36 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:

• Surveys:
  • 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.
• Experiments:
  • 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.
• Content Analysis:
  • 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:

• Experimental Designs: These designs involve manipulating one or more variables, while controlling for extraneous variables, to determine cause-and-effect relationships. JARS-Quant provides specific guidelines based on the method of participant assignment:
  • 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).
• Clinical Trials: These studies, often falling under experimental designs, specifically evaluate the effects of health-related interventions (e.g., therapies, medications) on health outcomes. JARS-Quant highlights additional reporting standards for clinical trials:
  • 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.
• Special Designs: JARS-Quant outlines reporting standards for studies with unique designs:
  • 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.