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Difference Between Qualitative and Quantitative Research

• Quantitative research is rooted in functionalism and aims for theory testing using structured data collection methods such as surveys and experiments.
• Qualitative research is rooted in interpretivism and focuses on theory development through a rich, detailed understanding of human experiences.

Philosophical Assumptions

• The functionalist paradigm is associated with quantitative research
• This seeks generalizability through replication and objectivity and identifies cause-effect relationships.
• Theory development occurs through hypothesis testing in the functionalist paradigm.
• The interpretivist paradigm is associated with qualitative research
• Knowledge is constructed based on participant experiences.
• The researcher is an instrument in understanding meaning within the interpretivist paradigm.

Core Skills for Qualitative Research

• Observation involves systematically recording behaviors and contexts.
• Interviewing involves conducting open-ended, structured, or semi-structured discussions.
• Coding and Analysis involves identifying key concepts, themes, and relationships.
• Building connections to theory involves developing insights that contribute to broader academic discussions.

Ensuring Trustworthiness in Qualitative Research

• Descriptive validity ensures accuracy in reporting observations.
• Interpretive validity ensures understanding participants' meaning accurately.
• Theoretical validity connects findings to broader concepts.
• Generalizability or transferability assess if findings can be applied to other contexts.

Grounded Theory (Strauss & Corbin, 1998)

• It is an inductive method that builds theory from data rather than testing existing hypotheses.
• This uses systematic coding including open, axial, and selective coding to generate insights.

Data Collection in Qualitative Research

• It uses interviews, observations, documents, and archival records.
• It can be used in single case studies for deep understanding of one entity, multiple case studies for comparison across cases, and process studies for longitudinal analysis of changes over time.

Interviewing Techniques

• Open-ended questions avoid leading or biased responses.
• Allowing participants to lead lets them introduce topics they find important.
• Adjusting questions based on responses uses follow-up questions to explore deeper insights.

Ethical Considerations in Interviewing

• Researchers must obtain informed consent.
• Researchers must respect participant privacy and emotional well-being.

Reflexivity in Qualitative Research

• It is acknowledging researcher bias and influence on data collection.
• It uses strategies to reduce bias.
• Memo-writing is a strategy to reduce bias
• Member-checking validates interpretations with participants for bias

What Is Coding?

• It is systematically categorizing data to identify patterns, themes, and relationships.
• It helps in reducing large amounts of qualitative data into manageable insights.

Types of Coding

• Open coding identifies initial themes and categories.
• Axial coding connects different categories to understand relationships.
• Selective coding develops a central storyline or theoretical model.

The Gioia Method (Gioia et al., 2013)

• A structured qualitative data analysis technique balances participant perspectives with researcher interpretations.
• Involves data collection, first-order coding, second-order themes, data structure, and theory building.

Key Steps of the Gioia Method

• Data collection involves multiple rounds of interviews and document analysis.
• First-order coding involves the identification of participants raw statements.
• Second-Order Themes are the development of broader concepts based on patterns.
• Data structure is based on organizing themes into a visual representation.
• Theory building involves explaining the relationships between concepts.

Case Selection in Qualitative Research

• Critical cases test or refine an existing theory.
• Extreme cases capture unusual or unexpected behaviors.
• Revelatory cases provide access to new insights on an underexplored topic.

The Eisenhardt Method (Eisenhardt, 1989)

• It is rigorous case study method that blends theoretical sampling, constant comparison, and cross-case analysis.
• This focuses on theory building rather than simply describing phenomena.

Key Features of the Eisenhardt Method

• Constant comparison iterates data and emerging theory.
• Replication logic treats each case as a separate experiment.
• Cross-case analysis identifies patterns across different settings.

Case Selection Strategies for The Eisenhardt Method

• Matched pairs design selects cases with similar characteristics but different outcomes.
• Polar types compare extreme success and failure cases.
• Common process design studies the same process across different settings.

Developing Theoretical Arguments

• Moving beyond description explains why patterns occur.
• Identifying boundary conditions determines when a theory applies and when it doesn't.

What Are Process Studies? (Langley, 1999)

• They focuses on how events unfold over time.
• They are used for studying organizational change, decision-making, and strategy formation.

Key Strategies in Process Research

• Temporal bracketing divides data into meaningful time periods.
• Visual mapping creates timelines of events to identify patterns.
• Turning nouns into verbs emphasizes processes (e.g., "strategizing" instead of "strategy").

Developing Theory from Data

• Generating concepts involves identifying surprising findings or gaps in literature.
• Refining categories involves splitting or merging concepts for clarity.
• Building theoretical models creates abstract representations of observed phenomena.

How to Write the Methods Section

• For a single case study design (Gioia & Langley), data was collected, coded, and analyzed.
• Uses tables, quotes, and thematic structures to present findings.
• For multiple case study design (Eisenhardt), cases are selected, analyzed, and compared.

How to Structure the Findings Section

• Periodization organizes findings into meaningful phases.
• Data tables presents first-order and second-order codes.
• Linking findings to theory demonstrates how the data contributes to existing research.

Presenting Theoretical Contributions

• Clarify the research puzzle.
• Compare findings with alternative theoretical perspectives.
• Highlight practical and managerial implications.

Writing Process Tips

• Iterative writing includes writing multiple drafts to refine arguments.
• Engaging with literature strengthens findings by linking them to past research.
• Avoiding common pitfalls ensures clarity, coherence, and logical flow in writing.

Ethnographic Observation (Spradley's Method)

• Is a qualitative research method that involves systematically observing people in their natural settings to understand behaviors, interactions, and cultural norms.

James Spradley's Domains of Descriptive Observation:

• Space involves identifying the physical layout of the setting.
• Actors are the people involved in the setting.
• Activities are the specific actions people are engaged in.
• Objects are physical items in the setting.
• Acts are small individual behaviors.
• Events are larger activities composed of multiple acts.
• Time is the sequencing of events.
• Goals are what participants aim to accomplish.
• Feelings are emotional expressions observed.

Observation: Findings & Practical Insights

• Should be descriptive rather than interpretative.
• Pay attention to patterns of interaction and the social norms that shape behaviors.
• A report should include objective details and avoid assumptions.
• Effective observations provide rich contextual data that help frame further research.

Conversational Interviewing

• A flexible, open-ended interviewing technique that allows participants to express themselves without rigid structure.
• An interview protocol can be a guide used to structure an interview while maintaining flexibility

Open-Ended Questions in Interviews

• Questions should allow for detailed responses, rather than simple yes/no answers.

Reflexivity in Interviewing

• It is the process of being aware of one's own biases and preconceptions while conducting research.

Best Practices for Interviewing

• Build rapport with the interviewee to encourage honest responses.
• Start with broad, open-ended questions and let participants lead the conversation.
• Avoid leading questions that may bias responses.
• Use probing follow-up questions to gain deeper insights (e.g., "Can you elaborate on that?").
• Record and transcribe interviews for accurate data collection.

Common Challenges in Interviewing

• Social desirability bias occurs when participants may give answers they think the interviewer wants to hear.
• Memory recall issues involve when participants struggle to accurately remember past events.
• Interviewer bias is the interviewer's own perspective may influence the way questions are framed.

Coding Qualitative Data

• Coding is the process of categorizing qualitative data to identify themes and patterns.
• Open coding is the first step of analysis, where key phrases and ideas are labeled.
• Axial coding is the identification of relationships between different codes.
• Selective coding is the development of a central narrative or theory based on patterns.

How to Code Data

• Read the data carefully and highlight important phrases.
• Assign descriptive labels to words or sentences that capture key ideas.
• Look for patterns and recurring concepts.
• Refine categories by grouping similar codes together.

Key Questions to Ask During Coding

• What is happening in this data?
• What stands out as significant?
• How do different codes relate to each other?
• What concepts are emerging that might contribute to theory?

Coding: Common Mistakes

• Over-coding involves assigning too many different codes, making analysis complicated.
• Forcing categories tries to fit data into predefined concepts rather than letting themes emerge naturally.
• Ignoring contradictions means valuable insights from anomalies in the data are ignored.

Coding with Al (Atlas.ti & ChatGPT)

• Al-assisted coding uses machine learning tools like Atlas.ti and ChatGPT to analyze qualitative data.

Al coding vs Manual coding

• While Al can speed up the coding process, it lacks the contextual understanding that human researchers provide.

Data Triangulation

• Involves comparing Al-generated codes with manual coding to ensure accuracy.

Using Al in Coding

• Start with manual coding to gain first-hand insights.
• Only use Al tools (Atlas.ti, ChatGPT) after completing initial manual coding.
• Compare Al-generated codes with manual ones.
• Validate Al-generated codes by checking for relevance and consistency.

Limitations of Al in Qualitative Research

• Al lacks deep contextual understanding and may misinterpret complex themes.
• Al over-generates single codes, leading to redundant categories.
• There are ethical concerns about data privacy and bias in Al algorithms.

Al Coding best practices

• Use Al as a support tool rather than relying on it entirely.
• Disclose Al usage in research write-ups.
• Manually review Al-generated outputs to refine categories.

From Data to Theory

• Theoretical saturation is the point at which new data no longer generates new insights.
• A conceptual model a visual representation of key themes and their relationships.
• Theoretical sampling is based on selecting data sources based on their potential to refine theory.

Steps to Move from Data to Theory

• Generate initial codes, then identify interesting patterns in the data
• Refine and organize codes by merging similar categories, drop irrelevant ones.
• Stabilize codes, then compare codes with existing theories.
• Develop a Conceptual Model and visually identify second-order categories (higher-level themes).
• Create visual diagrams to represent relationships.
• Label connections between concepts to explain causal mechanisms.

Mistakes in Theory Development

• Jumping to conclusions too early without fully exploring patterns.
• Forcing data to fit existing theories rather than letting new insights emerge.
• Ignoring contradictions that might refine or challenge theories.

Advise on Theory Development

• Good theory comes from suspending rigid scientific correctness and allowing for creative exploration.

Key Components of a Methods Section

• Research Settings should explain where and how the study was conducted and justify why this setting is relevant.
• In data Collection, describe how data was gathered and explain the sampling strategy.
• Present data Analysis: Detail coding procedures including first-order/open coding, and second-order themes, and any Al tools used.
• Link to theory: Show how the emerging categories relate to existing research and Jjustify why certain relationships between concepts were identified.
• Organize any findings by second-order themes and Support each theme with direct quotations from data, and Provide a conceptual model to illustrate relationships.

Mistakes in Writing Methods

• A study should avoid being Vague about data collection and analysis.
• Avoid not clearly defining codes and categories.
• Avoid Failing to justify theoretical connections.

Writing: Final Thoughts

• Methods should be transparent and replicable.
• Avoid overloading with technical jargon.
• Provide real examples to support descriptions.

Course Readings for Exam Preparation

• Shah, S. K., & Corley, K. G. (2006).
• Qualitative research plays a crucial role in theory development, while quantitative research primarily focuses on theory testing.
• To build better theories, researchers should combine both approaches.
• Authors emphasize the need for methodological pluralism in management research and propose using grounded theory as a key tool for qualitative analysis.
• Data alone does not generate theory-researchers do (Mintzberg, 1979).
• Qualitative research helps in developing new theoretical insights by exploring rich, contextualized data.
• Functionalist Paradigm (Quantitative Approach) assumes objective reality that can be measured.
• Interpretivist Paradigm (Qualitative Approach) assumes multiple social realities shaped by individuals.

Grounded Theory as a Bridge Between Quantitative and Qualitative Research

• It was Developed by Glaser & Strauss (1967) and is an inductive research method that builds theories directly from data.
• In this method, Researchers collect rich, in-depth data and refine theories through constant comparison.
• It implements steps for analyzing this theory, including Open, Axial, and Selective Coding as well as Theoretical Sampling
• This theory allows for data-driven discovery of concepts and provides flexibility

To counter bias that may arise, try to increase the trustworthiness in the following ways:

• Ensure Credibility (Internal Validity) through Prolonged engagement in the field
• Or using Triangulation (Using multiple data sources).
• Then use Peer debriefing to check biases.
• Aim to provide detailed context for any findings that may be generalized external to the experiment
• This Includes linking insights to existing theory.
• Show Dependability and Reliability through transparent strategy and coding.
• And objectify the experiment by Confirmability- Separating researcher interpretations
• As well as Keeping a record of decisions

Combining Quantitative and Qualitative Research

• Is valuable because it used Triangulation (Jick, 1979) to verify new insights
• While reducing "singular bias"
• Good practice balances simplicity, accuracy, and generalizability, for better holistic understanding of studied topic
• Calls for Methodological Pluralism where should not be seen as opposites but as complementary approaches
• "Theory-building requires rich qualitative insights before large-scale testing can validate findings."
• And, in theory, researchers should be trained in both methods to maximize research impact

Suddaby’s Critiques of "Grounded Theory"

• Suddaby critiques the misuse and misinterpretation of grounded theory in management research.
• Highlights misunderstandings and emphasizes its inductive and iterative nature
• GT must be based on symbolic and interactive pragmatism, meaning it can not start with prior knowledge
• Its primary attributes include: Constant Data comparisons, Theoretical sampling, and abstraction of concepts
• GT cannot be an excuse to ignore data, Just not content analysis or word counts
• Cannot be for testing pre-set hypothesis, cannot be simple, or easy to implement
• GT involves practical recommendations for researchers that are flexible, focus on discovery
• GT helps create novel theories where only few may exist, allowing for researchers to produce powerful findings
• Remember that in the end, theory is an inductive research method that builds from systematic qualitative data analysis
• In Grounded Theory analysis: Always strive to maintain constant comparisons and follow the 6 Key Steps to good testing and data analysis

Maxwell

• Critique argues the traditional notions of validity in research for qualitative require different criteria than quantitative
• To this point the most helpful course that can be taken is the one that produces a well structured data set
• The researcher must aim to ensure descriptive and Interpretive validity (of facts and interpretations),
• Through Member checking (asking participants to confirm interpretations) of thick description (Providing rich data to support the interpretative
• Aim to provide theoretical validity + Generalizability
• Overall be well practiced in the understanding of any data and its components

Van Maanen

• Article aimed at critiquing purely ethnographic “facts”, to which he states ethnography involves a good amount of interpretation and subjectivity
• The researcher should focus on all 9 points, aiming to interpret behaviors AND be able to distinguish fact for fiction overall
• Remember It’s imperative to find balance in analysis, especially because most people dont realize any internal biases of the process
• When approaching experiments try to implement as much control to a natural field so that data is accurate.
• Ethnography should be purely objective, and the researchers need to constantly question their assumptions
• Also, Researchers need to be consistently questioning themselves and interpretations of data in their experiment

Gioia

• Article discusses method that is systematic to inductive qualitative research aimed at improving data in theory development.
• While maintaining creativity in research, it provides a structured way to develop theories from qualitative data that balances systematic creativity
• This requires that one can achieve Scholarly rigor, especially during the systematic links between data
• Also, Traditional research focuses on construct measurement when dealing with its design
• Whereas, Inductive research (Grounded Theory) is about concept development — creating new theoretical insights from data.
  • Solution: The Gioia Methodology introduces a structured yet flexible approach to building theory from qualitative data.
• Key: Remember that research should focus and interpret theories as if they're the product of outside factors.
• This implies researchers can analyze data without biases (outside of their own control- as long as they are aware)
• While letting voices of the participants drive future experimental conceptual development
• Overall the method is an effective theory connection system, well used in multiple disciplines
• It ensures easy navigation, while allowing new concepts discover

Eisenhardt

• Article is designed to show theory data from case studies, developing new theoretical constructs that show how each case study can be interpreted and developed
• The method is inductive and iterative, each case serving is meant to serve as an experiment for any emerging case
• As a study is done more data is accrued that shows various test results
• Case based research is most helpful when existing theories don't fully explain the data
• Or when explaining social complexities
• Multiple cases show better results than single case studies
• When interviews introduce bias due to retrospective sensemaking and social desirability effects, always:

1. Use multiple informants to cross-check perspectives.
2. Combine interviews with archival data, observations, and real-time case tracking.
3. Balance retrospective cases (historical analysis) with real-time cases (ongoing observation).

Langley

• Article details extracting data for new theories from event and time based research with 7 key data strategies
• There is a goal to develop generalizable, accurate, and useful theories from complex and dynamic data"
• In this approach data has to deal with sequences of events over time, and always be considered for possible outcomes
• There has to be flexibility in experimental parameters that allows one or group to analyze these sequences effectively.
• To help Langley identifies seven distinct process data (Qualitative):
• Narrative data that Maintains richness and complexity.
• Quanitifcation of numeric systems of process into statistical models.
• Multiple Templates - Allison (1971),
• Time-based theory strategies with the aim of generating theoretical and constructive arguments
• Overall consider for what data suits what test, also remember each approach carries strengths alongside weakness

Grahman

• Article provides comparison of 3 major data collections:

1. Gioia (meaning-based study)
2. Eisenhardt (comparative cases)
3. Langley (Time/ event based), each showing similarities, differences, and practices with qualitative research.

• Key that all theories, that no matter what system are used, must develop strong theoretical contributions
• Also, methods must depend on goals, and follow distinct processes (which when followed correctly,) generate useful results

Grodai

• Article is for helping researchers actively categorize data so that rigor is improved and can lead to better data

1. Researchers must actively engage with their data by posing questions.
2. Questions like "What is happening here?" and "What does this data suggest?" can uncover new theoretical directions.

• This Helps with better generating categories as well as highlighting contradictions to what is expected
• By being aware of and articulating new categorization moves, researchers can Improve transparency, rigor, and methodological clarity.
• To this extent researchers can improve, then they can be more reliable

Klag

• Article designed to address the “conceptual leap,” is often mysterious and difficult to formalize in research methodologies.
• To the goal, theory of abduction where data becomes an active element
• There should exist the following 4 dialectical disagreements:

1. Deliberation versus Serendipity.
2. Engagement versus Detachment
3. Having - versus rejecting outside influences from a Outside Source.
4. An author or speaker should be able to connect with any connections

Pratt

• Editorial provides practical guidance for writing and reviewing qualitative research, which should include a section on 2 major concerns that could risk the report
  1. Balance must exist b/w telling just data, and interpreting or mis interpreting data
  2. Also one of the biggest thing that one needs to fix is the need to use quantitative tools one would consider using often. (ie more numbers bad)
• Remember Good process and high quality test have 5 features:
  1. Explain the test
  2. How to use
  3. What the process was
  4. What data or processes must be considered
  5. What you were or tried to accomplish
• This will make more high quality writing overall.