4.2 Explanatory sequential design
6 min read•august 21, 2024
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
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- Emphasizes quantitative and analysis as the primary component of the study
- designed to support and expand upon quantitative findings
- Typically allocates more resources and time to the 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 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 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
Key Terms to Review (16)
Audience Analysis: Audience analysis is the process of understanding the characteristics, needs, and preferences of a specific group of people who will consume or interact with a particular message or content. This analysis helps tailor communication strategies to resonate with the audience's values, interests, and demographics, ensuring that the message is effective and relevant. Recognizing the audience's background, knowledge level, and expectations can lead to more impactful communication and successful outcomes.
Complementarity: Complementarity refers to the relationship between qualitative and quantitative research methods, where each method enriches and strengthens the overall understanding of a research question. This concept is particularly important because it highlights how different data types can be used together to provide a fuller picture, improving the validity and reliability of research findings.
Convergent Parallel Design: Convergent parallel design is a mixed methods research approach that involves collecting and analyzing both qualitative and quantitative data simultaneously, but separately, and then comparing the results to draw conclusions. This design allows researchers to gain a comprehensive understanding of a research question by triangulating findings from different data sources, thus enriching the overall insights while maintaining the integrity of each method.
Data analysis: Data analysis is the process of systematically applying statistical and logical techniques to describe, summarize, and interpret quantitative and qualitative data. This practice is essential in research as it helps uncover patterns, relationships, and insights from data, guiding decision-making and conclusions. Different designs and methodologies shape how data is collected and analyzed, influencing the overall effectiveness of the research outcomes.
Data Collection: Data collection refers to the systematic process of gathering information from various sources to analyze and interpret for research purposes. This process is crucial in ensuring that the research is built on accurate and reliable evidence, enabling researchers to draw valid conclusions and make informed decisions.
Data Integration: Data integration is the process of combining data from different sources to provide a unified view and facilitate analysis. It plays a crucial role in research designs by allowing researchers to merge quantitative and qualitative data, which helps in drawing comprehensive conclusions. This approach enables researchers to leverage the strengths of various data types, enhancing the richness of findings and supporting more informed decisions.
Explanatory sequential design: Explanatory sequential design is a mixed methods research approach where qualitative data is collected and analyzed after the initial quantitative data has been gathered. This method helps researchers better understand quantitative results by using qualitative insights to explain or expand on the numerical findings. The combination of both data types allows for a more comprehensive understanding of research questions, enhancing the depth and context of the findings.
Follow-up qualitative study: A follow-up qualitative study is a research approach that is conducted after an initial quantitative phase, aiming to gather deeper insights and understanding about the findings from the first phase. It typically involves in-depth interviews, focus groups, or open-ended surveys that allow researchers to explore participants' thoughts, feelings, and experiences related to specific quantitative results. This type of study helps to clarify, explain, or expand upon the numerical data gathered earlier, providing a richer context for interpretation.
Grounded Theory: Grounded theory is a qualitative research methodology that aims to develop theories based on data systematically gathered and analyzed from the field. It emphasizes generating theories directly from empirical data rather than testing existing theories, allowing researchers to build a deeper understanding of social processes and interactions.
Message effectiveness: Message effectiveness refers to how well a communication achieves its intended purpose, whether that be to inform, persuade, or engage an audience. It encompasses factors like clarity, relevance, emotional impact, and the ability to inspire action or change in the audience's thoughts or behaviors. In research designs, especially explanatory sequential designs, assessing message effectiveness helps in understanding the relationship between communication strategies and audience responses.
Qualitative phase: The qualitative phase is a critical component in mixed methods research that focuses on collecting and analyzing non-numerical data to gain insights into participants' experiences, perspectives, and social contexts. This phase typically involves methods such as interviews, focus groups, and observations to explore complex phenomena in depth. It serves as a foundation for understanding underlying patterns and meanings that can inform subsequent quantitative research.
Quantitative phase: The quantitative phase refers to a structured segment of research that emphasizes the collection and analysis of numerical data to understand patterns, relationships, or outcomes. This phase often relies on statistical methods to derive insights and can serve different roles depending on the research design, such as confirming findings from qualitative data or identifying variables for further exploration.
Sample size considerations: Sample size considerations refer to the process of determining the number of participants needed in a study to ensure that results are valid, reliable, and generalizable. This involves balancing statistical power, the potential for error, and practical constraints like time and resources. Adequate sample size is crucial for both descriptive research, where it helps in providing an accurate snapshot of a population, and explanatory sequential design, where it ensures robust findings across multiple stages of data collection.
Social Constructivism: Social constructivism is a theory that posits that individuals construct their understanding of the world through interactions with others and the social context in which they live. This perspective emphasizes that knowledge is not merely discovered but rather created through social processes, such as communication and collaboration, shaping how individuals perceive reality. It suggests that meaning and understanding arise from cultural and contextual influences, making it essential in examining the development of knowledge in various fields.
Triangulation: Triangulation is a research method used to enhance the validity and reliability of findings by combining multiple perspectives, data sources, or methodologies in a study. This approach allows researchers to cross-verify results and gain a more comprehensive understanding of a phenomenon by integrating qualitative and quantitative data, or using various research designs. It helps in identifying patterns and inconsistencies, ultimately leading to richer insights and more robust conclusions.
Validity Issues: Validity issues refer to the concerns regarding the accuracy and credibility of research findings. They evaluate whether the study measures what it intends to measure and if the conclusions drawn from the research can be considered legitimate. This aspect is crucial for maintaining the integrity of research, especially in designs that rely on sequential phases, as it ensures that results from one phase accurately inform the next.