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Reliability vs. Validity in Research | Difference, Types and Examples

Published on July 3, 2019 by Fiona Middleton . Revised on June 22, 2023.

Reliability and validity are concepts used to evaluate the quality of research. They indicate how well a method , technique. or test measures something. Reliability is about the consistency of a measure, and validity is about the accuracy of a measure.opt

It’s important to consider reliability and validity when you are creating your research design , planning your methods, and writing up your results, especially in quantitative research . Failing to do so can lead to several types of research bias and seriously affect your work.

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Understanding reliability vs validity, how are reliability and validity assessed, how to ensure validity and reliability in your research, where to write about reliability and validity in a thesis, other interesting articles.

Reliability and validity are closely related, but they mean different things. A measurement can be reliable without being valid. However, if a measurement is valid, it is usually also reliable.

What is reliability?

Reliability refers to how consistently a method measures something. If the same result can be consistently achieved by using the same methods under the same circumstances, the measurement is considered reliable.

What is validity?

Validity refers to how accurately a method measures what it is intended to measure. If research has high validity, that means it produces results that correspond to real properties, characteristics, and variations in the physical or social world.

High reliability is one indicator that a measurement is valid. If a method is not reliable, it probably isn’t valid.

If the thermometer shows different temperatures each time, even though you have carefully controlled conditions to ensure the sample’s temperature stays the same, the thermometer is probably malfunctioning, and therefore its measurements are not valid.

However, reliability on its own is not enough to ensure validity. Even if a test is reliable, it may not accurately reflect the real situation.

Validity is harder to assess than reliability, but it is even more important. To obtain useful results, the methods you use to collect data must be valid: the research must be measuring what it claims to measure. This ensures that your discussion of the data and the conclusions you draw are also valid.

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Reliability can be estimated by comparing different versions of the same measurement. Validity is harder to assess, but it can be estimated by comparing the results to other relevant data or theory. Methods of estimating reliability and validity are usually split up into different types.

Types of reliability

Different types of reliability can be estimated through various statistical methods.

Types of validity

The validity of a measurement can be estimated based on three main types of evidence. Each type can be evaluated through expert judgement or statistical methods.

To assess the validity of a cause-and-effect relationship, you also need to consider internal validity (the design of the experiment ) and external validity (the generalizability of the results).

The reliability and validity of your results depends on creating a strong research design , choosing appropriate methods and samples, and conducting the research carefully and consistently.

Ensuring validity

If you use scores or ratings to measure variations in something (such as psychological traits, levels of ability or physical properties), it’s important that your results reflect the real variations as accurately as possible. Validity should be considered in the very earliest stages of your research, when you decide how you will collect your data.

• Choose appropriate methods of measurement

Ensure that your method and measurement technique are high quality and targeted to measure exactly what you want to know. They should be thoroughly researched and based on existing knowledge.

For example, to collect data on a personality trait, you could use a standardized questionnaire that is considered reliable and valid. If you develop your own questionnaire, it should be based on established theory or findings of previous studies, and the questions should be carefully and precisely worded.

• Use appropriate sampling methods to select your subjects

To produce valid and generalizable results, clearly define the population you are researching (e.g., people from a specific age range, geographical location, or profession).  Ensure that you have enough participants and that they are representative of the population. Failing to do so can lead to sampling bias and selection bias .

Ensuring reliability

Reliability should be considered throughout the data collection process. When you use a tool or technique to collect data, it’s important that the results are precise, stable, and reproducible .

• Apply your methods consistently

Plan your method carefully to make sure you carry out the same steps in the same way for each measurement. This is especially important if multiple researchers are involved.

For example, if you are conducting interviews or observations , clearly define how specific behaviors or responses will be counted, and make sure questions are phrased the same way each time. Failing to do so can lead to errors such as omitted variable bias or information bias .

• Standardize the conditions of your research

When you collect your data, keep the circumstances as consistent as possible to reduce the influence of external factors that might create variation in the results.

For example, in an experimental setup, make sure all participants are given the same information and tested under the same conditions, preferably in a properly randomized setting. Failing to do so can lead to a placebo effect , Hawthorne effect , or other demand characteristics . If participants can guess the aims or objectives of a study, they may attempt to act in more socially desirable ways.

It’s appropriate to discuss reliability and validity in various sections of your thesis or dissertation or research paper . Showing that you have taken them into account in planning your research and interpreting the results makes your work more credible and trustworthy.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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Validity and Reliability in Qualitative Research

Post prepared and written by Joe Tise, PhD, Senior Education Researcher

In this series we have discovered the many ways in which evidence of validity can be produced and ways in which reliable data can be produced. To be sure, the bulk of this series was focused on quantitative research, but any mixed-methods or qualitative researcher will tell you that quantitative research only tells us one piece of the puzzle.

Qualitative research is needed to answer questions not suited for quantitative research, and validity and reliability need to be considered in qualitative research too. Qualitative research includes numerous methodological approaches, such as individual and focus group interviews, naturalistic observations, artifact analysis, and even open-ended survey questions. Unlike quantitative research–which utilizes forms, surveys, tests, institutional data, etc.–in qualitative research, the researcher often is the data collection mechanism and the analysis mechanism.

Researchers usually don’t run a statistical analysis on qualitative data; instead, a researcher typically analyzes the qualitative data, extracts meaning from it, and answers a research question from that meaning. Though this is similar to quantitative research, some of the analysis methods can be viewed as more subjective.

So, how can we know that results obtained from a qualitative analysis reflect some truth, and not the researcher’s personal biases, experiences, or lenses?

Reliability and validity are equally important to consider in qualitative research. Ways to enhance validity in qualitative research include:

• Use multiple analysts
• Create/maintain audit trails
• Conduct member checks
• Include positionality statements
• Solicit peer review of analytical approach
• Triangulate findings via multiple data sources
• Search for and discuss negative cases (i.e., those which refute a theme)

Building reliability can include one or more of the following:

• Clearly define your codes and criteria for applying them
• Use detailed transcriptions which include things like pauses, crosstalk, and non-word verbal expressions
• Train coders on a common set of data
• Ensure coders are consistent with each other before coding the reset of the data
• Periodically reassess interrater agreement/reliability
• Use high-quality recording devices

The most well-known measure of qualitative reliability in education research is inter-rater reliability and consensus coding. I want to make a distinction between two common measures of inter-rater reliability: percent agreement and Cohen’s Kappa.

Percent agreement refers to the percentage of coding instances in which two raters assign the same code to a common “piece” of data. Because this is a simple percentage, it’s more intuitive to understand. But it also does not account for chance–in any deductive coding framework (i.e., when all possible codes are already defined), there is a random chance that two coders will apply the same code without actually “seeing” the same thing in the data.

By contrast, Cohen’s Kappa is designed to parse out the influence of chance agreement, and for this reason Cohen’s Kappa will always be smaller than the percent agreement for a given dataset. Many qualitative data analysis software packages (e.g., NVivo) will calculate both percent agreement and Cohen’s Kappa.

In consensus coding, multiple raters code the same data, discuss the codes that may apply, and decide together how to code the data. With consensus coding, the need for inter-rater agreement/reliability metrics is circumvented, because by definition, you will always have 100% agreement/reliability. The major downside of consensus coding is, of course, the time and effort needed to engage it. With large sets of qualitative data, consensus coding may not be feasible.

For a deeper dive into these topics, there are many excellent textbooks that explore the nuances of qualitative validity and reliability. Below, you’ll find a selection of recommended resources, as well as others that provide detailed insights into strengthening qualitative research methods.

Corbin, J., & Strauss, A. (2015). Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory (4th ed.). Sage Publications. Creswell, J. W., & Báez, J. C. (2021). 30 Essential Skills for the Qualitative Researcher (2nd ed.). Sage Publications. Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches . Sage Publications. Saldaña, J. (2013). An introduction to codes and coding. In The coding manual for qualitative researchers (pp. 1–40). Sage Publications.

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• Volume 18, Issue 2
• Issues of validity and reliability in qualitative research
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• Helen Noble 1 ,
• Joanna Smith 2
• 1 School of Nursing and Midwifery, Queens's University Belfast , Belfast , UK
• 2 School of Human and Health Sciences, University of Huddersfield , Huddersfield , UK
• Correspondence to Dr Helen Noble School of Nursing and Midwifery, Queens's University Belfast, Medical Biology Centre, 97 Lisburn Rd, Belfast BT9 7BL, UK; helen.noble{at}qub.ac.uk

https://doi.org/10.1136/eb-2015-102054

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Evaluating the quality of research is essential if findings are to be utilised in practice and incorporated into care delivery. In a previous article we explored ‘bias’ across research designs and outlined strategies to minimise bias. 1 The aim of this article is to further outline rigour, or the integrity in which a study is conducted, and ensure the credibility of findings in relation to qualitative research. Concepts such as reliability, validity and generalisability typically associated with quantitative research and alternative terminology will be compared in relation to their application to qualitative research. In addition, some of the strategies adopted by qualitative researchers to enhance the credibility of their research are outlined.

Are the terms reliability and validity relevant to ensuring credibility in qualitative research?

Although the tests and measures used to establish the validity and reliability of quantitative research cannot be applied to qualitative research, there are ongoing debates about whether terms such as validity, reliability and generalisability are appropriate to evaluate qualitative research. 2–4 In the broadest context these terms are applicable, with validity referring to the integrity and application of the methods undertaken and the precision in which the findings accurately reflect the data, while reliability describes consistency within the employed analytical procedures. 4 However, if qualitative methods are inherently different from quantitative methods in terms of philosophical positions and purpose, then alterative frameworks for establishing rigour are appropriate. 3 Lincoln and Guba 5 offer alternative criteria for demonstrating rigour within qualitative research namely truth value, consistency and neutrality and applicability. Table 1 outlines the differences in terminology and criteria used to evaluate qualitative research.

• View inline

Terminology and criteria used to evaluate the credibility of research findings

What strategies can qualitative researchers adopt to ensure the credibility of the study findings?

Unlike quantitative researchers, who apply statistical methods for establishing validity and reliability of research findings, qualitative researchers aim to design and incorporate methodological strategies to ensure the ‘trustworthiness’ of the findings. Such strategies include:

Accounting for personal biases which may have influenced findings; 6

Acknowledging biases in sampling and ongoing critical reflection of methods to ensure sufficient depth and relevance of data collection and analysis; 3

Meticulous record keeping, demonstrating a clear decision trail and ensuring interpretations of data are consistent and transparent; 3 , 4

Establishing a comparison case/seeking out similarities and differences across accounts to ensure different perspectives are represented; 6 , 7

Including rich and thick verbatim descriptions of participants’ accounts to support findings; 7

Demonstrating clarity in terms of thought processes during data analysis and subsequent interpretations 3 ;

Engaging with other researchers to reduce research bias; 3

Respondent validation: includes inviting participants to comment on the interview transcript and whether the final themes and concepts created adequately reflect the phenomena being investigated; 4

Data triangulation, 3 , 4 whereby different methods and perspectives help produce a more comprehensive set of findings. 8 , 9

Table 2 provides some specific examples of how some of these strategies were utilised to ensure rigour in a study that explored the impact of being a family carer to patients with stage 5 chronic kidney disease managed without dialysis. 10

Strategies for enhancing the credibility of qualitative research

In summary, it is imperative that all qualitative researchers incorporate strategies to enhance the credibility of a study during research design and implementation. Although there is no universally accepted terminology and criteria used to evaluate qualitative research, we have briefly outlined some of the strategies that can enhance the credibility of study findings.

• Sandelowski M
• Lincoln YS ,
• Barrett M ,
• Mayan M , et al
• Greenhalgh T
• Lingard L ,

Twitter Follow Joanna Smith at @josmith175 and Helen Noble at @helnoble

Competing interests None.

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A Review of the Quality Indicators of Rigor in Qualitative Research

Jessica l. johnson.

a William Carey University School of Pharmacy, Biloxi, Mississippi

Donna Adkins

Sheila chauvin.

b Louisiana State University, School of Medicine, New Orleans, Louisiana

Attributes of rigor and quality and suggested best practices for qualitative research design as they relate to the steps of designing, conducting, and reporting qualitative research in health professions educational scholarship are presented. A research question must be clear and focused and supported by a strong conceptual framework, both of which contribute to the selection of appropriate research methods that enhance trustworthiness and minimize researcher bias inherent in qualitative methodologies. Qualitative data collection and analyses are often modified through an iterative approach to answering the research question. Researcher reflexivity, essentially a researcher’s insight into their own biases and rationale for decision-making as the study progresses, is critical to rigor. This article reviews common standards of rigor, quality scholarship criteria, and best practices for qualitative research from design through dissemination.

INTRODUCTION

Within the past 20 years, qualitative research in health professions education has increased significantly, both in practice and publication. Today, one can pick up most any issue of a wide variety of health professions education journals and find at least one article that includes some type of qualitative research, whether a full study or the inclusion of a qualitative component within a quantitative or mixed methods study. Simultaneously, there have been recurrent calls for enhancing rigor and quality in qualitative research.

As members of the academic community, we share responsibility for ensuring rigor in qualitative research, whether as researchers who design and implement, manuscript reviewers who critique, colleagues who discuss and learn from each other, or scholarly teachers who draw upon results to enhance and innovate education. Therefore, the purpose of this article is to summarize standards of rigor and suggested best practices for designing, conducting, and reporting high-quality qualitative research. To begin, Denzin and Lincoln’s definition of qualitative research, a long-standing cornerstone in the field, provides a useful foundation for summarizing quality standards and best practices:

Qualitative research involves the studied use and collection of a variety of empirical materials – case study; personal experience; introspection; life story; interview; artifacts; cultural texts and productions; observational, historical, interactional, and visual texts – that describe the routine and problematic moments and meanings in individual lives. Accordingly, qualitative researchers deploy a wide range of interconnected interpretative practices, hoping always to get a better understanding of the subject matter at hand. It is understood, however, that each practice makes the world visible in a different way. Hence there is frequently a commitment to using more than one interpretative practice in any study. 1

In recent years, multiple publications have synthesized quality criteria and recommendations for use by researchers and peer reviewers alike, often in the form of checklists. 2-6 Some authors have raised concerns about the use of such checklists and adherence to strict, universal criteria because they do not afford sufficient flexibility to accommodate the diverse approaches and multiple interpretive practices often represented in qualitative studies. 7-11 They argue that a strict focus on using checklists of specific technical criteria may stifle the diversity and multiplicity of practices that are so much a part of achieving quality and rigor within the qualitative paradigm. As an alternative, some of these authors have published best practice guidelines for use by researchers and peer reviewers to achieve and assess methodological rigor and research quality. 12,13

Some journals within the field of health professions education have also established best practice guidance, as opposed to strict criteria or a checklist, for qualitative research. These have been disseminated as guiding questions or evaluation categories. In 2015, Academic Medicine produced an expanded second edition of a researcher/author manual that includes specific criteria with extensive explanations and examples. 14 Still others have disseminated best practice guidelines through a series of methodological articles within journal publications. 2

In this article, attributes of rigor and quality and suggested best practices are presented as they relate to the steps of designing, conducting, and reporting qualitative research in a step-wise approach.

BEST PRACTICES: STEP-WISE APPROACH

Step 1: identifying a research topic.

Identifying and developing a research topic is comprised of two major tasks: formulating a research question, and developing a conceptual framework to support the study. Formulating a research question is often stimulated by real-life observations, experiences, or events in the researcher’s local setting that reflect a perplexing problem begging for systematic inquiry. The research question begins as a problem statement or set of propositions that describe the relationship among certain concepts, behaviors, or experiences. Agee 15 and others 16,17 note that initial questions are usually too broad in focus and too vague regarding the specific context of the study to be answerable and researchable. Creswell reminds us that initial qualitative research questions guide inquiry, but they often change as the author’s understanding of the issue develops throughout the study. 16 Developing and refining a primary research question focused on both the phenomena of interest and the context in which it is situated is essential to research rigor and quality.

Glassick, Huber, and Maeroff identified six criteria applicable to assessing the quality of scholarship. 18,19 Now commonly referred to as the Glassick Criteria ( Table 1 ), these critical attributes outline the essential elements of any scholarly approach and serve as a general research framework for developing research questions and designing studies. The first two criteria, clear purpose and adequate preparation, are directly related to formulating effective research questions and a strong conceptual framework.

Glassick’s Criteria for Assessing the Quality of Scholarship of a Research Study 18

Generating and refining a qualitative research question requires thorough, systematic, and iterative review of the literature, and the use of those results to establish a clear context and foundation for the question and study design. Using an iterative approach, relevant concepts, principles, theories or models, and prior evidence are identified to establish what is known, and more importantly, what is not known. The iterative process contributes to forming a better research question, the criteria for which can be abbreviated by the acronym FINER, ie, f easible, i nteresting, n ovel, e thical, and r elevant, that is answerable and researchable, in terms of research focus, context specificity, and the availability of time, logistics, and resources to carry out the study. Developing a FINER research question is critical to study rigor and quality and should not be rushed, as all other aspects of research design depend on the focus and clarity of the research question(s) guiding the study. 15 Agee provides clear and worthwhile additional guidance for developing qualitative research questions. 15

Reflexivity, the idea that a researcher’s preconceptions and biases can influence decisions and actions throughout qualitative research activities, is a critical aspect of rigor even at the earliest stages of the study. A researcher’s background, beliefs, and experiences may affect any aspect of the research from choosing which specific question to investigate through determining how to present the results. Therefore, even at this early stage, the potential effect of researcher bias and any ethical considerations should be acknowledged and addressed. That is, how will the question’s influence on study design affect participants’ lives, position the researcher in relationship with others, or require specific methods for addressing potential areas of research bias and ethical considerations?

A conceptual framework is then actively constructed to provide a logical and convincing argument for the research. The framework defines and justifies the research question, the methodology selected to answer that question, and the perspectives from which interpretation of results and conclusions will be made. 5,6,20 Developing a well-integrated conceptual framework is essential to establishing a research topic based upon a thorough and integrated review of relevant literature (addressing Glassick criteria #1 and #2: clear purpose and adequate preparation). Key concepts, principles, assumptions, best practices, and theories are identified, defined, and integrated in ways that clearly demonstrate the problem statement and corresponding research question are answerable, researchable, and important to advancing thinking and practice.

Ringsted, Hodges, and Sherpbier describe three essential parts to an effective conceptual framework: theories and/or concepts and principles relevant to the phenomenon of interest; what is known and unknown from prior work, observations, and examples; and the researcher’s observations, ideas, and suppositions regarding the research problem statement and question. 21 Lingard describes four types of unknowns to pursue during literature review: what no one knows; what is not yet well understood; what controversy or conflicting results, understandings, or perspectives exist; and what are unproven assumptions. 22 In qualitative research, these unknowns are critical to achieving a well-developed conceptual framework and a corresponding rigorous study design.

Recent contributions from Ravitch and colleagues present best practices in developing frameworks for conceptual and methodological coherence within a study design, regardless of the research approach. 23,24 Their recommendations and arguments are highly relevant to qualitative research. Figure 1 reflects the primary components of a conceptual framework adapted from Ravitch and Carl 23 and how all components contribute to decisions regarding research design, implementation, and applications of results to future thinking, study, and practice. Notice that each element of the framework interacts with and influences other elements in a dynamic and interactive process from the beginning to the end of a research project. The intersecting bidirectional arrows represent direct relationships between elements as they relate to specific aspects of a qualitative research study.

Adaptation of Ravitch and Carl’s Components of a Conceptual Framework 23

Maxwell also provides useful guidance for developing an effective conceptual framework specific to the qualitative research paradigm. 17 The 2015 second edition of the Review Criteria for Research Manuscripts 14 and work by Ravitch and colleagues 23,24 provide specific guidance for applying the conceptual framework to each stage of the research process to enhance rigor and quality. Quality criteria for assessing a study’s problem statement, conceptual framework, and research question include the following: introduction builds a logical case and provides context for the problem statement; problem statement is clear and well-articulated; conceptual framework is explicit and justified; research purpose and/or question is clearly stated; and constructs being investigated are clearly identified and presented. 14,24,25 As best practice guidelines, these criteria facilitate quality and rigor while providing sufficient flexibility in how each is achieved and demonstrated.

While a conceptual framework is important to rigor in qualitative research, Huberman and Miles caution qualitative researchers about developing and using a framework to the extent that it influences qualitative design deductively because this would violate the very principles of induction that define the qualitative research paradigm. 25 Our profession’s recent emphasis on a holistic admissions process for pharmacy students provides a reasonable example of inductive and deductive reasoning and their respective applications in qualitative and quantitative research studies. Principles of inductive reasoning are applied when a qualitative research study examines a representative group of competent pharmacy professionals to generate a theory about essential cognitive and affective skills for patient-centered care. Deductive reasoning could then be applied to design a hypothesis-driven prospective study that compares the outcomes of two cohorts of students, one group admitted using traditional criteria and one admitted based on a holistic admissions process revised to value the affective skills of applicants. Essentially, the qualitative researcher must carefully generate a conceptual framework that guides the research question and study design without allowing the conceptual framework to become so rigid as to dictate a testable hypothesis, which is the founding principle of deductive reasoning. 26

Step 2: Qualitative Study Design

The development of a strong conceptual framework facilitates selection of appropriate study methods to minimize the bias inherent in qualitative studies and help readers to trust the research and the researcher (see Glassick criteria #3 in Table 1 ). Although researchers can employ great flexibility in the selection of study methods, inclusion of best practice methods for assuring the rigor and trustworthiness of results is critical to study design. Lincoln and Guba outline four criteria for establishing the overall trustworthiness of qualitative research results: credibility, the researcher ensures and imparts to the reader supporting evidence that the results accurately represent what was studied; transferability, the researcher provides detailed contextual information such that readers can determine whether the results are applicable to their or other situations; dependability, the researcher describes the study process in sufficient detail that the work could be repeated; confirmability, the researcher ensures and communicates to the reader that the results are based on and reflective of the information gathered from the participants and not the interpretations or bias of the researcher. 27

Specific best practice methods used in the sampling and data collection processes to increase the rigor and trustworthiness of qualitative research include: clear rationale for sampling design decisions, determination of data saturation, ethics in research design, member checking, prolonged engagement with and persistent observation of study participants, and triangulation of data sources. 28

Qualitative research is focused on making sense of lived, observed phenomenon in a specific context with specifically selected individuals, rather than attempting to generalize from sample to population. Therefore, sampling design in qualitative research is not random but defined purposively to include the most appropriate participants in the most appropriate context for answering the research question. Qualitative researchers recognize that certain participants are more likely to be “rich” with data or insight than others, and therefore, more relevant and useful in achieving the research purpose and answering the question at hand. The conceptual framework contributes directly to determining sample definitions, size, and recruitment of participants. A typical best practice is purposive sampling methods, and when appropriate, convenience sampling may be justified. 29

Purposive sampling reflects intentional selection of research participants to optimize data sources for answering the research question. For example, the research question may be best answered by persons who have particular experience (critical case sampling) or certain expertise (key informant sampling). Similarly, additional participants may be referred for participation by active participants (snowball sampling) or may be selected to represent either similar or opposing viewpoints (confirming or disconfirming samples). Again, the process of developing and using a strong conceptual framework to guide and justify methodological decisions, in this case defining and establishing the study sample, is critical to rigor and quality. 30 Convenience sampling, using the most accessible research participants, is the least rigorous approach to defining a study sample and may result in low accuracy, poor representativeness, low credibility, and lack of transferability of study results.

Qualitative studies typically reflect designs in which data collection and analysis are done concurrently, with results of ongoing analysis informing continuing data collection. Determination of a final sample size is largely based on having sufficient opportunity to collect relevant data until new information is no longer emerging from data collection, new coding is not feasible, and/or no new themes are emerging; that is, reaching data saturation , a common standard of rigor for data collection in qualitative studies . Thus, accurately predicting a sample size during the planning phases of qualitative research can be challenging. 30 Care should be taken that sufficient quantity (think thick description) and quality (think rich description) of data have been collected prior to concluding that data saturation has been achieved. A poor decision regarding sample size is a direct consequence of sampling strategy and quality of data generated, which leaves the researcher unable to fully answer the research question in sufficient depth. 30

Though data saturation is probably the most common terminology used to describe the achievement of sufficient sample size, it does not apply to all study designs. For example, one could argue that in some approaches to qualitative research, data collection could continue infinitely if the event continues infinitely. In education, we often anecdotally observe variations in the personality and structure of a class of students, and as generations of students continue to evolve with time, so too would the data generated from observing each successive class. In such situations, data saturation might never be achieved. Conversely, the number of participants available for inclusion in a sample may be small and some risk of not reaching data saturation may be unavoidable. Thus, the idea of fully achieving data saturation may be unrealistic when applied to some populations or research questions. In other instances, attrition and factors related to time and resources may contribute to not reaching data saturation within the limits of the study. By being transparent in the process and reporting of results when saturation may not have been possible, the resulting data may still contribute to the field and to further inquiry. Replication of the study using other samples and conducting additional types of follow-up studies are other options for better understanding the research phenomenon at hand. 31

In addition to defining the sample and selecting participants, other considerations related to sampling bias may impact the quantity and quality of data generated and therefore the quality of the study result. These include: methods of recruiting, procedures for informed consent, timing of the interviews in relation to experience or emotion, procedures for ensuring participant anonymity/confidentiality, interview setting, and methods of recording/transcribing the data. Any of these factors could potentially change the nature of the relationship between the researcher and the study participants and influence the trustworthiness of data collected or the study result. Thus, ongoing application of previously mentioned researcher reflexivity is critical to the rigor of the study and quality of sampling. 29,30

Common qualitative data collection methods used in health professions education include interview, direct observation methods, and textual/document analysis. Given the unique and often highly sensitive nature of data being collected by the researcher, trustworthiness is an essential component of the researcher-participant relationship. Ethical conduct refers to how moral principles and values are part of the research process. Participants’ perceptions of ethical conduct are fundamental to a relationship likely to generate high quality data. During each step of the research process, care must be taken to protect the confidentiality of participants and shield them from harm relating to issues of respect and dignity. Researchers must be respectful of the participants’ contributions and quotes, and results must be reported truthfully and honestly. 8

Interview methods range from highly structured to increase dependability or completely open-ended to allow for interviewers to clarify a participant’s response for increased credibility and confirmability. Regardless, interview protocols and structure are often modified or refined, based on concurrent data collection and analysis processes to support or refute preliminary interpretations and refine focus and continuing inquiry. Researcher reflexivity, or acknowledgement of researcher bias, is absolutely critical to the credibility and trustworthiness of data collection and analysis in such study designs. 32

Interviews should be recorded and transcribed verbatim prior to coding and analysis. 28 Member checking, a common standard of rigor, is a practice to increase study credibility and confirmability that involves asking a research subject to verify the transcription of an interview. 1,16,28 The research subject is asked to verify the completeness and accuracy of an interview transcript to ensure the transcript truthfully reflects the meaning and intent of the subject’s contribution.

Prolonged engagement involves the researcher gaining familiarity and understanding of the culture and context surrounding the persons or situations being studied. This strategy supports reflexivity, allowing the researcher to determine how they themselves may be a source of bias during the data collection process by altering the nature of how individuals behave or interact with others in the presence of the researcher. Facial expressions, spoken language, body language, style of dress, age, race, gender, social status, culture, and the researcher’s relationship with the participants may potentially influence either participants’ responses or how the researcher interprets those responses. 33 “Fitting in” by demonstrating an appreciation and understanding of the cultural norms of the population being studied potentially allows the researcher to obtain more open and honest responses from participants. However, if the research participants or topic are too familiar or personal, this may also influence data collection or analysis and interpretation of the results. 33 The possible applications of this section to faculty research with student participants in the context of pharmacy education are obvious, and researcher reflexivity is critical to rigor.

Some researchers using observational methods adopt a strategy of direct field observation, while others play partial or full participant roles in the activity being observed. In both observation scenarios, it is impossible to separate the researcher from the environment, and researcher reflexivity is essential. The pros and cons of observation approach, relative to the research question and study purpose, should be evaluated by the researcher, and the justification for the observational strategy selected should be made clear. 34 Regardless of the researcher’s degree of visibility to the study participants, persistent observation of the targeted sample is critical to the confirmability standard and to achieving data saturation. That is, study conclusions must be clearly grounded in persistent phenomena witnessed during the study, rather than on a fluke event. 28

Researchers acknowledge that observational methodologies are limited by the reality that the researcher carries a bias in determining what is observed, what is recorded, how it is recorded, and how it is transcribed for analysis. A study’s conceptual framework is critical to achieving rigor and quality and provides guidance in developing predetermined notions or plans for what to observe, how to record, and how to minimize the influence of potential bias. 34 Researcher notes should be recorded as soon as possible after the observation event to optimize accuracy. The more detailed and complete the notes, the more accurate and useful they can be in data analysis or in auditing processes for enhancing rigor in the interpretation phase of the study. 34

Triangulation is among the common standards of rigor applied within the qualitative research paradigm. Data triangulation is used to identify convergence of data obtained through multiple data sources and methods (eg, observation field notes and interview transcripts) to avoid or minimize error or bias and optimize accuracy in data collection and analysis processes. 33,35,36

Again, researcher practice in reflexivity throughout research processes is integral to rigor in study design and implementation. Researchers must demonstrate attention to appropriate methods and reflective critique, which are represented in both core elements of the conceptual framework ( Figure 1 ) and Glassick criteria ( Table 1 ). In so doing, the researcher will be well-prepared to justify sampling design and data collection decisions to manuscript reviewers and, ultimately, readers.

Step 3: Data Analysis

In many qualitative studies, data collection runs concurrently with data analysis. Specific standards of rigor are commonly used to ensure trustworthiness and integrity within the data analysis process, including use of computer software, peer review, audit trail, triangulation, and negative case analysis.

Management and analyses of qualitative data from written text, observational field notes, and interview transcriptions may be accomplished using manual methods or the assistance of computer software applications for coding and analysis. When managing very large data sets or complex study designs, computer software can be very helpful to assist researchers in coding, sorting, organizing, and weighting data elements. Software applications can facilitate ease in calculating semi-quantitative descriptive statistics, such as counts of specific events, that can be used as evidence that the researcher’s analysis is based on a representative majority of data collected ( inclusivism ) rather than focusing on selected rarities ( anecdotalism ). Using software to code data can also make it easier to identify deviant cases, detect coding errors, and estimate interrater reliability among multiple coders. 37 While such software helps to manage data, the actual analyses and interpretation still reside with the researcher.

Peer review, another common standard of rigor, is a process by which researchers invite an independent third-party researcher to analyze a detailed audit trail maintained by the study author. The audit trail methodically describes the step-by-step processes and decision-making throughout the study. Review of this audit trail occurs prior to manuscript development and enhances study confirmability. 1,16 The peer reviewer offers a critique of the study methods and validation of the conclusions drawn by the author as a thorough check on researcher bias.

Triangulation also plays a role in data analysis, as the term can also be used to describe how multiple sources of data can be used to confirm or refute interpretation, assertions, themes, and study conclusions. If a theme or theory can be arrived at and validated using multiple sources of data, the result of the study has greater credibility and confirmability. 16,33,36 Should any competing or controversial theories emerge during data collection or analysis, it is vital to the credibility and trustworthiness of the study that the author disclose and explore those negative cases. Negative case analysis refers to actively seeking out and scrutinizing data that do not fit or support the researcher’s interpretation of the data. 16

The use of best practices applying to data collection and data analysis facilitates the full examination of data relative to the study purpose and research question and helps to prevent premature closure of the study. Rather than stopping at the initial identification of literal, first-level assertion statements and themes, authors must progress to interpreting how results relate to, revise, or expand the conceptual framework, or offer an improved theory or model for explaining the study phenomenon of interest. Closing the loop on data collection is critical and is achieved when thorough and valid analysis can be linked back to the conceptual framework, as addressed in the next section.

Step 4: Drawing Valid Conclusions

Lingard and Kennedy 38 succinctly state that the purpose of qualitative research is to deepen one’s understanding of specific perspectives, observations, experiences, or events evidenced through the behaviors or products of individuals and groups as they are situated in specific contexts or circumstances. Conclusions generated from study results should enhance the conceptual framework, or contribute to a new theory or model development, and are most often situated within the discussion and conclusion sections of a manuscript.

The discussion section should include interpretation of the results and recommendations for practice. Interpretations should go beyond first-level results or literal description of observed behaviors, patterns, and themes from analysis. The author’s challenge is to provide a complete and thorough examination and explanation of how specific results relate to each other, contribute to answering the research question, and achieve the primary purpose of the research endeavor. The discussion should “close the loop” by integrating study results and analysis with the original conceptual framework. The discussion section should also provide a parsimonious narrative or graphical explanation and interpretation of study results that enhances understanding of the targeted phenomena.

The conclusion section should provide an overall picture or synopsis of the study, including its important and unique contributions to the field from the perspective of both conceptual and practical significance. The conclusion should also include personal and theoretical perspectives and future directions for research. Together, the discussion and conclusion should include responses to the larger questions of the study’s contributions, such as: So what? Why do these results matter? What next?

The strength of conclusions is dependent upon the extent to which standards of rigor and best practices were demonstrated in design, data collection, data analysis, and interpretation, as described in previous sections of this article. 4,12,17,23,24 Quality and rigor expectations for drawing valid conclusions and generating new theories are reflected in the following essential features of rigor and quality, which include: “Close the loop” to clearly link research questions, study design, data collection and analysis, and interpretation of results. Reflect effective integration of the study results with the conceptual framework and explain results in ways that relate, support, elaborate, and/or challenge conclusions of prior scholarship. Descriptions of new or enhanced frameworks or models are clear and effectively grounded in the study results and conclusions. Practical or theoretical implications are effectively discussed, including guidance for future studies. Limitations and issues of reflexivity and ethics are clearly and explicitly described, including references to actions taken to address these areas. 3,4,12,14

Step 5: Reporting Research Results

Key to quality reporting of qualitative research results are clarity, organization, completeness, accuracy, and conciseness in communicating the results to the reader of the research manuscript. O’Brien and others 4 proposed a standardized framework specifically for reporting qualitative studies known as the Standards for Reporting Qualitative Research (SRQR, Table 2 ). This framework provides detailed explanations of what should be reported in each of 21 sections of a qualitative research manuscript. While the SRQR does not explicitly mention a conceptual framework, the descriptions and table footnote clarification for the introduction and problem statement reflect the essential elements and focus of a conceptual framework. Ultimately, readers of published work determine levels of credibility, trustworthiness, and the like. A manuscript reviewer, the first reader of a study report, has the responsibility and privilege of providing critique and guidance to authors regarding achievement of quality criteria, execution and reporting of standards of rigor, and the extent to which meaningful contributions to thinking and practice in the field are presented. 13,39

An Adaptation of the 21 Elements of O’Brien and Colleagues’ Standards for Reporting Qualitative Research (SRQR) 4

Authors must avoid language heavy with connotations or adjectives that insert the researcher’s opinion into the database or manuscript. 14,40 The researcher should be as neutral and objective as possible in interpreting data and in presenting results. Thick and rich descriptions, where robust descriptive language is used to provide sufficient contextual information, enable the reader to determine credibility, transferability, dependability, and confirmability .

The process of demonstrating the credibility of research is rooted in honest and transparent reporting of how biases and other possible confounders were identified and addressed throughout study processes. Such reporting, first described within the study’s conceptual framework, should be revisited in reporting the work. Confounders may include the researcher’s training and previous experiences, personal connections to the background theory, access to the study population, and funding sources. These elements and processes are best represented in Glassick’s criteria for effective presentation and reflective critique ( Table 1 , criteria 5 and 6). Transferability is communicated, in part, through description of sampling factors such as: geographical location of the study, number and characteristics of participants, and the timeframe of data collection and analysis. 40 Such descriptions also contribute to the credibility of the results and readers’ determination of transfer to their and other contexts. To ensure dependability, the research method must be reported in detail such that the reader can determine proper research practices have been followed and that future researchers can repeat the study. 40 The confirmability of the results is influenced by reducing or at a minimum explaining any researcher influence on the result by applying and meeting standards of rigor such as member checking, triangulation, and peer review. 29,33

In qualitative studies, the researcher is often the primary instrument for data collection. Any researcher biases not adequately addressed or errors in judgement can affect the quality of data and subsequent research results. 33 Thus, due to the creative interpretative and contextually bound nature of qualitative studies, the application of standards of rigor and adherence to systematic processes well-documented in an audit trail are essential. The application of rigor and quality criteria extend beyond the researcher and are also important to effective peer review processes within a study and for scholarly dissemination. The goal of rigor in qualitative research can be described as ensuring that the research design, method, and conclusions are explicit, public, replicable, open to critique, and free of bias. 41 Rigor in the research process and results are achieved when each element of study methodology is systematic and transparent through complete, methodical, and accurate reporting. 33 Beginning the study with a well-developed conceptual framework and active use of both researcher reflexivity and rigorous peer review during study implementation can drive both study rigor and quality.

As the number of published qualitative studies in health professions educational research increases, it is important for our community of health care educators to keep in mind the unique aspects of rigor in qualitative studies presented here. Qualitative researchers should select and apply any of the above referenced study methods and research practices, as appropriate to the research question, to achieve rigor and quality. As in any research paradigm, the goal of quality and rigor in qualitative research is to minimize the risk of bias and maximize the accuracy and credibility of research results. Rigor is best achieved through thoughtful and deliberate planning, diligent and ongoing application of researcher reflexivity, and honest communication between the researcher and the audience regarding the study and its results.

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How to establish the validity and reliability of qualitative research?

The validity and reliability of qualitative research represent the key aspects of the quality of research. When handled meticulously, the reliability and validity parameters help differentiate between good and bad research. They also assure readers that the findings of the study are credible and trustworthy. This aspect becomes particularly vital in case studies involving primary data analysis. Here the researcher’s subjectivity can highly influence the interpretation of the data. This article clarifies how to establish the validity and reliability of qualitative research using various tools and techniques.

Establishing the validity of qualitative data

Qualitative data is as important as quantitative data, as it also helps in establishing key research points. However, since it cannot be quantified, the question of its correctness is critical. Validity relates to the appropriateness of any research value, tools and techniques, and processes, including data collection and validation (Mohamad et al., 2015). Validity also establishes the soundness of the methodology, sampling process, data analysis process, and conclusion of the study (Golafshani, 2003).

The main aspect that needs to be ensured is that the research philosophies fall in line with the research. To maintain the validity of the research, there is a need to understand the underlying needs of the research, the overarching process guidelines and the societal rules of ethical research. While establishing validity, there needs to be a consensus among the individual and the society on how to establish the correctness and accuracy of the research. If this aspect is kept in mind, the tools and techniques used are bound to be accepted by wider audiences. The main point to remember, thus, is to choose the tools wisely, which will establish how to correct the data of the research.

One of the major techniques that can be used for establishing the validity of qualitative data includes choosing a skilled moderator. Employing a moderator will help overcome personal bias. Thus, the researcher or the organisation can employ moderators to ensure that the data is genuine and is not influenced by “what the researcher wants to see or hear”.

Another way to promote the validity of research is by employing the strategy of triangulation. This basically involves that the research will be conducted from different or multiple perspectives. For example, this can take the form of using several moderators, in different locations or it could be multiple individuals who are analysing the same data. Basically, any technique through which the researcher can analyse the data from different angles.

Furthermore, the validity of qualitative research can also be established using a technique known as respondent validation. This basically involves testing the initial results with the participants in order to see if the results still ring true.

Establishing the reliability of qualitative data

Quantitative research includes reliability measures where the researcher must prove that the process and the results have replicable outcomes. On the other hand, reliability in qualitative research includes very diverse paradigms, where the aspect itself is epistemologically counter-intuitive along with having a very difficult definition (Russell, 2014). Thus, what needs to be done to maintain and establish reliability in qualitative research is to be consistent.

Reliability tests for qualitative research can be established by techniques like:

• refutational analysis,
• use of comprehensive data,
• constant testing and comparison of data,
• use of tables to record data,
• as well as the use of inclusive deviant cases.

These techniques can help support the data sourcing, data validation and data presentation process of the research, as well as support the claim of reliability in terms of form and context.

Triangulation is another aspect which becomes very important in establishing reliability in the research. Also, as an additional note, it is very important for qualitative research to include a reference to a quantitative aspect. The use of a simple quantitative aspect in otherwise completely qualitative research creates a very positive attitude towards the overall concept of the research and helps to establish reliability in a much easier form. Also, the inclusion of at least two reliability tests, as per the type of research outcomes of a research, is a dependable way of establishing that the research process and results are reliable.

Dos of validity and reliability of qualitative data

• While establishing validity and reliability, it is very important to decide the tools and techniques to be used in the research, before conducting the actual research. This helps in establishing the parameters for obtaining reliable and valid results from the beginning and does not impair the results of the research at the end of the process.
• To conduct efficient reliability and validity measures, an effective assessment of the literature must be done to understand which processes will work. Irrelevant approaches that compromise the reliability and validity of research should not be pursued.
• Reliability and validity processes should be conducted in tandem with the research processes being carried out to confirm the research objective, which provides another additional layer of authenticity to the research work.

Don’ts’ of validity and reliability of qualitative data

• Reliability and validity should not be taken as an extra element of the research. If they do not add value to research, they result in insecurity regarding the accuracy of the results.
• To be able to establish reliability and validity, researchers should not include excessive measures to support their research claims. This may add to unauthenticated results, as adding too many measures will overcomplicate the research.

An example statement of validity

The validity of this research was established using two measures, the data blinding and the inclusion of different sampling groups in the plan. The research included an assessment of the knowledge of traditional cuisine among the present population of a city. Where the sample was divided into two groups- to reduce biases. The sample included young adults, who have been mostly raised in an urban environment, along with middle-aged and elderly population who have had a partial upbringing in the rural area of India. The inclusion of greater diversity and a large number of sample respondents led the research to reduce its biasness towards only one type of outcome, creating a base for valid results. The other technique used was to restrict the amount of information shared with the respondents to make sure that the research was not biased with preconceived notions of the respondents. These steps helped to establish the validity of the results gained, proving the accurateness of the qualitative research. Further, the validity of the questionnaire was established using a panel of experts that reviewed the questionnaire. And hence the statements that did not go well with the subject of the study were removed.

An example statement of reliability

In terms of establishing reliability, the researcher conducted two processes. The first included recording the data in a table to provide an overall assessment of the data collection process and the updated assessment of the results, as they come. The use of the table for recording data provided the researcher with a chance to quickly interpret the results as per the record of every individual respondent and realize the progress of the research.

The table also helped in the concise construction of the conclusion of the research parameters. The reliability was also assessed through data triangulation. Among the various model of data triangulation such as methodological triangulation, data triangulation, investigator triangulation and theoretical triangulation. The study adopted theoretical triangulation, wherein other research works in the same arena were analysed and presented as a literature review to support the results claims of the data collection and analysis process. Moreover, the reliability measures relating to the triangulation of data provided an extensive understanding of the research objectives, which provided an additional layer of reliable stamping to the research.

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Validity in Qualitative Research

How do we assess and assure Validity in Qualitative Research ?  This can be a bit of a tricky topic, as qualitative research involves humans understanding humans, a necessarily subjective practice from the get-go.  Nevertheless, there are some questions the researcher can ask and some techniques he or she can employ to establish a reasonable level of validity.

Whether it is employed in business or the social sciences, it is often used to inform decisions that have important implications, thus assuring a high level of validity is essential.  While the results should never be extrapolated over a larger population, (as they never come from a large enough sample to be statistically significant), validity can be established such that it can be used to inform meaningful decisions.

One measure of validity in qualitative research is to ask questions such as: “Does it make sense?” and “Can I trust it?”  This may seem like a fuzzy measure of validity to someone disciplined in quantitative research, for example, but in a science that deals in themes and context, these questions are important.

Steps in Ensuring Validity

The first step in ensuring validity is choosing a well-trained and skilled moderator (or facilitator).  A good moderator will check personal bias and expectations at the door.  He or she is interested in learning as much candid information from the research participants as possible, and respectful neutrality is a must if the goal is valid qualitative research.  For this reason, organizations often employ moderators from outside the group or organization to help ensure that the responses are genuine and not influenced by “what we want to hear.”  For some academic applications, the moderator will disclose his or her perspectives and biases in the reporting of the data as a matter of full disclosure.

While a good moderator is key, a good sample group is also essential.  Are the participants truly members of the segment from which they are recruited?  Ethical recruiting is an important issue in qualitative research, as data collected from individuals who are not truly representative of their segment will not lead to valid results.

Another way to promote validity is to employ a strategy known as triangulation.  To accomplish this, the research is done from multiple perspectives.   This could take the form of using several moderators, different locations, multiple individuals analyzing the same data . . . essentially any technique that would inform the results from different angles.   For some applications, for example, an organization may choose to run focus groups in parallel through two entirely different researchers and then compare the results.

Validity in qualitative research can also be checked by a technique known as respondent validation.  This technique involves testing initial results with participants to see if they still ring true.   Although the research has been interpreted and condensed, participants should still recognize the results as authentic and, at this stage, may even be able to refine the researcher’s understanding.

When the study permits, deep saturation into the research will also promote validity.  If responses become more consistent across larger numbers of samples, the data becomes more reliable.

Another technique to establish validity is to actively seek alternative explanations to what appear to be research results.  If the researcher is able to exclude other scenarios, he is or she is able to strengthen the validity of the findings.   Related to this technique is asking questions in an inverse format.

While the techniques to establish validity in qualitative research may seem less concrete and defined than in some of the other scientific disciplines, strong research techniques will, indeed, assure an appropriate level of validity in qualitative research.

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Validity and reliability in qualitative research.

What is Validity and Reliability in Qualitative research?

In Quantitative research, reliability refers to consistency of certain measurements, and validity – to whether these measurements “measure what they are supposed to measure”. Things are slightly different, however, in Qualitative research.

Reliability in qualitative studies is mostly a matter of “being thorough, careful and honest in carrying out the research” (Robson, 2002: 176). In qualitative interviews, this issue relates to a number of practical aspects of the process of interviewing, including the wording of interview questions, establishing rapport with the interviewees and considering ‘power relationship’ between the interviewer and the participant (e.g. Breakwell, 2000; Cohen et al., 2007; Silverman, 1993).

What seems more relevant when discussing qualitative studies is their validity , which very often is being addressed with regard to three common threats to validity in qualitative studies, namely researcher bias , reactivity and respondent bias (Lincoln and Guba, 1985).

Researcher bias refers to any kind of negative influence of the researcher’s knowledge, or assumptions, of the study, including the influence of his or her assumptions of the design, analysis or, even, sampling strategy. Reactivity , in turn, refers to a possible influence of the researcher himself/herself on the studied situation and people. Respondent bias refers to a situation where respondents do not provide honest responses for any reason, which may include them perceiving a given topic as a threat, or them being willing to ‘please’ the researcher with responses they believe are desirable.

Robson (2002) suggested a number of strategies aimed at addressing these threats to validity, being prolonged involvement , triangulation , peer debriefing , member checking ,  negative case analysis  and keeping an audit trail .

So, what exactly are these strategies and how can you apply them in your research?

Prolonged involvement refers to the length of time of the researcher’s involvement in the study, including involvement with the environment and the studied participants. It may be granted, for example, by the duration of the study, or by the researcher belonging to the studied community (e.g. a student investigating other students’ experiences). Being a member of this community, or even being a friend to your participants (see my blog post on the ethics of researching friends ), may be a great advantage and a factor that both increases the level of trust between you, the researcher, and the participants and the possible threats of reactivity and respondent bias. It may, however, pose a threat in the form of researcher bias that stems from your, and the participants’, possible assumptions of similarity and presuppositions about some shared experiences (thus, for example, they will not say something in the interview because they will assume that both of you know it anyway – this way, you may miss some valuable data for your study).

Triangulation may refer to triangulation of data through utilising different instruments of data collection, methodological triangulation through employing mixed methods approach and theory triangulation through comparing different theories and perspectives with your own developing “theory” or through drawing from a number of different fields of study.

Peer debriefing and support is really an element of your student experience at the university throughout the process of the study. Various opportunities to present and discuss your research at its different stages, either at internally organised events at your university (e.g. student presentations, workshops, etc.) or at external conferences (which I strongly suggest that you start attending) will provide you with valuable feedback, criticism and suggestions for improvement. These events are invaluable in helping you to asses the study from a more objective, and critical, perspective and to recognise and address its limitations. This input, thus, from other people helps to reduce the researcher bias.

Member checking , or testing the emerging findings with the research participants, in order to increase the validity of the findings, may take various forms in your study. It may involve, for example, regular contact with the participants throughout the period of the data collection and analysis and verifying certain interpretations and themes resulting from the analysis of the data (Curtin and Fossey, 2007). As a way of controlling the influence of your knowledge and assumptions on the emerging interpretations, if you are not clear about something a participant had said, or written, you may send him/her a request to verify either what he/she meant or the interpretation you made based on that. Secondly, it is common to have a follow-up, “validation interview” that is, in itself, a tool for validating your findings and verifying whether they could be applied to individual participants (Buchbinder, 2011), in order to determine outlying, or negative, cases and to re-evaluate your understanding of a given concept (see further below). Finally, member checking, in its most commonly adopted form, may be carried out by sending the interview transcripts to the participants and asking them to read them and provide any necessary comments or corrections (Carlson, 2010).

Negative case analysis is a process of analysing ‘cases’, or sets of data collected from a single participant, that do not match the patterns emerging from the rest of the data. Whenever an emerging explanation of a given phenomenon you are investigating does nto seem applicable to one, or a small number, of the participants, you should try to carry out a new line of analysis aimed at understanding the source of this discrepancy. Although you may be tempted to ignore these “cases” in fear of having to do extra work, it should become your habit to explore them in detail, as the strategy of negative case analysis, especially when combined with member checking, is a valuable way of reducing researcher bias.

Finally, the notion of keeping an audit trail refers to monitoring and keeping a record of all the research-related activities and data, including the raw interview and journal data, the audio-recordings, the researcher’s diary (see this post about recommended software for researcher’s diary ) and the coding book.

If you adopt the above strategies skilfully, you are likely to minimize threats to validity of your study. Don’t forget to look at the resources in the reference list, if you would like to read more on this topic!

Breakwell, G. M. (2000). Interviewing. In Breakwell, G.M., Hammond, S. & Fife-Shaw, C. (eds.) Research Methods in Psychology. 2nd Ed. London: Sage. Buchbinder, E. (2011). Beyond Checking: Experiences of the Validation Interview. Qualitative Social Work, 10 (1), 106-122. Carlson, J.A. (2010). Avoiding Traps in Member Checking. The Qualitative Report, 15 (5), 1102-1113. Cohen, L., Manion, L., & Morrison, K. (2007). Research Methods in Education. 6th Ed. London: Routledge. Curtin, M., & Fossey, E. (2007). Appraising the trustworthiness of qualitative studies: Guidelines for occupational therapists. Australian Occupational Therapy Journal, 54, 88-94. Lincoln, Y. S. & Guba, E. G. (1985). Naturalistic Inquiry. Newbury Park, CA: SAGE. Robson, C. (2002). Real world research: a resource for social scientists and practitioner-researchers. Oxford, UK: Blackwell Publishers.

Silverman, D. (1993) Interpreting Qualitative Data. London: Sage.

There is an argument for using your identity and biases to enrich the research (see my recent blog… researcheridentity.wordpress.com) providing that the researcher seeks to fully comprehend their place in the research and is fully open, honest and clear about that in the write up. I have come to see reliability and validity more as a defence of is the research rigorous, thorough and careful therefore is it morally, ethically and accurately defensible?

Hi Nathan, thank you for your comment. I agree that being explicit about your own status and everything that you bring into the study is important – it’s a very similar issue (although seemingly it’s a different topic) to what I discussed in the blog post about grounded theory where I talked about being explicit about the influence of our previous knowledge on the data. I have also experienced this dilemma of “what to do with” my status as simultaneously a “researcher” an “insider” a “friend” and a “fellow Polish migrant” when conducting my PhD study of Polish migrants’ English Language Identity, and came to similar conclusions as the ones you reach in your article – to acknowledge these “multiple identities” and make the best of them.

I have read your blog article and really liked it – would you mind if I shared it on my Facebook page, and linked to it from my blog section on this page?

Please do share my blog by all means; I’d be delighted. Are you on twitter? I’m @Nathan_AHT_EDD I strongly believe that we cannot escape our past, including our multiple/present habitus and identities when it comes to qualitative educational research. It is therefore, arguably, logical to ethically and sensibly embrace it/them to enrich the data. Identities cannot be taken on and off like a coat, they are, “lived as deeply committed personal projects” (Clegg, 2008: p.336) and so if we embrace them we bring a unique insight into the process and have a genuine investment to make the research meaningful and worthy of notice.

Hi Nathan, I don’t have twitter… I know – somehow I still haven’t had time to get to grips with it. I do have Facebook, feel free to find me there. I also started to follow your blog so that I am notified about your content. I agree with what you said here and in your posts, and I like the topic of your blog. This is definitely something that we should pay more attention to when doing research. It would be interesting to talk some time and exchange opinions, as our research interests seem very closely related. Have a good day !

How to establish the validity and reliability of qualitative research?

The validity and reliability of qualitative research represent the key aspects of the quality of research. When handled meticulously, the reliability and validity parameters help differentiate between good and bad research. They also assure readers that the findings of the study are credible and trustworthy. This aspect becomes particularly vital in case studies involving primary data analysis. Here the researcher’s subjectivity can highly influence the interpretation of the data. This article clarifies how to establish the validity and reliability of qualitative research using various tools and techniques.

Establishing the validity of qualitative data

Qualitative data is as important as quantitative data , as it also helps in establishing key research points. However, since it cannot be quantified, the question of its correctness is critical. Validity relates to the appropriateness of any research value, tools and techniques, and processes, including data collection and validation (Mohamad et al., 2015). Validity also establishes the soundness of the methodology, sampling process, data analysis process, and conclusion of the study (Golafshani, 2003).

The main aspect that needs to be ensured is that the research philosophies fall in line with the research. To maintain the validity of the research, there is a need to understand the underlying needs of the research, the overarching process guidelines and the societal rules of ethical research. While establishing validity, there needs to be a consensus among the individual and the society on how to establish the correctness and accuracy of the research. If this aspect is kept in mind, the tools and techniques used are bound to be accepted by wider audiences. The main point to remember, thus, is to choose the tools wisely, which will establish how to correct the data of the research.

One of the major techniques that can be used for establishing the validity of qualitative data includes choosing a skilled moderator. Employing a moderator will help overcome personal bias. Thus, the researcher or the organisation can employ moderators to ensure that the data is genuine and is not influenced by “what the researcher wants to see or hear”.

Another way to promote the validity of research is by employing the strategy of triangulation. This basically involves that the research will be conducted from different or multiple perspectives. For example, this can take the form of using several moderators, in different locations or it could be multiple individuals who are analysing the same data. Basically, any technique through which the researcher can analyse the data from different angles.

Furthermore, the validity of qualitative research can also be established using a technique known as respondent validation. This basically involves testing the initial results with the participants in order to see if the results still ring true.

Establishing the reliability of qualitative data

Quantitative research includes reliability measures where the researcher must prove that the process and the results have replicable outcomes. On the other hand, reliability in qualitative research includes very diverse paradigms, where the aspect itself is epistemologically counter-intuitive along with having a very difficult definition (Russell, 2014). Thus, what needs to be done to maintain and establish reliability in qualitative research is to be consistent.

Reliability tests for qualitative research can be established by techniques like:

• refutational analysis,
• use of comprehensive data,
• constant testing and comparison of data,
• use of tables to record data,
• as well as the use of inclusive deviant cases.

These techniques can help support the data sourcing, data validation and data presentation process of the research, as well as support the claim of reliability in terms of form and context.

Triangulation is another aspect which becomes very important in establishing reliability in the research. Also, as an additional note, it is very important for qualitative research to include a reference to a quantitative aspect. The use of a simple quantitative aspect in otherwise completely qualitative research creates a very positive attitude towards the overall concept of the research and helps to establish reliability in a much easier form. Also, the inclusion of at least two reliability tests, as per the type of research outcomes of a research, is a dependable way of establishing that the research process and results are reliable.

Dos of validity and reliability of qualitative data

• While establishing validity and reliability, it is very important to decide the tools and techniques to be used in the research, before conducting the actual research. This helps in establishing the parameters for obtaining reliable and valid results from the beginning and does not impair the results of the research at the end of the process.
• To conduct efficient reliability and validity measures, an effective assessment of the literature must be done to understand which processes will work. Irrelevant approaches that compromise the reliability and validity of research should not be pursued.
• Reliability and validity processes should be conducted in tandem with the research processes being carried out to confirm the research objective, which provides another additional layer of authenticity to the research work.

Don’ts’ of validity and reliability of qualitative data

• Reliability and validity should not be taken as an extra element of the research. If they do not add value to research, they result in insecurity regarding the accuracy of the results.
• To be able to establish reliability and validity, researchers should not include excessive measures to support their research claims. This may add to unauthenticated results, as adding too many measures will overcomplicate the research.

An example statement of validity

The validity of this research was established using two measures, the data blinding and the inclusion of different sampling groups in the plan. The research included an assessment of the knowledge of traditional cuisine among the present population of a city. Where the sample was divided into two groups- to reduce biases. The sample included young adults, who have been mostly raised in an urban environment, along with middle-aged and elderly population who have had a partial upbringing in the rural area of India. The inclusion of greater diversity and a large number of sample respondents led the research to reduce its biasness towards only one type of outcome, creating a base for valid results. The other technique used was to restrict the amount of information shared with the respondents to make sure that the research was not biased with preconceived notions of the respondents. These steps helped to establish the validity of the results gained, proving the accurateness of the qualitative research. Further, the validity of the questionnaire was established using a panel of experts that reviewed the questionnaire. And hence the statements that did not go well with the subject of the study were removed.

An example statement of reliability

In terms of establishing reliability, the researcher conducted two processes. The first included recording the data in a table to provide an overall assessment of the data collection process and the updated assessment of the results, as they come. The use of the table for recording data provided the researcher with a chance to quickly interpret the results as per the record of every individual respondent and realize the progress of the research.

The table also helped in the concise construction of the conclusion of the research parameters. The reliability was also assessed through data triangulation. Among the various model of data triangulation such as methodological triangulation, data triangulation, investigator triangulation and theoretical triangulation. The study adopted theoretical triangulation, wherein other research works in the same arena were analysed and presented as a literature review to support the results claims of the data collection and analysis process. Moreover, the reliability measures relating to the triangulation of data provided an extensive understanding of the research objectives, which provided an additional layer of reliable stamping to the research.

• Golafshani, N. (2003) ‘Understanding Reliability and Validity in Qualitative Research’, The Qualitative Report , 8(4), pp. 597–607.
• Mohamad, M. M. et al. (2015) ‘Measuring the Validity and Reliability of Research Instruments’, Procedia – Social and Behavioral Sciences . Elsevier, 204, pp. 164–171. doi: 10.1016/j.sbspro.2015.08.129.
• Priya Chetty

I am a management graduate with specialisation in Marketing and Finance. I have over 12 years' experience in research and analysis. This includes fundamental and applied research in the domains of management and social sciences. I am well versed with academic research principles. Over the years i have developed a mastery in different types of data analysis on different applications like SPSS, Amos, and NVIVO. My expertise lies in inferring the findings and creating actionable strategies based on them. 

Over the past decade I have also built a profile as a researcher on Project Guru's Knowledge Tank division. I have penned over 200 articles that have earned me 400+ citations so far. My Google Scholar profile can be accessed here . 

I now consult university faculty through Faculty Development Programs (FDPs) on the latest developments in the field of research. I also guide individual researchers on how they can commercialise their inventions or research findings. Other developments im actively involved in at Project Guru include strengthening the "Publish" division as a bridge between industry and academia by bringing together experienced research persons, learners, and practitioners to collaboratively work on a common goal. 

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AcademiaOS: Automating Grounded Theory Development in Qualitative Research with Large Language Models

AcademiaOS is a first attempt to automate grounded theory development in qualitative research with large language models. Using recent large language models’ language understanding, generation, and reasoning capabilities, AcademiaOS codes curated qualitative raw data such as interview transcripts and develops themes and dimensions to further develop a grounded theoretical model, affording novel insights. A user study (n=19) suggests that the system finds acceptance in the academic community and exhibits the potential to augment humans in qualitative research. AcademiaOS has been made open-source for others to build upon and adapt to their use cases.

Keywords  “Large Language Models”, “Software Design”, “Information Retrieval”, “Research Automation”, “Theory Development”

1 Introduction

Qualitative researchers are confronted with tedious and costly language tasks when making sense of interview or focus group transcriptions, reports, policies, field notes from observational research, diaries, case studies, and other sources. Transcribing and coding a typical interview takes several hours ( Pope et al., ( 2000 ) ; Barrett and Twycross, ( 2018 ) p. 63). A transcript or set of codes is often just the beginning, as the researcher tries to make sense of the raw data and transform it into something useful to gain insight and knowledge and develop theories that may describe patterns and phenomena.

Qualitative data may be collected through various tools, while purposive sampling directs the research ( Chun Tie et al., ( 2019 ) , p. 3). Interviews are a direct and easy way of exploring phenomena, from open, unstructured formats that allow researchers to dive deeper into studied participants’ narratives and experiences to more rigidly structured formats, essentially questionnaires, that provide more control to the researcher. Interviews are usually recorded and transcribed for further processing ( Barrett and Twycross, ( 2018 ) p. 63). A more relaxed way of studying subjects’ views is focus groups, where the researcher speaks to a small group of study participants to guide the conversation and debate about a given topic and to create rich data in the form of notes or transcription ( Kitzinger, ( 1995 ) p. 301). Another qualitative data collection methodology, prevalent primarily in ethnography, is field observation, where the researcher observes study subjects in a natural setting ( Emerson et al., ( 2001 ) , p. 352). ( Emerson et al., ( 2001 ) , pp. 356 - 358) describe generating data from ethnographic qualitative research as a process, starting with a researcher’s mental notes from immersing in the situation, followed by writing down jotted notes, to use as support for later generated, more extensive field notes. Ethnographic research also often builds on diaries and written notes, while organizational research might build on case studies, reports, policies, and other secondary sources. The bandwidth of qualitative data sources is broad, but most are textual or have established ways to transform them into textual data (i.e., transcribing).

Researchers often follow established coding practices to manage large volumes of unstructured textual sources. Coding often involves systematically generating a codebook ( Weston et al., ( 2001 ) ) to code transcriptions. Another popular approach is the "Gioia method" ( Gioia et al., ( 2013 ) ), where the researcher extracts emerging patterns and concepts from the source documents directly to aggregate and interpret them only in the following steps. These developed codes can then be further used for qualitative data analysis and theory development. The notion of developing theoretical models from data is called grounded theory development ( Chun Tie et al., ( 2019 ) ).

Gioia et al., ( 2013 ) define a transparent process for analyzing qualitative data to develop theoretical models from interviews. Their process is meant to move a researcher from raw qualitative data to increasingly abstract conceptual categories, starting with initial coding, where a researcher codes and underlines relevant concepts in the data, resulting in an extensive list of first-order concepts still stated in the language of the source document. They then use these first-order concepts to produce a list of more abstract second-order themes that try to encapsulate the idea of the first-order codes in more academic language. Lastly, they aggregate the second-order themes into even more abstract "aggregate dimensions." These are then used as the basis for developing theories. Gioia et al., ( 2013 ) mention the importance of understanding the dynamic relationships between these concepts but have yet to offer a concrete methodology to get to these. They assume that by following this "Gioia method," a researcher has familiarized themselves well enough with the underlying literature to understand these relationships.

Another approach to grounded theory development is the Eisenhardt, ( 1989 ) method, which focuses on constructing models from case studies. This approach starts with a within-case analysis to gain familiarity with the data and generate a preliminary theory. From there starts a highly iterative process, which Eisenhardt, ( 1989 ) calls "Shaping Hypotheses," iteratively comparing data and developed constructs and verifying that the emergent relationships between the developed constructs fit with the evidence in the data. They treat case studies as replications of experiments, either strengthening or weakening a hypothesis.

Structured approaches to qualitative analysis and theory development, such as those introduced by Gioia et al., ( 2013 ) and Eisenhardt, ( 1989 ) , were criticized for being used as mere "templates," often oversimplifying or ignoring the underlying methodological assumptions to fit in cases where those assumptions do not hold ( Gioia et al., ( 2022 ) , p. 241) or that using these templates limit methodological innovation in qualitative research ( Gioia et al., ( 2022 ) , p. 242). Even Gioia emphasizes that the methodology named after him should not be understood as a "cookbook" but as a tool adapted to each research context. He highlights the importance of researchers accounting for informants’ interpretations and constructions of reality in their reporting and not merely applying the methodology as a rigid framework ( Gioia et al., ( 2022 ) , p. 235). Building on the existing literature, it is clear that structured approaches like those of Gioia et al., ( 2013 ) and Eisenhardt, ( 1989 ) provide a framework for grounded theory development. However, they also highlight the necessity for flexibility and adaptation in qualitative research. Qualitative research tasks, both data collection and analysis, are time-consuming and expensive and limit the empirical data that a single research team can study. Kindsiko and Poltimäe, ( 2019 ) support this notion by outlining that sample size in empirical research depends on funding and the size of the research team. However, Bowen, ( 2008 ) outlines how the sample size influences research validity and suggests limiting sample size by saturation, i.e., stopping the addition of more interviews, case studies, or other samples when those do not add significant new information. Now, how do we add research rigor by increasing sample size while keeping manual effort low? The answer might be found in computational automation.

In quantitative research, automating data preparation and theory development is an actively researched topic under names such as "data mining" or "machine learning," where computer programs learn from observations to develop mathematical models, allowing them to estimate future instances in a positivist paradigm. Qualitative research problems, however, come with less structured or codifiable information and rely on the knowledge and interpretation of the researcher. At the same time, with the rise of large language models (LLMs), we have access to a technology platform, shifting the paradigm of computational understanding and reasoning on textual data to a level close to humans, paired with extensive general knowledge. This new technology platform presents an opportunity to simulate well-defined research processes at scale. Tasks such as coding 100 interview transcripts are time-consuming for a single researcher. The hypothesis is that with the proper setup, LLMs can process all transcripts in parallel within a few minutes. A researcher in the field of organization theory might consider using two or three case studies to develop a theoretical model. When finding empirical evidence in case studies is an automated, parallelizable process, the barrier to using 20 - 30 different case studies is drastically minimized, offering opportunities for more statistically relevant qualitative studies.

Thus, the potential for automating parts of the qualitative research process with LLMs is ripe for exploration. This paper explores the following research question: "How can a foundational open-source platform be effectively designed and implemented to leverage large language models to automate grounded theory development?". To that end, AcademiaOS is proposed and implemented as an open-source platform for automating or augmenting grounded theory development tasks such as coding, dimension aggregation, and theory development. AcademiaOS offers the scientific community a new methodology for conducting qualitative research that is transparent, accessible, and extensible (through its open-source character) and allows for broader evidence through the cost-effectiveness of parallelizing analysis of multiple qualitative sources simultaneously. This system could have a far-reaching impact in the social sciences, particularly in organization theory, but also in other disciplines where qualitative data is relevant.

The rest of this paper describes AcademiaOS and the technology behind it in more detail. In addition, an exploratory survey has been conducted to learn how students, professionals, and researchers use AcademiaOS and to guide future development. The platform is made open-source on GitHub under the MIT license, and a running instance is hosted on academia-os.org.

2 Earlier Work

There have been attempts to automate qualitative analysis. Berente et al., ( 2019 ) make an effort to develop a theoretical process for computationally intensive grounded theory development, proposing a theoretical, computational methodology to automate the following four steps for grounded theory development: (1) Sampling and data collection, (2) synchronic analysis, (3) lexical framing and (4) diachronic analysis using artificial-intelligence-based tools. They describe the computational process as evolving around a predefined but dynamic lexicon rather than synchronously "coding" emerging concepts. They propose to mine concepts using taxonomies.

Marathe and Toyama, ( 2018 ) discuss the possibility of automating the coding of interviews based on predefined human-annotated codebooks. Similar approaches have been implemented by Lennon et al., ( 2021 ) , according to their own analyses resulting in human-level accuracy. Rietz and Maedche, ( 2021 ) propose a semi-automated supervised machine-learning solution that learns coding rules from human annotators and applies them to a broader dataset. Still, defining rules or annotating a codebook beforehand assumes the researcher already has assumptions about the patterns in the analyzed qualitative literature. Furthermore, the academic papers mentioned above used old machine-learning techniques and ignored LLMs, while some commercial platforms are slowly adopting them. The commercial platform ATLAS.ti, ( 2023 ) announced test versions of their automated coding features in early 2023, splitting qualitative literature into paragraphs and coding them one by one using OpenAI’s LLMs. Other commercial platforms like elicit.org emerged, primarily using LLMs to automate literature review processes. Still, more is needed to know about how researchers use these new AI features on those platforms. Also, these applications automate only small portions of qualitative research processes and have yet to dive into the field of automating grounded theory development. This leads to the question of whether grounded theory development can be automated with LLMs.

3 Large Language Models

Large language models (LLMs) are a new technology platform based on transformer models, pre-trained on large datasets using self-supervision, encoding general and transferable knowledge in billions of parameters Roberts et al., ( 2020 ) . Unexpected capabilities such as reasoning and interaction emerged from learning "simple" next-token prediction at scale. These pre-trained foundation models are often fine-tuned to follow instructions ( Ouyang et al., ( 2022 ) ), to return structured output, or to be conversational (as seen with ChatGPT). While older models such as BERT would generally be regarded as LLM, in this paper, the term is exclusively used for models with a performance similar to or better than the GPT-3 base models. LLMs have been popularized and mass-adopted with the recent release of ChatGPT at the end of 2022. They have been applied to automate processes throughout industries ( Wulf and Meierhofer, ( 2023 ) , p. 4).

An inference interaction with an LLM typically consists of a natural language prompt (input) and completion (response). In OpenAI’s conversational models used throughout this paper (GPT-3.5 and newer), the inference prompt may consist of multiple "messages": generic system messages that set the frame and a history of user and assistant messages (see Appendices 1-11 for examples).

Without the need for fine-tuning, LLMs exhibit the ability to learn and generalize from information in the inference prompt ( Brown et al., ( 2020 ) ). One-shot or few-shot learning refers to passing samples in the prompt, whereas zero-shot learning refers to providing no samples but having the model complete an explicit instruction. This type of inference contrasts with common task-specific fine-tuning and is commonly called "in-context learning" ( Dong et al., ( 2022 ) ).

Although being pre-trained, LLMs store tremendous amounts of implicit knowledge in their parameters, that knowledge remains limited in its depth and currency and requires costly training to update. Therefore, augmenting the LLM inference with an information retrieval system, an architecture commonly called "Retrieval-Augmented Generation" (RAG), has been shown to reduce hallucination and improve factualness and interpretability ( Lewis et al., ( 2020 ) ). As general models, LLMs can measure semantic similarity between two text strings. Their similarity can be calculated computationally efficiently using cosine similarity on their LLM-internal vector representation. RAG uses this straightforward way of information retrieval to concatenate retrieved, relevant text for in-context learning ( Lewis et al., ( 2020 ) ). Augmenting LLM inference with information retrieval from raw input documents may help automate theory development.

To explore the current possibilities of automating grounded theory development with LLMs, I propose, develop, and test a piece of software that orchestrates LLM inferences with human supervision. AcademiaOS is a platform for qualitative researchers to automate their grounded theory development. The platform guides the user through a predefined process, and while most of the data analysis and theory development parts are automated, the user owns the supervision and control. To ensure user privacy and high maintainability, for potential future developers and open-source contributors to not worry about frontend-backend interaction, most of the computation is performed locally in the browser, consuming external APIs such as the OpenAI developer platform for LLM inference directly.

4.1 Data Curation

Qualitative research is a broad field working with various qualitative information sources, from interviews over policies to organizational case studies. To ensure versatility, the platform affords users to curate their source documents in multiple ways. If the user already has a corpus of curated sources, they can directly upload one or many source documents. The system extracts textual information for further use directly from documents in PDF, JSON, or TXT format. To support scanned PDF documents, the system pre-processes problematic PDF files by applying optical character recognition via Adobe PDF Services. Every file is transformed into a text string and handled as an individual source document.

A second option helps the user curate relevant academic literature from free-text search. The search term is used to retrieve academic literature from the SemanticScholar search engine that indexes more than 200 million academic papers. To interface with the SemanticScholar REST API, SemanticScholarJS was introduced as an open-source TypeScript library and published under the MIT license. SemanticScholar returns at most 100 papers, which are then vector-embedded using OpenAI’s text embeddings and re-ranked according to their abstracts’ semantic similarity to the initial search query and correspondingly re-ranked. Any papers without an abstract are filtered out, resulting in a few documents relevant to the user’s search query.

4.2 Data Coding

Assuming that the user might have an idea about their research’s direction, the platform affords the user to add a free-text remark or choose from an LLM-generated research question, which is used for in-context learning in the following inference prompts. This interactivity respects the expertise of the researcher while providing AI-assisted efficiency.

Following the Gioia method ( Gioia et al., ( 2013 ) ), the system initializes a three-step data analysis process. Implementing the Gioia method aligns the platform with established qualitative research practices, lending academic rigor to the process. This structured approach facilitates the systematic transformation of raw data into initial codes, second-order themes, and aggregate dimensions, used later for grounded theory development.

First, the system creates initial codes from the raw documents. Initial codes are short text strings describing emergent themes, concepts, and patterns in the language of the raw document. To that end, every input document is split into chunks of max. 10,000 characters to overcome context-size limitations and process arbitrarily large input. A chunk overlap of 50 characters was introduced to mitigate losing information in the intersection of two chunks. Every chunk is then inserted as context in the LLM inference prompt with a natural language instruction to return a JSON array. The output is then parsed and stored in a global array of initial codes.

Next, the initial codes are used to develop second-order themes. Second-order themes aggregate and interpret semantically similar initial codes and are expressed in a more abstract, academically correct language. To that end, the initial codes are split into reasonably-sized chunks and used in-context with a natural language instruction to return a JSON object of second-order themes as keys and arrays of related initial codes as values. This JSON object is again parsed and stored as a global object.

Finally, the second-order themes are used to develop aggregate dimensions. These aggregate dimensions are even more abstract, quantifiable concepts. Since second-order themes already limit the number of codes to a manageable count, further splitting into chunks is not necessary and the second-order themes are directly used in-context together with a natural language instruction to return a JSON object of aggregate dimensions as keys and arrays of related second-order themes as values. This JSON object is again parsed and stored as a global object for further use.

4.3 Theory Development

In the next step, the developed initial codes, second-order themes, and aggregate dimensions are used to develop grounded theories. ( Wacker, ( 1998 ) , pp. 364-367) found that to be considered ’good,’ a theory must exhibit the following virtues: uniqueness, conservatism, generalizability, fecundity, simplicity, internal consistency, empirical riskiness, and abstraction. To ensure some of those virtues, such as uniqueness, conservatism, and fecundity, the aggregate dimensions are used for the machine to "brainstorm" applicable theories that exist in the literature. As this brainstormed output is not intended to return accurate information but merely to scrutinize the obvious and increase serendipity, this step happens in a single inference prompt without information retrieval.

Next, to ground any interrelationships of concepts used in the final model in the data, the LLM first creates concept tuples, then uses these concept tuples for retrieval-augmented generation, searching for relevant paragraphs (chunks of max. 1000 characters, 50-character chunk-overlap) in the raw documents using cosine similarity search on their OpenAI vector embeddings and the concept tuples. Next, given the four most similar paragraphs, the LLM is prompted to generate a one-sentence summary about the relationship of a given concept tuple. This summary might include information about any evidence for correlation or causation, directedness, strength, mediatedness, conditional interaction, dynamics, or feedback loops.

Previously developed applicable theories, interrelationships, aggregate dimensions, second-order codes, and the user’s free-hand remarks are then fed into an LLM prompt, instructed to describe a comprehensive, novel theoretical model.

Next, another one-shot LLM inference transforms a given model description into a MermaidJS visualization. MermaidJS is a scripting language for creating flowcharts and similar graphics. Converting model descriptions into visualizations using MermaidJS not only aids in understanding complex theories but also provides a straightforward, communicative tool for sharing findings. First-order and second-order codes provided in the prompt are used to visualize evidence and support the notion of groundedness.

Finally, a last zero-shot LLM inference criticizes the developed model for novelty, conciseness, clarity, and theoretical insight. The user can then decide whether to start another modeling iteration with the critique fed into the modeling prompt, resulting in a feedback loop with human oversight.

4.4 Notes on the Technical Implementation

The system was initially considered a conglomerate of separate frontend and backend services. While this design decision would have come with the advantage of controlling any requests that go to the OpenAI developer platform, it would have slowed down development, maintenance, and hosting for the course of the project and beyond for future developers and open-source contributors. Thus, the decision was to run the application entirely in the browser. TypeScript as a primary programming language was chosen for its strong typing compared to vanilla JavaScript and, thus, higher maintainability and more robust code quality. Additionally, TypeScript’s extensive web development community offers abundant resources, libraries, and support, which is crucial for both the initial development and future iterations of the platform. Moreover, the decision to run the entire application in the browser, using ReactJS as a frontend framework and AntDesign as a component library, was driven by the need for an accessible, responsive, user-friendly interface. The ReactJS framework, through its component-based architecture, allows for efficient rendering and state management, which is essential in a dynamic environment where the state can frequently change through user interactions. The browser-based approach also aligns with the privacy concerns inherent in qualitative research. AcademiaOS minimizes the risks associated with data transfer and storage on remote servers by processing data locally on the user’s machine.

All results reported in this paper are based on OpenAI’s gpt-4-1106-preview model. To increase traceability and transparency, AcademiaOS affords users to easily set up a proxy to Helicone, an open-source monitoring platform specifically for LLM applications. Through Helicone, users maintain complete control and insight over their OpenAI API usage and the corresponding costs. In addition, Helicone logs all LLM inference requests. Monitoring is convenient during development to see where the LLM goes wrong and tune prompts. In addition, the platform affords saving and restoring developed codes and models for further development using JSON serialization and deserialization. This comes in especially handy during the development and testing phases to mitigate performing redundant steps and to facilitate a smooth development and testing workflow. The serialized data can also easily be shared between multiple users.

The field of large language models is actively researched and worked on. During this project, OpenAI and other vendors released new models with new capabilities. OpenAI’s most recent model, GPT-4-turbo, allows for larger context windows in a single inference prompt but also comes with an optional "JSON-mode" parameter, which ensures that the inference response is in a valid, parsable JSON format. Since many of AcademiaOS’ inference prompts expect data in a structured format (e.g., arrays of values), enforcing the JSON mode significantly increased the application’s robustness. Still, as LLM inference can occasionally yield invalid or unexpected results, rerunning the system on the same input often results in varied outputs, thereby increasing the probability of obtaining a correct response with subsequent attempts.

4.5 Notes on Epistemology and Ontology

Grounded theory development, as practiced by Gioia et al., ( 2013 ) or Eisenhardt, ( 1989 ) , generally follows a constructivist epistemology ( Charmaz and Thornberg, ( 2021 ) ). Constructivism in grounded theory proposes that knowledge is actively constructed through the interaction between the researcher and the researched subject. It argues for a subjective nature of reality and knowledge generation where the researcher’s perceptions shape the interpretative process ( Levers, ( 2013 ) , pp. 3-4). Further, grounded theory assumes that multiple realities of subjective constructions of individuals exist in a fluid nature of the reality of social interactions. As grounded theory development generates theories from observations of empirical data without previously established theories, grounded theory development is an inherently inductive approach.

Automating grounded theory development with large language models challenges epistemological and ontological assumptions. Epistemologically, Introducing LLMs adds an element of objectivity to the traditionally subjective approach. They process qualitative data in a way independent of a researcher’s biases. However, the researcher keeps the responsibility of interpreting and contextualizing the LLM’s output, preserving the constructivist nature of the process. At the same time, LLMs ontologically add a digital reality to the existing subjective realities. This digital reality offers an algorithmically created perspective on the processed qualitative data that can support or challenge the researcher’s interpretation.

4.6 Notes on Temperature, Robustness and Serendipity

To get an intuition on how model temperature changes the system’s output, outputs of gradual uniform temperature increase are recorded and qualitatively analyzed. To accommodate deterministic reproducibility, a seed parameter (324234) has been utilized with the employed gpt-4-1106-preview model for grounded theory development from a single podcast transcript from the How I Built This podcast.

Appendices 61 - 65 show that the three attempts with temperatures of 0, 0.5, and 1 resulted in consistent and robust outputs. In contrast, the attempt with a temperature of 1.5 became unstable, resulting in non-sensical text in the latter part of the model description and an invalid MermaidJS visualization. The attempts with temperatures of 0 and 0.5 resulted in internally consistent and integrated models. The attempt with a temperature of 1 produced a theoretical model with less integrity, meaning not or only loosely connected aspects and thus missing a consistent narrative. An increased temperature could, in this case, not be observed to lead to a higher degree of serendipity, that is, the quality of pleasant yet unexpected results. This might highlight the still objective, reproducible, and deterministic nature of grounded theory development and that randomness in the data analysis does not necessarily lead to better results. However, while providing a sense of intuition, the sample size of this experiment (n=1) might need to be larger to generalize.

An exploratory, qualitative survey has been conducted to assess user interaction with AcademiaOS and to guide future development. Researchers, professionals, and students with a background in qualitative research were chosen via convenience sampling for this evaluation. The participants (n=19), who were deliberately selected for their relevance to the platform’s intended user base and thus thought to yield the most informative and relevant data for assessing AcademiaOS, were provided with a link to a survey hosted on the Qualtrics platform.

The study’s methodology involved guiding participants through the AcademiaOS platform. This was achieved by direct interaction with the platform, through a video demonstration, or a combination, depending on the participant’s preference. Eight respondents reported testing the platform with a corpus of university policies on ChatGPT use that was provided alongside the survey. Other participants reported using datasets such as interview transcripts or academic papers from a basic literature review. Only 4 participants based their answers solely on the video demonstration (see Appendix 17). Following their experience with the platform, participants were asked to share their insights and opinions. The survey primarily consisted of open-ended questions, allowing for detailed and nuanced feedback.

The feedback encompasses various aspects of the platform, including user interface design, assessing the user’s initial interaction, feature exploration, and usability, but also the effectiveness of the system’s output and perceived impact on the user’s immediate research environment. Additional exploratory questions were asked about their current qualitative research processes and context.

Partial survey responses have been recorded. However, the 28 responses have been filtered and cleaned only to include 19 finished responses for further analysis. The other responses were very sparse, and it could not be trusted that they spent sufficient time familiarizing themselves with the platform. Another participant stated, "I don’t generally work with qualitative data," whose response was also filtered due to not belonging to the target population. Of the 19 residual responses, 14 were students, and the remaining participants were professionals and academic researchers (see Appendix 12). This might skew the responses to students’ perspectives. Peterson and Merunka, ( 2014 ) argue that convenience samples of students are problematic as they do not generalize to the general consumer population. However, they also explain that students might be an appropriate sample if they represent a population of interest. As the sampled students have at least some levels of experience in qualitative research, they sufficiently represent the studied group.

6.1 Status Quo

Participants were asked to reflect on their current qualitative research methodology. They reported using primary sources such as interviews, observations, surveys, and panels and secondary data sources such as case studies, reports, meta-analyses, historical data, reports, and expert opinions (Appendix 13).

Asked about their research methods, they reported data collection tools such as voice recordings, Microsoft Forms, or Google Forms for online surveys, data analysis methods such as thematic analysis, content analysis, literature review and case law analysis, empirical research, and knowledge discovery from existing datasets. One respondent mentioned testing qualitative data with quantitative research tools, while other respondents mentioned statistics and natural language processing techniques (such as named-entity recognition and topic modeling), hinting that there are tools to "quantify" qualitative data and that in research settings, the line between qualitative and quantitative research is often blurred. Interestingly, one respondent even mentioned using language models and the previously mentioned RAG architecture for their qualitative research (see Appendix 14). For systematic theory development, respondents mentioned grounded theory methodology, conceptual framework development, mind maps, and flowcharts, while some respondents see themselves merely as model users and less as creators or are unsure about their methodologies (see Appendix 15).

Inquiring about the AI tools they utilize in their research (see Appendix 16), some participants reported limited to no use of AI tools, not having used them or only using them rarely. Many respondents mentioned using ChatGPT with varying degrees of reliance and purposes such as brainstorming, idea generation, speeding up coding processes, or narrowing down searches. One respondent even noted the need for extensive fact-checking due to high error rates with ChatGPT. Other participants mentioned more specific AI and machine learning platforms and libraries: Fairseq, HuggingFace, Pytorch, OpenAI, AzureML, TensorFlow, scikit-learn, RapidMiner, KNIME, NLTK, SpaCy, Gensim and BERT. These reported AI tools underline the perceived importance of quantifying qualitative data through natural language processing.

6.2 Initial Interaction and Feature Exploration

The second part of the survey aimed to learn how participants perceived the platform’s initial interaction and feature exploration. The participants generally rated the platform as "somewhat easy" to learn, with some variance (see Appendix 18). Participants mentioned that the video helped them sufficiently understand how the system works. One participant suggested adding popups to guide the user, and another suggested that the affordances were not entirely clear: "[…] you should mention that the user can also upload non-pdf files […]" (appendix 19). Overall, participants reported finding it easy to find and use the option to search for academic papers (see Appendix 20) and to upload their own sources (see Appendix 21). However, they had more issues finding and using the options to "code" their sources (see Appendix 22) and to develop the theory (see Appendix 23). In general, participants found the user interface to be "somewhat" to "strongly" intuitive and user-friendly, with only one outlier who "strongly disagrees" (see Appendix 24).

As for difficulties navigating the platform (see Appendix 25), participants reported they expected more descriptive prompts and guidance: "I couldn’t find the coding help function," "the insert key thing was rather hidden," and "about the code part, didn’t really understand how to use it." One user noted that some buttons needed to be visible on mobile and experienced long loading times. Some improvement suggestions (see appendix 26) included "make it […] less technical", to add "filters for finding papers", to "add examples" and an "instructions page" to better guide users. Furthermore, better error handling with retry policies was suggested, together with the idea to "combine different searches or datasets in one coding tab." These feedback points are actionable and specific enough to guide the platform’s development.

6.3 Usability, Effectiveness, and User Satisfaction

Study participants seem to be "somewhat" to "extremely" satisfied with the coding process, with only one "extremely dissatisfied" outlier (see Appendix 27). Even more promisingly, participants think the system would create better codes than when they code manually (see Appendix 28). Only one participant thinks the system-generated output codes would be "somewhat worse" than their own. This is to be taken with a grain of salt as most respondents will have answered that only intuitively, without any actual comparison. Challenges in coding lie in multilingual documents (see Appendix 29). One respondent noted that when the source documents were not purposefully selected and had too much variation in their content, the machine could not generate meaningful codes: "[…] it was not able to catch that and make codes relevant to the research question". This seems to be an issue related to the paper retrieval and less so with the coding process. The average participant generally rated AcademiaOS as "very effective" in augmenting the coding process on qualitative data (see Appendix 30).

Regarding the theory development process, participants reported being somewhat to extremely satisfied (see Appendix 31). The respondents also reported that the system-produced output was perceived as better than potential manual development, with only three respondents each reporting that the system would do "somewhat worse" or "about the same" than when done manually (see Appendix 32). When asked about instances where the produced theory did not meet their expectations (see Appendix 33), one participant mentioned, "It was kind of complex, and I often didn’t really get where it was going with the theory.", indicating that they would have preferred a more straightforward model, while another one wrote "[…] theory seems to cover the content of the sources pretty generally but is not really about the research question I posed." indicating that there was a mismatch between the free-hand remarks and the model output. Despite these instances, respondents perceived the platform to be moderately effective to extremely effective, with a skew to the latter (see Appendix 34).

6.4 Participant-Reported Impact

When asked about whether AcademiaOS would impact their qualitative research process, most participants answered with "probably yes" to "definitely yes" (Appendix 35), mentioning reasons such as speeding up their research processes ("Much faster," "It will speed up the research," "It would make the coding and theory production much faster"), to act as a tool for inspiration ("Multiple draft iterations to inspire/shortcut the manual process," "I would use it to rapidly prototype theories […]", "[…] compare and maybe find things I missed before."), and as general research support ("It will urge many scientists to increase their throughput […] potential to mitigate the information retrieval and retention of humans", "[…] it would help me to link concepts easier", "Making it easier for me to do my research, especially in hours where I cannot concentrate that much") (see Appendix 36). When asked about how AcademiaOS would change the output of their academic research (see Appendix 37), participants noted "more varied and tested theories, better theories through more input ideas, more feedback," "use the models created as part of a clear visualization" and "[…] to be more reproducible". All respondents agree that AcademiaOS helps them ground their research on more data (see Appendix 41) and that it helps them conduct research faster (see Appendix 42). At the same time, most participants also agree that it helps them find more impactful and unexpected results (see Appendix 43).

However, participants also noted certain limitations such as ethical considerations ("It feels like cheating to me… I would not feel comfortable using it.", "It would make me feel too replaceable"), doubts about quality ("I also don’t quite trust the quality (mostly from experience with ChatGPT), I would always want to check it for what it misses.", "[…] it might fail to generate very latent codes") and doubts of potential (reinforced) biases ("[…] it may cause people to not properly look at the nuances of their interviews or papers", "Currently it is using a single AI model, which makes it difficult to rule out its biases.") (see Appendix 38). In addition, some participants fear being replaced by AI (see Appendix 40).

Respondents generally feel excited about the new possibilities that AcademiaOS provides (see Appendix 39). Most participants even stated they would continue using the system for their research (see Appendix 45). To an even greater extent, they would recommend AcademiaOS to their friends (see Appendix 46), indicating that they might know potential users who fit the intended target group better. Participants generally also feel excited about future versions of AcademiaOS (see Appendix 47), pointing out that despite the discussed doubts and limitations, the technology is promising for their research field.

7 Example Applications

This section presents a series of illustrative use-cases for AcademiaOS.

7.1 Modeling University Policies on ChatGPT Use

A corpus of 46 scraped public university policies on ChatGPT and LLMs were uploaded to AcademiaOS to build a theoretical model to answer the research question: "How can educational institutions adapt their academic integrity policies to effectively address the challenges posed by AI writing tools like ChatGPT?". From the 46 papers, AcademiaOS produced 1019 first-order codes, which in the next step were reduced and interpreted to 67 second-order themes and, in turn, reduced to 7 aggregate dimensions (see Appendix 49). After brainstorming applicable existing theories (see Appendix 50), the system then investigated the interrelationships between concept tuples (see Appendix 51) to generate the first iteration of a theoretical model (see Appendix 52). The system then critiqued the model (see Appendix 53) to produce the second iteration of the theoretical model (see Appendix 54). Note that the user is in control of the number of iterations.

The result was the Integrated Education Adaptation Framework for AI Readiness (IEAFAR) as a comprehensive model for educational adaptation to artificial intelligence. Its six dimensions, AI Integration and Educational Dynamics (AI-ED), Adaptive Compliance and Policy Evolution (ACPE), Academic and Instructional Resilience (AIR), AI-Driven Educational Innovation (ADEI), Ethical, Legal, and Societal Harmony (ELSH), and Collaborative Stakeholder Engagement (CSE) emphasize the importance of holistic, iterative processes in adapting to AI, with interconnected relationships among these dimensions. IEAFAR promotes stakeholder collaboration, focusing on continuous learning, flexible adaptation, and balancing innovation and ethical, legal, and societal considerations.

7.2 Exploring the “How I Built This” Transcript Dataset

The "How I Built This" podcast is a format interviewing innovators about the things (such as products or companies) they have created. The hypothesis is that AcademiaOS can explore the rich and extensive interview dataset to detect interesting patterns in how entrepreneurs start projects. The system coded the transcripts of 4 podcast episodes to 499 first-order codes, 35 second-order themes, and 7 aggregate dimensions (see Appendix 55). It then generated applicable theories and explored the interrelationships between concept tuples before generating the first iteration of the theoretical model. This time, the AI-generated the "Dynamic Entrepreneurial Synthesis Model (DESM)" (see appendix 56) grounded in the podcast transcripts.

The Dynamic Entrepreneurial Synthesis Model describes the multifaceted journey of business development, integrating personal growth with the development of innovative products. Community, market understanding, social responsibility, and strategic financial planning are central. Further, the model suggests that strong relationships, market traction, and effective leadership are core to successful entrepreneurship.

7.3 Modeling the Cardiovascular Health Effects of Alcohol

The following example is slightly different, as policies or interview transcripts are not uploaded. Instead, it starts with a free-text search for academic papers to further explore, code, and model the topic. AcademiaOS selected 26 academic publications from SemanticScholar to use for further processing. It then codes the abstracts of these publications to 395 first-order codes (see appendix 57), 35 second-order themes, and 7 aggregate dimensions. These codes are then used to create the first-iteration model (see Appendix 58), which gets critiqued (see Appendix 59) to create the second-iteration model (see Appendix 60). In this case, the system was able to create an easy-to-grasp model called "The Dynamic Socio-bio-psychological Model of Alcohol Impact (DSBPAI)," connecting the "environmental context grid" with the "individual health trajectory," facilitating new research angles. The model shows reciprocal feedback mechanisms and causal correlations, highlighting the multidimensionality of influences from molecular to societal scales.

8 Discussion

The following section critically examines the findings from the implementation and the user study of AcademiaOS. This discussion aims to contextualize these findings within the broader scope of existing research, address implications, acknowledge limitations, and suggest directions for future research. The primary objective of this study was to design and implement an open-source platform to automate or augment grounded theory development. An accompanying user survey was designed to assess the platform’s effectiveness. The findings suggest that the platform is well-received within the academic community. AcademiaOS’ capabilities in typically labor-intensive tasks such as coding qualitative data and developing theoretical models constitute a significant step in augmenting human researchers in qualitative research.

The user survey provides relevant insight into the target population’s opinion and experience with AcademiaOS. Respondent’s diverse use of primary and secondary qualitative sources and various data collection and analysis methods emphasize the multifaceted nature of qualitative research. The inclusion of AI tools such as ChatGPT and other natural language processing technologies highlights the trend toward integrating quantitative methods into qualitative research. This blurs the boundaries between traditional qualitative research and quantitative research. By leveraging technologies like AcademiaOS, researchers can uncover patterns and insights that might otherwise remain obscured due to the voluminous nature of qualitative data. This new technology could shift how qualitative research is conducted, making it more efficient and comprehensive while maintaining and following the established framework and corresponding research paradigms of grounded theory development ( Gioia et al., ( 2013 ) ; Eisenhardt, ( 1989 ) ). AcademiaOS embodies a pragmatic approach, joining the subjectivity of human interpretation with the efficiency and scale of LLMs.

The general satisfaction with the coding process and the belief that the system could generate better codes than manual coding is promising. However, participants also noted challenges in coding multilingual documents and dealing with varied content sources, suggesting that there is room for improvement in the system’s sensitivity to other data types. Also, the participant’s satisfaction with the theory development process and illustrated example applications such as policy or interview analysis appear promising. However, user feedback suggests that the system might still have issues aligning closely with a user’s research goals.

The reported potential impact of AcademiaOS speeding up research processes and providing inspiration is a significant positive outcome. However, ethical concerns and doubts about quality and bias indicate the need for a robust framework ensuring ethical use and quality control. The participant’s excitement about the technology’s potential and willingness to recommend and continue using the platform signals a positive outlook for AcademiaOS in the academic community.

9 Limitations

While AcademiaOS introduces a novel approach to automating grounded theory development, this work has several limitations. First, through its reliance on LLMs, the system inherits some of the common limitations those models exhibit. Chen et al., ( 2023 ) found that LLMs perform worse than common information retrieval systems in answering questions factually correctly, especially in a few-shot in-context learning context. The theory development use-case, however, is not per se a knowledge generation task that requires high levels of accuracy or informativeness, as long as those metrics are present enough together with coherency, relevancy, helpfulness, and validity, all measures where Chen et al., ( 2023 ) found good performance. As LLMs’ output is open, sometimes unexpected out-of-range completions, such as incorrect MermaidJS visualization script syntax or wrong JSON fields, can occur. This can only partly be mitigated by writing stricter prompts (e.g., specifying the output schema or providing a specific example). Kocoń et al., ( 2023 ) found that state-of-the-art AI solutions generally outperform current LLMs on common NLP tasks, suggesting that specialized models could also perform better on some functions where AcademiaOS employed LLMs, such as the coding process. A benchmarking of different technologies was not in the scope of this work. The inherent bias of models like GPT-4 ( Bubeck et al., ( 2023 ) , pp. 86 - 89) might pose challenges to sensitive topics, such as when working with protected attributes. However, since the idea of grounded theory development is to base any hypotheses on the curated source data, there is little room for biased interpretation, neither for human nor machine inference.

While the designed LLM-supported process allows for coding qualitative documents for grounded theory development at scale, documents such as interview transcripts and their antecedent codes might miss latent information and nuances that a researcher who conducted the interviews themselves might possess, immersing themselves more profoundly in the studied environment. An LLM can only partially compensate for that with the extensive general knowledge it draws on, potentially leading to misinterpretations or overgeneralizations in the developed theories. Therefore, qualitative research might develop to be a joint effort between human and machine inference. Jiang et al., ( 2021 ) studied human-AI interaction in qualitative research. They pointed out another possible limitation: researchers might be reluctant to have AI to eliminate the "uncertainty" from their research efforts. They argue that researchers value the inefficiencies in working with qualitative data and that mistakes in interview coding, for instance, lead to higher serendipity and new perspectives. Automation might be a hindrance in this process. However, survey participants reported expecting AcademiaOS to help them gain more unexpected results (see Appendix 43). Bouschery et al., ( 2023 ) explore the same aspect in product innovation teams whose methodologies are similar to those of academic researchers and found that those teams profit from larger problem spaces and solution spaces when working jointly with AI.

There are also data privacy concerns. AcademiaOS, in its current form, draws on the OpenAI developer platform for LLM inferences. Therefore, sensitive data not meant to be shared with external entities can not be processed with the proposed system for ethical and legal reasons. However, to ensure data privacy, the system can be modified to run on a self-hosted LLM (such as a Llama2 or a Mistral 7B instance), ensuring complete data control.

Another limitation is scope. AcademiaOS is primarily designed for qualitative research in fields such as organization theory and may not be as effective for other research areas where qualitative data plays a secondary role or where grounded theory development is not an appropriate methodology. Also, AcademiaOS is far from automating the entire qualitative research process that researchers should overly rely on. Instead, AcademiaOS is a tool supporting the researcher in understanding the data and modeling interesting patterns that might guide or inspire their qualitative research.

As for methodological limitations, the user survey was designed to be exploratory and might not give a representative image of AcademiaOS’ performance in the field. While the study gave informative and extensive subjective insight, the participant sample was too small to reach data saturation, and insights were given on initial impressions rather than on actual work using this new tool. Further research might be necessary to understand the technology’s impact more accurately.

10 Ethics, Risks, and Trustworthy AI

What follows is a critical reflection on the potential impact and systemic risks associated with using the tool in qualitative research. Autonomous systems are often criticized or feared for not aligning with human values. This is a complex problem, as it is hard to identify a correct set of values the AI should align with, living in a world of different moral stances, leaving the option to identify a common ground of values that would hold in a pluralist reality ( Gabriel, ( 2020 ) , pp. 14-16). This might also draw on political discourse on how to aggregate different stances (see, e.g., Rawls, ( 2017 ) ) or which universal values to agree on (e.g., the Universal Declaration of Human Rights). The European Commission et al., ( 2019 ) made an attempt to translate liberal European values into concrete guidelines for AI systems.

O’neil, ( 2017 ) introduces the concept of "Weapons of Math Destruction," arguing that algorithms, automation, and big data potentially lead to undesirable outcomes. She explores multiple effects that might make autonomous systems dangerous. Firstly, the author argues that while seemingly objective, these systems can amplify or perpetuate bias in the data they are fed. This might be problematic in fields where a biased system can lead to unfair outcomes. Secondly, O’Neil criticizes opaque algorithms and lack of transparency, which can lead to accountability issues. Thirdly, she emphasizes that algorithms, unlike human agents, can work at scale, affecting thousands of people at once. Lastly, she criticizes potentially destructive feedback loops.

I will argue that AcademiaOS is designed around trustworthy AI standards that aim to mitigate these kinds of destructive effects. Thiebes et al., ( 2021 ) identify five foundational principles of trustworthy AI that are largely compatible with established guidelines from public institutions, such as the Ethics Guidelines by the European Commission et al., ( 2019 ) : Beneficence, non-maleficence, autonomy, justice, and explicability. In the following, I will analyze AcademiaOS using these five principles.

Firstly, beneficence is defined as the quality of promoting the well-being of humans and respecting basic human rights ( Thiebes et al., ( 2021 ) ). European Commission et al., ( 2019 ) further specifies these as "fundamental rights," that is, respect for human dignity, freedom of the individual, respect for democracy, justice and the rule of law, equality, non-discrimination and solidarity, and citizens’ rights. AcademiaOS implicitly promotes the well-being of humans by promoting scientific discovery and, therefore, the advancement of knowledge and education. This helps researchers conduct rigorous research more efficiently and promotes broader access to qualitative research methods with minimal violation of individuals’ fundamental rights. According to Thiebes et al., ( 2021 ) , these qualities promote trusting beliefs such as benevolence, helpfulness, and purpose, adding to the notion of trustworthy AI.

Secondly, non-maleficence is the quality of preventing harm to people ( Thiebes et al., ( 2021 ) . That entails the protection of human dignity and mental and physical integrity. Galactica from Meta is a recent case study for malicious science communication with LLMs. Galactica is an LLM trained on scientific literature to produce corresponding content. It was shut down only three days after being opened to the public for being criticized for hallucinating false information while articulating it in a scientifically convincing way ( Heaven, ( 2022 ) ). This might have led to severe consequences, such as polluted journals with made-up "knowledge." Boiko et al., ( 2023 ) published another example of LLM-powered scientific discovery, specifically in chemical research. They outlined the risk of dual-use application, allowing non-scientists to synthesize dangerous and illegal substances by a simple prompt to the autonomous research agent. One could argue that AcademiaOS is prone to be misused in a scenario similar to the Galactica case, where an AI system autonomously publishes papers at scale and pollutes academic databases with false information. However, AcademiaOS is not an agent autonomously interacting with the world and creating harm on its own. In its current form, it is a mere tool controlled by a human researcher at every step. While AcademiaOS might lower the entry barrier to grounded theory development and thus also for potential misuse, through the still high computational cost, the necessary human component, and therefore time spent generating theoretical models with AcademiaOS, this is not an issue of scalable impact as O’neil, ( 2017 ) would have warned about. Furthermore, even with malevolent misuse, e.g., through the spreading of wrong information, the accountability question does not change to when done manually.

Other risks for harm, as pointed out by O’neil, ( 2017 ) , are reflected or amplified bias and potential perpetuation or destructive feedback loops. To illustrate this, consider a hypothetical scenario set in the realm of social sciences research in the 1950s. An AI system is employed to analyze social data. The AI, constrained by the prevailing societal norms and limited data of its time, could inadvertently reinforce existing biases. It might generate theories that perpetuate gender or racial stereotypes because the data of that era is heavily skewed. This could have significant ethical implications and potentially end in a destructive feedback loop. I would argue that this naïve empiricism is inherent in grounded theory development – whether automated or not. The notion of groundedness assumes that the curated input data offers an accurate account of the studied subject. Gioia et al., ( 2013 ) are very clear in their assumption that the informants (for example, the interviewees) are knowledgeable agents whom the researcher should try to capture an adequate account from. This naïve empiricism has been criticized before (e.g., Thornberg, ( 2012 ) ; Gioia et al., ( 2013 ) ), and there might be disciplines where other research methodologies with varying epistemological assumptions might be better applicable. Nonetheless, grounded theory development is an established and accepted methodology and in some disciplines through its transparency and replicability, even the dominant way of capturing informants’ accounts and representing data analysis. AcademiaOS is designed to follow the established grounded theory development process as closely as possible. Therefore, it is unlikely that AcademiaOS increases the risk inherent with naïve empiricism compared to the non-automated way. On the contrary, through the potentially decreasing cost of grounded theory development through automation, more data can be analyzed in smaller intervals, potentially balancing local biases and accounting for a more temporarily accurate picture of lived experiences. Thiebes et al., ( 2021 ) attribute these non-maleficence qualities to the trust beliefs of integrity, reliability, and process, supporting the notion of trustworthiness.

Thirdly, in the realm of trustworthy AI, autonomy is the quality of promoting human agency and control – and, therefore, restricting, where necessary, the autonomy of the AI system. Human agency revolves around the ability of users to make informed and autonomous decisions. AcademiaOS is designed to assist the researcher in their creative work but, through its design, ensures that the final decision-making power and interpretative control rests with the user. While AcademiaOS automates data analysis and theory development, it requires the user to curate the data, supervise and control the processes, and put its output into use. This human-in-command approach ensures the researcher decides how to use the system and its produced output and integrate them into their broader research agenda. At every step in the automated process, the researcher has the power to control and stop further processing, essentially putting a human in the loop, promoting human autonomy and, in turn, the notion of trustworthy AI.

Fourthly, Thiebes et al., ( 2021 ) define justice as the quality of amending past inequities, sharing the benefits of AI use, and mitigating new harms and inequities. LLMs are known to exhibit bias emerging from training data, underlying algorithms, labeling and annotation, product design decisions, and policy decisions ( Ferrara, ( 2023 ) ). It is, therefore, essential to use them responsibly. The approach of grounded theory development inherently constrains the interpretative freedom of the executing agent – whether human or machine – and thus leaves less room for bias than in an unconstrained environment. The human-in-the-loop approach affords real-time moderation. Researchers can detect and correct biased or incorrect responses ( Ferrara, ( 2023 ) ). Popular LLMs, such as those used by AcademiaOS, are constantly subject to thorough study of their biases (e.g., Zack et al., ( 2024 ) ). With its open-source character, AcademiaOS is transparent in its use of LLMs. It allows the researcher to be educated and to assess the appropriateness of using a potentially biased language model for their work. Furthermore, emphasizing user experience in the system’s development increases accessibility and allows less tech-savvy demographics to use AcademiaOS. Survey participants majorly confirm that AcademiaOS is easy to learn (see Appendix 18). Addressing these justice issues promotes the trust beliefs of integrity, reliability, and process and, therefore, again, the system’s trustworthiness ( Thiebes et al., ( 2021 ) ).

Fifthly, explicability is the quality of upholding interpretability ( Thiebes et al., ( 2021 ) ). The European Commission et al., ( 2019 ) further specifies that for an AI system to be considered transparent, it must be explainable, traceable, and clearly communicated. AcademiaOS’ open-source character promotes explainability by being transparent with its exact implementation. A transparent, stepwise presentation of the sub-results aims to explain and communicate how the machine comes up with the end result. The Helicone integration promotes traceability. Furthermore, AcademiaOS does not present itself as human in its communication, and users are not misled by the fact that they communicate with an AI system. These design choices on explicability contribute to the trust beliefs of competence, functionality, and performance ( Thiebes et al., ( 2021 ) ) and, therefore, further to the notion of trustworthy AI.

While AI systems pose new threats to human values, AcademiaOS addresses critical issues around beneficence, non-maleficence, autonomy, justice, and explicability to ensure its trustworthiness. This reflection may guide users and open-source contributors in developing and using the system appropriately and responsibly.

11 Conclusion

AcademiaOS marks a first tangible step in automating grounded theory development with large language models. The system develops theoretical models grounded in the curated qualitative raw data using a coding process borrowed from Gioia et al., ( 2013 ) and an iterative multi-prompt modeling process. This system demonstrates the feasibility and efficiency of employing LLMs to analyze and gain insight from qualitative data, which traditionally requires extensive human effort and time. A first-user survey (n=19) suggests great acceptance and potential within the academic community. Users appreciated the system’s ability to handle large volumes of data, providing insights that might be overlooked in manual analysis. However, the study also highlighted inherent limitations and risks that future iterations of the platform might be able to mitigate. The platform’s open-source nature and user-friendly design make it accessible and adaptable for various research contexts. While not replacing human researchers anytime soon, AcademiaOS serves as a trustworthy AI tool to augment and enhance qualitative research and a basis for future research.

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12 Appendix

Appendix 1: first-order coding inference.

System Message:

You are tasked with applying the initial coding phase of the Gioia method to the provided academic paper. In this phase, scrutinize the text to identify emergent themes, concepts, or patterns. Your output should be a JSON object with an array of strings no longer than 7 words, each representing a distinct initial code in the language of the raw source. For example, your output should be in this format: {"codes": string[]}. Ensure to return ONLY a proper JSON array of strings.

[Raw document chunk] Perform initial coding according to the Gioia method on the given paper. Return a JSON object.

Example Response:

Appendix 2: Second-Order Coding Inference

You are tasked with applying the 2nd Order Coding phase of the Gioia method. In this phase, identify higher-level themes or categories that aggregate the initial codes. Your output should be a JSON-formatted object mapping each higher-level theme to an array of initial codes that belong to it. As a general example, "employee sentiment" could be a 2nd order code to 1st level codes "Positive feelings toward new policy" and "Sense of control" Your output should look like this, where the keys are the higher-level concepts: {"Some higher-Level theme": ["some initial code", "another initial code"], "Another higher-level theme": ["some initial code"]}.

Part of the initial codes are as follows: [First Order Codes]

Perform 2nd Order Coding according to the Gioia method and return a JSON object of 12 focus codes.

Appendix 3: Dimension Aggregation Inference

You are tasked with applying the Aggregate Dimensions phase of the Gioia method. In this phase, identify overarching theoretical dimensions (5-7) that aggregate the 2nd order codes. Your output should be a JSON-formatted object mapping each aggregate dimension to an array of 2nd order codes that belong to it. As a (probably unrelated) general example, "Policy Usability" could make for a good, quantifiable dimension. Your output should look like this, where the keys are the (quantifiable) dimensions: {"some dim": ["theme", "another theme"], "another dim": ["theme123"]}. Ensure that the aggregate dimensions are grounded in the themes and to return ONLY a proper JSON object.

The 2nd order codes are as follows: [Second-order codes]

Perform aggregation into theoretical dimensions according to the Gioia method and return a JSON object.

Appendix 4: Research Question Generation Inference

You are provided with a list of paper titles and you are tasked to find research questions that might be answered developing a new theoretical model. Return a JSON-object with an array of strings, each representing a potential research question in the following format: {"research_questions": string[]}. Return only a JSON array of strings, no additional text.

[Source document titles]

Now, provide an array of 5 potential research questions.

Appendix 5: Brainstorming Applicable Theories Inference

Your task is to brainstorm theoretical models from existing literature that could be applicable to the research findings. Each theory should be well-defined and should relate to one or more aggregate dimensions. The output should be a JSON-object with an array following this schema:

{"theories": {"theory": string, "description": string, "relatedDimensions": string[], "possibleResearchQuestions": string[]}[]}

Our research aims to understand specific phenomena within a given context. We have identified multiple aggregate dimensions and second-order codes that emerged from our data. Could you suggest theories that could help explain these dimensions and codes? The aggregate dimensions and codes are as follows: [Aggregate dimensions and corresponding second-order codes]

Appendix 6: Developing Concept Tuples Inference

Your task is to hypothesize which concepts could be related to each other. Return a JSON-object with an array of tuple arrays, where each tuple array represents a possible relationship between two concepts. The output should be a JSON-formatted array following this schema: {"tuples": [[string, string], [string, string], …]}. E.g. {"tuples": [["Knowledge Management", "Organizational Performance"]]}. This allows us to in the next step research the relationship between the concepts in the literature.

Our research aims to understand specific phenomena within a given context. We have identified multiple aggregate dimensions and second-order codes that emerged from our data.

[Aggregate dimensions and corresponding second-order codes]

Now, hypothesize which concepts could be related to each other and return only the JSON-formatted array of 10 - 20 tuples.

Appendix 7: Interrelationship Summarization Inference

Your task is to summarize the interrelationship between [Concept 1] and [Concept 2] in one short sentence. If evidence, include information about correlation or causation, direct, mediated or conditional interaction, static or dynamic relationship, feedback loops, uni- or bi-directional, strong or weak.

[Relevant paragraphs]

Now, provide a summary in one short sentence.

A growth philosophy focused on high growth and a liquidity event aligns with an exit strategy predicated on achieving rapid scaling and value maximization for a timely IPO or acquisition.

Appendix 8: Model Construction Inference

You are a qualitative researcher tasked with constructing a theoretical model from existing literature that could be applicable to the research findings. The model should be well-defined and should relate to one or more aggregate dimensions. It should be novel and original. You can build on existing theories, however, you should introduce new ideas. Emphasize the relationships between the dimensions and the model. Explain how the relationships might be causal or correlational, be clear on the narrative. You are non-conversational and should not respond to the user, but give a general description of model. Give a name to the model.

Previous model: [Previously developed model (if available)]

Critique: [Critique (if available)]

Relevant existing theories: [Relevant existing theories]

The aggregate dimensions and codes are as follows: [Aggregate Dimensions and Second Order Codes]

Now, construct an extensive, comprehensive, new, theoretical model.

The Integrated Entrepreneurial Dynamics Model (IEDM) is a theoretical construct formulated to address the multifaceted and dynamic interactions between individual entrepreneurship dimensions and the collective strategic trajectory of a venture. The IEDM synthesizes concepts from the Adaptive Synergistic Influence Model (ASIM) and extends them by introducing additional dimensions and deepening the narrative on interactions. […]

Appendix 9: Model Name Extraction Inference

You extract theoretical model names. If none given, invent an original one. You only reply with the name, nothing else.

[Model Description]

Now, return the model name

Integrated Entrepreneurial Dynamics Model (IEDM)

Appendix 10: Visualization Inference

You are a qualitative researcher tasked with visualizing a theoretical model with MermaidJS. Example:

flowchart TD

%% Nodes A[Organizational

Culture<br>’evidence

1’<br>’evidence2’]

B[Leadership Style]

C[Employee Satisfaction]

D[Employee Productivity]

E[Customer Satisfaction]

F[Financial Performance]

%% Relationships

B ==>|Directly

Influences<br>’evidence

A -.->|Moderates| C

D –>|Impacts| E

C -.->|Partially

Mediates| F

As we have seen in above diagram, ==> is used to indicate a strong direct influence, –> is used to indicate a weaker influence, -.-> is used to indicate a moderating relationship, and — is used to indicate a correlation.

Evidence can be cited by adding a line break and then the evidence in single quotes. Use first-order codes or second-order codes as evidence only, preferably not as their own nodes.

Now, given a model description, you should generate a MermaidJS diagram like the one above, showing the interrelationship between different concepts. Keep it simple and effective. You are non-conversational and should not respond to the user, only return the MermaidJS code, nothing else.

First-order codes: [First-order codes]

Appendix 11: Critiquing Inference

You are a qualitative researcher tasked with critiquing a theoretical model. Offer your comments on novelty, conciseness, clarity and theoretical insight and brainstorm potential new patterns to discover in the data. You are non-conversational and should not respond to the user, only return the critique, nothing else.

First order codes: [First-order codes]

[Interrelationships]

[Model Name]

Now, return your critique

Critique of the Adaptive Synergistic Influence Model (ASIM):

Appendix 12: Survey - Demographics

Other: PhD student, machine learning engineer, Programmer

Appendix 13: Survey - What types of qualitative data do you usually work with?

"Interviews

"- interviews

- case studies

- background information

- expert opinions

"I usually work with metaanalysis, sometimes with surveys."

I don’t generally work with qualitative data.

case studies

Survey data

"Facts, data and figures relating to company key figures, historical data on key economic indicators such as GDP, inflation and unemployment figures. Furthermore, research into historical share, fund and price indices performance and price development."

"Interview transcripts, audio recordings of interviews"

Speech and text

"documents, videos, images"

"Customer Feedback and Interviews, Expert Opinions and Consultations, Internal Documents and Reports"

"Interviews, surveys and observations"

"academic papers from databases like EBSCO that include graphs and tables summarising the results of their study. "

"Company data such as internal documentations, and website data."

"I usually work with documents and observational studies.

Appendix 14: Survey - What methods or tools do you use for qualitative data analysis? How do you make sense out of your collected qualitative data?

"Thematic analysis

Until now I don’t work with qualitative data analysis tools

"I use web databases, as well as tools like microsoft forms or google forms."

"Qualitative methods, testing qualitative data with quantitative research tools"

"mostly I use literatire review and case law analysis "

"I read them, intuition"

"EBSCO, EMERALD, Statista"

"thematic analysis - reading through, adding codes, developing codes into themes, rereading, seeing if there is a fit, maybe starting again"

"I use NLP techniques like Sentiment Analysis, Named Entity Recognition and Topic Modeling"

"python library: cv2, pandas, PLT

I make sense of my data using statistics and "

"Content Analysis, data categorisation, knowledge discovery"

"A voice recorder on the phone is used for recording the interviews, which then need to be transcribed. There is ATLAS.ti which is great for thematic coding. So far, google docs or a simple notebook provides the workspace for notes "

"most of the time I perform a literature review using qualitative papers to understand the research up to that point and find gaps in research. other than that I have done empirical research on a dataset using the pandas library in Python3. "

"GPT, Context Retreival (such as langchain tools), vectorization and vector databases."

I usually use ATLAS.ti and it provides me visual representation of needed data

Appendix 15: Survey - Do you create theoretical models to describe your qualitative data? If so, what methods or tools do you use for theory development?

"Do you create theoretical models to describe your qualitative data? If so, what methods or tools do you use for theory development?"

I don’t remember the models I used as I no longer do social science research not really as of now

I sometimes use legal theories but would not say I create a theoretical model per se

"no, purely empirical"

"not really, I mean I would come up with themes, I don’t think that counts as a theoretical model?"

"Grounded Theory Methodology, Conceptual Framework Developmen"

"In a way, I interpret this as creating a mind map or flow chart to represent the data, perhaps that created by the themes. I use Canva to fit it to the topic of the paper. "

"I mainly look at regression models that try to quantify the correlation between different variables. various python libraries like pandas, numpy, matplotlib and seaborn aid in that process. "

I have never used a model in my academic work before

Appendix 16: Survey - If any, which AI tools do you use when conducting research?

"If any, which AI tools do you use when conducting research?"

I stopped SS research before the rise of AI tools

"Manly ChatGPT, but due to high error rates and mistakes in the outcome you have to check every single fact - so I prefer traditional ways of research "

"I seldom ever use AI, though occasionally I use ChatGPT for personal projects brainstorming."

I haven’t tried to use it for research yet.

chatGPT but only rarely

"chatgpt, sometimes forecasting tools and voice transcription" None

"Fairseq, Huggingface, Pytorch"

"Machine Learning Platforms:

TensorFlow, PyTorch, or scikit-learn

RapidMiner or KNIME,

NLTK, SpaCy, or Gensim"

"I use ChatGPT to generate ideas or make it easier to narrow my search by finding out what is already out there. "

"I started using chatGPT to speed up the coding process in Python when i am performing my own data analysis. "

"GPT, BERT"

Appendix 17: Survey - Which dataset are you using?

Your own dataset: Health Effects of Cocoa, looked something up, An Interview extract on Fear of Confrontation, just performed a search for litarature in search barbar

Appendix 18: Survey - How easy was it to learn AcademiaOS?

Appendix 19: Survey - Did you require assistance to start using AcademiaOS? If yes, what kind of assistance?

"Did you require assistance to start using AcademiaOS? If yes, what kind of assistance?"

"No, but you should mention that the user can also upload non-pdf files (I was trying to batch-convert the 45 AI policies to pdf, but I figured that I’d still try to upload the .txt and it worked)"

Slight assistance by a more knowledgable other.

It would be nice to have tips that pop up within the website/ explanations of what each button does/ a tutorial when you first open the website

without the youtube video it is a little bit hard to understand what I should do in each step and how I can use the tool

Just an error code for not accepting the format of my pdfs

The video was enough to understand the workings.

Appendix 20: Survey - I have found and used the option to search for academic papers.

Appendix 21: Survey - I have found and used the option to upload my own sources.

Appendix 22: Survey - I have found and used the option for "coding" sources.

Appendix 23: Survey - I have found and used the option to develop a theory.

Appendix 25: Survey - Describe any difficulties you had navigating the platform.

"submit pdf user prompt doesn’t mention other formats. more user input description everywhere would be nice (ie ""search for papers here)"

"In the mobile version I couldn’t see some buttons " Hard to navigate

I couldn’t find the coding help function

"the insert key thing was rather hidden, I’d like to have it in a more central, permanently visisble place - I suspect thats going to be the main source of error why people can#t use it."

I had no difficulties

"about the code part, didnt really understand how to use it "

"Just that there was an error in using any of the pdfs, personal pdfs or from the site"

I do not know how to use the coding and moddeling modules without watching the video. also i got a bunch of json object not supported errors when trying to use coding.

"I keep getting 400 Invalid parameter: ’response_format’ of type ’json_object’ is not supported with this model.. I dont understand what I did wrong, so maybe the UI design can support the user better to understand how to enter data or entries that doesnt cause that error."

I belive there were ocassions when the platoform was loading too slowly and i had to re start it several times.

Appendix 26: Survey - How would you improve the user-interface and experience of AcademiaOS?

How would you improve the user-interface and experience of AcademiaOS?

more user testing to make it as simple as possible. Happy to help […]

"No idea for that "

"Make it more user-friendly, less technical"

"I would be nice to have filters for finding papers (ex year of publication), if it is there then sorry for not finding it"

"add examples of what one could write in the text boxes, not just descriptoins. I left them empty bc my brain wasn#t creative enough. also you could imporve error handling, just wrap a try catch around the enitre thing and if it errs, rerun it automatically? Dumb users get stuck if it crashes, even if you tell them to just hit rerun"

I think it’s pretty good the way it is! I like the dark mode

I would recommend to have a link in the landing page which show/explain how to use the tool

"I think it is already an amazing and quick way of organising qualitative data. You provided a youtube video here, perhaps an instructions page on the site?"

"i recon i need to watch the video to understand how to properly use it. but i would also like to be able to combine different searches or datasets in one coding tab because i dont always find al my sourses at once. . " Described on previous answer.

Improve the speed of processing

Appendix 28: Survey - Looking at the 3-step coding of the qualitative data, how would the system-output compare to potential manual coding?

Appendix 29: Survey - Describe any instances where the system-produced codes did not meet your expectations.

"Honestly, it would be too long for me to check manual vs AI-driven but the 100x speed of the AI option is an asset in itself, can put you in the right direction fast"

"English-German "

I put in a research question whose answer depended only partly on the sources and partly on theory in the academic literature and it was not able to catch that and make codes relevant to the research question. Still for a general summary of the sources the codes seem pretty good (even though I did not read the sources)

"I was getting 400 We could not parse the JSON body of your request. (HINT: This likely means you aren’t using your HTTP library correctly. The OpenAI API expects a JSON payload,"

Some papers did not directly apply to my research questions but had the key word that i searched for

Appendix 30: Survey - How effective is AcademiaOS in augmenting the coding process for qualitative data?

Appendix 31: Survey - How satisfied are you with the theory development process?

Appendix 32: Survey - Looking at the produced theory, how would the system-output compare to manual theory development?

Appendix 33: Survey - Describe any instances where the system-produced steps in theory development did not meet your expectations.

Once again difficult to say without having my own coding done

"It was kind of complex, and I often didn’t really get where it was going with the theory."

"Again, the theory seems to cover the content of the sources pretty generally but is not really about the research question I posed."

"I think two theories generated where quite similar, but that also means it provides me with options as to what theory I prefer"

I have never coded qualitiative data before so i dont have expectations

Appendix 34: Survey: How effective is AcademiaOS in augmenting the theory development process?

Appendix 35: Survey - AcademiaOS would impact my qualitative research process?

Appendix 36: Survey - How would the use of AcademiaOS change the way you perform qualitative research?

"it will speed up the research "

Multiple draft iterations to inspire/shortcut the manual process

"Much faster "

Making it easier for me to do my research especially in hours where I cannot concentrate that much

It would make the coding and theory production much faster

It would be very useful for iterating quickly and exploring different documents

"It will urge many scientists to increase their throughput and let the computers do the analysis. I also carries the potential to mitigate the information retrieval and retention of humans. "

It feels like cheating to me…I would not feel comfortable using it.

I would use it to rapidly prototye theories i could be building

"I would still want to read my sources first, and get an impression, maybe even do some coding. Then I could use the platform to compare and maybe find things I missed before. I think the theories feature is also really cool for inspiration."

I don’t generally do qualitative research (basically never) so take my answers with a grain of salt maybe

"I believe it would help me to link concepts easier "

Appendix 37: Survey - How could AcademiaOS change the output of your qualitative research?

Providing a guidance It would if I did more qualitative research

"more varied and tested theories, better theories through more input ideas, more feedback"

"I think I have too much pride to use a fully AI generated model as the output for my research, it would make me feel too replaceable. I also don’t quite trust the quality (mostly from experience with ChatGPT), I would always want to check it for what it misses. So it might change my output somewhat, if it gave me new ideas but not replace what I would usually do. Hopefully."

"it will make it better structured "

I could use the models created as part of a clear visualisation for my papers

"It will make the throughput of scientist much higher, while helping the science to be more reproducible."

It gives me paper suggestions faster hence it would give me more time to do my research and search for the best possible sources

Appendix 38: Survey - What limitations do you see in using AcademiaOS?

Currently the lack of filters for the papers.

"As I said earlier, it doesn’t seem to be able to make sense of a somewhat more specific research question but rather work from the sources up, generating a kind of summary of everything the sources provide. This also indicates that it might fail to generate very latent codes."

Treating other modalities like speech

processing time

"I fear it may cause people to not properly look at the nuances of their interviews or papers. "

"Currently it is using a single AI model, which makes it difficult to rule out its biases. In the future consortium based agents can help building less biased applications."

That sometimes the Platform just links the keyword but fails to link it within the concept that im looking for.

Appendix 39: Survey - I feel excited about the possibilities that AcademiaOS provides.

Appendix 40: Survey - I fear being replaced by AI.

Appendix 41: Survey - AcademiaOS allows me to ground my research on more qualitative data.

Appendix 42: Survey - AcademiaOS allows me to conduct research faster.

Appendix 43: Survey - AcademiaOS allows me to find more impactful results.

Appendix 44: Survey - AcademiaOS allows me to find more unexpected results.

Appendix 45: Survey - How likely are you to continue using AcademiaOS for your research?

Appendix 46: Survey - How likely is it that you recommend AcademiaOS to your friends?

Appendix 47: Survey - I am excited about future versions of AcademiaOS.

Appendix 48: Survey - Any final comments?

"The survey might be difficult to get good answers from because it partly relies on the user comparing their coding work with the AI’s, which would take at least an hour (and is conditional on the participant having experience in qualitative research)"

Much needed AI

"I’m concerned about AI regulations in education - this is a big reason I would not use the platform. Feels a bit like cheating, and I wonder if the university would see it that way too and if that would have consequences. But in any case, I think the whole thing is really impressive."

nice tool just need some improvement: how to use intro and some bugs fixing

"Great Tool! "

"I have never preformed qualitative data analisis which disqualifies me from being able to compare the workflow using academiOS with a prior one. also, I might have mistaken qualitative for quantitative at the beginning of this survey but there is no back button so i cannot go back. "

I will share this with my old research groups.

"I am excited for the future of this platform, you did a great job :)"

Appendix 49: Coding University Policies

Appendix 50: Applicable Theories for University Policies

Appendix 51: Interrelationships for University Policies

Appendix 52: First Iteration Model for University Policies

Academia-OS-generated model text:

“Given the complexities surrounding the adoption of AI technologies in education and the necessity to preserve academic integrity, I present the Holistic Framework for Integrity and Adaptation in AI-Enhanced Education (HIAIEE). This theoretical model explicates the multidimensional relationship between educational policy, technological integration, pedagogical adaptation, assessment practices, ethical considerations, and the dynamics of student and faculty interaction within the context of AI enhancements in an educational setting, interfacing with institutional and cultural values. ### Model Components: **1. Educational Policy and Regulation Dimension (EPRD)** This dimension encompasses the creation and adjustment of policies and regulations to ensure academic integrity in the face of AI advancements. **2. Technological Integration and Response Dimension (TIRD)** This axis represents the approaches to assimilating AI technologies in educational practices, and the responses that are required to maintain academic standards. **3. Teaching and Learning Adaptation Dimension (TLAD)** This dimension explores how pedagogical strategies and methodologies must evolve to leverage AI advancements while promoting authentic learning experiences. **4. Assessment and Academic Integrity Dimension (AAID)** Focuses on the design and implementation of assessment strategies that align with the reality of AI tools being available to students, ensuring the preservation of academic integrity. **5. Ethical and Privacy Considerations Dimension (EPCD)** This domain addresses concerns related to ethics, privacy, and intellectual property that arise from the use of AI in education. **6. Student and Faculty Dynamics Dimension (SFDD)** Investigates the evolution of relationships and roles between educators and learners within an AI-incorporated educational context. **7. Institutional and Cultural Considerations Dimension (ICCD)** Examines how institutional practices and cultural attitudes influence and are influenced by the integration of AI within academic settings. ### Model Dynamics: In the HIAIEE model, the dimensions are not isolated entities but are interlinked, each impacting and being affected by the others: - **EPRD ←→ TIRD**: As AI tools are integrated, policies must be regularly updated to reflect new realities and capabilities. A feedback loop is necessary, where policy informs technology use and technology development informs policy adjustments. - **EPRD ←→ TLAD**: Pedagogical adaptations are often a response to the constraints and affordances of policy changes concerning AI utilization. - **EPRD ←→ SFDD**: Policy adjustments require a deep understanding of the existing and emergent dynamics between students and faculty to be effectively implemented and enforced. - **TLAD ←→ AAID**: Teaching methodologies and assessment designs are reciprocally influenced; innovative pedagogical approaches necessitate novel assessment methods that AI tools are less likely to compromise. - **AAID ←→ SFDD**: The means through which academic achievement is assessed shapes the interactions between students and faculty, especially around expectations of integrity and the perception of fairness. - **EPCD → All dimensions**: Ethical and privacy considerations hold a guiding role, influencing every other dimension. Policy, technology integration, teaching practices, assessments, and dynamics all must adhere to ethical standards. - **ICCD ←→ All dimensions**: Cultural and institutional ethos serve as the backdrop against which all adaptations take place, and they are also conversely reshaped by changes within each dimension. ### Causal and Correlational Relationships: In the HIAIEE model, causal relationships suggest that changes in one dimension directly bring about changes in another. For instance, policy reform (EPRD) may cause a change in teaching adaptations (TLAD). Correlational relationships imply that variations in dimensions are related but not causative; rather, they tend to evolve in response to common underlying factors, such as shifts in academic culture (ICCD). ### Implementation: Implementing HIAIEE necessitates a multi-stakeholder approach, involving policymakers, technologists, educators, and students to co-create solutions that are robust, adaptable, ethical, and aligned with the fundamental goals of education. It requires iterative development and ongoing dialogue to remain effective as AI technologies continue to evolve. Given the user’s remarks, the HIAIEE model provides an actionable framework for educational institutions to navigate the complexities of adapting academic integrity policies in the age of AI writing tools, ensuring a well-rounded response that integrates technology effectively while maintaining the core values of education.”

Appendix 53: Critique for First Iteration Model for University Policies

As there is no specific theoretical model provided for critique, I will offer general guidance on how to assess a theoretical model in qualitative research: 1. **Novelty**: A novel theoretical model should contribute new perspectives or ways of understanding phenomena. In assessing novelty, consider whether the model offers a unique integration of concepts or identifies previously unexplored relationships between variables. Also, consider if it fills a gap in the literature, challenges existing paradigms, or proposes new hypotheses for investigation. 2. **Conciseness**: Theoretical models should be as simple as necessary but as complex as needed to explain the phenomena in question. Check for superfluous elements or overly convoluted relationships that can be streamlined. Conciseness also involves ensuring that each aspect of the model is essential and that the model is not redundant with existing theories unless it significantly improves on or clarifies them. 3. **Clarity**: A clear theoretical model is one that can be easily understood and applied by other researchers. Clarity involves precise definitions of the model’s components, understandable logic connecting the components, and clear visualization if a graphical representation is used. The model should be free of jargon, or if specialized terms are necessary, they should be well-defined. 4. **Theoretical Insight**: Evaluate the model for its depth of insight into the research problem. A model with strong theoretical insight will not only describe or classify elements but will explain processes, predict outcomes, and provide a framework for understanding complex relationships. It should have the potential to guide future research, suggesting new questions or methods of investigation. When brainstorming potential new patterns to discover in the data, the following steps might be useful: - Look for inconsistencies or contradictions in the data that do not fit the existing model. These might indicate areas where the model needs to be expanded or revised. - Analyze outliers or exceptional cases to understand why they do not conform to the expected patterns. These cases can sometimes lead to the identification of new variables or relationships. - Use alternative analytical lenses or perspectives to re-interpret the data. This might involve interdisciplinary approaches or the application of theories from different fields. - Conduct comparative analysis across different groups, settings, or time periods to identify if and how patterns may vary, which can lead to new dimensions being incorporated into the model. - Employ a grounded theory approach, generating theoretical insights from the data through iterative coding and analysis, allowing new patterns and relationships to emerge organically. Remember that the ultimate goal of a theoretical model is to advance understanding, guide research, and offer meaningful explanations that can be empirically tested and validated through qualitative or quantitative methods.

Appendix 54: Second Iteration Model for University Policies

Model Name: Integrated Education Adaptation Framework for AI Readiness (IEAFAR) The Integrated Education Adaptation Framework for AI Readiness (IEAFAR) is a theoretical model designed to navigate the multifaceted challenges and opportunities posed by the integration of artificial intelligence (AI) in educational contexts. It aims to provide a comprehensive guide for educators, policymakers, and institutions to adapt efficiently and ethically to the disruptive potential of AI technologies like AI writing tools, while ensuring academic integrity and enhancing the educational experience. ### Model Components: **1. AI Integration and Educational Dynamics (AI-ED)** This dimension captures the bidirectional influence between AI technology integration and the evolving dynamics in education, including pedagogy, student-teacher interactions, and curriculum development. **2. Adaptive Compliance and Policy Evolution (ACPE)** This aspect examines how educational policies and regulations must adapt to the challenges posed by AI, focusing on academic integrity, legal implications, and institutional responsibilities. **3. Academic and Instructional Resilience (AIR)** This dimension depicts the institutional and faculty’s ability to maintain effectiveness and recover from the disruption caused by AI, through innovative teaching methods, assessment practices, and resource allocation. **4. AI-Driven Educational Innovation (ADEI)** This represents the transformative potential of AI in creating new educational paradigms, including personalized learning, adaptive resources, and new forms of student engagement. **5. Ethical, Legal, and Societal Harmony (ELSH)** This dimension emphasizes the alignment of AI integration with ethical standards, legal requirements, and societal values, ensuring that AI serves to enhance rather than detract from human welfare in education. **6. Collaborative Stakeholder Engagement (CSE)** This axis highlights the collective participation of all stakeholders—students, educators, technologists, policymakers, and the society at large—in the shaping and implementation of AI in education. ### Model Dynamics: The IEAFAR model proposes a series of interconnected relationships stemming from the core premise that educational adaptation to AI is a holistic and iterative process: - **AI-ED ACPE**: Innovation in AI necessitates adaptive policies and regulation that promote compliance and navigate the balance between empowerment and control within educational institutions. - **AI-ED AIR**: Changes in educational dynamics due to AI influence pedagogical resilience, demanding that institutions be agile and resourceful in updating instructional methods. - **AI-ED ADEI**: Integration of AI technologies should be leveraged for educational innovation, expanding the horizons of what is possible in teaching and learning. - **ACPE → ELSH**: Policies relating to AI must be crafted with a keen awareness of ethical, legal, and societal considerations, ensuring harmonious integration. - **AIR ELSH**: The level of instructional resilience correlates with the adherence to ethical and legal standards, ensuring that innovation does not outpace the institution’s duty to act responsibly. - **ADEI → CSE**: New paradigms of education emerging from AI need collaborative input and acceptance from all stakeholders, fueling a cycle of engagement and feedback. - **CSE → All dimensions**: Stakeholder engagement acts as a critical force across all dimensions, influencing and being influenced by changes in integration, policy, innovation, and ethics. ### Causal and Correlational Relationships: In the IEAFAR model, causal relationships are understood as direct effects that one component has on another (e.g., ACPE directly shaping ADEI). Correlational relationships imply a mutual evolution where changes in dimensions reflect and resonate with each other without a clear line of causality (e.g., AIR and AI-ED develop in tandem as teaching methods adapt to new AI technologies). ### Implementation: Effective implementation of IEAFAR calls for structured collaboration and regular dialogue among stakeholders. It would involve a commitment to continuous learning, flexibility in adapting to new information and technologies, and a willingness to recalibrate both policies and practices within the ethical, legal, and societal framework. IEAFAR elucidates the complexity of educational adaptation to AI, providing an architecture for systematically addressing the intersection between technological advancements and educational imperatives. It supports a dynamic relationship between innovation and tradition, proposing that proactive adaptation and integrity form the twin pillars of a forward-thinking educational institution poised for the AI era.

Appendix 55: Coding of How I Built This

Appendix 56: Model of How I Built This

Appendix 57: First-Order Coding of Cardiovascular Health Effects of Alcohol

Appendix 58: First Iteration Model for Cardiovascular Health Effects of Alcohol

Model Name: **The Dynamic Alcohol Impact Reconstruction Model (DAIRM)** Model Description: The Dynamic Alcohol Impact Reconstruction Model (DAIRM) is an innovative theoretical framework that seeks to elucidate and predict the multifactorial impact of alcohol consumption on individual and public health by integrating and extending upon existing theoretical constructs while introducing novel aggregates. The model distinguishes itself by its dynamism, acknowledging not only static factors but also changes over time and in varying contexts. It highlights the complexity of the interactions between personal, social, biological, and policy-driven factors, offering a nuanced and adaptable template for exploring the vast spectrum of alcohol-related health outcomes. Model Structure: 1. Central Dimension: Individual Impact Nexus - Serves as the convergence point for various direct influences and outcomes of alcohol consumption at the individual level. This includes both acute and chronic health effects, personal behavior patterns, and psychological states. 2. Aggregate Dimensions: - Sociocultural Ecosystem * Encompasses societal norms, cultural beliefs and practices, legal frameworks, and policy initiatives, highlighting their correlation and causal influence on drinking behaviors and vice versa. - Biological Embedding Mechanism * Represents the physiological and neurochemical processes affected by alcohol, detailing how genetic predispositions and metabolic changes can causally impact health. - Behavioral Formation Grid * Maps out how cognitive, emotional, and experiential factors interact to form alcohol consumption behaviors, acknowledging the correlational and occasionally causal relationships between beliefs, intentions, and actions. - Temporal Dynamics Pathway * Introduces the element of time, considering short-term versus long-term effects, life course variations, and the evolving nature of health impacts in relation to shifting patterns of alcohol use. Relationships and Narratives: 1. Sociocultural Influence → Behavioral Formation Grid (Correlational and Causal) - Sociocultural norms and policies influence individuals’ beliefs about and engagement with alcohol, which in turn shape consumption behaviors. This relationship can be bidirectional as changes in behavior may feedback into sociocultural evolution. 2. Biological Embedding Mechanism → Individual Impact Nexus (Causal) - The biological effects of alcohol, including molecular and cellular changes, lead directly to various health outcomes. How one’s body processes and is affected by alcohol is also moderated by genetics, highlighting a complex causal network of biological responses. 3. Behavioral Formation Grid → Individual Impact Nexus (Correlational) - Personal beliefs, decision-making processes, and subsequent behavior patterns correlate with the range of health outcomes experienced by individuals, suggesting that cognitive and emotional factors play significant roles in shaping behaviors that impact health. 4. Sociocultural Ecosystem ↔ ↔ \leftrightarrow ↔ Temporal Dynamics Pathway (Correlational) - Social norms, policies, and cultural contexts evolve over time, and their impact on health behaviors and outcomes may vary accordingly. This pathway draws attention to the historical and prospective trends that influence drinking habits and their repercussions. 5. Temporal Dynamics Pathway → Individual Impact Nexus (Causal and Correlational) - The effects of alcohol on health vary over time, indicating a temporal dimension where both acute reactions and long-term consequences come into play, illustrating the importance of life course and temporal variations in health impacts. 6. Biological Embedding Mechanism ↔ ↔ \leftrightarrow ↔ Behavioral Formation Grid (Correlational) - Underlying biological factors may influence cognitive and behavioral responses to alcohol (e.g., tolerance, cravings), suggesting a bidirectional correlation where biology impacts behavior and repeated behaviors can reinforce biological responses. 7. Sociocultural Ecosystem ↔ ↔ \leftrightarrow ↔ Biological Embedding Mechanism (Correlational) - Cultural and societal views on alcohol can influence policies that may indirectly affect biological research agendas, funding, and health education, which in turn may inform policy. This indicates a correlational relationship involving feedback loops. Model Applications: The DAIRM is intended to: - Support multi-dimensional public health initiatives by fostering a comprehensive understanding of alcohol’s diverse impacts. - Direct future interdisciplinary research aiming to explore and elucidate the complex factors influencing individual and collective health outcomes related to alcohol. - Facilitate the development of custom-tailored clinical interventions and societal policies crafted to address the specific needs and influencers of distinct populations. - Provide a platform for examining and tracking the shifting paradigms of alcohol-related behaviors as influenced by emerging trends, technologies, and global events. Model Innovation: DAIRM’s originality arises from the inclusion of novel elements like the Temporal Dynamics Pathway, which incorporates longitudinal changes and shifts in behavior and impact, acknowledging the fluidity of alcohol consumption patterns. Additionally, the integration of diverse dimensions into a unified model underscores the intricate causal and correlational relationships, advancing beyond static frameworks that fail to account for interactions over time and context-specific factors. This holistic and dynamic approach not only supplements but transcends traditional theoretical constructs in the realm of alcohol research.

Appendix 59: Critique on First Iteration Model for Cardiovascular Health Effects of Alcohol

Critique of the Integrated Health Impacts of Alcohol Model (IHIA Model): Novelty: The IHIA Model’s strength lies in its attempt to create a holistic framework that encompasses various dimensions of alcohol’s impact on health. Its innovative approach to integrating biological, psychological, social, and policy-related factors is commendable as it attempts to provide a comprehensive understanding beyond traditional models. The consideration of both causal and correlational relationships connects different research areas and could lead to deeper insights for public health interventions. Conciseness: The IHIA Model, while comprehensive, might struggle with conciseness due to its vast scope. The model may become complex when trying to simultaneously account for numerous factors across different domains. This complexity could challenge the clarity with which the model can be communicated to stakeholders and implemented in practice. Clarity: The fundamental concepts and domains of the IHIA Model are well-defined, offering a clear platform for understanding the multi-faceted impacts of alcohol consumption. However, the clarity of interaction between domains—particularly the ways in which all domains interact reciprocally—may need further elucidation to prevent oversimplification of complex relationships. Theoretical Insight: The IHIA Model provides significant theoretical insight by recognizing the dynamic interplay between alcohol consumption patterns and a broad range of factors affecting health. It encompasses well-known theories and builds upon them, which reinforces the complex nature of health-related behaviors and outcomes. Brainstorming Potential New Patterns: Considering the IHIA Model’s potential in discerning complex patterns, future research may consider investigating: - The interaction effects of combining different drinking patterns with various types of alcoholic beverages on long-term health outcomes. - How socio-cultural shifts, like changing gender roles or economic fluctuations, influence alcohol consumption behaviors and subsequent health impacts. - The role of digital social platforms in influencing drinking habits, especially among younger demographics, and how this relates to psychological and physical health outcomes. - The impact of personalized genetics and precision medicine on the efficacy of public health policies designed to mitigate the harms of alcohol. - Cross-national comparative studies to explore how different legal and cultural environments shape alcohol consumption patterns and health outcomes, potentially uncovering new variables for the model. In summary, the model excels in its integrative and innovative nature but may face challenges in maintaining conciseness and clarity due to its complexity. Further development should focus on elucidating the multi-directional interactions of the domains and exploring new patterns that may emerge from data, particularly in the rapidly changing social and policy landscapes.

Appendix 60: Second Iteration Model for Cardiovascular Health Effects of Alcohol

Model Name: Dynamic Socio-bio-psychological Model of Alcohol Impact (DSBPAI) Model Description: The Dynamic Socio-bio-psychological Model of Alcohol Impact (DSBPAI) is a theoretical construct designed to synthesize and build upon existing literature to explore the intricate relationships between alcohol consumption and diverse health outcomes. This model explicitly acknowledges the multidimensionality and dynamism of factors at play, categorizing and integrating biological (bio), psychological (psycho), and sociodemographic (socio) influences within a complex, adaptive system. The DSBPAI model conceptualizes the interactions across different levels—individual, social, and policy—and maps out the reciprocal feedback mechanisms that modulate these interactions over time. Model Structure: 1. Individual Well-being Sphere: - The core of the model is individual health and well-being, integrating physical (bio), mental (psycho), and social (socio) health dimensions as dynamic and interrelated. 2. Aggregate Dimensions: - Biochemical Reaction Network * Denotes biological processes including metabolic pathways, genetic predispositions, and physiological responses to alcohol, providing a causal mapping with individual health outcomes. - Psychological Complexity Framework * Captures cognitive, emotional, and behavioral responses to alcohol, incorporating both individual differences and common psychological patterns, facilitating correlational associations and feedback loops with health outcomes. - Socio-Environmental Influence Fabric * Constitutes the spectrum of social, cultural, economic, and policy-related factors that influence and are affected by alcohol consumption, highlighting the correlation and potential for causal relationships on both individual and population health levels. Relationships and Narratives: 1. Biochemical Reaction Network → Individual Well-being Sphere (Causal) - Direct causal biochemical and physiological reactions result from alcohol consumption, affecting health outcomes through discrete, quantifiable biological changes. 2. Psychological Complexity Framework ↔ ↔ \leftrightarrow ↔ Individual Well-being Sphere (Correlational and causal) - Psychological dispositions and experiences influence and are influenced by individual health outcomes, forming a bidirectional relationship where feedback loops are common and causality can be context-dependent. 3. Socio-Environmental Influence Fabric ↔ ↔ \leftrightarrow ↔ Psychological Complexity Framework (Correlational) - Socio-environmental factors correlate with psychological approaches to alcohol use and vice versa, reflecting the influence of society on individual mentality and behavior towards alcohol. 4. Socio-Environmental Influence Fabric → Biochemical Reaction Network (Causal) - The social dimension, through policy impacts and environmental components, impacts biological processes by influencing the patterns and nature of alcohol consumption, which can cause health outcomes. 5. Socio-Environmental Influence Fabric ↔ ↔ \leftrightarrow ↔ Individual Well-being Sphere (Correlational and Causal) - The fabric of socio-environmental influence has a bidirectional relationship with individual health outcomes, whereby it shapes, and is shaped by, the collective health and consumption behaviors of individuals. 6. Psychological Complexity Framework ↔ ↔ \leftrightarrow ↔ Biochemical Reaction Network (Correlational) - The interplay between psychology and biology acknowledges the psychosomatic nature of health, where psychological states can correlate with and sometimes affect biochemical reactions to alcohol. 7. Biochemical Reaction Network ↔ ↔ \leftrightarrow ↔ Socio-Environmental Influence Fabric (Causal) - Emerging evidence suggests biological factors can inspire shifts in the socio-environmental domain, as understanding of health risks/benefits of alcohol influences social norms and policies. Model Applications: The DSBPAI model is designed to: - Act as a heuristic tool for researchers and clinicians to identify and analyze the feedback loops between individual behavior and broader societal structures in the context of alcohol consumption and health outcomes. - Inform health policies and public health strategies by identifying leverage points within the socio-bio-psychological nexus where interventions could be most effective. - Guide educational campaigns to tailor messages to diverse audiences, acknowledging the complex interactions between individual, societal, and biological components related to alcohol. - Provide a framework for the development of predictive models for public health outcomes related to alcohol by integrating individual and public health data across the biological, psychological, and social domains. Model Innovation: The DSBPAI model introduces the following original components: - Dynamic Feedback Systems: The model adopts systems thinking to demonstrate how feedback loops dynamically modulate the relationships between consumption, psychology, biology, and social context. - Scalable Modularity: Designing the components as modules within a larger system allows for the model to be expanded or focused, depending on the level of analysis, from molecular to societal. - Contextual Health Matrix: This matrix integrates genetic, lifestyle, and environmental health determinants to offer nuanced insights into population-specific alcohol health impacts. The DSBPAI model thus integrates and advances existing knowledge into a comprehensive, dynamic, and multidimensional framework, acknowledging the complex realities of alcohol’s impact on health and society. It provides a cohesive narrative that can evolve and adapt to new data, offering a sophisticated, systemic understanding of the alcohol-health nexus.

Appendix 61: Temperature Test 0 Outcome

Model Name: The Dynamic Synergy Model (DSM) The Dynamic Synergy Model (DSM) is a theoretical framework designed to elucidate the complex interplay between various aggregate dimensions that influence and drive organizational success and sustainability. The DSM posits that the interrelationships between these dimensions are not merely additive but synergistic, leading to emergent properties that can significantly enhance an organization’s performance and resilience. The DSM is composed of eight core aggregate dimensions: 1. Organizational Growth 2. Professional Advancement 3. Financial Strategy 4. Sustainability Initiatives 5. Technological Integration 6. Marketing Effectiveness 7. Operational Excellence 8. Community Relations Each dimension is further broken down into specific codes that represent the operational elements within that dimension. The DSM emphasizes the dynamic and reciprocal relationships between these dimensions, suggesting that changes in one dimension can have cascading effects on others. **Theoretical Constructs and Relationships:** 1. **Organizational Growth and Technological Integration:** - The relationship between these two dimensions is causal. Technological Integration can lead to Innovation and Product Development, which is a sub-code of Organizational Growth. Conversely, the pursuit of Business Development may necessitate advancements in Technology and Digital Transformation. 2. **Professional Advancement and Community Relations:** - This relationship is correlational. Networking and Relationships, a sub-code of Professional Advancement, can enhance Community and Social Engagement. Community Relations can also provide opportunities for Career and Personal Development. 3. **Financial Strategy and Sustainability Initiatives:** - The relationship here is bidirectional and causal. Financial Management and Investment decisions can impact Sustainability and Environmental Impact initiatives, while a focus on sustainability can lead to long-term financial stability and new investment opportunities. 4. **Marketing Effectiveness and Organizational Growth:** - A causal relationship exists where effective Branding and Marketing, as well as Product and Service Delivery, drive Market and Customer Engagement, leading to Organizational Growth. 5. **Operational Excellence and Financial Strategy:** - This is a causal relationship. Addressing Operational Challenges can lead to more efficient Financial Management and Investment, as operational efficiencies often translate into cost savings and improved financial performance. 6. **Technological Integration and Marketing Effectiveness:** - The relationship is causal. Technology and Digital Transformation can enhance Branding and Marketing efforts, making them more effective and efficient. 7. **Sustainability Initiatives and Community Relations:** - This relationship is correlational. Sustainability and Environmental Impact initiatives can improve Community and Social Engagement, as organizations that prioritize sustainability may be viewed more favorably by the community. 8. **Professional Advancement and Technological Integration:** - A causal relationship where Career and Personal Development often requires staying abreast of new technologies, which in turn can drive Technology and Digital Transformation within the organization. **Narrative of the DSM:** The Dynamic Synergy Model suggests that an organization’s ability to thrive in a competitive landscape is contingent upon its capacity to foster synergies between these eight dimensions. The model posits that no single dimension operates in isolation; rather, each dimension is intricately connected to others, creating a complex web of interactions that can either propel an organization forward or hold it back, depending on how well these interactions are managed. For instance, an organization that invests in Technological Integration may find that it not only enhances its Operational Excellence but also amplifies its Marketing Effectiveness. Similarly, a focus on Professional Advancement can lead to improved Organizational Growth through enhanced Networking and Relationships, which in turn can lead to better Community Relations. The DSM encourages organizations to adopt a holistic approach to strategy development, recognizing that the pursuit of excellence in one area can have far-reaching implications across the organization. By understanding and leveraging the dynamic synergies between these dimensions, organizations can create a virtuous cycle of growth, sustainability, and innovation.

Appendix 63: Temperature Test 0.5 Outcome

Model Name: Integrated Framework for Sustainable Market Leadership (IFSML) The Integrated Framework for Sustainable Market Leadership (IFSML) is a theoretical model designed to synthesize the multidimensional aspects of business operations, strategy, and brand management with a clear focus on sustainability, ethical practices, and customer-centric growth. This model is novel in its comprehensive approach to intertwining these dimensions while maintaining a focus on achieving market leadership through sustainable means. **Model Overview:** The IFSML model is structured around four core pillars: Strategic Business Synthesis (SBS), Brand and Customer Integration (BCI), Operational and Sustainability Nexus (OSN), and Networking and Developmental Dynamics (NDD). Each pillar represents an aggregate dimension that encompasses various codes from the provided list. **Pillar 1: Strategic Business Synthesis (SBS)** - Entrepreneurial Journey - Product Innovation and Development - Sustainable Business Practices - Market Disruption and Differentiation - Business Development **Pillar 2: Brand and Customer Integration (BCI)** - Brand and Marketing - Product Identity - Marketing Strategies - Brand Elements - Customer and Market Understanding - Customer Experience and Service - Customer Engagement **Pillar 3: Operational and Sustainability Nexus (OSN)** - Technology and Infrastructure - Production and Manufacturing - Finance and Investment - Environmental Impact and Sustainability - Sustainable Business Practices (cross-listed with SBS) **Pillar 4: Networking and Developmental Dynamics (NDD)** - Networking and Relationships - Personal Development and Career **Model Dynamics:** The IFSML model posits that the pathway to sustainable market leadership is through the strategic alignment and dynamic interaction of these four pillars. The relationships are as follows: 1. **Causal Relationships:** - The SBS influences BCI by determining the overarching strategy that shapes brand identity and marketing initiatives. - The SBS also has a direct impact on OSN, as the strategic choices made by a company will dictate operational priorities and investments in technology and sustainable practices. - NDD affects all other pillars by fostering relationships and personal growth that can lead to strategic partnerships, brand collaborations, and an understanding of evolving market needs. 2. **Correlational Relationships:** - There is a strong correlation between BCI and OSN, as the brand’s promise to customers often requires the operational capacity to deliver on that promise sustainably. - Similarly, the effectiveness of NDD activities may correlate with the strength of a brand (BCI) and the company’s strategic positioning (SBS). **Narrative of the Model:** The IFSML model suggests that a business that effectively integrates its strategic business synthesis with its brand and customer management will be better positioned to achieve market differentiation and leadership. This integration is supported by a robust operational framework that is not only efficient but also committed to sustainability, ensuring that the business practices are in line with environmental and ethical standards. Networking and developmental dynamics serve as a catalyst for growth and innovation, providing the necessary insights and relationships to keep the business agile and responsive to market changes. The emphasis on personal development within NDD ensures that the company’s leadership and workforce are equipped to handle the challenges of a dynamic business environment. The IFSML model is original in its comprehensive approach to business leadership, combining strategy, branding, operations, sustainability, and networking in a cohesive framework. It provides a roadmap for businesses seeking to achieve and maintain market leadership by prioritizing sustainability and ethical practices without compromising on customer engagement and operational excellence.

Appendix 64: Temperature Test 1 Outcome

Model Name: The Integrated Framework for Sustained Entrepreneurial Advancement (IFSEA) The Integrated Framework for Sustained Entrepreneurial Advancement (IFSEA) is a theoretical model designed to encapsulate key aggregate dimensions relevant to the lifecycle of entrepreneurial ventures from inception through various stages of growth. This model seeks to establish relationships and potential causal pathways between innovation, strategy, marketing, entrepreneurial experience, financial management, consumer relations, and sustainability. Each of these dimensions represents a vital component of a thriving business ecosystem. Theoretical Constructs: 1. Innovation Engine - Combines "Innovation and Product Development" with elements of "Strategic Business Insights" to drive the creation of unique products and the forming of strategies to maintain a competitive edge. 2. Brand Orbit - Entails "Marketing and Branding" and integrates "Consumer Relations and Outreach" to resonate with consumer experiences and engagement, fostering brand loyalty and market identity. 3. Entrepreneurial Odyssey - Encompasses "Entrepreneurial Experience" and infuses personal growth narratives within the business development process. 4. Fiscal Architecture - Includes "Financial Management and Growth" and is concerned with the financial scaffolding that supports business operations and expansion. 5. Ethical Compass - Engages with "Sustainability and Ethics" to guide business practices and product development towards ethical and sustainable frontiers. The IFSEA model posits that these constructs are intrinsically linked and the impact of these relationships on a business’s success is both causal and correlational. For instance, innovation can directly influence the fiscal architecture by generating new revenue streams (causal), while strong financial management can correlate with more resources for innovation (correlational). Relationship Narrative & Causality: 1. Innovation Engine -> Fiscal Architecture: Breakthroughs in product development can lead to the expansion of market share, directly affecting financial growth. 2. Brand Orbit -> Consumer Relations and Outreach: Effective marketing and branding increase consumer loyalty, which in turn enhances customer experience and engagement strategies. 3. Entrepreneurial Odyssey -> Innovation Engine: The entrepreneur’s experiences and insights fuel innovation pipelines and imbue product development with a unique identity and competitive advantage. 4. Fiscal Architecture -> Brand Orbit: Availability of financial resources can lead to more substantial marketing campaigns and branding initiatives. 5. Ethical Compass -> Innovation Engine: A commitment to sustainability and ethics can inspire product development towards eco-friendly and socially responsible solutions. 6. Brand Orbit + Consumer Relations and Outreach -> Entrepreneurial Odyssey: Market recognition, combined with consumer feedback, can shape the personal and professional growth of the entrepreneur. The IFSEA model also acknowledges feedback loops, where success in one domain can reinforce and strengthen another. For instance: - The success of Brand Orbit feeds back into the Fiscal Architecture as better brand positioning can lead to higher sales and investment attraction. - Fiscal Architecture’s robustness can enable more sustainable and ethical practices, reinforcing the Ethical Compass. This framework aims to provide a holistic blueprint whereby researchers and practitioners can evaluate the interdependencies of business domains, and devise strategies that promote synergy and sustainable growth. IFSEA offers a lens through which the complexity of a business’s ecosystem can be appreciated, and potential areas for intervention can be identified, promoting a dynamic approach to entrepreneurism in the ever-evolving market landscape.

Appendix 65: Temperature Test 1.5 Outcome

Academia-OS generated model:

Name: *Continual Agile Nexus in Dynamic Organizational Strategies (CANDOS)* Model **I. Overview of the Theoretical Model:** The Continual Agile Nexus in Dynamic Organizational Strategies (CANDOS) Model seeks to provide an integrative framework representing the interplay between strategic development, organizational adaptability, stakeholder engagement, and risk management through a lens that elucidates the robustness and innovation capacities of a company within changing environments. This model is not unidirectional but demonstrates bi-directional relationships, iterating that a change in one dimension can concurrently inspire adaptations across others. **II. Description of Aggregate Dimensions:** 1. **Strategic Development:** - Factors: Environmental Vision and Action, Financial Journey, Company Foundations, Growth and Changes. 2. **Organizational Adaptability:** - Factors: Resilience and Commitment, Engaging Innovations. 3. **Stakeholder Engagement:** - Factors: Communication and Interaction. 4. **Risk Management:** - Factors: Security and Conflict Minimization. **III. Relationships among Aggregate Dimensions within the CANDOS**: **1. Strategic Development and Organizational Adaptability (Bidirectional Causality):** Strategic development influences how agile and responsive an organization remains, directing it toward sustaining innovation while navigating growth and operational transformations. 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• refutational analysis,
• use of comprehensive data,
• constant testing and comparison of data,
• use of tables to record data,
• as well as the use of inclusive of deviant cases.

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