Explanatory sequential design is a powerful approach in communication research that combines quantitative and qualitative methods. It starts with collecting and analyzing quantitative data, then uses qualitative methods to explain or elaborate on those findings. This two-phase approach provides a comprehensive understanding of communication phenomena.

The design allows researchers to leverage the strengths of both quantitative and qualitative approaches. It's particularly useful when quantitative results alone are insufficient to fully address research questions, enabling researchers to dive deeper into statistically significant findings or explain unexpected results.

Definition of explanatory sequential design

• Explanatory sequential design integrates quantitative and qualitative research methods in a two-phase approach
• Researchers first collect and analyze quantitative data, then use qualitative methods to explain or elaborate on the initial findings
• This design allows for a comprehensive understanding of research questions in communication studies by combining statistical analysis with in-depth exploration of participant perspectives

Purpose and rationale

• Aims to provide a more complete picture of communication phenomena by leveraging strengths of both quantitative and qualitative approaches
• Allows researchers to explain unexpected quantitative results or dive deeper into statistically significant findings
• Particularly useful when quantitative results alone are insufficient to fully address research questions in communication research

Key characteristics

Quantitative priority

• Emphasizes quantitative data collection and analysis as the primary component of the study
• Qualitative phase designed to support and expand upon quantitative findings
• Typically allocates more resources and time to the quantitative phase of the research

Sequential timing

• Conducts quantitative and qualitative phases in a strict, sequential order
• Quantitative data collection and analysis always precede qualitative phase
• Allows researchers to use quantitative results to inform qualitative data collection strategies and participant selection

Integration of data

• Combines quantitative and qualitative findings during interpretation phase
• Uses qualitative data to provide context and explanation for quantitative results
• Integrates both data types to develop a more comprehensive understanding of communication phenomena

Steps in explanatory sequential design

Quantitative data collection

• Gathers numerical data through surveys, experiments, or content analysis
• Uses probability sampling techniques to ensure representativeness
• Employs standardized instruments to measure communication variables (attitudes, behaviors, media consumption)

Quantitative data analysis

• Applies statistical techniques to analyze quantitative data
• Includes descriptive statistics, inferential tests, and multivariate analyses
• Identifies significant relationships, trends, or differences in communication patterns

Qualitative data collection

• Selects participants based on quantitative results (extreme cases, typical cases)
• Conducts interviews, focus groups, or observations to gather in-depth information
• Develops interview guides or observation protocols informed by quantitative findings

Qualitative data analysis

• Employs coding techniques to identify themes and patterns in qualitative data
• Uses constant comparative method or thematic analysis to interpret participant responses
• Explores contextual factors and individual experiences related to quantitative results

Integration of findings

• Compares and contrasts quantitative and qualitative results
• Identifies how qualitative data explain or elaborate on quantitative findings
• Develops a cohesive narrative that combines both data types to address research questions

Advantages of explanatory sequential design

• Provides a comprehensive understanding of communication phenomena by combining breadth and depth
• Allows researchers to explain unexpected quantitative results through qualitative inquiry
• Enhances credibility of findings through triangulation of methods
• Facilitates development of targeted interventions or communication strategies based on integrated insights

Limitations and challenges

• Time-consuming and resource-intensive due to sequential nature of data collection
• Requires expertise in both quantitative and qualitative research methods
• Potential for disconnect between quantitative and qualitative phases if not carefully planned
• Difficulty in obtaining Institutional Review Board approval for two-phase studies
• Challenges in integrating and presenting findings from different methodological approaches

Applications in communication research

• Investigating media effects by combining survey data with in-depth interviews
• Exploring organizational communication patterns through network analysis and employee interviews
• Examining health communication campaigns using message effectiveness metrics and focus groups
• Studying intercultural communication competence through standardized assessments and ethnographic observations

Explanatory vs exploratory sequential design

• Explanatory design starts with quantitative data, while exploratory begins with qualitative
• Explanatory aims to explain quantitative results, exploratory uses qualitative findings to develop quantitative instruments
• Explanatory emphasizes quantitative priority, exploratory often prioritizes qualitative phase
• Explanatory is more theory-driven, exploratory is more inductive and hypothesis-generating

Sampling considerations

Quantitative phase sampling

• Employs probability sampling techniques to ensure representativeness
• Determines sample size based on statistical power analysis
• Considers population characteristics and research questions when selecting sampling frame
• May use stratified or cluster sampling to capture diverse communication contexts

Qualitative phase sampling

• Utilizes purposive sampling based on quantitative results
• Selects participants who can provide insights into specific quantitative findings
• May focus on extreme cases, typical cases, or demographically diverse participants
• Sample size determined by data saturation rather than statistical considerations

Data analysis strategies

Statistical analysis techniques

• Descriptive statistics to summarize quantitative data (means, standard deviations, frequencies)
• Inferential tests to examine relationships and differences (t-tests, ANOVA, regression)
• Factor analysis to identify underlying constructs in communication scales
• Structural equation modeling to test complex relationships between variables

Qualitative coding methods

• Open coding to identify initial concepts and themes in qualitative data
• Axial coding to establish relationships between categories
• Selective coding to develop core themes and integrate findings
• Constant comparative method to refine and validate emerging themes

Validity and reliability concerns

• Ensures quantitative instrument validity through pilot testing and expert review
• Assesses reliability of quantitative measures using Cronbach's alpha or test-retest methods
• Employs member checking and peer debriefing to enhance qualitative trustworthiness
• Uses triangulation of data sources and methods to increase overall validity
• Addresses potential researcher bias through reflexivity and audit trails

Ethical considerations

• Obtains informed consent for both quantitative and qualitative phases
• Protects participant confidentiality and anonymity, especially when linking data across phases
• Ensures fair selection of participants for qualitative follow-up
• Addresses power dynamics in qualitative interviews or focus groups
• Considers cultural sensitivity in instrument design and data collection procedures

Reporting results

Quantitative results presentation

• Presents descriptive statistics in tables or graphs
• Reports inferential test results using appropriate statistical notation
• Includes effect sizes and confidence intervals for key findings
• Uses visual aids (scatterplots, bar charts) to illustrate relationships or trends

Qualitative results presentation

• Organizes findings by themes or categories identified in analysis
• Incorporates direct quotes from participants to support interpretations
• Uses diagrams or concept maps to illustrate relationships between themes
• Provides rich descriptions of context and participant experiences

Integrated findings discussion

• Synthesizes quantitative and qualitative results to address research questions
• Explains how qualitative findings elaborate on or clarify quantitative results
• Discusses convergence or divergence between quantitative and qualitative data
• Develops a cohesive narrative that integrates both data types

Software tools for analysis

• Utilizes SPSS, R, or SAS for quantitative statistical analysis
• Employs NVivo, ATLAS.ti, or MAXQDA for qualitative data management and coding
• Uses mixed methods software (Dedoose) to integrate quantitative and qualitative data
• Applies visualization tools (Tableau, PowerBI) to create integrated data displays

Examples in communication studies

• Media effects research combining survey data on social media use with in-depth interviews about user experiences
• Organizational communication study using social network analysis and follow-up interviews to explore information flow
• Health communication campaign evaluation integrating message recall metrics with focus groups on message interpretation
• Intercultural communication competence assessment combining standardized scales with ethnographic observations of cross-cultural interactions