4.2 Explanatory sequential design
7 min read•august 21, 2024
combines quantitative and qualitative methods in a two-phase approach. It uses qualitative data to explain quantitative findings, providing a comprehensive understanding of complex communication phenomena through statistical analysis and in-depth exploration.
This approach addresses limitations of single-method research, enhancing explanatory power by combining statistical trends with contextual insights. It facilitates deeper understanding of unexpected or contradictory quantitative results, making it valuable for communication research.
Overview of explanatory sequential design
- Combines quantitative and methods in a two-phase approach within Advanced Communication Research Methods
- Allows researchers to use qualitative data to explain and interpret quantitative findings
- Provides a comprehensive understanding of complex communication phenomena through integration of statistical analysis and in-depth exploration
Purpose and rationale
- Addresses limitations of single-method approaches in communication research
- Enhances explanatory power by combining statistical trends with contextual insights
- Facilitates deeper understanding of unexpected or contradictory quantitative results
Strengths of explanatory approach
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- Provides clear, distinct stages for implementation and analysis
- Allows for in-depth exploration of quantitative findings
- Enhances credibility of results through methodological
- Facilitates communication of findings to diverse audiences (academics, practitioners)
Limitations of explanatory approach
- Time-consuming process due to sequential nature
- Requires expertise in both quantitative and qualitative methods
- Potential for disconnect between quantitative and qualitative phases
- May be challenging to integrate divergent findings
Key characteristics
- Two-phase design with distinct quantitative and qualitative stages
- Emphasis on explaining quantitative results through qualitative inquiry
- Integration of methods occurs primarily during interpretation phase
Quantitative priority
- Greater emphasis placed on quantitative data and analysis
- Quantitative results guide the focus of subsequent qualitative phase
- Statistical findings form the foundation for qualitative exploration
- Allows for generalization of results to broader populations
Sequential timing
- Quantitative phase conducted first, followed by qualitative phase
- Clear temporal separation between and analysis stages
- Allows for careful planning and refinement of qualitative protocols
- Provides opportunity to address unexpected quantitative findings
Integration points
- Initial integration occurs during development of qualitative protocols
- Major integration takes place during interpretation of combined results
- Quantitative results inform participant selection for qualitative phase
- Final integration synthesizes statistical trends with qualitative themes
Phases of implementation
- Follows a structured, linear process with distinct stages
- Allows for careful planning and execution of each research phase
- Facilitates clear communication of research process to stakeholders
Quantitative data collection
- Utilizes , , or content analysis techniques
- Employs probability sampling methods for generalizability
- Focuses on measuring variables and testing hypotheses
- Gathers numerical data to identify trends and relationships
Quantitative data analysis
- Applies statistical techniques (descriptive, inferential)
- Identifies significant relationships, differences, or patterns
- Generates results that guide qualitative inquiry
- May include factor analysis, regression, or structural equation modeling
Qualitative data collection
- Employs , , or observations
- Selects participants based on quantitative results
- Explores reasons behind statistical findings
- Gathers rich, descriptive data to provide context and depth
Qualitative data analysis
- Utilizes coding techniques to identify themes and patterns
- Applies interpretive approaches (thematic analysis, grounded theory)
- Seeks to explain and elaborate on quantitative findings
- Generates thick descriptions and conceptual frameworks
Sampling strategies
- Crucial for connecting quantitative and qualitative phases
- Ensures appropriate participants are selected for qualitative follow-up
- Influences the depth and breadth of explanatory insights
Participant selection model
- Selects participants for qualitative phase based on quantitative results
- Focuses on individuals who can best explain significant findings
- May target extreme or outlier cases for in-depth exploration
- Allows for purposeful sampling to address specific research questions
Variation selection model
- Chooses participants to represent maximum variation in quantitative results
- Seeks to capture diverse perspectives and experiences
- Enhances transferability of findings across different contexts
- Facilitates exploration of factors influencing variability in outcomes
Data integration techniques
- Critical for synthesizing quantitative and qualitative findings
- Ensures coherent narrative across both research phases
- Enhances overall explanatory power of the study
Connecting quantitative and qualitative phases
- Uses quantitative results to inform qualitative data collection
- Develops interview protocols based on statistical findings
- Selects participants for qualitative phase using quantitative data
- Ensures logical flow between research phases
Merging results for interpretation
- Compares and contrasts quantitative and qualitative findings
- Identifies areas of convergence and divergence in results
- Develops joint displays to visually represent integrated findings
- Synthesizes statistical trends with qualitative themes for comprehensive understanding
Research questions
- Guide the overall research process and focus of inquiry
- Reflect the sequential nature of the explanatory design
- Ensure alignment between quantitative and qualitative phases
Quantitative research questions
- Focus on relationships, differences, or trends in variables
- Often phrased as hypotheses or predictions
- Addressed through statistical analysis of numerical data
- May include questions about prevalence, frequency, or magnitude
Qualitative research questions
- Explore reasons, contexts, or meanings behind quantitative findings
- Often open-ended and focused on "how" or "why" aspects
- Addressed through in-depth analysis of textual or observational data
- May evolve based on quantitative results and emerging themes
Validity considerations
- Essential for ensuring rigor and trustworthiness of research
- Addresses potential threats to validity in both quantitative and qualitative phases
- Requires attention to integration and overall design
Quantitative validity issues
- Focuses on , , and reliability
- Addresses potential confounding variables and selection bias
- Ensures appropriate sampling and measurement techniques
- May include pilot testing of instruments and statistical power analysis
Qualitative validity issues
- Emphasizes credibility, transferability, dependability, and confirmability
- Employs techniques such as member checking and peer debriefing
- Addresses researcher bias and reflexivity
- Ensures thick descriptions and appropriate data saturation
Mixed methods validity
- Considers legitimation of integrated findings
- Addresses potential threats to integration validity
- Ensures appropriate sample integration and meta-inferences
- May include techniques such as data transformation and typology development
Reporting results
- Presents findings in a clear, logical sequence
- Reflects the two-phase nature of the explanatory sequential design
- Integrates quantitative and qualitative results for comprehensive understanding
Quantitative results presentation
- Reports descriptive statistics and inferential analyses
- Utilizes tables, graphs, and charts to visualize data
- Highlights significant findings and trends
- Identifies areas requiring further qualitative exploration
Qualitative results presentation
- Describes themes, patterns, and illustrative quotes
- Utilizes narrative descriptions and conceptual models
- Provides context and depth to quantitative findings
- Highlights participant perspectives and experiences
Integrated findings
- Synthesizes quantitative and qualitative results
- Develops joint displays to visually represent integration
- Addresses how qualitative findings explain quantitative results
- Presents a coherent narrative that combines statistical trends and thematic insights
Applications in communication research
- Demonstrates versatility of explanatory sequential design across various subfields
- Highlights potential for addressing complex communication phenomena
- Illustrates integration of quantitative and qualitative methods in real-world research
Media effects studies
- Investigates causal relationships between media exposure and audience outcomes
- Quantitative phase measures media consumption and effects
- Qualitative phase explores underlying mechanisms and contextual factors
- Integrates statistical trends with audience interpretations and experiences
Organizational communication research
- Examines communication processes and outcomes in workplace settings
- Quantitative phase assesses communication patterns and organizational variables
- Qualitative phase explores employee perceptions and organizational culture
- Combines survey data with in-depth interviews for comprehensive understanding
Health communication studies
- Investigates effectiveness of health messages and interventions
- Quantitative phase measures message impact and behavior change
- Qualitative phase explores audience reception and contextual influences
- Integrates experimental designs with focus groups or interviews
Challenges and ethical considerations
- Addresses potential obstacles in implementing explanatory sequential design
- Highlights ethical issues specific to mixed methods research
- Provides guidance for researchers in navigating complex research processes
Time and resource requirements
- Demands significant time investment due to sequential nature
- Requires careful planning and coordination of research phases
- May necessitate additional funding for extended data collection
- Involves managing large datasets and diverse analytical techniques
Researcher expertise
- Necessitates proficiency in both quantitative and qualitative methods
- Requires skills in integrating and synthesizing diverse data types
- May involve collaboration between specialists in different methodologies
- Demands flexibility in adapting research design based on emerging findings
Ethical issues in sequential designs
- Addresses informed consent across multiple research phases
- Considers confidentiality when linking quantitative and qualitative data
- Manages potential participant fatigue in longitudinal designs
- Ensures equitable selection of participants for qualitative follow-up
Comparison with other designs
- Highlights unique features of explanatory sequential design
- Facilitates selection of appropriate mixed methods approach
- Addresses strengths and limitations relative to alternative designs
Explanatory vs exploratory sequential
- Explanatory begins with quantitative, exploratory with qualitative
- Explanatory focuses on explaining results, exploratory on instrument development
- Explanatory has quantitative priority, exploratory may have qualitative priority
- Explanatory integrates at interpretation, exploratory at data collection
Explanatory sequential vs convergent parallel
- Explanatory collects data sequentially, convergent collects simultaneously
- Explanatory prioritizes quantitative, convergent gives equal weight to both
- Explanatory integrates at interpretation, convergent throughout analysis
- Explanatory allows for targeted qualitative inquiry, convergent provides broader perspective
Software tools for analysis
- Facilitates efficient data management and analysis
- Addresses needs for both quantitative and qualitative phases
- Supports integration of diverse data types
Quantitative analysis software
- Includes statistical packages (SPSS, SAS, R)
- Facilitates descriptive and inferential analyses
- Provides tools for data visualization and reporting
- Supports advanced techniques (factor analysis, structural equation modeling)
Qualitative analysis software
- Includes platforms for coding and thematic analysis (NVivo, ATLAS.ti)
- Facilitates organization and retrieval of qualitative data
- Supports development of conceptual models and visual representations
- Allows for team-based coding and analysis
Mixed methods software options
- Includes specialized tools for integrating quantitative and qualitative data
- Facilitates creation of joint displays and integrated visualizations
- Supports data transformation and typology development
- Enables seamless movement between quantitative and qualitative analyses
Key Terms to Review (23)
Complementarity: Complementarity refers to the concept where different types of data or methods in research complement each other to provide a fuller understanding of a phenomenon. This approach emphasizes how qualitative and quantitative methods can work together, enhancing the overall insights gained from research by integrating diverse perspectives and approaches.
Data analysis: Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. It involves using statistical and computational techniques to interpret and visualize data, which can enhance understanding and provide insights that influence various research designs and approaches.
Data collection: Data collection refers to the systematic process of gathering, measuring, and analyzing information from various sources to answer research questions or evaluate outcomes. This process is crucial in various research designs, ensuring the integrity and relevance of findings by employing specific methods tailored to the study's objectives.
Experiments: Experiments are systematic procedures used to investigate cause-and-effect relationships by manipulating independent variables and measuring their impact on dependent variables. This method allows researchers to establish control over variables, minimizing confounding factors, and providing a clearer understanding of causal links. Through the use of experiments, researchers can gather empirical data that supports or refutes theoretical claims, which is essential in fields that emphasize positivism and structured methodologies.
Explanatory sequential design: Explanatory sequential design is a mixed methods research approach where the researcher first collects and analyzes quantitative data, followed by qualitative data to further explain or elaborate on the initial findings. This design is particularly useful for providing a deeper understanding of quantitative results by using qualitative insights to clarify and contextualize them, often leading to richer interpretations.
External Validity: External validity refers to the extent to which the results of a study can be generalized to, or have relevance for, settings, people, times, and measures beyond the specific conditions of the research. This concept is essential for determining how applicable the findings are to real-world situations and populations.
Focus groups: Focus groups are a qualitative research method where a small group of participants engage in a guided discussion to gather insights about their perceptions, opinions, and attitudes towards a specific topic or product. This method allows researchers to collect diverse perspectives and explore the underlying reasons behind participant responses, making it valuable in various research contexts.
Integration of findings: Integration of findings refers to the process of combining and synthesizing data from different sources or phases of research to provide a comprehensive understanding of the research problem. This process is crucial in mixed methods research, where qualitative and quantitative data are merged to create a holistic view of the research questions being investigated.
Internal Validity: Internal validity refers to the extent to which a study can establish a causal relationship between variables, free from the influence of external factors or biases. It is crucial for determining whether the outcomes of an experiment truly result from the manipulation of independent variables rather than other confounding variables.
Interviews: Interviews are qualitative data collection methods where a researcher engages in direct conversation with participants to gather in-depth information about their thoughts, feelings, experiences, and behaviors. This technique is fundamental for understanding individual perspectives and can vary in structure from highly structured to completely unstructured formats, depending on the research approach.
Jennifer C. Greene: Jennifer C. Greene is a prominent scholar known for her significant contributions to the field of mixed methods research, particularly in education and social science. Her work emphasizes the importance of integrating qualitative and quantitative approaches to enhance research findings, making them more comprehensive and applicable. Greene's insights have influenced various research designs and have established frameworks that help researchers understand when and how to effectively combine different methodologies.
John W. Creswell: John W. Creswell is a prominent scholar known for his contributions to qualitative and mixed methods research. His work emphasizes the importance of understanding various research designs, including how they can be utilized to address complex research questions effectively. His frameworks for qualitative, quantitative, and mixed methods research have become foundational in the field, guiding researchers in their methodological choices.
Mixed methods: Mixed methods refers to a research approach that combines both quantitative and qualitative techniques in order to gain a more comprehensive understanding of a research problem. This method allows researchers to leverage the strengths of both types of data, providing richer insights and validating findings through triangulation. It connects well with various philosophical frameworks and designs, enhancing the research process.
Phase One: Phase One refers to the initial stage in an explanatory sequential design where quantitative data is collected and analyzed before qualitative data is gathered. This phase establishes a foundational understanding of the research problem and informs the subsequent qualitative phase, allowing researchers to explore deeper insights and meanings behind the quantitative results.
Phase Two: Phase Two refers to the second stage in an explanatory sequential design, which is a mixed-method research approach that combines quantitative and qualitative methods. In this phase, researchers typically collect qualitative data to further explore or explain the results obtained in Phase One, which usually involves quantitative data collection. This integration of both data types allows for a deeper understanding of the research problem by providing context and insights that numbers alone cannot convey.
Primary data: Primary data refers to the information collected firsthand for a specific research purpose. This type of data is original and unique, as it is gathered directly from participants or sources through methods such as surveys, interviews, or experiments. The use of primary data allows researchers to tailor their inquiries and gather detailed, specific information relevant to their study.
Purposive sampling: Purposive sampling is a non-probability sampling technique where researchers select participants based on specific characteristics or criteria relevant to the study. This method is particularly useful for obtaining in-depth insights from a targeted group, ensuring that the sample aligns closely with the research objectives and questions.
Qualitative Research: Qualitative research is a method of inquiry that focuses on understanding human behavior, experiences, and social phenomena through the collection of non-numerical data. It emphasizes depth over breadth, allowing researchers to explore complex issues, contexts, and meanings in a more nuanced way than quantitative approaches. This type of research is closely tied to various philosophical perspectives that shape its methods and interpretations.
Quantitative research: Quantitative research is a systematic investigation that primarily focuses on quantifying relationships, behaviors, and phenomena through statistical analysis and numerical data. This method relies on structured tools such as surveys and experiments to collect measurable data, allowing researchers to identify patterns, test hypotheses, and make predictions. By emphasizing objectivity and replicability, quantitative research aligns closely with scientific methods and plays a critical role in establishing generalizable findings across various fields.
Random sampling: Random sampling is a method used in research to select a subset of individuals from a larger population, where each individual has an equal chance of being chosen. This technique helps ensure that the sample accurately represents the population, reducing bias and allowing for generalizations about the broader group.
Secondary data: Secondary data refers to information that has been collected and published by someone other than the researcher for a different purpose. This type of data can include statistics, research articles, reports, and historical records, and is often used to provide context or support for a new research project. It is valuable because it saves time and resources, allowing researchers to build upon existing knowledge instead of starting from scratch.
Surveys: Surveys are a research method used to collect data from a predetermined group of respondents through questionnaires or interviews. They are essential for understanding opinions, behaviors, and characteristics of populations and are often utilized to gather quantitative data that can be statistically analyzed.
Triangulation: Triangulation is a method used in research to enhance the credibility and validity of study findings by combining multiple perspectives, data sources, or methodologies. This approach allows researchers to cross-verify results, providing a more comprehensive understanding of the research problem and reducing biases that may arise from relying on a single method or source.