical Research
• DOI: https:// doi. org/10.4135/9781452274881
• Keywords: analysis of covariance , analysis of variance , correlation , covariance , dependent variables , independent variables , regression Show all Show less
• Print ISBN: 9781412956963
• Online ISBN: 9781452274881
• Buy the book icon link

Subject index

Written for communication students, Quantitative Research in Communication provides practical, user-friendly coverage of how to use statistics, how to interpret SPSS printouts, how to write results, and how to assess whether the assumptions of various procedures have been met. Providing a strong conceptual orientation to techniques and procedures that range from the “moderately basic” to “highly advanced,” the book provides practical tips and suggestions for quantitative communication scholars of all experience levels.

In addition to important foundational information, each chapter that covers a specific statistical procedure includes suggestions for interpreting, explaining, and presenting results; realistic examples of how the procedure can be used to answer substantive questions in communication; sample SPSS printouts; and a detailed summary of a published communication journal article using that procedure.

Features Engaged Research application boxes stimulate thought and discussion, illustrating how particular research methods can be used to answer very practical, civic-minded questions.

Realistic examples at the beginning of each chapter show how the chapter's procedure could be used to answer a substantive research question.

Examples and application activities geared toward the emerging trend of service learning encourage students to do projects oriented toward their community or campus.

Summaries of journal articles demonstrate how to write statistical results in APA style and illustrate how real researchers use statistical procedures in a wide variety of contexts, such as tsunami warnings, date requests, and anti-drug public service announcements.

How to Decipher Figures show students how to “read” the statistical shorthand presented in the quantitative results of an article and also, by implication, show them how to write up results.

Quantitative Research in Communication is ideal for courses in Quantitative Methods in Communication, Statistical Methods in Communication, Advanced Research Methods (undergraduate), and Introduction to Research Methods (Graduate) in departments of communication, educational psychology, psychology, and mass communication.

Front Matter

• Chapter 1: Introduction to Quantitative Research
• Chapter 2: Using Statistics in Quantitative Research
• Chapter 3: Independent Samples or Student's t Test
• Chapter 4: ONEWAY Analysis of Variance
• Chapter 5: Factorial ANOVA
• Chapter 6: Analysis of Covariance
• Chapter 7: Multivariate ANOVA
• Chapter 8: Chi-Square Statistic
• Chapter 9: Simple Bivariate Correlation
• Chapter 10: Multiple Regression
• Chapter 11: Factor Analysis
• Chapter 12: Advanced Modeling Techniques
• Understanding Advanced Modeling
• Describing the Model
• Understanding the Test
• Two Methods of Analysis
• Ordinary Least Squares
• Maximum Likelihood Estimation
• Choosing a Technique
• Other Advanced Modeling Approaches
• SEM in Communication Research
• Chapter 13: Meta-Analysis
• Mathematical Argument for Meta-Analysis
• The Practical Argument for Meta-Analysis
• Structure of Writing in Meta-Analysis
• The Literature Review
• Methods for Conducting the Meta-Analysis
• Results Section Reporting of a Meta-Analysis
• Meta-Analysis in Communication Research

Back Matter

• Appendix A: Critical Values for the t Statistic
• Appendix B: Critical Values for the Chi-Square Statistic
• Appendix C: Critical Values for the f Statistic
• Appendix D: Critical Values for the r Statistic
• About the Authors

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