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2.3: Inductive or Deductive? Two Different Approaches

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Learning Objectives

• Describe the inductive approach to research, and provide examples of inductive research.
• Describe the deductive approach to research, and provide examples of deductive research.
• Describe the ways that inductive and deductive approaches may be complementary.

Theories structure and inform sociological research. So, too, does research structure and inform theory. The reciprocal relationship between theory and research often becomes evident to students new to these topics when they consider the relationships between theory and research in inductive and deductive approaches to research. In both cases, theory is crucial. But the relationship between theory and research differs for each approach. Inductive and deductive approaches to research are quite different, but they can also be complementary. Let’s start by looking at each one and how they differ from one another. Then we’ll move on to thinking about how they complement one another.

Inductive Approaches and Some Examples

In an inductive approach to research, a researcher begins by collecting data that is relevant to his or her topic of interest. Once a substantial amount of data have been collected, the researcher will then take a breather from data collection, stepping back to get a bird’s eye view of her data. At this stage, the researcher looks for patterns in the data, working to develop a theory that could explain those patterns. Thus when researchers take an inductive approach, they start with a set of observations and then they move from those particular experiences to a more general set of propositions about those experiences. In other words, they move from data to theory, or from the specific to the general. Figure 2.5 outlines the steps involved with an inductive approach to research.

Figure 2.5 Inductive Research

There are many good examples of inductive research, but we’ll look at just a few here. One fascinating recent study in which the researchers took an inductive approach was Katherine Allen, Christine Kaestle, and Abbie Goldberg’s study (2011)Allen, K. R., Kaestle, C. E., & Goldberg, A. E. (2011). More than just a punctuation mark: How boys and young men learn about menstruation. Journal of Family Issues, 32 , 129–156. of how boys and young men learn about menstruation. To understand this process, Allen and her colleagues analyzed the written narratives of 23 young men in which the men described how they learned about menstruation, what they thought of it when they first learned about it, and what they think of it now. By looking for patterns across all 23 men’s narratives, the researchers were able to develop a general theory of how boys and young men learn about this aspect of girls’ and women’s biology. They conclude that sisters play an important role in boys’ early understanding of menstruation, that menstruation makes boys feel somewhat separated from girls, and that as they enter young adulthood and form romantic relationships, young men develop more mature attitudes about menstruation.

In another inductive study, Kristin Ferguson and colleagues (Ferguson, Kim, & McCoy, 2011)Ferguson, K. M., Kim, M. A., & McCoy, S. (2011). Enhancing empowerment and leadership among homeless youth in agency and community settings: A grounded theory approach. Child and Adolescent Social Work Journal, 28 , 1–22. analyzed empirical data to better understand how best to meet the needs of young people who are homeless. The authors analyzed data from focus groups with 20 young people at a homeless shelter. From these data they developed a set of recommendations for those interested in applied interventions that serve homeless youth. The researchers also developed hypotheses for people who might wish to conduct further investigation of the topic. Though Ferguson and her colleagues did not test the hypotheses that they developed from their analysis, their study ends where most deductive investigations begin: with a set of testable hypotheses.

Deductive Approaches and Some Examples

Researchers taking a deductive approach take the steps described earlier for inductive research and reverse their order. They start with a social theory that they find compelling and then test its implications with data. That is, they move from a more general level to a more specific one. A deductive approach to research is the one that people typically associate with scientific investigation. The researcher studies what others have done, reads existing theories of whatever phenomenon he or she is studying, and then tests hypotheses that emerge from those theories. Figure 2.6 outlines the steps involved with a deductive approach to research.

Figure 2.6 Deductive Research

While not all researchers follow a deductive approach, as you have seen in the preceding discussion, many do, and there are a number of excellent recent examples of deductive research. We’ll take a look at a couple of those next.

In a study of US law enforcement responses to hate crimes, Ryan King and colleagues (King, Messner, & Baller, 2009)King, R. D., Messner, S. F., & Baller, R. D. (2009). Contemporary hate crimes, law enforcement, and the legacy of racial violence. American Sociological Review, 74 , 291–315.hypothesized that law enforcement’s response would be less vigorous in areas of the country that had a stronger history of racial violence. The authors developed their hypothesis from their reading of prior research and theories on the topic. Next, they tested the hypothesis by analyzing data on states’ lynching histories and hate crime responses. Overall, the authors found support for their hypothesis.

In another recent deductive study, Melissa Milkie and Catharine Warner (2011)Milkie, M. A., & Warner, C. H. (2011). Classroom learning environments and the mental health of first grade children. Journal of Health and Social Behavior, 52 , 4–22. studied the effects of different classroom environments on first graders’ mental health. Based on prior research and theory, Milkie and Warner hypothesized that negative classroom features, such as a lack of basic supplies and even heat, would be associated with emotional and behavioral problems in children. The researchers found support for their hypothesis, demonstrating that policymakers should probably be paying more attention to the mental health outcomes of children’s school experiences, just as they track academic outcomes (American Sociological Association, 2011).The American Sociological Association wrote a press release on Milkie and Warner’s findings: American Sociological Association. (2011). Study: Negative classroom environment adversely affects children’s mental health. Retrieved from asanet.org/press/Negative_Cla...tal_Health.cfm

Complementary Approaches?

While inductive and deductive approaches to research seem quite different, they can actually be rather complementary. In some cases, researchers will plan for their research to include multiple components, one inductive and the other deductive. In other cases, a researcher might begin a study with the plan to only conduct either inductive or deductive research, but then he or she discovers along the way that the other approach is needed to help illuminate findings. Here is an example of each such case.

In the case of my collaborative research on sexual harassment, we began the study knowing that we would like to take both a deductive and an inductive approach in our work. We therefore administered a quantitative survey, the responses to which we could analyze in order to test hypotheses, and also conducted qualitative interviews with a number of the survey participants. The survey data were well suited to a deductive approach; we could analyze those data to test hypotheses that were generated based on theories of harassment. The interview data were well suited to an inductive approach; we looked for patterns across the interviews and then tried to make sense of those patterns by theorizing about them.

For one paper (Uggen & Blackstone, 2004),Uggen, C., & Blackstone, A. (2004). Sexual harassment as a gendered expression of power. American Sociological Review, 69 , 64–92. we began with a prominent feminist theory of the sexual harassment of adult women and developed a set of hypotheses outlining how we expected the theory to apply in the case of younger women’s and men’s harassment experiences. We then tested our hypotheses by analyzing the survey data. In general, we found support for the theory that posited that the current gender system, in which heteronormative men wield the most power in the workplace, explained workplace sexual harassment—not just of adult women but of younger women and men as well. In a more recent paper (Blackstone, Houle, & Uggen, 2006),Blackstone, A., Houle, J., & Uggen, C. “At the time I thought it was great”: Age, experience, and workers’ perceptions of sexual harassment. Presented at the 2006 meetings of the American Sociological Association. Currently under review. we did not hypothesize about what we might find but instead inductively analyzed the interview data, looking for patterns that might tell us something about how or whether workers’ perceptions of harassment change as they age and gain workplace experience. From this analysis, we determined that workers’ perceptions of harassment did indeed shift as they gained experience and that their later definitions of harassment were more stringent than those they held during adolescence. Overall, our desire to understand young workers’ harassment experiences fully—in terms of their objective workplace experiences, their perceptions of those experiences, and their stories of their experiences—led us to adopt both deductive and inductive approaches in the work.

Researchers may not always set out to employ both approaches in their work but sometimes find that their use of one approach leads them to the other. One such example is described eloquently in Russell Schutt’s Investigating the Social World (2006).Schutt, R. K. (2006). Investigating the social world: The process and practice of research . Thousand Oaks, CA: Pine Forge Press. As Schutt describes, researchers Lawrence Sherman and Richard Berk (1984)Sherman, L. W., & Berk, R. A. (1984). The specific deterrent effects of arrest for domestic assault. American Sociological Review, 49 , 261–272. conducted an experiment to test two competing theories of the effects of punishment on deterring deviance (in this case, domestic violence). Specifically, Sherman and Berk hypothesized that deterrence theory would provide a better explanation of the effects of arresting accused batterers than labeling theory . Deterrence theory predicts that arresting an accused spouse batterer will reduce future incidents of violence. Conversely, labeling theory predicts that arresting accused spouse batterers will increase future incidents. Figure 2.7 summarizes the two competing theories and the predictions that Sherman and Berk set out to test.

Figure 2.7 Predicting the Effects of Arrest on Future Spouse Battery

Sherman and Berk found, after conducting an experiment with the help of local police in one city, that arrest did in fact deter future incidents of violence, thus supporting their hypothesis that deterrence theory would better predict the effect of arrest. After conducting this research, they and other researchers went on to conduct similar experimentsThe researchers did what’s called replication. We’ll learn more about replication in Chapter 3. in six additional cities (Berk, Campbell, Klap, & Western, 1992; Pate & Hamilton, 1992; Sherman & Smith, 1992).Berk, R., Campbell, A., Klap, R., & Western, B. (1992). The deterrent effect of arrest in incidents of domestic violence: A Bayesian analysis of four field experiments. American Sociological Review, 57 , 698–708; Pate, A., & Hamilton, E. (1992). Formal and informal deterrents to domestic violence: The Dade county spouse assault experiment. American Sociological Review, 57 , 691–697; Sherman, L., & Smith, D. (1992). Crime, punishment, and stake in conformity: Legal and informal control of domestic violence. American Sociological Review, 57 , 680–690. Results from these follow-up studies were mixed. In some cases, arrest deterred future incidents of violence. In other cases, it did not. This left the researchers with new data that they needed to explain. The researchers therefore took an inductive approach in an effort to make sense of their latest empirical observations. The new studies revealed that arrest seemed to have a deterrent effect for those who were married and employed but that it led to increased offenses for those who were unmarried and unemployed. Researchers thus turned to control theory, which predicts that having some stake in conformity through the social ties provided by marriage and employment, as the better explanation.

Figure 2.8 Predicting the Effects of Arrest on Future Spouse Battery: A New Theory

What the Sherman and Berk research, along with the follow-up studies, shows us is that we might start with a deductive approach to research, but then, if confronted by new data that we must make sense of, we may move to an inductive approach. Russell Schutt depicts this process quite nicely in his text, and I’ve adapted his depiction here, in Figure 2.9.

KEY TAKEAWAYS

• The inductive approach involves beginning with a set of empirical observations, seeking patterns in those observations, and then theorizing about those patterns.
• The deductive approach involves beginning with a theory, developing hypotheses from that theory, and then collecting and analyzing data to test those hypotheses.
• Inductive and deductive approaches to research can be employed together for a more complete understanding of the topic that a researcher is studying.
• Though researchers don’t always set out to use both inductive and deductive strategies in their work, they sometimes find that new questions arise in the course of an investigation that can best be answered by employing both approaches.

Monty Python and Holy Grail :

(click to see video)

Do the townspeople take an inductive or deductive approach to determine whether the woman in question is a witch? What are some of the different sources of knowledge (recall Chapter 1) they rely on?

• Think about how you could approach a study of the relationship between gender and driving over the speed limit. How could you learn about this relationship using an inductive approach? What would a study of the same relationship look like if examined using a deductive approach? Try the same thing with any topic of your choice. How might you study the topic inductively? Deductively?

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Inductive Vs Deductive Research

Table of Contents

Inductive and deductive research are two different approaches to conducting a research study. While they are different in their orientation and methods, they can both be used to generate new knowledge and advance scientific understanding.

Inductive Research

Inductive research is a bottom-up approach to research where a researcher starts with specific observations and data and then works to generate general principles or theories. This approach involves collecting data and analyzing it to identify patterns, themes, or categories. From these patterns, the researcher can develop theories or concepts that can explain the observations made in the data. Inductive research is often used in qualitative research, case studies, and grounded theory research.

Deductive Research

Deductive research is a top-down approach to research where a researcher starts with a theory or hypothesis and then tests it through data collection and analysis. This approach involves testing a specific hypothesis or theory and then drawing conclusions based on the results of the analysis. Deductive research is often used in quantitative research, experimental research, and survey research.

Difference between Inductive and Deductive Research

Here are some key differences between inductive and deductive research:

Both inductive and deductive research have their strengths and weaknesses, and the choice of which to use depends on the nature of the research question, the research objectives, and the available resources. Inductive research is useful when a researcher wants to explore a new area of inquiry or when there is limited theory or previous research available, while deductive research is useful when a researcher wants to test a specific theory or hypothesis.

Also see Research Methods

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Muhammad Hassan

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Please note you do not have access to teaching notes, qualitative research: deductive and inductive approaches to data analysis.

Qualitative Research Journal

ISSN : 1443-9883

Article publication date: 31 October 2018

Issue publication date: 15 November 2018

The purpose of this paper is to explain the rationale for choosing the qualitative approach to research human resources practices, namely, recruitment and selection, training and development, performance management, rewards management, employee communication and participation, diversity management and work and life balance using deductive and inductive approaches to analyse data. The paper adopts an emic perspective that favours the study of transfer of human resource management practices from the point of view of employees and host country managers in subsidiaries of western multinational enterprises in Ghana.

Design/methodology/approach

Despite the numerous examples of qualitative methods of data generation, little is known particularly to the novice researcher about how to analyse qualitative data. This paper develops a model to explain in a systematic manner how to methodically analyse qualitative data using both deductive and inductive approaches.

The deductive and inductive approaches provide a comprehensive approach in analysing qualitative data. The process involves immersing oneself in the data reading and digesting in order to make sense of the whole set of data and to understand what is going on.

Originality/value

This paper fills a serious gap in qualitative data analysis which is deemed complex and challenging with limited attention in the methodological literature particularly in a developing country context, Ghana.

• Qualitative
• Emic interviews documents

Azungah, T. (2018), "Qualitative research: deductive and inductive approaches to data analysis", Qualitative Research Journal , Vol. 18 No. 4, pp. 383-400. https://doi.org/10.1108/QRJ-D-18-00035

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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Quantitative and Qualitative Approaches to Generalization and Replication–A Representationalist View

In this paper, we provide a re-interpretation of qualitative and quantitative modeling from a representationalist perspective. In this view, both approaches attempt to construct abstract representations of empirical relational structures. Whereas quantitative research uses variable-based models that abstract from individual cases, qualitative research favors case-based models that abstract from individual characteristics. Variable-based models are usually stated in the form of quantified sentences (scientific laws). This syntactic structure implies that sentences about individual cases are derived using deductive reasoning. In contrast, case-based models are usually stated using context-dependent existential sentences (qualitative statements). This syntactic structure implies that sentences about other cases are justifiable by inductive reasoning. We apply this representationalist perspective to the problems of generalization and replication. Using the analytical framework of modal logic, we argue that the modes of reasoning are often not only applied to the context that has been studied empirically, but also on a between-contexts level. Consequently, quantitative researchers mostly adhere to a top-down strategy of generalization, whereas qualitative researchers usually follow a bottom-up strategy of generalization. Depending on which strategy is employed, the role of replication attempts is very different. In deductive reasoning, replication attempts serve as empirical tests of the underlying theory. Therefore, failed replications imply a faulty theory. From an inductive perspective, however, replication attempts serve to explore the scope of the theory. Consequently, failed replications do not question the theory per se , but help to shape its boundary conditions. We conclude that quantitative research may benefit from a bottom-up generalization strategy as it is employed in most qualitative research programs. Inductive reasoning forces us to think about the boundary conditions of our theories and provides a framework for generalization beyond statistical testing. In this perspective, failed replications are just as informative as successful replications, because they help to explore the scope of our theories.

Introduction

Qualitative and quantitative research strategies have long been treated as opposing paradigms. In recent years, there have been attempts to integrate both strategies. These “mixed methods” approaches treat qualitative and quantitative methodologies as complementary, rather than opposing, strategies (Creswell, 2015 ). However, whilst acknowledging that both strategies have their benefits, this “integration” remains purely pragmatic. Hence, mixed methods methodology does not provide a conceptual unification of the two approaches.

Lacking a common methodological background, qualitative and quantitative research methodologies have developed rather distinct standards with regard to the aims and scope of empirical science (Freeman et al., 2007 ). These different standards affect the way researchers handle contradictory empirical findings. For example, many empirical findings in psychology have failed to replicate in recent years (Klein et al., 2014 ; Open Science, Collaboration, 2015 ). This “replication crisis” has been discussed on statistical, theoretical and social grounds and continues to have a wide impact on quantitative research practices like, for example, open science initiatives, pre-registered studies and a re-evaluation of statistical significance testing (Everett and Earp, 2015 ; Maxwell et al., 2015 ; Shrout and Rodgers, 2018 ; Trafimow, 2018 ; Wiggins and Chrisopherson, 2019 ).

However, qualitative research seems to be hardly affected by this discussion. In this paper, we argue that the latter is a direct consequence of how the concept of generalizability is conceived in the two approaches. Whereas most of quantitative psychology is committed to a top-down strategy of generalization based on the idea of random sampling from an abstract population, qualitative studies usually rely on a bottom-up strategy of generalization that is grounded in the successive exploration of the field by means of theoretically sampled cases.

Here, we show that a common methodological framework for qualitative and quantitative research methodologies is possible. We accomplish this by introducing a formal description of quantitative and qualitative models from a representationalist perspective: both approaches can be reconstructed as special kinds of representations for empirical relational structures. We then use this framework to analyze the generalization strategies used in the two approaches. These turn out to be logically independent of the type of model. This has wide implications for psychological research. First, a top-down generalization strategy is compatible with a qualitative modeling approach. This implies that mainstream psychology may benefit from qualitative methods when a numerical representation turns out to be difficult or impossible, without the need to commit to a “qualitative” philosophy of science. Second, quantitative research may exploit the bottom-up generalization strategy that is inherent to many qualitative approaches. This offers a new perspective on unsuccessful replications by treating them not as scientific failures, but as a valuable source of information about the scope of a theory.

The Quantitative Strategy–Numbers and Functions

Quantitative science is about finding valid mathematical representations for empirical phenomena. In most cases, these mathematical representations have the form of functional relations between a set of variables. One major challenge of quantitative modeling consists in constructing valid measures for these variables. Formally, to measure a variable means to construct a numerical representation of the underlying empirical relational structure (Krantz et al., 1971 ). For example, take the behaviors of a group of students in a classroom: “to listen,” “to take notes,” and “to ask critical questions.” One may now ask whether is possible to assign numbers to the students, such that the relations between the assigned numbers are of the same kind as the relations between the values of an underlying variable, like e.g., “engagement.” The observed behaviors in the classroom constitute an empirical relational structure, in the sense that for every student-behavior tuple, one can observe whether it is true or not. These observations can be represented in a person × behavior matrix 1 (compare Figure 1 ). Given this relational structure satisfies certain conditions (i.e., the axioms of a measurement model), one can assign numbers to the students and the behaviors, such that the relations between the numbers resemble the corresponding numerical relations. For example, if there is a unique ordering in the empirical observations with regard to which person shows which behavior, the assigned numbers have to constitute a corresponding unique ordering, as well. Such an ordering coincides with the person × behavior matrix forming a triangle shaped relation and is formally represented by a Guttman scale (Guttman, 1944 ). There are various measurement models available for different empirical structures (Suppes et al., 1971 ). In the case of probabilistic relations, Item-Response models may be considered as a special kind of measurement model (Borsboom, 2005 ).

Constructing a numerical representation from an empirical relational structure; Due to the unique ordering of persons with regard to behaviors (indicated by the triangular shape of the relation), it is possible to construct a Guttman scale by assigning a number to each of the individuals, representing the number of relevant behaviors shown by the individual. The resulting variable (“engagement”) can then be described by means of statistical analyses, like, e.g., plotting the frequency distribution.

Although essential, measurement is only the first step of quantitative modeling. Consider a slightly richer empirical structure, where we observe three additional behaviors: “to doodle,” “to chat,” and “to play.” Like above, one may ask, whether there is a unique ordering of the students with regard to these behaviors that can be represented by an underlying variable (i.e., whether the matrix forms a Guttman scale). If this is the case, we may assign corresponding numbers to the students and call this variable “distraction.” In our example, such a representation is possible. We can thus assign two numbers to each student, one representing his or her “engagement” and one representing his or her “distraction” (compare Figure 2 ). These measurements can now be used to construct a quantitative model by relating the two variables by a mathematical function. In the simplest case, this may be a linear function. This functional relation constitutes a quantitative model of the empirical relational structure under study (like, e.g., linear regression). Given the model equation and the rules for assigning the numbers (i.e., the instrumentations of the two variables), the set of admissible empirical structures is limited from all possible structures to a rather small subset. This constitutes the empirical content of the model 2 (Popper, 1935 ).

Constructing a numerical model from an empirical relational structure; Since there are two distinct classes of behaviors that each form a Guttman scale, it is possible to assign two numbers to each individual, correspondingly. The resulting variables (“engagement” and “distraction”) can then be related by a mathematical function, which is indicated by the scatterplot and red line on the right hand side.

The Qualitative Strategy–Categories and Typologies

The predominant type of analysis in qualitative research consists in category formation. By constructing descriptive systems for empirical phenomena, it is possible to analyze the underlying empirical structure at a higher level of abstraction. The resulting categories (or types) constitute a conceptual frame for the interpretation of the observations. Qualitative researchers differ considerably in the way they collect and analyze data (Miles et al., 2014 ). However, despite the diverse research strategies followed by different qualitative methodologies, from a formal perspective, most approaches build on some kind of categorization of cases that share some common features. The process of category formation is essential in many qualitative methodologies, like, for example, qualitative content analysis, thematic analysis, grounded theory (see Flick, 2014 for an overview). Sometimes these features are directly observable (like in our classroom example), sometimes they are themselves the result of an interpretative process (e.g., Scheunpflug et al., 2016 ).

In contrast to quantitative methodologies, there have been little attempts to formalize qualitative research strategies (compare, however, Rihoux and Ragin, 2009 ). However, there are several statistical approaches to non-numerical data that deal with constructing abstract categories and establishing relations between these categories (Agresti, 2013 ). Some of these methods are very similar to qualitative category formation on a conceptual level. For example, cluster analysis groups cases into homogenous categories (clusters) based on their similarity on a distance metric.

Although category formation can be formalized in a mathematically rigorous way (Ganter and Wille, 1999 ), qualitative research hardly acknowledges these approaches. 3 However, in order to find a common ground with quantitative science, it is certainly helpful to provide a formal interpretation of category systems.

Let us reconsider the above example of students in a classroom. The quantitative strategy was to assign numbers to the students with regard to variables and to relate these variables via a mathematical function. We can analyze the same empirical structure by grouping the behaviors to form abstract categories. If the aim is to construct an empirically valid category system, this grouping is subject to constraints, analogous to those used to specify a measurement model. The first and most important constraint is that the behaviors must form equivalence classes, i.e., within categories, behaviors need to be equivalent, and across categories, they need to be distinct (formally, the relational structure must obey the axioms of an equivalence relation). When objects are grouped into equivalence classes, it is essential to specify the criterion for empirical equivalence. In qualitative methodology, this is sometimes referred to as the tertium comparationis (Flick, 2014 ). One possible criterion is to group behaviors such that they constitute a set of specific common attributes of a group of people. In our example, we might group the behaviors “to listen,” “to take notes,” and “to doodle,” because these behaviors are common to the cases B, C, and D, and they are also specific for these cases, because no other person shows this particular combination of behaviors. The set of common behaviors then forms an abstract concept (e.g., “moderate distraction”), while the set of persons that show this configuration form a type (e.g., “the silent dreamer”). Formally, this means to identify the maximal rectangles in the underlying empirical relational structure (see Figure 3 ). This procedure is very similar to the way we constructed a Guttman scale, the only difference being that we now use different aspects of the empirical relational structure. 4 In fact, the set of maximal rectangles can be determined by an automated algorithm (Ganter, 2010 ), just like the dimensionality of an empirical structure can be explored by psychometric scaling methods. Consequently, we can identify the empirical content of a category system or a typology as the set of empirical structures that conforms to it. 5 Whereas the quantitative strategy was to search for scalable sub-matrices and then relate the constructed variables by a mathematical function, the qualitative strategy is to construct an empirical typology by grouping cases based on their specific similarities. These types can then be related to one another by a conceptual model that describes their semantic and empirical overlap (see Figure 3 , right hand side).

Constructing a conceptual model from an empirical relational structure; Individual behaviors are grouped to form abstract types based on them being shared among a specific subset of the cases. Each type constitutes a set of specific commonalities of a class of individuals (this is indicated by the rectangles on the left hand side). The resulting types (“active learner,” “silent dreamer,” “distracted listener,” and “troublemaker”) can then be related to one another to explicate their semantic and empirical overlap, as indicated by the Venn-diagram on the right hand side.

Variable-Based Models and Case-Based Models

In the previous section, we have argued that qualitative category formation and quantitative measurement can both be characterized as methods to construct abstract representations of empirical relational structures. Instead of focusing on different philosophical approaches to empirical science, we tried to stress the formal similarities between both approaches. However, it is worth also exploring the dissimilarities from a formal perspective.

Following the above analysis, the quantitative approach can be characterized by the use of variable-based models, whereas the qualitative approach is characterized by case-based models (Ragin, 1987 ). Formally, we can identify the rows of an empirical person × behavior matrix with a person-space, and the columns with a corresponding behavior-space. A variable-based model abstracts from the single individuals in a person-space to describe the structure of behaviors on a population level. A case-based model, on the contrary, abstracts from the single behaviors in a behavior-space to describe individual case configurations on the level of abstract categories (see Table 1 ).

Variable-based models and case-based models.

From a representational perspective, there is no a priori reason to favor one type of model over the other. Both approaches provide different analytical tools to construct an abstract representation of an empirical relational structure. However, since the two modeling approaches make use of different information (person-space vs. behavior-space), this comes with some important implications for the researcher employing one of the two strategies. These are concerned with the role of deductive and inductive reasoning.

In variable-based models, empirical structures are represented by functional relations between variables. These are usually stated as scientific laws (Carnap, 1928 ). Formally, these laws correspond to logical expressions of the form

In plain text, this means that y is a function of x for all objects i in the relational structure under consideration. For example, in the above example, one may formulate the following law: for all students in the classroom it holds that “distraction” is a monotone decreasing function of “engagement.” Such a law can be used to derive predictions for single individuals by means of logical deduction: if the above law applies to all students in the classroom, it is possible to calculate the expected distraction from a student's engagement. An empirical observation can now be evaluated against this prediction. If the prediction turns out to be false, the law can be refuted based on the principle of falsification (Popper, 1935 ). If a scientific law repeatedly withstands such empirical tests, it may be considered to be valid with regard to the relational structure under consideration.

In case-based models, there are no laws about a population, because the model does not abstract from the cases but from the observed behaviors. A case-based model describes the underlying structure in terms of existential sentences. Formally, this corresponds to a logical expression of the form

In plain text, this means that there is at least one case i for which the condition XYZ holds. For example, the above category system implies that there is at least one active learner. This is a statement about a singular observation. It is impossible to deduce a statement about another person from an existential sentence like this. Therefore, the strategy of falsification cannot be applied to test the model's validity in a specific context. If one wishes to generalize to other cases, this is accomplished by inductive reasoning, instead. If we observed one person that fulfills the criteria of calling him or her an active learner, we can hypothesize that there may be other persons that are identical to the observed case in this respect. However, we do not arrive at this conclusion by logical deduction, but by induction.

Despite this important distinction, it would be wrong to conclude that variable-based models are intrinsically deductive and case-based models are intrinsically inductive. 6 Both types of reasoning apply to both types of models, but on different levels. Based on a person-space, in a variable-based model one can use deduction to derive statements about individual persons from abstract population laws. There is an analogous way of reasoning for case-based models: because they are based on a behavior space, it is possible to deduce statements about singular behaviors. For example, if we know that Peter is an active learner, we can deduce that he takes notes in the classroom. This kind of deductive reasoning can also be applied on a higher level of abstraction to deduce thematic categories from theoretical assumptions (Braun and Clarke, 2006 ). Similarly, there is an analog for inductive generalization from the perspective of variable-based modeling: since the laws are only quantified over the person-space, generalizations to other behaviors rely on inductive reasoning. For example, it is plausible to assume that highly engaged students tend to do their homework properly–however, in our example this behavior has never been observed. Hence, in variable-based models we usually generalize to other behaviors by means of induction. This kind of inductive reasoning is very common when empirical results are generalized from the laboratory to other behavioral domains.

Although inductive and deductive reasoning are used in qualitative and quantitative research, it is important to stress the different roles of induction and deduction when models are applied to cases. A variable-based approach implies to draw conclusions about cases by means of logical deduction; a case-based approach implies to draw conclusions about cases by means of inductive reasoning. In the following, we build on this distinction to differentiate between qualitative (bottom-up) and quantitative (top-down) strategies of generalization.

Generalization and the Problem of Replication

We will now extend the formal analysis of quantitative and qualitative approaches to the question of generalization and replicability of empirical findings. For this sake, we have to introduce some concepts of formal logic. Formal logic is concerned with the validity of arguments. It provides conditions to evaluate whether certain sentences (conclusions) can be derived from other sentences (premises). In this context, a theory is nothing but a set of sentences (also called axioms). Formal logic provides tools to derive new sentences that must be true, given the axioms are true (Smith, 2020 ). These derived sentences are called theorems or, in the context of empirical science, predictions or hypotheses . On the syntactic level, the rules of logic only state how to evaluate the truth of a sentence relative to its premises. Whether or not sentences are actually true, is formally specified by logical semantics.

On the semantic level, formal logic is intrinsically linked to set-theory. For example, a logical statement like “all dogs are mammals,” is true if and only if the set of dogs is a subset of the set of mammals. Similarly, the sentence “all chatting students doodle” is true if and only if the set of chatting students is a subset of the set of doodling students (compare Figure 3 ). Whereas, the first sentence is analytically true due to the way we define the words “dog” and “mammal,” the latter can be either true or false, depending on the relational structure we actually observe. We can thus interpret an empirical relational structure as the truth criterion of a scientific theory. From a logical point of view, this corresponds to the semantics of a theory. As shown above, variable-based and case-based models both give a formal representation of the same kinds of empirical structures. Accordingly, both types of models can be stated as formal theories. In the variable-based approach, this corresponds to a set of scientific laws that are quantified over the members of an abstract population (these are the axioms of the theory). In the case-based approach, this corresponds to a set of abstract existential statements about a specific class of individuals.

In contrast to mathematical axiom systems, empirical theories are usually not considered to be necessarily true. This means that even if we find no evidence against a theory, it is still possible that it is actually wrong. We may know that a theory is valid in some contexts, yet it may fail when applied to a new set of behaviors (e.g., if we use a different instrumentation to measure a variable) or a new population (e.g., if we draw a new sample).

From a logical perspective, the possibility that a theory may turn out to be false stems from the problem of contingency . A statement is contingent, if it is both, possibly true and possibly false. Formally, we introduce two modal operators: □ to designate logical necessity, and ◇ to designate logical possibility. Semantically, these operators are very similar to the existential quantifier, ∃, and the universal quantifier, ∀. Whereas ∃ and ∀ refer to the individual objects within one relational structure, the modal operators □ and ◇ range over so-called possible worlds : a statement is possibly true, if and only if it is true in at least one accessible possible world, and a statement is necessarily true if and only if it is true in every accessible possible world (Hughes and Cresswell, 1996 ). Logically, possible worlds are mathematical abstractions, each consisting of a relational structure. Taken together, the relational structures of all accessible possible worlds constitute the formal semantics of necessity, possibility and contingency. 7

In the context of an empirical theory, each possible world may be identified with an empirical relational structure like the above classroom example. Given the set of intended applications of a theory (the scope of the theory, one may say), we can now construct possible world semantics for an empirical theory: each intended application of the theory corresponds to a possible world. For example, a quantified sentence like “all chatting students doodle” may be true in one classroom and false in another one. In terms of possible worlds, this would correspond to a statement of contingency: “it is possible that all chatting students doodle in one classroom, and it is possible that they don't in another classroom.” Note that in the above expression, “all students” refers to the students in only one possible world, whereas “it is possible” refers to the fact that there is at least one possible world for each of the specified cases.

To apply these possible world semantics to quantitative research, let us reconsider how generalization to other cases works in variable-based models. Due to the syntactic structure of quantitative laws, we can deduce predictions for singular observations from an expression of the form ∀ i : y i = f ( x i ). Formally, the logical quantifier ∀ ranges only over the objects of the corresponding empirical relational structure (in our example this would refer to the students in the observed classroom). But what if we want to generalize beyond the empirical structure we actually observed? The standard procedure is to assume an infinitely large, abstract population from which a random sample is drawn. Given the truth of the theory, we can deduce predictions about what we may observe in the sample. Since usually we deal with probabilistic models, we can evaluate our theory by means of the conditional probability of the observations, given the theory holds. This concept of conditional probability is the foundation of statistical significance tests (Hogg et al., 2013 ), as well as Bayesian estimation (Watanabe, 2018 ). In terms of possible world semantics, the random sampling model implies that all possible worlds (i.e., all intended applications) can be conceived as empirical sub-structures from a greater population structure. For example, the empirical relational structure constituted by the observed behaviors in a classroom would be conceived as a sub-matrix of the population person × behavior matrix. It follows that, if a scientific law is true in the population, it will be true in all possible worlds, i.e., it will be necessarily true. Formally, this corresponds to an expression of the form

The statistical generalization model thus constitutes a top-down strategy for dealing with individual contexts that is analogous to the way variable-based models are applied to individual cases (compare Table 1 ). Consequently, if we apply a variable-based model to a new context and find out that it does not fit the data (i.e., there is a statistically significant deviation from the model predictions), we have reason to doubt the validity of the theory. This is what makes the problem of low replicability so important: we observe that the predictions are wrong in a new study; and because we apply a top-down strategy of generalization to contexts beyond the ones we observed, we see our whole theory at stake.

Qualitative research, on the contrary, follows a different strategy of generalization. Since case-based models are formulated by a set of context-specific existential sentences, there is no need for universal truth or necessity. In contrast to statistical generalization to other cases by means of random sampling from an abstract population, the usual strategy in case-based modeling is to employ a bottom-up strategy of generalization that is analogous to the way case-based models are applied to individual cases. Formally, this may be expressed by stating that the observed qualia exist in at least one possible world, i.e., the theory is possibly true:

This statement is analogous to the way we apply case-based models to individual cases (compare Table 1 ). Consequently, the set of intended applications of the theory does not follow from a sampling model, but from theoretical assumptions about which cases may be similar to the observed cases with respect to certain relevant characteristics. For example, if we observe that certain behaviors occur together in one classroom, following a bottom-up strategy of generalization, we will hypothesize why this might be the case. If we do not replicate this finding in another context, this does not question the model itself, since it was a context-specific theory all along. Instead, we will revise our hypothetical assumptions about why the new context is apparently less similar to the first one than we originally thought. Therefore, if an empirical finding does not replicate, we are more concerned about our understanding of the cases than about the validity of our theory.

Whereas statistical generalization provides us with a formal (and thus somehow more objective) apparatus to evaluate the universal validity of our theories, the bottom-up strategy forces us to think about the class of intended applications on theoretical grounds. This means that we have to ask: what are the boundary conditions of our theory? In the above classroom example, following a bottom-up strategy, we would build on our preliminary understanding of the cases in one context (e.g., a public school) to search for similar and contrasting cases in other contexts (e.g., a private school). We would then re-evaluate our theoretical description of the data and explore what makes cases similar or dissimilar with regard to our theory. This enables us to expand the class of intended applications alongside with the theory.

Of course, none of these strategies is superior per se . Nevertheless, they rely on different assumptions and may thus be more or less adequate in different contexts. The statistical strategy relies on the assumption of a universal population and invariant measurements. This means, we assume that (a) all samples are drawn from the same population and (b) all variables refer to the same behavioral classes. If these assumptions are true, statistical generalization is valid and therefore provides a valuable tool for the testing of empirical theories. The bottom-up strategy of generalization relies on the idea that contexts may be classified as being more or less similar based on characteristics that are not part of the model being evaluated. If such a similarity relation across contexts is feasible, the bottom-up strategy is valid, as well. Depending on the strategy of generalization, replication of empirical research serves two very different purposes. Following the (top-down) principle of generalization by deduction from scientific laws, replications are empirical tests of the theory itself, and failed replications question the theory on a fundamental level. Following the (bottom-up) principle of generalization by induction to similar contexts, replications are a means to explore the boundary conditions of a theory. Consequently, failed replications question the scope of the theory and help to shape the set of intended applications.

We have argued that quantitative and qualitative research are best understood by means of the structure of the employed models. Quantitative science mainly relies on variable-based models and usually employs a top-down strategy of generalization from an abstract population to individual cases. Qualitative science prefers case-based models and usually employs a bottom-up strategy of generalization. We further showed that failed replications have very different implications depending on the underlying strategy of generalization. Whereas in the top-down strategy, replications are used to test the universal validity of a model, in the bottom-up strategy, replications are used to explore the scope of a model. We will now address the implications of this analysis for psychological research with regard to the problem of replicability.

Modern day psychology almost exclusively follows a top-down strategy of generalization. Given the quantitative background of most psychological theories, this is hardly surprising. Following the general structure of variable-based models, the individual case is not the focus of the analysis. Instead, scientific laws are stated on the level of an abstract population. Therefore, when applying the theory to a new context, a statistical sampling model seems to be the natural consequence. However, this is not the only possible strategy. From a logical point of view, there is no reason to assume that a quantitative law like ∀ i : y i = f ( x i ) implies that the law is necessarily true, i.e.,: □(∀ i : y i = f ( x i )). Instead, one might just as well define the scope of the theory following an inductive strategy. 8 Formally, this would correspond to the assumption that the observed law is possibly true, i.e.,: ◇(∀ i : y i = f ( x i )). For example, we may discover a functional relation between “engagement” and “distraction” without referring to an abstract universal population of students. Instead, we may hypothesize under which conditions this functional relation may be valid and use these assumptions to inductively generalize to other cases.

If we take this seriously, this would require us to specify the intended applications of the theory: in which contexts do we expect the theory to hold? Or, equivalently, what are the boundary conditions of the theory? These boundary conditions may be specified either intensionally, i.e., by giving external criteria for contexts being similar enough to the ones already studied to expect a successful application of the theory. Or they may be specified extensionally, by enumerating the contexts where the theory has already been shown to be valid. These boundary conditions need not be restricted to the population we refer to, but include all kinds of contextual factors. Therefore, adopting a bottom-up strategy, we are forced to think about these factors and make them an integral part of our theories.

In fact, there is good reason to believe that bottom-up generalization may be more adequate in many psychological studies. Apart from the pitfalls associated with statistical generalization that have been extensively discussed in recent years (e.g., p-hacking, underpowered studies, publication bias), it is worth reflecting on whether the underlying assumptions are met in a particular context. For example, many samples used in experimental psychology are not randomly drawn from a large population, but are convenience samples. If we use statistical models with non-random samples, we have to assume that the observations vary as if drawn from a random sample. This may indeed be the case for randomized experiments, because all variation between the experimental conditions apart from the independent variable will be random due to the randomization procedure. In this case, a classical significance test may be regarded as an approximation to a randomization test (Edgington and Onghena, 2007 ). However, if we interpret a significance test as an approximate randomization test, we test not for generalization but for internal validity. Hence, even if we use statistical significance tests when assumptions about random sampling are violated, we still have to use a different strategy of generalization. This issue has been discussed in the context of small-N studies, where variable-based models are applied to very small samples, sometimes consisting of only one individual (Dugard et al., 2012 ). The bottom-up strategy of generalization that is employed by qualitative researchers, provides such an alternative.

Another important issue in this context is the question of measurement invariance. If we construct a variable-based model in one context, the variables refer to those behaviors that constitute the underlying empirical relational structure. For example, we may construct an abstract measure of “distraction” using the observed behaviors in a certain context. We will then use the term “distraction” as a theoretical term referring to the variable we have just constructed to represent the underlying empirical relational structure. Let us now imagine we apply this theory to a new context. Even if the individuals in our new context are part of the same population, we may still get into trouble if the observed behaviors differ from those used in the original study. How do we know whether these behaviors constitute the same variable? We have to ensure that in any new context, our measures are valid for the variables in our theory. Without a proper measurement model, this will be hard to achieve (Buntins et al., 2017 ). Again, we are faced with the necessity to think of the boundary conditions of our theories. In which contexts (i.e., for which sets of individuals and behaviors) do we expect our theory to work?

If we follow the rationale of inductive generalization, we can explore the boundary conditions of a theory with every new empirical study. We thus widen the scope of our theory by comparing successful applications in different contexts and unsuccessful applications in similar contexts. This may ultimately lead to a more general theory, maybe even one of universal scope. However, unless we have such a general theory, we might be better off, if we treat unsuccessful replications not as a sign of failure, but as a chance to learn.

Author Contributions

MB conceived the original idea and wrote the first draft of the paper. MS helped to further elaborate and scrutinize the arguments. All authors contributed to the final version of the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank Annette Scheunpflug for helpful comments on an earlier version of the manuscript.

1 A person × behavior matrix constitutes a very simple relational structure that is common in psychological research. This is why it is chosen here as a minimal example. However, more complex structures are possible, e.g., by relating individuals to behaviors over time, with individuals nested within groups etc. For a systematic overview, compare Coombs ( 1964 ).

2 This notion of empirical content applies only to deterministic models. The empirical content of a probabilistic model consists in the probability distribution over all possible empirical structures.

3 For example, neither the SAGE Handbook of qualitative data analysis edited by Flick ( 2014 ) nor the Oxford Handbook of Qualitative Research edited by Leavy ( 2014 ) mention formal approaches to category formation.

4 Note also that the described structure is empirically richer than a nominal scale. Therefore, a reduction of qualitative category formation to be a special (and somehow trivial) kind of measurement is not adequate.

5 It is possible to extend this notion of empirical content to the probabilistic case (this would correspond to applying a latent class analysis). But, since qualitative research usually does not rely on formal algorithms (neither deterministic nor probabilistic), there is currently little practical use of such a concept.

6 We do not elaborate on abductive reasoning here, since, given an empirical relational structure, the concept can be applied to both types of models in the same way (Schurz, 2008 ). One could argue that the underlying relational structure is not given a priori but has to be constructed by the researcher and will itself be influenced by theoretical expectations. Therefore, abductive reasoning may be necessary to establish an empirical relational structure in the first place.

7 We shall not elaborate on the metaphysical meaning of possible worlds here, since we are only concerned with empirical theories [but see Tooley ( 1999 ), for an overview].

8 Of course, this also means that it would be equally reasonable to employ a top-down strategy of generalization using a case-based model by postulating that □(∃ i : XYZ i ). The implications for case-based models are certainly worth exploring, but lie beyond the scope of this article.

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Dr Deborah Gabriel

Inductive and deductive approaches to research

The main difference between inductive and deductive approaches to research is that whilst a deductive approach is aimed and testing theory, an inductive approach is concerned with the generation of new theory emerging from the data.

A deductive approach usually begins with a hypothesis, whilst an inductive approach will usually use research questions to narrow the scope of the study.

For deductive approaches the emphasis is generally on causality, whilst for inductive approaches the aim is usually focused on exploring new phenomena or looking at previously researched phenomena from a different perspective.

Inductive approaches are generally associated with qualitative research, whilst deductive approaches are more commonly associated with quantitative research. However, there are no set rules and some qualitative studies may have a deductive orientation.

One specific inductive approach that is frequently referred to in research literature is grounded theory, pioneered by Glaser and Strauss.

This approach necessitates the researcher beginning with a completely open mind without any preconceived ideas of what will be found. The aim is to generate a new theory based on the data.

Once the data analysis has been completed the researcher must examine existing theories in order to position their new theory within the discipline.

Grounded theory is not an approach to be used lightly. It requires extensive and repeated sifting through the data and analysing and re-analysing multiple times in order to identify new theory. It is an approach best suited to research projects where there the phenomena to be investigated has not been previously explored.

The most important point to bear in mind when considering whether to use an inductive or deductive approach is firstly the purpose of your research; and secondly the methods that are best suited to either test a hypothesis, explore a new or emerging area within the discipline, or to answer specific research questions.

Citing This Article

Gabriel, D. (2013). Inductive and deductive approaches to research.  Accessed on ‘date’   from https://deborahgabriel.com/2013/03/17/inductive-and-deductive-approaches-to-research/

Gabriel, D., 2013. Inductive and deductive approaches to research.  Accessed on ‘date’  from https://deborahgabriel.com/2013/03/17/inductive-and-deductive-approaches-to-research/

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200 thoughts on “ Inductive and deductive approaches to research ”

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Hi, yes the explanation was helpful because it was simple to read and pretty much, straight to the point. It has given me a brief understanding for what I needs. Thanks — Chantal

It was very supportive for me!! thank you!!

thank you so much for the information. it was simple to read and also brief concise and straight to the point. thank you.

Deborah, thanks for this elaboration. but I am asking is it possible to conduct a deductive inclined research and also generate a theory, or add to the theory. I have been asked by my supervisor whether I am just testing hypothesi or my aim is to contribute/generate a theory. my studies is more of a quantitative nature. Thanks

Deductive research is more aimed towards testing a hypothesis and therefore is an approach more suited to working with quantitative data. The process normally involves reproducing a previous study and seeing if the same results are produced. This does not lend itself to generating new theories since that is not the object of the research. Between inductive and deductive approaches there is also a third approach which I will write a post on shortly – abdductive.

Dear Deborah, it has been very long time since you posted this article. However, I can testify that it is very helpful for a novis reseracher like me. 

Ms. Deborah, thank you for given info, but i was in confusion reg, differences between deductive, inductive, abductive and (new one) Hypothetico-deductive approaches. Can it be possible to email the differences, its applications, tools used and scientific nature, to build a theory using quantitative survey method. My email Id is: [email protected] Iam writing my PhD thesis based on this . it is part of my 3 chapter. Thanking you awaiting for your mail soon.

Hi Madhu. As a PhD student you need to take the time to read the appropriate literature on research approaches and attend workshops/conferences to better understand methodology. You cannot take short cuts by by asking someone (me) to simply provide you with ready answers to your queries – especially when I do not have the time to do so!

Great saying…every student at every level, should be critical analysts and critical thinkers.

Thank u.. but there is a little mistake which is deductive approach deals with qualitative and inductive appoech deals with quantitative

No – you have it the wrong way round – I suggest you read the article again and also engage in wider reading on research methods to gain a deeper understanding.

A clear cut concept with example.

Deborah I appreciate so much in this article…I got what I am looking for… thanks for your contribution.

Thanks so much

It is simple, easy to differentiate and understandable.

Thank u for the information, it really helps me.

Exactly, your work is simple and clear, that there are two research approaches, Inductive and deductive.Qualitative and Quantative approaches You gave clear differences in a balance, simple to understand, I suppose you are a teacher by profession.   This is how we share knowledge,and you become more knowledgable

Thank you Lambawi, I am glad that these posts are proving useful. I will endeavour to add some more in the coming weeks!

This has been helpful. thanks for posting

Thank you very much for sharing knowledge with me. It really much helpful while preparing my college exam. Thank –:)

Thank you ever so much for making it simple and easily understandable. Would love to see more posts.

Best wishes

The explanation is simple and easy to understand it has helped to a lot thank you

very helpful and explained simply. thanks

Explanation is simple…. it was a great help for my exam preparation.

Excellent presentation please!

Very helpful information and a clear, simple explanation. Thank you

Thanks; this has been helpful in preparation for my forthcoming exams

This is fantastic, I have greatly beneffitted from this straight forward illustration

Thanks…i will benifited to read this

Thanks for your help. Keep it like that so that will be our guide towards our destinations.

Thank-you for your academic insights.

Thank you for your clarification. Well understood.

Hi, I had a question would you call process tracing technique an inductive or deductive approach? or maybe both? Hope you can help me with the same.

Hi Achin. Process tracing is a qualitative analytical tool and therefore inductive rather than deductive, since its purpose is to identify new phenomena. You might find this journal article useful:

http://www.ukcds.org.uk/sites/default/files/uploads/Understanding-Process-Tracing.pdf

Preparing for my Research Methods exams and I'm grateful for your explanations. This is a full lecture made simple. Thank you very much.

I am very thankful for this information, madam you are just good. If you are believer, allow me to say, May God bless you with more knowledge and good health.

Hi Rasol, glad that the post was useful and  – yes – I am a believer so thank you for the blessing!

Dear Deborah 

I am currently doing Btech in forensic with Unisa would you be so kind and help with this question below and may I use your services while Iam doing this degree Please 

Hi I have question that goes like these "If the reseacher wanted to conduct reseach in a specific context to see whether it supports an establisblished theory" the reseacher would be conducting 

1. case study 

2. deductive research 

3. exploratory resarch 

4. inductive methods 

please help me to choose the right one 

Yours Faithfully 

Baba Temba 

Deductive, which is not exploratory but designed to test a hypothesis. So this is unlike to be case study research but a quantitative study.

Hi Deborah, I have been struggling with my research methods proposal, in finding the right methodology for my study.  This is the only explanation out of all the books that I have read which really enables me to truly understand the meaning of Grounded Theory for which you describe as an inductive.  I just would like to say thank you for your explanation as this has helped me in a way, which I thought I would never get.  Thank you Destini 

You are most welcome!

Very useful piece of information. Thank you!  

Very impressing work, may god bless you with more mighty knowledge.

In fact this has been very usefull information for me in my research,. It's very clear and easy to understand looking at the choice of language ,etc God bless you!!

Hi Ms Deborah Gabriel, I am from the backward area of Pakistan, which is known as “North Waziristan”. Unfortunately famous for terrorism, as from that background, you can understand the weakness of my educational background. I am struggling for MBA degree, and I was searching for Deductive and Inductive approaches, and then I found your best explained article here, already praised by many people. I will just add this “Thank you, May GOD Bless You” I will be highly honoured if you would like to contact me on my email. My email is [email protected] Thanking you for your time and efforts.

Is it possible to use deductive approach in research concerning what has happened in an industry?

If you are seeking to test a hypothesis then yes. 

Thank you very much this information has been extremely helpful. I can now progress with my dissertation. 

Thanks for that good work Deborah. It has taken me quite a short time to read and understand. Kindly please help me understand what am required to write in this case where my teacher gave me this question: "Explain the process of deduction and induction research approaches".

Please refer to the recommended reading:  https://deborahgabriel.com/recommended-reading/

Many thanks to you, I really appreciate u on ur information provided basically on theories and approaches to understanding research.

Thank you very much.

Good work Deborah.

Thank you so much!! The distinction between the two approaches is clear and concise. Most other websites tend to go into long discussions without really getting to the point. This was very helpful. 

Thank you , useful explanation

It is a very fruitful post. I would like to ask if the objective of my research is to develop an extended process from the existing processes. And I am going to use qualitative and quantitative research methods, because my research phenomenon requires to study the individual meanings and perceptions and then uses the findings from the qualitative study and also the theoretical study as inputs for the quantitative study. Finally, I will use the findings of the theoretical study and the quantitative study in developing the extended process. So, which approach to follow in this case?

Dear Tamer, Your question is too hypothetical for me to offer a response. But in any event, you are the only one who can decide whether an inductive or deductive approach is appropriate for your research project. This is where methodology comes in – which is about determining what research methods will be most effective in answering your research questions and which are in sync with your approach (e.g. critical, feminist etc). 

my dissertation is in the same situation. and I also feel struggle to choose my research approach. I guess its a combination of inductive and deductive. using the deductive approach to test what was found in the literature, and use an inductive approach to examine the themes that emerged from qualitative data.

Thanks much! 

What do you think about the approach with quantitative analyses that start with data to generate theories? Typically data mining techniques fit into my example. 

This is a question of methodology – research methods must be selected based on the discipline, research questions and approach to the study. For example, If you are seeking to ascertain how many people read the news on their smartphones then a quantitiative method is most appropriate. On the other hand. if you are seeking to delve into why  some people read the news on their smartphones, then clearly a qualitative method is required.

Awesome response, I was looking at the same thing in my postgraduate class.

What if I’m using secondary sources? Which would be more appropriate qualitative or quantitative?

The question of inductive or deductive approaches arise only in relation to ‘primary’ research – that is when you are undertaking your own study. In your own study, secondary sources would appear under a Literature Review. However, if you are doing a dissertation, say for an undergraduate degree where you are not undertaking primary research then inductive or deductive approaches are not applicable. I hope this clarifies.

Your comments are really good and easy to understand. Hope to contact you for my project. keep up the good work. thank you

The last paragraph stated ‘The most important point to bear in mind when considering whether to use an inductive or deductive approach is firstly the purpose of your research; and secondly the methods that are best suited to either test a hypothesis, explore a new or emerging area within the discipline, or to answer specific research questions. However my question is if my research is about answering specific research questions in a qualitative research. Am I to use the inductive or the deductive or the mixture of the two?

Hi Ola, if your research questions are qualitatively focused – that is seeking to find out the whys and the hows as opposed to ‘what’ and ‘how many’ then certainly an inductive approach is most appropriate. This is because inductive aims to find new theories emerging from the data whereas deductive is centred on testing a hypothesis rather than exploring research questions.

Thank u so much.it was difficult for me to understand but with ur help the job is complete

Points of distinction top notch. Absolutely fantastic. Straight to the point. Was really helpful. Keep up the good work.

Thanks for the inforation Deborah. It was  useful

Thank you so much, this was something I was never able to grasp so well! I found this site while searching the difference between the two on Google. I am a PHD Scholar, now it seems I will be visiting this site frequently and seeking your help 🙂

Hi Deborah. Thank you for the input.It clearly exemplifies the difference. In your response to one of the questions, you have highlighted a lot of 'what'  will qualify the research as quantitative. I have developed 4 research questions, 3 are on 'what's and 1 'why'. The what is because my sample of analysis is multimodal text. Will my study still fall under qualitative? Thank you in advance, Deborah. I appreciate it very much. 

Hi Zilla, It is hard to provide a definitive answer without knowing what your research questions are (although time does not permit me to provide individual responses). So I will reiterate that the question of whether to adopt an inductive or deductive approach to a research project is relevant for ‘primary’ research – that is, research that you undertake yourself. Factors that influence your decision should rest on whether you are seeking to explore the ‘whys’ and ‘hows’ of human experience, generating new levels or understanding or simply wish to test a hypothesis or use a large sample in order to generalise results to the wider population. You say that your sample is multimodal text – that is simply text plus media such as videos, pictures etc. My question to you is whether this multimodal text has been generated from primary research – i.e interviews you conducted, photos you took and/or videos that you filmed of research subjects? If that is the case then I would presume that this would be a qualitative research project that would lend itself to an inductive approach,since I cannot imagine that you would be able to work with a very large sample of multimodal text. If the multimodal text is not generated from your own primary resesarch then this is secondary research that might be included in your literature review but would fall outside the scope of your analysis.

Dear Deborah I just want to ask you to help me with generation of theory. Steps that need to be followed

Mongwai Michael

Thanks a lot for showing me the best way to understand the basic difference between two approaches of research.

Dear Aliyu, time does not permit me to provide responses on your individual projects. Therefore, my aim is to equip you with the understanding of different approaches so that you have both the confidence and competence to make appropriate decisions on the most suitable methodological approaches to your research.

Beautiful stuff you are giving us Deborah.

Deborah, thank you so much for your explanation, I'm clear now.

I am gathering quantitative data to develop a model to represent the behavior of a material using an existing model. I subsequently used this model to simulate the material behavior with a computer program. this is a reversal approach to previour reaeasch in these area. usually the computer simulation is used to obtain quantitative data without experiment. Could you please kindly let me know what is my reasearch method Thanks

Please see my response to Aliyu on 8 November.

Dr, your explanation about inductive research and deductive, is meaningful to postgraduate students. What is your suggestion on my research topic: use of handset by primary six pupils for games, rather for home works and readings, what is the research approach that will be suitable?

Very brief and well explained. Thank you Deborah.

Thank you Dr. Gabriel, good informationl; will come back. 

It has actually helped – a similar question was asked last year in my schools,that prompted me to search for it while preparing for my exam. Today the same question appeared and I used your explanation as my response to the question. Thanks.

Hi Deborah, your explanations are comprehensible. I understand this topic thanks to you. May I ask you question? What are  the similarities between inductive and deductive reasoning?

That’s like asking what the similarities are between quantitative and qualitative research! Focus on what your research objectives are and then choose the approach that will be most efefctive in meeting these objectives. 

Thanks so much Have got what I really want here

Enlightening facts. Thank you.

Thanks Deborah for the explanation but, i want to ask if descriptive is inductive or deductive approach? God bless you

it is really good explanation

Can I ask one question? I am going to research how technology is changing the hotel industry particularly at the hotel front desk so is that inductive or deductive approach? I believe deductive approach because the aim of my research is to investigate current used technology at hotel front desk. So what do you think please let me know Thank you very much indeed.

Please refer to my post on conceptual frameworks to take you through the key steps in developing a research project – you will find your answer there:  https://deborahgabriel.com/2015/02/14/using-conceptual-frameworks-in-qualitative-research/ 

The information provide is quite helpful, thanks after all….

I was confused about these approaches but your information has helped me a lot – reasonable and authentic.

I've got the answers,thx.

It is very clear and concise.

Thank you, it was right on point.

Thank you, I used this solution for my assignment.

Thank you so much Deborah. I am currently doing my dissertation and most of my lecturers have recommeded us to use Research Methods for Business Students by Mark Saunders, Philip Lewis and Adrian Thornhill. I have found the book very hard to understand especially when I'm wrtiting up the methodology section as I have to talk about deductive and inductive approaches.  You have simplified it and explained it well. Also you have made it so so easy to understand. Everyone should be reading this. Thank you so so, so much.

You’re very welcome. Good luck with your dissertation!

Thank you D now I am aware of these two!

I found it so easy to understand the difference between deductive theory and inductive- it's so helpful. Many Thanks. 

Deborah, your work is precise,well organized and relevant.Thank you very much.

Thanks for the explanation, it has cleared my doubts. 

Thanks Dr. Deb, I am satisfied – it was really useful.

Hi Doc, thank you for making things simpler for me. I will always be incontact with your website. Stay forever blessed. 

Thank you for the information. It really helps me.

Hi Deborah, i just went through the abductive approach which is combination of inductive and deductive Approach. I found it a little confusing when I tried to know by my own from e sources. But after going through the conversation in this page helped me a lot. Thank u very much. If u can share your email I can share my report made for my pre PhD comprehensive viva. My profession is teaching and my area of research is International HRM. Title is Knowledge and Learning Model among effective repatriation . If anybody is doing reserach in the same area, plse feel free to reach me at [email protected]. Thank u all again

Thx for the information.

Hi Deborah Thank you very much for the article. it is informative. My question is what approach am i supposed to take if i am doing a research that is both qualitative and quantitative. I am doing research on the feasibility of establishing renewable energy systems in a developing country. I am using a simulation software to generate a model to analyse the technical and economic data (Quantitative) but i have to use interviews to capture social and polical views from industry experts (Qualitative). So which approach is best in such a scenario? Thank you

In a mixed methods study, the quanitiative dimension of the study usually functions to capture preliminary data, with the qualitative dimension being the primary method that answers the research questions. In any case, in a mixed methods study you must peform both quantitative and qualitative data analysis – separately. In reference to your specifc study you need to refer back to your reearch questions and the aims and objectives of your study. Is your primary objective to develop a model for a renewable energy system or is it to determine whether industry experts see the viability of the model? If it is the latter then the approach should be inductive. I would advise you to consult your supervisor or someone in your discipline, as I am not an engineer.

Very informative.

Your explaination of inductive & deductive approaches to research is clear to understand. 

Your explanation of concepts is succint and easily conceivable. Helpful.

Thank you so much Deborah. I am currently writing my research on resource curse theory and I will like to have your private mail for private discussions. Thank you

No problem – you can use the contact form and your message will go directly to my email address.

Thanks for differentiating the two in easy and pragmetic manner.

Thank you Deborah, that was a simple, clear explanation helpful for sure.

I like the way you simplified everything,was really helpful for my assignment. Please how do I reference this work? Thank you

Reference it as an online source:

Gabriel,D.(2013). Inductive and deductive approaches to research. Avaolable from: https://deborahgabriel.com/2013/03/17/inductive-and-deductive-approaches-to-research/  

Thank you Deborah, this is very helpful to me and others.

Great insight, simple and clear; I now get the difference thanks for sharing.

Thanks for the very good explanation and comparison.

A very simple and straightforward guidance to students. 

Hi Debrah , It is really interesting to get valuable points from your statments about deductive reasoning. However it seems short . It will be helpful for us if you write more. Thanks

Dear Almaz, thank you for your feedback. The post was only intended to provide a brief overview of the subject – to better understand inductive and deductive approaches to research I strongly recommend immersing yourself in the available literature. 

Perhaps, you can suggest 1 or 2 widely cited scholars (to read) that argue that deductive approaches can also be used in a qualitative methodology which is interpretive/subjective in nature.

Thank you once again!

You are definitely on point.

Thank you so much.

Thank you so much Deborah.

Such inspiring work.

Your literature is helping us a lot here at the Ivory Tower Makerere University Kampala Uganda.

I am glad to hear that. What are you studying?

Hi Deborah. I am Iftikhar from United Arab Emirates (UAE).  I am conducting a research on learning preferences of Generation Z youth, and one of my research questions is "What are the learning preferences of UAE Gen Z youth and how matching of L&D program delivery with these learning preferences affect Gen Z interest in organizational L&D programs?"  Now as for existing literature, a lot has already been written on this but in the West; there is practically no formal research literature available on this topic in UAE.  Therefore, I am taking the Western literature outcomes and applying these in UAE context to see the results. My questions are: a. Will this research be treated as "Deductive' or "Inductive"? b.  Should I choose 'Quantitative" or "Qualitative' approach? Wishing you all the best.  

Thanks Deborah. but I'm confused. You said deductive approach is used in quantitative research and it test a theory and inductive approach is used in qualitative research to generate a theory. So what is grounded theory?

Thanks a lot for such a good explanation, Deborah!

Thank you very much. It was simple to understand.

Lovely….this was very helpful…simple and straightforward. 

Thanks. It is so useful. Best Regards.

This has been troubling me for a while. It is often said that the interpretive paradigm typically goes with inductive approaches and methods involving observation, interviews and research into archives. But then if concepts are to emerge from the data without theoretical preconceptions, how come it is often said that the research design, choice of case studies, and initial coding in thematic analysis can be theory driven? Wouldn’t that make the approach deductive (i.e. about testing theory). Or, how does theory coming before the research design fit with an inductive approach? In my experience so far authors seem to evade this point.

HALLO D! I REALLY LIKE YOUR WORK IT REALLY HELPED ME IN MY RESEARCH.

Thank You so very much Deborah. I really got to uncover what puzzled me on deductive versus inductive approaches.

Thanks Dr.Gabriel. It was very simple and useful. Now I understood the differecne b/w deductive and inductive method.  

Thanx for sharing with us. It is very useful for my dissertation.  Your topic clarifies the difference between inductive and deductive research.

Hi Dr.Gabriel, I am doing a research to apply a theory into service industry which is more commonly practiced in manufacturing industry ( known as Lean approach), my aim is to apply this approach into banking operation, the objective is to find the elements/processes in the bank operation that actually increase the cost or decrease the service quality. If I want to conduct a research to find those elements in a bank operation. should I use Inductive approach? what is your advise?  Thanks 

Hi Deborah, Thank you for a great article! It made it very clear the differenece between deductive and inductive.  I'd like to ask you the following: – Is possible to have inductive study with hypotheses and use semi-structured interviews to answer these hypotheses and research questions?   thank you very much for your reply!   Alina

Hi Alina, I’m glad my post has been useful for you. In answer to your question, I think maybe you are confusing research questions with hypotheses. Research questions guide the overall study and ensure that when designing interview questions – they are structured in the most effective way to elicit responses that address the research questions. Hypotheses are linked to deductive studies where researchers aim to test presumptions/predictions about phenomenon – this is not the same as research questions.

Hi Deborah Thanks for an intersting piece of work presented. Am kindly inquiring how i can get along with literature review and conceptual framemework on the topic 'IDPs and Solid Waste Management' and objectives; exploring everyday practice around solid waste management; finding out how social networks move and merge into new spaces for waste management and establish connections between waste management and social lfestyle. Thanks Hakimu

Hi Deborah thank you for a great article . I have a question for you I am doing my research work and I have some issue about theory construction. Basically I am a beginner in social research – I have no idea about constructing new theory. Please let me know about theory construction and what is a procedure  – how can I construct theory and also about steps of this method? I hope you will understand my words. Thank you.

Dear Amna, Welcome to the world of research – we all have to start somewhere! If you're new to social research I would recommend you join the Social Research Association (SRA) who provide training and a wealth of resources for researchers. With regards to theory – unless you are researching new phenomena that has never been researched before or are developing a completely new approach (unlikely) you will not be creating 'new' theory with your research project. You will be using existing theory in your approach and embed theoretical perspectives into your methodology. You will also likely use relevant theories when analysing your data. However, before you think about theory you need to develop your methodology – see my other post: methods and methodology .

Thanks so much. This post was very helpful and easy to understand!

Hi Deborah, Thank you for the precise and helpful information .. I need your help as I feel a little bit confused. i am doing a case study of airline corporate image. it is the newest crisis scenario in my country related to our regional carrier. I think, i among the pioneers doing the case study research for this airline company. I used the conceptual framework from other previous conducted study. It was conducted in quantitative manner. If i used the conceptual framework as my guidance for my literature review and interview question construction, is that okay if i do not use inductive for the case study because i do not build a new theory. If i just compare and argue with the previous finding and the model used, is it consider as deductive approach in case study? Based on my reading, i found some researchers used deductive approach in their case study. they tested the hypotheses..but i just compare my finding with the model used from the previous research. For your information, i did documentation, direct observation and interview (trigulation) with ex-passengers and aviation expert. What do you think? .Please help me..i am stuck. Thank you

Thank you Doctor, it’s straightforward valuable piece of knowledge. It may require a little bit of referencing. Furthermore, adding citation line below will be useful for academic use.

Thank you very much!

Thank you for a crisp and nice post. It helped me.

I am very beginner in research, and its really very helpful.

Thanks writer,

Thank you very much…

wow wow wow great work Deborah. I now have a clear insight of the differences,,,, kudos!

Thank you …it’s helpful for me.

Good job.It helped me to find the question’s answer in my mid-term. Thenk you.

My study is ethnographic research specifically it studies about culture, tradition and lifestyle of an ethnic groups. I think my research is inductive, is it right?

Thank you, I feel same as most the above commentators. Very well written – written in a way that I (who for the first time heard of these two types of researcher methods,) felt like I got a gist of what they are and how they are different.

Thanks Deborah G! Your articles have helped me a lot.

your article is simple to understand and please keep it up thanks.

Hi, It is really helpful me to get sorted these concepts in research field in simple manner.Thanks for that and really appreciate it.

Very clear explanation about inductive and deductive appraoches in research. I like it.

I appreciate your clear and precise explanations, thank you.

Thanks for this, it’s clear, concise and easy to understand – very helpful.

That was just perfect. I have been reading about these methods from my course book offered by university for 3 days but I couldn’t understand their differences. Now it is completely clear. Thanks a lot.

Is that possible to have both in our research? I mean, what if we choose an inductive approach and then when we go forward make some assumptions to answer research questions?

If you are undertaking inductive research then you don’t make any assumptions as you are looking for micro theories to emerge from your analysis of the data. You cannot start with inductive and then switch to deductive – it must be one or the other. You don’t make assumptions to answer research questions – you analyse the findings to do that.

It is precise and clear. Thanks

Great work and explanation and also the researcher herself is very energetic and motivated to help others… world is because of people like you. thanks

Dear madam, I’m a undergraduate studying engineering and I want to know famous researches done solely based on inductive and deductive separately….if you can give me some examples it will be very helpful to me….

You can look this up yourself, through your library and learning resources at your institution…

Dear Deborah, Thank you for the precise explanation of inductive and deductive approaches.

When analysing data in a qualitative study, could you use both inductive and deductive methods as a triangulation technique?

Hi Irene, a mixed methods study might involve both a quantitative method – e.g. survey and qualitative – e.g. interviews. But the overall approach would still be inductive as the quantitative element normally shapes the qualitative and the overall aim would still be to gain in-depth understandings rather than generalise findings. Mixed methods does as you say, create academic rigour through triangulation.

Dear Deborah Thank you in advance for using your precious time to reply to my question. God bless you. l am doing my master degree dissertation on Green Supply Chain Management practices in the United Kingdom automotive industry. The research philosophy that I adopt is this: interpretivist epistemological and constructivism ontological. The methodology is as follows: Interpretivism – inductive – mono-method qualitative -survey. My question is this: Can online survey questionnaire be used with the inductive approach? Most books that l am reading are linking online survey with quantitative data.

Quite educative. Thank you Dr Deborah, it’s so useful to my study.

Thank you Dr Gabriel. It was really useful abstract. Kindly help me to enlighten with more details on grounded theory, dependent and independent variables.

Thank you Dr Gabriel for sharing your knowledge with all of us. Highly appreciated. I have just started my Ph.D. program, and I’m still struggled to deciding which paradigm I have to use! I have one question: My research idea talks about the readiness of an organization toward IoT (factors that affect the organization readiness towards IoT). note*: The IoT technology is still not implemented in the organization context that I want to study that is why I’m going to study the readiness. so what is your suggestion for me regarding which paradigm I have to use?

Hi Mutasem, Congratulations on embarking on your PhD program! Your research paradigm should reflect your positionality, your values and in essence, how you view the world. You need to think critically and reflectively about this. For example, you say you plan to study the readiness of an organization to implement IT. One approach to this study could be examining what factors might shape that readiness – i.e power structures that confer equality/inequality, and there is also the question of how the adoption of IT could help to create more inclusivity and diversity (which contribute to greater productivity & profitability). This of course is a different proposition from merely focusing on technical issues as opposed to social/political ones that also shape technology use.

THANK YOU SO MUCH FOR YOUR KIND RESPONSE

Dear Dr Gabriel

I am currently busy with my Masters in Interior design. My research aim is to determine (possibly explore as Its not currently making sense) the discrepancy that exists between the designer and a specific user group of a Healthcare environment. I have used provisional coding as a first cycle method (which identified a set of themes by which to analyze a Healthcare environment). These themes (conceptual framework) informed my interviews etc. From there the findings were analysed to see to what extent the designer aims correspond with the way in which the user group experiences the space (through the various themes). I initially thought that it was a deductive study but as it is a qualitative study I was abit worried. From what I understood from previous comments, the inductive/deductive is only applicable in the primary research, would that then mean that my study takes the form of a inductive approach? (although my questions are ‘what’ and ‘how’).

Kind regards Anienke

Hi Deborah, your posts are quite simple and useful. Great! Thanks for posts!

Thanks for posting this, I would say that this article is one of the most useful explanation I have found so far. But I also have a question, hopefully you will be able to help me out. If my research question is about understanding “How important are loyalty programmes for customers in welness field. I use quantitative method (Online survey) to collect data from respondents, what is my approach for research inductive and deductive?

Dr Deborah Gabriel, Honestly, I have gone through your explanation on inductive and deductive approaches to research work and I’m very pleased with the write up. I want to sincerely thank you for your contribution to the existing body of knowledge. Regards, Clemduze.

Thank you Dr Gabriel for explaining the differences between inductive and deductive approaches in research. Your explanation helped me understand these two concepts as I am working on the early portions of my dissertation in General Psychology.

Thank you so much. This has been very useful. I now know I can use both Inductive and deductive if i am carrying out a mixed method research. Both can be used depending on my research questions.

It was helpful.

How can I be at this time replying to this worthy and simple explanation? It’s well placed and and explained. Thank you D.

Thanks for your article. How do I reference you in my work?

APA Gabriel, D. (2013). Inductive and deductive approaches to research. Accessed on ‘date’ from https://deborahgabriel.com/2013/03/17/inductive-and-deductive-approaches-to-research/

Harvard Gabriel, D., 2013. Inductive and deductive approaches to research. Accessed on ‘date’ from https://deborahgabriel.com/2013/03/17/inductive-and-deductive-approaches-to-research/

Can social research be purely deductive?

‘Social’ research – i.e research within the social sciences, can be qualitative or quantitative, and therefore can be inductive or deductive. It depends on what the research objectives are as to which approach is taken – and as my article states, these questions are explored through research methodology.

Very important points are discussed in your article in simplified terms.

Thanks very much!

Appreciated the discussion – it is well simplified and easy to understand.

Comments are closed.

Inductive and/or Deductive Research Designs

• First Online: 27 October 2022

Cite this chapter

• Md. Shahidul Haque 4  

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This chapter aims to introduce the readers, especially the Bangladeshi undergraduate and postgraduate students to some fundamental considerations of inductive and deductive research designs. The deductive approach refers to testing a theory, where the researcher builds up a theory or hypotheses and plans a research stratagem to examine the formulated theory. On the contrary, the inductive approach intends to construct a theory, where the researcher begins by gathering data to establish a theory. In the beginning, a researcher must clarify which approach he/she will follow in his/her research work. The chapter discusses basic concepts, characteristics, steps and examples of inductive and deductive research designs. Here, also a comparison between inductive and deductive research designs is shown. It concludes with a look at how both inductive and deductive designs are used comprehensively to constitute a clearer image of research work.

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Haque, M.S. (2022). Inductive and/or Deductive Research Designs. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_5

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Logical Reasoning. Inductive Vs Deductive Reasoning 

Written by Jeena Timalsina on May 20, 2024

Have you ever wondered how Sherlock Holmes always solved crimes? How do businesses come up with ideas and decide on launching new products or upgrading their service?

The answer lies in logical reasoning, and today we will learn how Big Data plays a crucial role in this process.

Everything we do online generates data, the zettabytes of data accumulated from millions of us(users) become Big Data. 

Each action we perform in cyberspace leaves a data trail. From what we search on Google, which result do we click on, how many seconds do we spend on the particular page, and other forms of engagement are valuables for the website.  

These same actions that don’t seem like anything to us are goldmines that turn businesses into multi-billion dollar enterprises. But to make sense of it all and use it to their advantage, they need logical reasoning.

Logical reasoning is the process of using logic, or systematic methods of thinking to arrive at a conclusion or make a decision. 

One makes inferences, draws conclusions, and evaluates arguments based on evidence and principles of logic. 

Even Sherlock Holmes relied on it! More precisely, the fictional sleuth counted heavily on inductive reasoning and deductive reasoning during his investigation. 

Going through his mountain of evidence with witness statements, fingerprints, surveillance footage, crime scene photographs, and more. 

There is a presumption that Holmes inclined more toward inductive reasoning because he portrays keen observation skills that act as a basis for his investigation, but he also used deductive reasoning with established facts extensively. 

Both categories fall under the umbrella of logical reasoning. 

By the end of this article, you will realize how big-data insights are absolute necessities for businesses to make inductive and deductive inferences. 

Now without further ado, let’s answer…

What is inductive and deductive reasoning?

Inductive reasoning is the act of using specific scenarios to make generalized conclusions. It is also called cause-and-effect reasoning. The bottom-up approach relies on limited specific observations and evidence is used to draw conclusions that can be applied more widely. This approach generates new premises and hypotheses based on observed patterns. 

For example, you see a phenomenon repeatedly occur like each time you eat a peanut, your throat starts itching so you conclude that you might have a peanut allergy. This conclusion is drawn from observation, and experience, along with a tinge of probability to make our arguments reliable. 

Hence in inductive reasoning, we make observations, discern a pattern, and infer a theory. 

Whereas, deductive reasoning is the process of using a general principle or premise (a factual statement we believe to be true) to arrive at a specific, guaranteed conclusion. This is also called the top-down approach. It’s like starting with a well-established rule and applying it to specific cases.

For example, the statement ‘I need to reach the office by 10 AM’, is a general premise. Following that, the next statement is a fact ‘It takes 40 minutes to reach the office if I take my car’. Based on that, we can deduce that ‘I need to leave the house at 9:20 AM, at the latest to arrive at the office on time’. These syllogisms are good ways to test deductive reasoning and the validity of the argument. 

Thus, deductive reasoning ensures the truth of the conclusion if the premises are also true and valid. 

Now that we have a basic idea of the two reasoning approaches. 

Let’s jump to the…

Key differences of Inductive vs. deductive reasoning. 

Data for inductive and deductive reasoning.

You can use qualitative data from the web to uncover patterns and trends in your industry for inductive reasoning. 

Qualitative data from the internet include trending searches, social media mentions, discussions, and online reviews. 

These are critical details you should never miss out on during analysis to understand your customer’s preferences, pain points, and public sentiment toward your business. You can identify emerging trends with access to real-time data and perform proactive analysis to predict the future behavior of your website visitors. 

Along the same lines, for A/B testing, targeted marketing, and risk assessment, you can utilize quantitative data from the web for deductive inference in business decision-making. 

Imagine how by comparing prices with your competitors, or competitor monitoring, you can always refurbish your pricing strategy and identify the most competitive price for your service that boosts user engagement and retention. 

You can also design targeted advertising campaigns that resonate with your target audience, and figure out what works best for you and the user’s experience. 

Limitations

Like Superman’s kryptonite, it’s needless to say that both of these approaches have their limitations. 

The reliability of a conclusion from inductive logic which depends on the completeness of observations is questionable. When you have insufficient or biased evidence then the results from skewed data lead to inaccurate generalizations. Similarly, how if your sample is not representative of the entire population then the inferences may not reflect the broader reality.   

On the other hand, conclusions from deductive reasoning depend on facts and complete certainty of the premises. However, in the practicality of business settings, the data collected by themselves or in-house can have countless abnormalities and uncertainties. Using that for analysis and insights can wreak havoc upon your business. 

It is critical to test the data with a rigorous and robust QA mechanism for analysis and sound conclusions. But rest assured, Grepsr has got you covered with the quality dataset you need, at your doorstep. 

Abductive Reasoning 

Although it isn’t talked about enough, abductive reasoning is also a type of logical inference. 

In simple terms, it’s taking your best shot or making a probable conclusion from an incomplete set of observations or from what you know at the moment. 

While inductive reasoning generalizes conclusions from particular instances by observing data and deductive reasoning guarantees a conclusion as it is based on established facts, abductive reasoning infers the best possible explanation of a situation as the correct conclusion.  

For instance, if you see a stack of books on a medical student’s desk that seems higher than usual, you infer that their exams are probably around the corner. 

Lawyers also use it to build their cases when the evidence is lacking. The prosecutor or defender tries their best to construct a coherent narrative that explains the available facts, even if some of their assumptions are not fully substantiated. 

Hence, abductive reasoning is educated guessing and deducing a chain of events that could have resulted in a specific conclusion. This is the least reliable and uncertain method for making decisions because it lacks enough evidence and completeness. 

Hence, for accuracy, relevance, and precision in your decision-making, you can rely on Grepsr ’s quality data for a comprehensive business data strategy. 

and remember,

The absence of evidence is not the evidence of absence.  Dr Carl Sagan

What this quote means is, if there is no evidence of something found yet, doesn’t mean that the thing doesn’t exist at all. Or, if something hasn’t happened yet, doesn’t mean it’s never going to happen even in the future.

The perfect metaphor to understand is how black swans, which people didn’t know existed were discovered in Australia in 1697 by the Dutch explorer Willem de Vlamingh.

Grepsr for actionable web data

Coming to an end, the point of this article is to make readers understand that to make valid, reliable, and guaranteed inductive and deductive inferences, data is a must-have. 

Leveraging high-quality data helps your business win from the strategic level and beyond, whereas relying on bad data is like fighting a losing battle, you’ll never get anywhere but end up digging yourself a hole due to inaccurate insights.  

Especially in a business context, high-quality, structured, and organized data is indispensable for inductive and deductive reasoning. Just like how Detective Holmes needed organized data with logic and astute observation to unravel his investigations and solve murder mysteries. 

Data extraction services like Grepsr glean insights from the noise to help you make a powerful data strategy. 

Cutting corners and compromising the data quality is never going to be a problem if you choose us as your trusted external data provider. We are the advocates of quality data – the cornerstone that can make or break your business. 

Therefore, for high-quality, consistent, accurate, valid, and real-time actionable data, remember Grepsr . 

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• Open access
• Published: 18 May 2024

Determinants of appropriate antibiotic and NSAID prescribing in unscheduled outpatient settings in the veterans health administration

• Michael J. Ward 1 , 2 , 3 , 4 ,
• Michael E. Matheny 1 , 4 , 5 , 6 ,
• Melissa D. Rubenstein 3 ,
• Kemberlee Bonnet 7 ,
• Chloe Dagostino 7 ,
• David G. Schlundt 7 ,
• Shilo Anders 4 , 8 ,
• Thomas Reese 4 &
• Amanda S. Mixon 1 , 9  

BMC Health Services Research volume  24 , Article number:  640 ( 2024 ) Cite this article

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Despite efforts to enhance the quality of medication prescribing in outpatient settings, potentially inappropriate prescribing remains common, particularly in unscheduled settings where patients can present with infectious and pain-related complaints. Two of the most commonly prescribed medication classes in outpatient settings with frequent rates of potentially inappropriate prescribing include antibiotics and nonsteroidal anti-inflammatory drugs (NSAIDs). In the setting of persistent inappropriate prescribing, we sought to understand a diverse set of perspectives on the determinants of inappropriate prescribing of antibiotics and NSAIDs in the Veterans Health Administration.

We conducted a qualitative study guided by the Consolidated Framework for Implementation Research and Theory of Planned Behavior. Semi-structured interviews were conducted with clinicians, stakeholders, and Veterans from March 1, 2021 through December 31, 2021 within the Veteran Affairs Health System in unscheduled outpatient settings at the Tennessee Valley Healthcare System. Stakeholders included clinical operations leadership and methodological experts. Audio-recorded interviews were transcribed and de-identified. Data coding and analysis were conducted by experienced qualitative methodologists adhering to the Consolidated Criteria for Reporting Qualitative Studies guidelines. Analysis was conducted using an iterative inductive/deductive process.

We conducted semi-structured interviews with 66 participants: clinicians ( N  = 25), stakeholders ( N  = 24), and Veterans ( N  = 17). We identified six themes contributing to potentially inappropriate prescribing of antibiotics and NSAIDs: 1) Perceived versus actual Veterans expectations about prescribing; 2) the influence of a time-pressured clinical environment on prescribing stewardship; 3) Limited clinician knowledge, awareness, and willingness to use evidence-based care; 4) Prescriber uncertainties about the Veteran condition at the time of the clinical encounter; 5) Limited communication; and 6) Technology barriers of the electronic health record and patient portal.

Conclusions

The diverse perspectives on prescribing underscore the need for interventions that recognize the detrimental impact of high workload on prescribing stewardship and the need to design interventions with the end-user in mind. This study revealed actionable themes that could be addressed to improve guideline concordant prescribing to enhance the quality of prescribing and to reduce patient harm.

Peer Review reports

Adverse drug events (ADEs) are the most common iatrogenic injury. [ 1 ] Efforts to reduce these events have primarily focused on the inpatient setting. However, the emergency department (ED), urgent care, and urgent primary care clinics are desirable targets for interventions to reduce ADEs because approximately 70% of all outpatient encounters occur in one of these settings. [ 2 ] Two of the most commonly prescribed drug classes during acute outpatient care visits that have frequent rates of potentially inappropriate prescribing include antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs). [ 3 , 4 ]

An estimated 30% of all outpatient oral antibiotic prescriptions may be unnecessary. [ 5 , 6 ] The World Health Organization identified overuse of antibiotics and its resulting antimicrobial resistance as a global threat. [ 7 ] The Centers for Disease Control and Prevention (CDC) conservatively estimates that in the US there are nearly 3 million antibiotic-resistant infections that cause 48,000 deaths annually. [ 8 ] Antibiotics were the second most common source of adverse events with nearly one ADE resulting in an ED visit for every 100 prescriptions. [ 9 ] Inappropriate antibiotic prescriptions (e.g., antibiotic prescription for a viral infection) also contribute to resistance and iatrogenic infections such as C. difficile (antibiotic associated diarrhea) and Methicillin-resistant Staphylococcus aureus (MRSA) . [ 8 ] NSAID prescriptions, on the other hand, result in an ADE at more than twice the rate of antibiotics (2.2%), [ 10 ] are prescribed to patients at an already increased risk of potential ADEs, [ 4 , 11 ] and frequently interact with other medications. [ 12 ] Inappropriate NSAID prescriptions contribute to serious gastrointestinal, [ 13 ] renal, [ 14 ] and cardiovascular [ 15 , 16 ] ADEs such as gastrointestinal bleeding, acute kidney injury, and myocardial infarction or heart failure, respectively. Yet, the use of NSAIDs is ubiquitous; according to the CDC, between 2011 and 2014, 5% of the US population were prescribed an NSAID whereas an additional 2% take NSAIDs over the counter. [ 11 ]

Interventions to reduce inappropriate antibiotic prescribing commonly take the form of antimicrobial stewardship programs. However, no such national programs exist for NSAIDs, particularly in acute outpatient care settings. There is a substantial body of evidence supporting the evidence of such stewardship programs. [ 17 ] The CDC recognizes that such outpatient programs should consist of four core elements of antimicrobial stewardship, [ 18 ] including commitment, action for policy and practice, tracking and reporting, and education and expertise. However, the opportunities to extend antimicrobial stewardship in EDs are vast. Despite the effectiveness, there is a recognized need to understand which implementation strategies and how to implement multifaceted interventions. [ 19 ] Given the unique time-pressured environment of acute outpatient care settings, not all antimicrobial stewardship strategies work in these settings necessitating the development of approaches tailored to these environments. [ 19 , 20 ]

One particularly vulnerable population is within the Veterans Health Administration. With more than 9 million enrollees in the Veterans Health Administration, Veterans who receive care in Veteran Affairs (VA) hospitals and outpatient clinics may be particularly vulnerable to ADEs. Older Veterans have greater medical needs than younger patients, given their concomitant medical and mental health conditions as well as cognitive and social issues. Among Veterans seen in VA EDs and Urgent Care Clinics (UCCs), 50% are age 65 and older, [ 21 ] nearly three times the rate of non-VA emergency care settings (18%). [ 22 ] Inappropriate prescribing in ED and UCC settings is problematic with inappropriate antibiotic prescribing estimated to be higher than 40%. [ 23 ] In a sample of older Veterans discharged from VA ED and UCC settings, NSAIDs were found to be implicated in 77% of drug interactions. [ 24 ]

Learning from antimicrobial stewardship programs and applying to a broader base of prescribing in acute outpatient care settings, it is necessary to understand not only why potentially inappropriate prescribing remains a problem for antibiotics, but for medications (e.g., NSAIDs) which have received little stewardship focus previously. This understanding is essential to develop and implement interventions to reduce iatrogenic harm for vulnerable patients seen in unscheduled settings. In the setting of the Veterans Health Administration, we sought to use these two drug classes (antibiotics and NSAIDs) that have frequent rates of inappropriate prescribing in unscheduled outpatient care settings, to understand a diverse set of perspectives on why potentially inappropriate prescribing continues to occur.

Selection of participants

Participants were recruited from three groups in outpatient settings representing emergency care, urgent care, and urgent primary care in the VA: 1) Clinicians-VA clinicians such as physicians, advanced practice providers, and pharmacists 2) Stakeholders-VA and non-VA clinical operational and clinical content experts such as local and regional medical directors, national clinical, research, and administrative leadership in emergency care, primary care, and pharmacy including geriatrics; and 3) Veterans seeking unscheduled care for infectious or pain symptoms.

Clinicians and stakeholders were recruited using email, informational flyers, faculty/staff meetings, national conferences, and snowball sampling, when existing participants identify additional potential research subjects for recruitment. [ 25 ] Snowball sampling is useful for identifying and recruiting participants who may not be readily apparent to investigators and/or hard to reach. Clinician inclusion criteria consisted of: 1) at least 1 year of VA experience; and 2) ≥ 1 clinical shift in the last 30 days at any VA ED, urgent care, or primary care setting in which unscheduled visits occur. Veterans were recruited in-person at the VA by key study personnel. Inclusion criteria consisted of: 1) clinically stable as determined by the treating clinician; 2) 18 years or older; and 3) seeking care for infectious or pain symptoms in the local VA Tennessee Valley Healthcare System (TVHS). TVHS includes an ED at the Nashville campus with over 30,000 annual visits, urgent care clinic in Murfreesboro, TN with approximately 15,000 annual visits, and multiple primary care locations throughout the middle Tennessee region. This study was approved by the VA TVHS Institutional Review Board as minimal risk.

Data collection

Semi-structured interview guides (Supplemental Table 1) were developed using the Consolidated Framework for Implementation Research (CFIR) [ 26 ] and the Theory of Planned Behavior [ 27 , 28 ] to understand attitudes and beliefs as they relate to behaviors, and potential determinants of a future intervention. Interview guides were modified and finalized by conducting pilot interviews with three members of each participant group. Interview guides were tailored to each group of respondents and consisted of questions relating to: 1) determinants of potentially inappropriate prescribing; and 2) integration into practice (Table. 1 ). Clinicians were also asked about knowledge and awareness of evidence-based prescribing practices for antibiotics and NSAIDs. The interviewer asked follow-up questions to elicit clarity of responses and detail.

Each interview was conducted by a trained interviewer (MDR). Veteran interviews were conducted in-person while Veterans waited for clinical care so as not to disrupt clinical operations. Interviews with clinicians and stakeholders were scheduled virtually. All interviews (including in-person) were recorded and transcribed in a manner compliant with VA information security policies using Microsoft Teams (Redmond, WA). The audio-recorded interviews were transcribed and de-identified by a transcriptionist and stored securely behind the VA firewall using Microsoft Teams. Study personnel maintained a recording log on a password-protected server and each participant was assigned a unique participant ID number. Once 15 interviews were conducted per group, we planned to review interviews with the study team to discuss content, findings, and to decide collectively when thematic saturation was achieved, the point at which no new information was obtained. [ 29 ] If not achieved, we planned to conduct at least 2 additional interviews prior to group review for saturation. We estimated that approximately 20–25 interviews per group were needed to achieve thematic saturation.

Qualitative data coding and analysis was managed by the Vanderbilt University Qualitative Research Core. A hierarchical coding system (Supplemental Table 2) was developed and refined using an iterative inductive/deductive approach [ 30 , 31 , 32 ] guided by a combination of: 1) Consolidated Framework for Implementation Research (CFIR) [ 26 ]; 2) the Theory of Planned Behavior [ 27 , 28 ]; 3) interview guide questions; and 4) a preliminary review of the transcripts. Eighteen major categories (Supplemental Table 3) were identified and were further divided into subcategories, with some subcategories having additional levels of hierarchical division. Definitions and rules were written for the use of each of the coding categories. The process was iterative in that the coding system was both theoretically informed and derived from the qualitative data. The coding system was finalized after it was piloted by the coders. Data coding and analysis met the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines. [ 33 ]

Four experienced qualitative coders were trained by independently coding two transcripts from each of the three participant categories. Coding was then compared, and any discrepancies resolved by reconciliation. After establishing reliability in using the coding system, the coders divided and independently coded the remaining transcripts in sequential order. Each statement was treated as a separate quote and could be assigned up to 21 different codes. Coded transcripts were combined and sorted by code.

Following thematic saturation, the frequency of each code was calculated to understand the distribution of quotes. Quotes were then cross-referenced with coding as a barrier to understand potential determinants of inappropriate prescribing. A thematic analysis of the barriers was conducted and presented in an iterative process with the research team of qualitative methodologists and clinicians to understand the nuances and refine the themes and subthemes from the coded transcripts. Transcripts, quotations, and codes were managed using Microsoft Excel and SPSS version 28.0.

We approached 132 individuals and 66 (50%) agreed to be interviewed. Participants included 25 clinicians, 24 stakeholders, and 17 Veterans whose demographic characteristics are presented in Table 2 . The clinicians were from 14 VA facilities throughout the US and 20 physicians, and five advanced practice providers. Of the clinicians, 21 (84%) worked in either an ED or urgent care while the remainder practiced in primary care. The 24 stakeholders included 13 (54%) clinical service chiefs or deputy chief (including medical directors), five (21%) national directors, and six (25%) experts in clinical content and methodology. The 17 Veterans interviewed included 15 (88%) who were seen for pain complaints.

Results are organized by the six thematic categories with several subthemes in each category. Themes and subthemes are presented in Table 3  and are visually represented in Fig.  1 . The six themes were: 1) perceived versus actual Veterans expectations about prescribing, 2) the influence of a time-pressured clinical environment on prescribing stewardship, 3) limited clinician knowledge, awareness, and willingness to use evidence-based care, 4) uncertainties about the Veteran condition at the time of the clinical encounter, 5) limited communication, and 6) technology barriers.

Visual representation of themes and subthemes from 66 clinician, stakeholder, and Veteran interviews

Theme 1: Perception that Veterans routinely expect a medication from their visit, despite clinical inappropriateness

According to clinicians, Veterans frequently expect to receive a prescription even when this decision conflicts with good clinical practice.

Certainly lots of people would say you know if you feel like you’re up against some strong expectations from the patients or caregivers or families around the utility of an antibiotic when it’s probably not indicated…In the emergency department the bias is to act and assume the worst and assume like the worst for the clinical trajectory for the patient rather than the reverse. [Clinician 49, Physician, ED]

In addition, stakeholders further stated that patient prescription expectations are quite influential and are likely shaped by Veterans’ prior experiences.

I think the patients, particularly for antibiotics, have strong feelings about whether they should or shouldn’t get something prescribed. [Stakeholder 34] You know I think the biggest challenge, I think, is adjusting patients’ expectations because you know they got better the last time they were doing an antibiotic. [Stakeholder 64]

Patient satisfaction and clinician workload may also influence the clinician’s prescription decision.

We have a lot of patients that come in with back pain or knee pain or something. We’ll get an x-ray and see there’s nothing actually wrong physically that can be identified on x-ray at least and you have to do something. Otherwise, patient satisfaction will dip, and patients leave angry. [Clinician 28, Physician, urgent care clinic] For some clinicians it’s just easier to prescribe an antibiotic when they know that’s the patient’s expectation and it shortens their in-room discussion and evaluation. [Clinician 55, Physician, ED]

Despite clinician perception, Veterans communicated that they did not necessarily expect a prescription and were instead focused on the clinical interaction and the clinician’s decision.

I’m not sure if they’ll give me [unintelligible] a prescription or what they’ll do. I don’t care as long as they stop the pain. [Patient 40, urgent care clinic] I don’t expect to [receive a prescription], but I mean whatever the doctor finds is wrong with me I will follow what he says. [Patient 31, ED]

Theme 2: Hectic clinical environments and unique practice conditions in unscheduled settings provide little time to focus on prescribing practices

Clinicians and stakeholders reported that the time-constrained clinical environment and need to move onto the next patient were major challenges to prescribing stewardship.

The number one reason is to get a patient out of your office or exam bay and move on to the next one. [Stakeholder 28] It takes a lot of time and you have to be very patient and understanding. So, you end up having to put a fair bit of emotional investment and intelligence into an encounter to not prescribe. [Stakeholder 1]

Stakeholders also noted that unique shift conditions and clinician perceptions that their patients were “different” might influence prescribing practices.

A common pushback was ‘well my patients are different.’ [Stakeholder 4] Providers who worked different types of shifts, so if you happened to work on a Monday when the clinics were open and had more adults from the clinics you were more likely to prescribe antibiotics than if you worked over night and had fewer patients. Providers who worked primarily holidays or your Friday prescribing pattern may be very different if you could get them into a primary care provider the next day. [Stakeholder 22]

Clinicians also reported that historical practices in the clinical environment practices may also contribute to inappropriate prescribing.

I came from working in the [outpatient] Clinic as a new grad and they’re very strict about prescribing only according to evidence-based practice. And then when I came here things are with other colleagues are a little more loose with that type of thing. It can be difficult because you start to adopt that practice to. [Clinician 61, Nurse Practitioner, ED]

Theme 3: Clinician knowledge, awareness, and willingness to use evidence-based care

Stakeholders felt that clinicians had a lack of knowledge about prescribing of NSAIDs and antibiotics.

Sometimes errors are a lack of knowledge or awareness of the need to maybe specifically dose for let’s say impaired kidney function or awareness of current up to date current antibiotic resistance patterns in the location that might inform a more tailored antibiotic choice for a given condition. [Stakeholder 37] NSAIDs are very commonly used in the emergency department for patients of all ages…the ED clinician is simply not being aware that for specific populations this is not recommended and again just doing routine practice for patients of all ages and not realizing that for older patients you actually probably should not be using NSAIDs. [Stakeholder 40]

Some clinicians may be unwilling to change their prescribing practices due to outright resistance, entrenched habits, or lack of interest in doing so.

It sounds silly but there’s always some opposition to people being mandated to do something. But there are some people who would look and go ‘okay we already have a handle on that so why do we need something else? I know who prescribes inappropriately and who doesn’t. Is this a requirement, am I evaluated on it? That would come from supervisors. Is this one more thing on my annual review?’ [Stakeholder 28] If people have entrenched habits that are difficult to change and are physicians are very individualistic people who think that they are right more often than the non-physician because of their expensive training and perception of professionalism. [Stakeholder 4]

Theme 4: Uncertainty about whether an adverse event will occur

Clinicians cited the challenge of understanding the entirety of a Veteran’s condition, potential drug-drug interactions, and existing comorbidities in knowing whether an NSAID prescription may result in an adverse event.

It’s oftentimes a judgement call if someone has renal function that’s right at the precipice of being too poor to merit getting NSAIDs that may potentially cause issues. [Clinician 43, Physician, inpatient and urgent care] It depends on what the harm is. So, for instance, you can’t always predict allergic reactions. Harm from the non-steroidals would be more if you didn’t pre-identify risk factors for harm. So, they have ulcer disease, they have kidney problems where a non-steroidal would not be appropriate for that patient. Or potential for a drug-drug interaction between that non-steroid and another medication in particular. [Clinician 16, Physician, ED]

Rather than be concerned about the adverse events resulting from the medication itself, stakeholders identified the uncertainty that clinicians experience about whether a Veteran may experience an adverse event from an infection if nothing is done. This uncertainty contributes to the prescription of an antibiotic.

My experience in working with providers at the VA over the years is that they worry more about the consequences of not treating an infection than about the consequences of the antibiotic itself. [Stakeholder 19] Sometimes folks like to practice conservatively and they’ll say even though I didn’t really see any hard evidence of a bacterial infection, the patient’s older and sicker and they didn’t want to risk it. [Stakeholder 16]

Theme 5: Limited communication during and after the clinical encounter

The role and type of communication about prescribing depended upon the respondent. Clinicians identified inadequate communication and coordination with the Veteran’s primary care physician during the clinical encounter.

I would like to have a little more communication with the primary doctors. They don’t seem to be super interested in talking to anyone in the emergency room about their patients… A lot of times you don’t get an answer from the primary doctor or you get I’m busy in clinic. You can just pick something or just do what you think is right. [Clinician 25, Physician, ED]

Alternatively, stakeholders identified post-encounter patient outcome and clinical performance feedback as potential barriers.

Physicians tend to think that they are doing their best for every individual patient and without getting patient by patient feedback there is a strong cognitive bias to think well there must have been some exception and reason that I did it in this setting. [Stakeholder 34] It’s really more their own awareness of like their clinical performance and how they’re doing. [Stakeholder 40]

Veterans, however, prioritized communication during the clinical encounter. They expressed the need for clear and informative communication with the clinician, and the need for the clinician to provide a rationale for the choice and medication-specific details along with a need to ask any questions.

I expect him to tell me why I’m taking it, what it should do, and probably the side effects. [Patient 25, ED] I’d like to have a better description of how to take it because I won’t remember all the time and sometimes what they put on the bottle is not quite as clear. [Patient 22, ED]

Veterans reported their desire for a simple way to learn about medication information. They provided feedback on the current approaches to educational materials about prescriptions.

Probably most pamphlets that people get they’re not going to pay attention to them. Websites can be overwhelming. [Patient 3, ED] Posters can be offsetting. If you’re sick, you’re not going to read them…if you’re sick you may glance at that poster and disregard it. So, you’re not really going to see it but if you give them something in the hand people will tend to look at it because it’s in their hand. [Patient 19, ED] It would be nice if labels or something just told me what I needed to know. You know take this exactly when and reminds me here’s why you’re taking it for and just real clear and not small letters. [Patient 7, ED]

Theme 6: Technology barriers limited the usefulness of clinical decision support for order checking and patient communication tools

Following the decision to prescribe a medication, clinicians complained that electronic health record pop-ups with clinical decision support warnings for potential safety concerns (e.g., drug-drug interactions) were both excessive and not useful in a busy clinical environment.

The more the pop ups, the more they get ignored. So, it’s finding that sweet spot right where you’re not constantly having to click out of something because you’re so busy. Particularly in our clinical setting where we have very limited amount of time to read the little monograph. Most of the time you click ‘no’ and off you go. (Clinician 16, Physician, ED) Some of these mechanisms like the EMR [electronic medical record] or pop-up decision-making windows really limit your time. If you know the guidelines appropriately and doing the right thing, even if you’re doing the right thing it takes you a long time to get through something. (Clinician 19, Physician, Primary care clinic)

For post-encounter communication that builds on Theme 5 about patient communication, patients reported finding using the VA patient portal (MyHealtheVet) challenging for post-event communication with their primary care physician and to review the medications they were prescribed.

I’ve got to get help to get onto MyHealtheVet but I would probably like to try and use that, but I haven’t been on it in quite some time. [Patient 22, ED] I tried it [MyHealtheVet] once and it’s just too complicated so I’m not going to deal with it. [Patient 37, Urgent care]

This work examined attitudes and perceptions of barriers to appropriate prescribing of antibiotics and NSAIDs in unscheduled outpatient care settings in the Veterans Health Administration. Expanding on prior qualitative work on antimicrobial stewardship programs, we also included an examination of NSAID prescribing, a medication class which has received little attention focused on prescribing stewardship. This work seeks to advance the understanding of fundamental problems underlying prescribing stewardship to facilitate interventions designed to improve not only the decision to prescribe antibiotics and NSAIDs, but enhances the safety checks once a decision to prescribe is made. Specifically, we identified six themes during these interviews: perceived versus actual Veteran expectations about prescribing, the influence of a time-pressured clinical environment on prescribing stewardship, limited clinician knowledge, awareness, and willingness to use evidence-based care, uncertainties about the Veteran condition at the time of the clinical encounter, limited communication, and technology barriers.

Sensitive to patient expectations, clinicians believed that Veterans would be dissatisfied if they did not receive an antibiotic prescription, [ 34 ] even though most patients presenting to the ED for upper respiratory tract infections do not expect antibiotics. [ 35 ] However, recent work by Staub et al. found that among patients with respiratory tract infections, receipt of an antibiotic was not independently associated with improved satisfaction. [ 36 ] Instead, they found that receipt of antibiotics had to match the patient’s expectations to affect patient satisfaction and recommended that clinicians communicate with their patients about prescribing expectations. This finding complements our results in the present study and the importance of communication about expectations is similarly important for NSAID prescribing as well.

A commitment to stewardship and modification of clinician behavior may be compromised by the time-pressured clinical environment, numerous potential drug interactions, comorbidities of a vulnerable Veteran population, and normative practices. The decision to prescribe medications such as antibiotics is a complex clinical decision and may be influenced by both clinical and non-clinical factors. [ 34 , 37 , 38 ] ED crowding, which occurs when the demand for services exceeds a system’s ability to provide care, [ 39 ] is a well-recognized manifestation of a chaotic clinical environment and is associated with detrimental effects on the hospital system and patient outcomes. [ 40 , 41 ] The likelihood that congestion and wait times will improve is unlikely as the COVID-19 pandemic has exacerbated the already existing crowding and boarding crisis in EDs. [ 42 , 43 ]

Another theme was the uncertainty in the anticipation of adverse events that was exacerbated by the lack of a feedback loop. Feedback on clinical care processes and patient outcomes is uncommonly provided in emergency care settings, [ 44 ] yet may provide an opportunity to change clinician behavior, particularly for antimicrobial stewardship. [ 45 ] However, the frequent use of ineffective feedback strategies [ 46 ] compromises the ability to implement effective feedback interventions; feedback must be specific [ 47 ] and address the Intention-to-Action gap [ 48 ] by including co-interventions to address recipient characteristics (i.e., beliefs and capabilities) and context to maximize impact. Without these, feedback may be ineffective.

An additional barrier identified from this work is the limited communication with primary care following discharge. A 2017 National Quality Forum report on ED care transitions [ 49 ] recommended that EDs and their supporting hospital systems should expand infrastructure and enhance health information technology to support care transitions as Veterans may not understand discharge instructions, may not receive post-ED or urgent care, [ 50 , 51 , 52 ] or may not receive a newly prescribed medication. [ 24 ] While there are existing mechanisms to communicate between the ED and primary care teams such as notifications when a Veteran presents to the ED and when an emergency clinician copies a primary care physician on a note, these mechanisms are insufficient to address care transition gaps and are variable in best practice use. To address this variability, the VA ED PACT Tool was developed using best practices (standardized processes, "closed-loop" communication, embedding into workflow) to facilitate and standardize communication between VA EDs and follow-up care clinicians. [ 53 ] While the ED PACT Tool is implemented at the Greater Los Angeles VA and can create a care coordination order upon ED discharge, its use is not yet widely adopted throughout the VA.

In the final theme about technology barriers, once the decision has been made to prescribe a medication, existing electronic tools that are key components of existing stewardship interventions designed to curtail potentially inappropriate prescriptions may be compromised by their lack of usability. For example, clinician and stakeholder interview respondents described how usability concerns were exacerbated in a time-pressured clinical environment (e.g., electronic health record clinical decision support tools). Clinical decision support is an effective tool to improve healthcare process measures in a diverse group of clinical environments; [ 54 ] however, usability remains a barrier when alerts must be frequently overridden. [ 55 , 56 ] Alert fatigue, as expressed in our interviews for order checking and recognized within the VA’s EHR, [ 57 , 58 ] may contribute to excessive overrides reducing the benefit of clinical decision support, [ 56 , 59 ] there was a notable lack of discussion about the decision to initiate appropriate prescriptions, which is a key action of the CDC’s outpatient antibiotic stewardship campaign. [ 18 ] Thus, a potentially more effective, albeit challenging approach, is to “nudge” clinicians towards appropriate prescribing and away from the initial decision to prescribe (e.g., inappropriate antibiotic prescribing for viral upper respiratory tract infections) with either default order sets for symptom management or to enhance prescription decisions through reminders about potential contraindications to specific indications (e.g., high risk comorbidities). Beyond EHR-based solutions that might change clinician behavior, the CDC’s outpatient antibiotic stewardship program provides a framework to change the normative practices around inappropriate prescribing and includes a commitment to appropriate prescribing, action for policy and change, tracking and reporting, and education and expertise. [ 18 ]

Another technical barrier faces patients through patient-facing electronic tools such as the VA’s MyHealtheVet portal, which was developed to enhance patient communication following care transitions and to allow Veterans to review their medications and to communicate with their primary care clinical team. Patient portals can be an effective tool for medication adherence [ 60 ] and offer promise to provide patient education [ 61 ] following a clinical encounter. However, they are similarly limited by usability concerns, representing an adoption barrier to broader Veteran use after unscheduled outpatient care visits [ 62 ], particularly in an older patient population.

These interviews further underscored that lack of usability of clinical decision support for order checking that arises from ineffective design and is a key barrier preventing health information technology from reaching its promise of improving patient safety. [ 63 ] A common and recognized reason for these design challenges include the failure to place the user (i.e., acute care clinician) at the center of the design process resulting in underutilization, workarounds, [ 64 ] and unintended consequences, [ 65 ] all of which diminish patient safety practices and fail to change clinician behavior (i.e., prescribing). Complex adaptive systems work best when the relative strengths of humans (e.g., context sensitivity, situation specificity) are properly integrated with the information processing power of computerized systems. [ 66 ] One potential approach to address usability concerns is through the integration of user-centered design into technology design represents an opportunity to design more clinician- and patient-centric systems of care to advance prescribing stewardship interventions that may have lacked broader adoption previously. As antimicrobial stewardship and additional prescribing stewardship efforts focus on time-pressured environments where usability is essential to adoption, taking a user-centered design approach to not only the development of electronic tools but also in addressing the identified barriers in prescribing represents a promising approach to enhance the quality of prescribing.

Limitations

The study findings should be considered in light of its limitations. First, the setting for this work was the Veterans Health Administration, the largest integrated health system in the US. Also, while we focused on the stewardship of two drug classes, there are numerous additional drug classes that are prescribed in these settings. Studies in other settings or on other drug classes may not generalize to other settings and drug classes. Second, while clinicians and stakeholder perspectives included diverse, national representation, the Veterans interviewed were local to the Tennessee Valley Healthcare System. Given the concurrent COVID-19 pandemic at the time of enrollment, most of the Veterans were seen for pain-related complaints, and only two infectious-related complaints were included. However, we also asked them about antibiotic prescribing. Clinician and stakeholder narratives may not completely reflect their practice patterns as their responses could be influenced by social desirability bias. Third, responses may be subject to recall bias and may influence the data collected. Finally, the themes and subthemes identified may overlap and have potential interactions. While we used an iterative process to identify discrete themes and subthemes, prescription decisions represent a complex decision process that are influenced by numerous patient and contextual factors and may not be completely independent.

Despite numerous interventions to improve the quality of prescribing, the appropriate prescription of antibiotics and NSAIDs in unscheduled outpatient care settings remains a challenge. Using the Veterans Health Administration, this study found that challenges to high quality prescribing include perceived Veteran expectations about receipt of medications, a hectic clinical environment deprioritizing stewardship, limited clinician knowledge, awareness, and willingness to use evidence-based care, uncertainty about the potential for adverse events, limited communication, and technology barriers. Findings from these interviews suggest that interventions should consider the detrimental impact of high workload on prescribing stewardship, clinician workflow, the initial decision to prescribe medications, and incorporate end-users into the intervention design process. Doing so is a promising approach to enhance adoption of high quality prescribing practices in order to improve the quality and patient outcomes from NSAID and antibiotic prescribing.

Availability of data and materials

De-identified datasets used and/or analysed during the current study will be made available from the corresponding author on reasonable request.

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Acknowledgements

This material is based upon work supported by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development (I01HX003057). The content is solely the responsibility of the authors and does not necessarily represent the official views of the VA.

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Conceptualization: MJW, ASM, MEM, DS, SA. Methodology: MJW, ASM, MEM, DS, KB, SA, TR. Formal analysis: KB, DS, CD, MJW. Investigation: MJW, MDR, DS. Resources: MJW, MEM. Writing—Original Draft. Preparation: MJW, ASM, KB, MDR. Writing—Review & Editing: All investigators. Supervision: MJW, ASM, MEM. Funding acquisition: MJW, MEM.

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Facilitators and barriers of HPV vaccination: a qualitative study in rural Georgia

• Courtney N. Petagna 1 ,
• Stephen Perez 1 ,
• Erica Hsu 1 ,
• Brenda M. Greene 2 ,
• Ionie Banner 1 ,
• Robert A. Bednarczyk 3 &
• Cam Escoffery 1  

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Introduction

Human papillomavirus (HPV) vaccination protects against HPV-associated cancers and genital warts. Healthy People 2030 goal for HPV vaccine uptake is 80%, but as of 2021, only 58.5% of adolescents are up to date in Georgia. The purpose of the study is to assess the attitudes, vaccine practices, facilitators, and barriers to receiving the HPV vaccine in southwest Georgia.

We conducted 40 semi-structured interviews in the United States from May 2020-Feburary 2022 with three different audiences (young adults, parents, and providers and public health professionals) guided by the P3 (patient-, provider-, practice-levels) Model. The audiences were recruited by multiple methods including fliers, a community advisory board, Facebook ads, phone calls or emails to schools and health systems, and snowball sampling. Young adults and parents were interviewed to assess their perceived benefits, barriers, and susceptibility of the HPV vaccine. Providers and public health professionals were interviewed about facilitators and barriers of patients receiving the HPV vaccine in their communities. We used deductive coding approach using a structured codebook, two coders, analyses in MAXQDA, and matrices.

Out of the 40 interviews: 10 young adults, 20 parents, and 10 providers and public health professionals were interviewed. Emerging facilitator themes to increase the uptake of the HPV vaccine included existing knowledge (patient level) and community outreach, providers’ approach to the HPV vaccine recommendations and use of educational materials in addition to counseling parents or young adults (provider level) and immunization reminders (practice level). Barrier themes were lack of knowledge around HPV and the HPV vaccine (patient level), need for strong provider recommendation and discussing the vaccine with patients (provider level), and limited patient reminders and health education information around HPV vaccination (practice level). Related to socio-ecology, the lack of transportation and culture of limited discussion about vaccination in rural communities and the lack of policies facilitating the uptake of the HPV vaccine (e.g., school mandates) were described as challenges.

These interviews revealed key themes around education, knowledge, importance of immunization reminders, and approaches to increasing the HPV vaccination in rural Georgia. This data can inform future interventions across all levels (patient, provider, practice, policy, etc.) to increase HPV vaccination rates in rural communities.

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Human papillomavirus (HPV) is a common sexually transmitted infection (STI) in the United States (US) with an estimated prevalence of 42.5 million people and an incidence of 13 million people per year [ 1 ]. HPV-associated cancers, including vulvar, vaginal, cervical, penile, anal, and oropharyngeal cancers, can develop years or decades following persistent HPV infection [ 2 , 3 ]. Between 2015 and 2019, it was estimated HPV caused 47,199 new cancer cases each year [ 2 ]. Georgia has an incidence rate of 12.9 per 100,000 persons of all HPV associated cancers compared to the United States at 11.8 per 100,000 persons [ 4 ]. Additionally, Georgia is ranked in the top 15 nationally for having high cervical cancer incidence rates (7.4 per 100,000 persons) and the national incident rate is 6.5 per 100,000 persons [ 4 ]. Due to the high incidence rates of HPV associated cancers, the Georgia Cancer Plan: 2019–2024 made targeting HPV associated cancers a priority in an effort to support cancer prevention efforts [ 5 ]. The objective related to this priority is (Objective 1): “To increase the number of females and males who complete the HPV vaccine series in accordance with the Advisory Committee on Immunization Practices (ACIP) and recommendations” [ 5 ].

HPV vaccine was developed to prevent HPV associated cancers and genital warts; [ 6 ] currently HPV vaccine is one of two cancer prevention vaccines available globally [ 7 ]. Previous research determined each HPV vaccine is safe and has at least 96% efficacy for preventing HPV-associated cancers [ 8 ]. HPV vaccination was recommended in the US for adolescent females in 2006, and for adolescent males in 2011 [ 9 ]. The Advisory Committee on Immunization Practices (ACIP) recommends vaccination from as young as 9 years old to age 26, with the possibility of receiving the vaccine up to the age of 45 through a shared decision making process between the provider and the patient [ 10 ]. The earlier a person receives the HPV vaccine before engaging in sexual activity, the better protected they will be from HPV-associated cancers and genital warts [ 11 ]. If the vaccine is initiated prior to the 15th birthday, vaccine recipients need to complete a two-dose vaccine series; if the first dose is given after the 15th birthday, vaccine recipients need to complete a three-dose series [ 12 ]. Healthy People 2030 offers standardized 10-year measurable health objectives for the United States. Among their target goals is to have 80% of adolescents aged 13 to 15 receive all recommended doses of the HPV vaccine. As of 2021, the current national rate is suboptimal at 58.5% [ 13 ].

According to the National Immunization Survey-Teen (NIS-Teen) data from 2022, 76.0% of adolescents aged 13–17 have received at least one HPV vaccine dose with 62.6% having completed the series [ 14 ]. Comparatively, other adolescent vaccines such as Tdap and meningococcal are closer to 90% for receiving one dose. [ 14 ] Compared to the national percentage from the NIS-Teen 2022, Georgia’s HPV vaccine initiation and up-to-date rates among adolescents aged 13–17 are 70.8% and 61.5%, respectively [ 15 ]. Adolescents residing in rural areas compared to urban areas have lower initiation (68% versus 77.8%) and up-to-date (49.2% versus 60.4%) HPV vaccination rates from NIS-Teen 2020 [ 16 ]. Similarly, in the District of Albany (rural GA), only 47.9% adolescents aged 13–17 were up-to-date on their HPV vaccinations, which is 13% lower than the rest of the state, provided by Georgia Registry of Immunization Transactions and Services (GRITS) [ 17 ]. Therefore, this shows a gap in HPV vaccine uptake in rural communities and understanding the reasons behind low vaccine rates is crucial to increasing vaccination efforts.

Research has examined facilitators and barriers at the patient- (adolescent & parent), provider-, and practice-levels. The facilitators at both the patient- and provider-levels are patient’s trust in the provider, knowledge of the vaccine, and self-efficacy in one’s own ability to discuss the vaccine [ 18 , 19 ]. For practice-level, the facilitators are the availability of the vaccine, scheduling future vaccine appointments, and prioritizing the vaccine [ 18 ]. The barriers at the patient- and provider-levels are the lack of knowledge and self-efficacy discussing the vaccine, concerns about safety and adverse effects, and not receiving provider recommendation for HPV vaccine [ 18 , 20 ]. The barriers for practice-level are lack of access to vaccine provider, clinic logistics, and reminder system. [ 19 ] Few studies have explored facilitators and barriers of receiving the HPV vaccine intersecting at multiple levels of the socio-ecological model (SEM), and even fewer have been conducted in rural southwest Georgia [ 18 , 21 , 22 , 23 , 24 ].

This qualitative study aimed to identify socio-ecological determinants influencing HPV vaccination uptake among parents, young adults, and public health professionals and providers in rural Georgia. We applied the P3 (patient-, provider-, practice-levels) Model to examine all three levels at the same time and how they impact each other, specifically around HPV vaccination [ 25 ]. At the patient level we assessed parents and young adults perceived susceptibility and severity. To assess all three levels we asked parents, young adults, providers and public health professionals about the facilitators and barriers of receiving the HPV vaccine series.

We conducted a cross-sectional qualitative study to assess attitudes, knowledge, perceived severity and susceptibility, and reasons for HPV vaccination uptake (or lack of) among parents and young adults. In addition, interviews with healthcare providers and public health professionals were conducted to assess their knowledge, attitudes, practices, and the facilitators and barriers to HPV vaccination in rural communities. Between September 2020 to March 2022, a series of 40 interviews were virtually conducted with participants from southwest Georgia. At the beginning of the interview, the participant was told about the study, their role, risks and benefits of the study, and consented to participate. After consent was given, the interview was recorded on Zoom or an audio recorder. Emory staff (coordinators and students) were trained on the study, interview guide and conducted the interviews. The interviews were between 30–45 min and participants were compensated with a $25 electronic gift card. The study was deemed exempt by the Institutional Review Board at Emory University.

Conceptual framework

This study was informed by the P3 Model and the SEM [ 25 , 26 ]. The P3 Model is a unique approach since it encompasses not one but all three levels (patient, provider, and practice) of the clinic approach and integrates key components of health promotion and behavioral theoretical models (e.g., Health Belief Model, Theory of Planned Behavior, and ecological models (SEM) to impact health outcomes (Fig. 1 ) [ 25 ]. Since the P3 Model integrates multiple theories into the model, we utilized the model to guide our study and focused on targeting each of the levels in the model. The SEM describes the interplay of different levels of health factors that may influence the uptake of health behaviors at the individual, interpersonal, organizational (i.e., health systems), community, and policy levels [ 26 ]. From the SEM, we included questions beyond the P3 Model including community and policy-level factors that facilitate or hinder vaccine uptake. The frameworks applied to this study address limitations in existing rural health literature on HPV vaccination by considering healthcare system components beyond patient-level factors influencing parents’ and young adults’ vaccination decisions [ 25 , 27 ].

The HPV vaccine applied to the P3 (practice, provider, and patient level) model

Eligibility

This study included diverse participant categories from parents, young adults, providers and public health professionals. The parent of a child category was split into two groups: 1) vaccinated and 2) unvaccinated. The eligibility criteria for parents with a vaccinated child is a parent whose child received at least one dose of the HPV vaccine series and the child were between the ages 9–17. The eligibility for parents with an unvaccinated child is a parent whose child did not receive any doses of the HPV vaccine series and were between the ages 9–17. To be eligible for the young adult category, the person had to be between the ages of 18–34. Providers and public health professionals had to be a person who worked in a clinical setting or public health department or public health organization. The eligibility criteria of the interview sample are in Table  1 .

Recruitment

We used snowball and convenience sampling methods for participant recruitment and recruited only from southwest Georgia, which consists of 33 counties. Participants (e.g., parents and young adults) were recruited from the Emory Prevention Research Center (EPRC) Community Advisory Board (CAB), Facebook advertisements, and emails. The CAB is comprised of community members and leaders, health providers or staff from health systems, the public health district, businesses, and non-profits in southwest Georgia. This CAB has been in existence for over 20 years and members typically commit for two–three years. The Facebook advertisements were posted on the EPRC Facebook page targeting parents and young adults who live in southwest Georgia. Some parents, young adults, and providers were recruited from elementary schools and universities by receiving cold emails from the study team. In the email, eligibility and demographic questions were asked such as the age of the child/young adult, has the child/young adult received the HPV vaccine series, and if not, whether there are any plans for them to receive the HPV vaccine series in the future. The last two questions were about county of residence and race. These two questions were asked to make sure the study team captured a diverse sample. Public health professionals were recruited from non-profits and health agencies through word of mouth, fliers, and emails. To ensure saturation was met we had at least 10 participants for each category.

Interview guide development

The qualitative study had three interview guides for different audiences (parents, young adults, and providers/public health professionals). The interview guides were informed by the P3 Model and the socio-ecological model [ 25 , 26 ]. The questions revolved around six topical categories, including: 1) knowledge, 2) facilitators to receipt of HPV vaccine, 3) barriers to receipt of HPV vaccine, 4) healthcare delivery factors, 5) community and resources, and 6) demographics. In addition, in the parent and young adult interview guides we asked about preventive care and interaction with providers around HPV vaccination. For providers, we also asked about promotional methods for the vaccine, the use of the Vaccine for Children’s program, staffing and supply issues, and if they have strategies or received training on how to talk to patients and parents about the vaccine (Table 2 ). Across all categories, we assessed participant demographics by asking demographic questions at the end of the interview before concluding the recording. The demographic questions included age, gender, race, and ethnicity (whether they are of Hispanic/Spanish descent). In addition, for providers and public health professionals, we inquired about their title and discipline, the organization they work for, and how long they have worked there. The interview guides and methods were reviewed by the study team and a working subgroup consisting of researchers from the EPRC and the EPRC CAB. The CAB members who participated are a healthcare provider, an infectious disease epidemiologist, and a health district deputy director. These CAB members, EPRC researchers, and our Emory team met three times before the data collection to guide the instrument development, recruitment methods, and data analysis plans. The results also were shared with them through several CAB meetings.

All the interviews except for two were recorded on Zoom. The two interviews not recorded on Zoom were recorded on an audio recorder since the interviews were conducted over the phone. The interviews were then transcribed by a professional transcription service. We applied a systematic method for thematic data analysis including iterative codebook development with deductive codes from the interview guide, first-round coding, secondary coding, refinement of the codebook, consensus, final analysis, and matrices of themes [ 28 ]. A codebook with definitions was developed using a deductive coding approach from the three interview guides (parent, young adult, and providers/public health professionals) and the P3 Model and inductive codes. All transcripts were uploaded to MAXQDA for analysis [ 29 ]. Two trained researchers coded each transcript with the secondary coder reviewing coding from the primary coder. If there were discrepancies, then the coders would meet to discuss and come to an agreement and add new codes to the codebook when needed [ 28 ]. Emerging themes were identified for barriers and facilitators across each of the levels and finalized until saturation was reached [ 30 ]. Themes were sorted by facilitators and barriers and then broken down further by each of the levels in the P3 Model and socio-ecological levels (community and policy) in matrices with stronger themes ranked first.

We had 10 young adults, 20 parents, and 10 providers and public health professionals (health system participants) participating in the qualitative study. The young adults were 80% female and 20% male, 60% Black, and 40% White, 90% non-Hispanic and 10% Hispanic. The parents were 95% female and 5% male, 60% Black, 35% White, and 5% did not specify their race. Adolescents of the parents were 53% female and 47% male, 42% were ages 9–12, 48% were ages 13–17 and 10% were 18 and over. Health system participants were 90% female, 10% male, 60% Black and 30% White and 10% not specified. More than half of all participants and providers identified as African American (60%), about a third identified as White (35%), and 5% of participants did not specify their race. Additional demographics of the sample are displayed in Tables  3. and  4 . The 40 participants reside in 11 out of the 33 counties in southwest Georgia and the top three counties are: Dougherty (52.5%), Lee (15%), and Colquitt (10%) seen in Supplemental Table 1.

Facilitators

There were facilitators for receiving the HPV vaccine identified at each of the three levels in the P3 Model. Facilitators at the patient level were having existing knowledge of HPV and the HPV vaccine, knowing the vaccine is safe, having knowledge on who can receive the vaccine and when, and having trusted individuals provide information about the HPV vaccine to their community. At the provider level, they were having efficacy of the vaccine, framing of the HPV vaccine to patients, and revisiting the HPV vaccination with hesitant parents. Facilitators at the practice level were immunization reminders, patient registries, the use of social media (e.g., educational videos), and other health clinics who support the vaccine. Immunization reminders was the most mentioned strategy mentioned across participants, both for the patients and providers to remind patients about the vaccine. See Table  5 for more facilitator quotes for each level.

Patient level

At the patient level, participants (primarily parents of vaccinated children and young adults both vaccinated and unvaccinated) consistently referenced having existing knowledge of HPV as a facilitator to increase uptake in administering the HPV vaccine. In describing the vaccine, participants referenced a basic understanding of which cancers it can prevent, and ages at which adolescents can receive the vaccine. Parents and young adults understood the safety of the vaccine, which assisted in having positive attitudes towards the vaccine. One parent described: "I’m going to say it’s [HPV vaccine] some insurance for your child’s behaviors and actions later on in life, you know? Lots of insurance." (Participant 7, parent of vaccinated child). For young adults, they learned about the vaccine on social media, and through school. Enlisting trustworthy individuals to connect community members with information within the community serves as another facilitator for increasing HPV vaccine uptake. A director of a non-profit alluded to this: "I think once they are educated, you know, by a trusted voice, you know, whether that’s their physician or, you know, pastor or somebody, whoever that trusted voice is for them, I think they’re more likely to be acceptable to that ." (Participant 39, health systems).

Provider level

At the provider level, parents of vaccinated children highlighted the approach their children’s providers took when discussing the HPV vaccine with them. The providers framed the vaccine as a preventative measure against other diseases. Providers often spoke of the efficacy the vaccine has against contracting and spreading sexually transmitted infections (STIs), and how those STIs may have more serious ramifications later in life. A parent described this perspective: "Just putting it out and putting the information out and let them stress that it is an STD just like any other STD. Of course, with repercussions in the future, and if you can prevent it, why not." (Participant 6, parent of vaccinated child) .

In addition to framing the discussion, several education and messaging strategies were viewed as successful facilitators; these included patient visits at clinics, health departments, and utilizing community events to educate local community members. Some health system participants offered effective strategies such as revisiting the topic with hesitant parents and using information sheets to allow the parents to learn about the vaccine and its importance. One provider described their approach: "And I try to re-educate if they didn’t, just because a lot of it is that they kind of don’t know what HPV is. They’ve heard of the vaccine. They understand that it’s a vaccine, but I don’t think they really know what HPV is and why they should be concerned about it." (Participant 40, health systems). Parents of vaccinated children emphasized the use of brochures and pamphlets offered by providers as effective learning strategies. Some reflected on how this allowed for parents to take their time learning about the vaccine, and its benefits. Others viewed the brochure as a first step towards having a deeper conversation with the provider. Ultimately, parents thought brochures may bridge the gap for parents who do not know enough about the vaccine but want to learn more about it.

Practice level

At the practice level, immunization reminders sent to parents and young adults were seen as effective strategies by parents whose children were vaccinated and vaccinated young adults for patients to receive their HPV vaccine doses. Reminders included different formats depending on the health system, including phone calls and reminder cards. Health system participants also recognized different strategies to ensure patients returned for subsequent doses. These included the use of patient registries and highlighting those due for immunizations, as well as through the standardized Georgia Registry of Immunization Transactions and Services (GRITS), the statewide immunization information system. As one provider described their practice’s strategy:

"We have what’s called precall-recall, and so once a month we print out a list of our patients here that either they’re coming due for a set of immunizations they’ll be turning 11 in the next month. We’ll send out a letter that says your child will be due for immunizations on this day. We won't specifically say what immunization, but we’ll say they’re due for immunizations…" (Participant 9, health systems) .

Community and policy levels

At the community and policy levels, parents with vaccinated children and health system participants discussed techniques of using central and familiar locations like schools to engage in community outreach. Another one was to have champions within the community. One provider described: “I’d say insight into the community, definitely, to get the word out. Because if you don’t have somebody from the community that also buys in, then they’re not going to participate, not going to show up” (Participant 25, health system nurse). For policy, health system participants mentioned vaccine programs, explaining : “I think we have a free program with HPV…We get them (adult patients) to sign something and then we can get it for free for people who are uninsured” (Participant 23, health system provider).

The barriers for receiving the HPV vaccine at each of the three levels in the P3 Model were the lack of information and dialogue around the HPV vaccine. At the patient level, the main barriers were a dearth of education on HPV and the HPV vaccine, misinformation, and stigma as is relates to STIs and sexual intercourse. At the provider level, a deficiency exists in direct provider-patient communication, including instances where providers fail to inform and recommend the HPV vaccine to their patients. At the practice level, there are a lack of systematic reminders for patient immunizations reminders, limited information, time, staff, and resources committed to the HPV vaccine (Table  6 ).

At the patient level, a persistent theme among parents of both vaccinated and unvaccinated children in our study focused on a dearth of knowledge among parents and their communities about the importance of vaccinating their children against HPV. They highlighted how it is not a common topic to be discussed among parents with their children. One young adult described their experience as a child, “They’re (doctor) like, oh yeah, we now offer the HPV vaccine. Is it something you want to get? And my mom was like, eh, no, she doesn’t need that right now. And I was like, okay. I don’t really want a shot either, so it’s fine with me.” (Participant 17, unvaccinated young adult). Not only is it not being discussed, but parents described not knowing where to go to find more information about the vaccine. Health system participants also discussed how parents often did not have the necessary knowledge about the vaccine to effectively make decisions on behalf of their children. Stemming from this lack of education is the impact that misinformation has surrounding the efficacy, safety, and utility of the HPV vaccine. Two non-vaccinated young adults address misinformation, one stated, “… they’re [young adults] very hesitant about getting like even the COVID vaccine, just because, you know, they heard rumors, oh, it has this in it, it has that in it… ” another stated, “They [young adults] look at social media and certain people may say this is what they do, this is what they don’t do, this is that. So, I think actually with social media and peer pressure that conveys a lot of the youth.”

A director of a non-profit described, “I think all of the conspiracy theories that are out there now, and it’s even worse since COVID, nobody trusts, or a lot of people don’t trust public health messages anymore.” (Participant 12, health systems). In this context, the participant emphasizes the challenge of discussing the vaccine with parents and how a lack of trust in public health complicates messaging strategies.

Coupled with this misinformation was the resulting stigma of discussing HPV due to it being a STI. Vaccinated and unvaccinated young adults, both parents of vaccinated and unvaccinated children, and health system participants described how some parents may be reluctant to vaccinate their child, because they perceive it to indicate their child could be engaging in sex, or receiving the vaccine encourages the child to be sexually active. As one parent described, “Well, I think part of it is that since it is sexually transmitted, I think that a lot of parents don’t want to really delve into that thought that their kids are being sexually active or may be sexually active soon” (Participant 1, parent of vaccinated child). Particularly in southwest Georgia, sexual intercourse is stigmatized. As one provider described,

“ I think the – I think stigma, because it is associated with sexual – a sexual nature. So, they kind of clam up like here in southwest Georgia, Bible belt, like it’s just kind of a – you know, you don’t speak of those things. Those are kind of taboo. Like everybody knows it’s occurring, but you don’t really want to I guess see your child doing – you know, doing things like that. So, I think it’s just the culture here” (Participant 15, health system nurse).

By attempting to discuss a vaccine to prevent STIs, health system participants believed this may contradict many who view teenage sexual health education as only relevant through abstinence.

At the provider level, parents of unvaccinated children and young adults (both vaccinated and unvaccinated) alluded to the dearth of direct communication with providers about the vaccine and revisiting the topic with their patients. Specifically, some parents described how their child’s doctor did not educate them on the reasons for getting the vaccine. As one parent described their experience with a doctor as:

"…they presented it, and asked did I want him to receive the vaccine, but at that time, I just had not had enough information on it personally, and with that, they did not give me any more information. And so, with that being said, you know, if my – if the doctor is not willing to provide more and give me more insight into it, any side effects, you know, statistics, and things of the sort, then you know, (laughs) yeah." (Participant 2, parent of unvaccinated child).

This parent highlighted how they may have been convinced had the doctor provided more details about the reason for vaccinating their child. Another parent with an unvaccinated child described providers not revisiting the HPV vaccine with them at later visits if the parent initially said “no.” Aligned with the lack of direct communication, providers were not informing and recommending the HPV vaccine to patients. As a director of a non-profit stated, “ I think maybe lack of consistent recommendations. You know, they may get tied up in, you know, other bunch of check list of things that they’ve got to do and then may – it just may not be consistent throughout the flow…” (Participant 39, health systems). A parent also felt the providers need to be speaking more about the HPV vaccine in the exam room. One parent described, “I feel they should be more open and mention it in an exam. I do. I feel like they should. Not just have the poster up, like in the hallway. They still should mention it. The same way that they’re stressing the COVID vaccine, they should stress that vaccine in the same manner, I think.” (Participant 35, parent of unvaccinated child) . Here, the parent wished the approach to HPV and the HPV vaccine was similar to the COVID-19 vaccine in order for them to understand its importance during their child’s visits.

At the practice level, participants described lack of systematic reminders for patient immunizations, limited time, resources, and staff allocated per patient, and lack of education in the clinic or medical offices. A parent of each a vaccinated and unvaccinated child referenced not receiving vaccine reminders. One of the parents stated: "Yeah. I think that like, for example, in my case, if there were an actual mailing that came to our house-…I would have seen it. I would have at least begun a conversation with my husband about it, and he was the one responsible for taking him to the pediatrician and getting it handled." (Participant 1, parent of vaccinated child) . Although their child was vaccinated, the need for a mailed reminder would have facilitated discussions between the parents about vaccinating their child. Similarly, a young adult who received their first shot did not return for their second shot since they did not know when to return to the doctor’s office. Other barriers at the practice level include limited information, time, staff, and resources dedicated to the HPV vaccine. Both parents and health system participants mentioned time being a factor. One provider stated,

“ Time would be one I would see, because with a lot of the things that we’re having to do now, you don’t’ have as much time to do the education as you would like to, and sometimes when you’re talking about sex and HPV, if it’s on a one to one basis, it’s hard to establish a rapport in five, ten minutes and get all the information that you need to get to them and then allow them to ask questions” (Participant 25, health system nurse).

As for the lack of resources, parents with a vaccinated child mentioned they have seen posters about measles, mumps, and rubella but not on the HPV vaccine and clinics not having enough of vaccines to distribute. A barrier widely mentioned across participants (parents, young adults unvaccinated, and health systems) were the differences between private practices and public health departments in rural communities. The differences between the two discussed were the patient-provider relationship and patient privacy differences. A parent explained:

“If you’re more familiar with the doctor you have more trust, and you’re more likely to take their advice. When you go to one of the local clinics, the convenient care clinics, it’s not a guarantee you’re going to get the same doctor. So, you may not be as comfortable having a certain conversation with one doctor as you would with a doctor that you’re used to seeing on a regular basis” (Participant 13, parent with a vaccinated child).

Another parent stated, “Private, is not private, and a lot of people may avoid going to the health department and would rather go to an outside pediatrician but don’t have the transportation to get there (Participant 6, parent with a vaccinated child). This parent explained health department layouts are openly structured and patients get called to a window to discuss their health information and people in the waiting room can hear those discussions, causing a lack of privacy for the patient, Similarly, a young adult unvaccinated also mentioned how privacy and courtesy of health professionals at certain clinics can be a barrier for patients. A lack of privacy is a concern at a patient level, while limited resources for transportation infrastructure affect the community at large.

Several barriers at the community and policy level were mentioned by participants. At the community level the barriers include inadequate transportation, and lack of information within the community about HPV and the HPV vaccine and resources. A parent alluded to how important having a car is: “If I didn’t have a car, I probably wouldn’t even – I would barely go to the doctor if I had to use public transportation” (Participant 27, parent with an unvaccinated child). There is public transportation, but it takes more time and some unvaccinated young adults also stated how rural communities are spread out, which makes it challenging to travel to clinics that are out of their town and far away. A young adult described the lack of discussion around the vaccine in rural Georgia communities: “No, just that there is really not a lot of talks about it. I definitely think there needs to be more communication about it for sure” (Participant 17, unvaccinated young adult). At the policy level, the two main barriers participants mentioned were the financial barriers and lack of policies facilitating the uptake of the HPV vaccine. A provider described not being able to provide the vaccine to a minor without parental consent, “…hey, we can’t give them to you, because you’re not 18. We can give you, you know, reproductive care, but we cannot give you any vaccine without your parents’ permission” (Participant 15, health system nurse).

Our study used the P3 Model framework and found common facilitators and barriers to receipt of the HPV vaccine in rural communities. Some of the facilitators we found were trusted individuals in the community, existing knowledge, and providers stating the vaccine is a cancer prevention tool. Parent participants from a study in Alabama reported that guidance from pediatricians or family physicians influenced their decision to vaccinate their children against HPV [ 31 ]. Another study in Montana noted parents may be more receptive to the HPV vaccine when it is discussed as a cancer prevention tool rather than an STI prevention tool [ 32 ]. A pivotal facilitator at the provider level in our study involved how providers phrase and frame the HPV vaccine to patients. Medical providers and public health stakeholders from a prior study in Montana identified a presumptive style of recommending the HPV vaccine. An announcement and conversation training HPV intervention for providers led to an increase in HPV vaccinations for adolescents ages 11–18 over those in a control group in North Carolina [ 33 , 34 ]. This style included offering the HPV vaccine with other immunizations such as meningococcal, HPV, and Tdap together, which was successful [ 32 , 34 ].

Additionally, our research revealed that immunization reminders were a key facilitator in improving HPV vaccination rates. A study in rural Alabama similarly reported that receiving appointment reminders via card, call, or text helped ensure all doses were received [ 35 ]. Similarly, healthcare provider participants from a study in Georgia highlighted the importance of scheduling subsequent HPV vaccine appointments before patients leave their first vaccination appointment and the use of reminder systems [ 18 ]. In addition to immunization reminders, health education material such as educational videos, and incorporating the use of social media were mentioned as strategies to engage people on the HPV vaccine by our participants. Our participants suggested that employing simplified strategies will better attract and engage the general population, especially those with lower health literacy. Previous research found that rural communities need increased access to education on HPV, the HPV vaccine, and sexual health [ 36 ].

In addition to examining facilitators within the P3 Model, we also examined community and policy-level factors. From our study, the community facilitators were trusted community key stakeholders and how they were instrumental in the development of interventions, [ 37 ] community and school education programs, [ 38 ] and county-wide social marketing campaigns [ 39 ]. Our participants mentioned having webinars and the use of school events and outreach is beneficial for increasing the uptake of the HPV vaccine. Future research could investigate interventions like technological reminders and capacity-building for rural healthcare systems to boost HPV vaccination rates. Additionally, future HPV vaccine promotion efforts could focus on community education and participation in campaigns such as the American Cancer Society’s Mission HPV Cancer Free [ 40 ]. At the policy level, some existing facilitators were the Vaccines for Children (VFC) program and private clinics instituting standing orders within their practice for the HPV vaccine [ 41 ]. The VFC was designed so children can receive vaccines regardless if the parent or guardian can afford the vaccines. Similarly, our study participants mentioned how beneficial vaccine programs are not only for children but for adults too.

Data from the 2010–2020 National Immunization Survey-Teen identified the following barriers to receiving the HPV vaccine: lack of knowledge, abstinence, safety concerns, and viewing the vaccine as unnecessary [ 42 ]. A lack of knowledge on HPV was reported as a prominent barrier among our rural participants, which has been observed across multiple rural-based studies [ 18 , 19 , 31 , 35 , 43 ]. Studies in rural Alabama found a lack of parental understanding about the HPV vaccine was a key barrier as reported by parents, pediatricians, and nurse participants [ 19 , 35 ]. Provider participants from a quantitative study reported vaccinating adolescent females (13–17 years old) at higher rates compared to pre-adolescent females (9–12 years old) [ 44 ]. Barriers such as parental discomfort and potential adverse side effects in vaccinating their pre-adolescent child against HPV, especially if the child has underlying health conditions can influence the age group disparity in vaccine uptake [ 44 , 45 ].

Another frequently discussed barrier in our study was stigma surrounding HPV as an STI and challenges in discussing sexual health, particularly given the conservative nature of southwest Georgia, located in the “Bible Belt” region. Previous research in the south (Georgia, Alabama, Kentucky, and North and South Carolina) found that parental perception of the HPV vaccination encouraging or permitting sexual activity discourages parents from having their child vaccinated against HPV [ 18 , 31 , 36 , 43 ]. Prior research with healthcare providers from Georgia noted providers avoid discussing sex at all when recommending the HPV vaccine due to STI stigma [ 18 ].

Similarly, a lack of provider recommendations or discussion about HPV was a prominent barrier among our participants, consistent with previous literature [ 18 , 35 , 42 ]. According to providers in Georgia, low provider confidence in the HPV vaccine can pose a barrier to giving patients strong recommendations for the vaccine [ 18 ]. A national survey examining the quality of physician recommendation for HPV vaccination revealed that physicians in the sample often lacked consistency, urgency, and timeliness in their recommendation of the HPV vaccine [ 46 ]. Strategies such as education from other lay health professionals such as community health workers or navigators, training or mentoring of providers through technology, or partnering with other health organizations may be possible intervention strategies to explore [ 47 ]. Provider training on strong recommendations and presumptive communication has been effective in approaching this discussion about the HPV vaccine with parents and/or adolescents and effective in promoting vaccination [ 33 , 48 ]. This type of training should be delivered to rural public health and healthcare providers to address these barriers [ 49 ].

Additionally, we found that a lack of patient reminders can hinder increases in HPV vaccination rates, which has been observed in prior studies. Reminder cards can easily be lost, so technology-based options, particularly email or text should be utilized based on individual patient preferences [ 43 ]. However, not all rural areas have the capacity to utilize text messaging based on limited cellar service [ 50 ]. Future efforts to increase HPV vaccination need to include relevant reminders for patients and/or caregivers. In addition to a lack of patient reminders, there is a lack of privacy within healthcare for patients. As shown in our study, patients commented on the lack of privacy in health departments and previous research highlights healthcare does not have appropriate privacy protections for patients [ 51 ]. Moreover, our participants noted that in rural areas, inadequate staffing and resources were also barriers to HPV vaccine uptake. A study in rural North and South Carolina also found that provider shortages in rural areas result in fewer opportunities for parents and adolescents to learn about the HPV vaccine [ 36 ].

From previous research, barriers at the community level consist of a lack of transportation and how it negatively affects people getting to their appointments to receive medical care [ 52 , 53 ]. Participants in our study mentioned how a lack of transportation is a challenge, especially when one does not own a car or is unable to drive. Future research can explore methods to increase vaccinations outside of clinical settings including community settings or in pharmacies, as recommended by the President’s Cancer Panel report [ 54 ].

Similarly, a lack of policies can impact the uptake of the HPV vaccine. For example, the HPV vaccine is not routinely mandated for school entry at the state level, unlike other vaccines such as Tdap. While all states and the District of Columbia have middle school entry requirements for Tdap vaccination, only four U.S. jurisdictions (Rhode Island, Virginia, Puerto Rico, and the District of Columbia) currently have HPV vaccination requirements [ 41 , 55 ]. Georgia did propose a bill in 2019 to allow adolescents younger than 16 to consent to vaccinations without parental consent, however, the bill did not pass [ 56 , 57 ]. Due to this, minors will need parental approval to receive the vaccine and our participants explained the difficulty of this. Other policy barriers are financial vaccine burdens on health systems that administer the vaccines and lack of reimbursement from insurance companies [ 58 ]. Future implementation and evaluation of HPV policies (e.g., school or policy requirements) could assess policy solutions and impacts on HPV vaccine uptake.

Strengthens and limitations

The strengths of this study include interviewing three categories of community stakeholders and receiving their insights on the facilitators and barriers of the HPV vaccine in rural Georgia. In addition, for parents and young adults, we received perspectives from those who received and did not receive the HPV vaccine. Using the P3 Model in our study and the subsequent findings, allowed for consideration of multi-level interventions for increasing HPV vaccination. Research has shown numerous programs that promote HPV vaccination operate at a single level [ 59 ]. Mostly focusing on either the patient or provider levels and a lack of focus on including facilitators and barriers at the practice level [ 60 , 61 , 62 ]. Public health professionals and providers can learn from these facilitators and barriers to test strategies at different levels to increase rural HPV vaccination rates. However, our study has some limitations. The study findings may not be generalizable outside of rural southwest Georgia to other states or regions. There were also delays in recruitment because of participants’ limited time due to the impact of the COVID-19 pandemic. We had to pause recruiting providers because the community asked us to due to the demands of the pandemic and we focused our efforts on recruiting parents and young adults for the study. Participants may have offered socially desirable responses during the interview regarding the HPV vaccine. Finally, although we used several methods to increase the reliability of the qualitative data analyses such as using verbatim transcripts, a structured, iterative codebook and training of coders, and two research team members coding each interview, there may be research biases in the analyses and interpretation of our data.

Identifying multi-level facilitators and barriers influencing HPV vaccination is necessary for increasing vaccine uptake, particularly in rural areas where vaccine coverage is disproportionately low. We found some key barriers at all three levels of the P3 model including misinformation, lack of knowledge, provider-patient communication, provider recommendation, lack of systematic reminders, and limited time and resources. These barriers highlight the need for future research to explore the effectiveness of the following strategies in rural communities: HPV vaccine education in rural communities through public health providers, provider training on strong recommendations, and technological health systems activities such as patient reminders.

Availability of data and materials

The data supports the findings of this study are available in tables and supplementary materials of this article. We can share general data matrices or summaries of the qualitative data if there are requested. Since this is a qualitative data and there is sensitive information about the vaccine and perhaps health systems, we will not share the actual transcripts. Contact Cam Escoffery at [email protected] about data availability.

Abbreviations

Community advisory board

Human papillomavirus

Patient-provider-practice-level Model

Socio-ecological Model

Sexually transmitted infections

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Acknowledgements

The authors would like to acknowledge the Emory Prevention Research Center Community Advisory Board members who assisted with recruiting participants and all the participants who provided their time and insight for this study.

This study was supported by Centers for Disease Control and Prevention, SIP 19–005 Cancer Prevention and Control Research Network, U48 DP006377 and Winship Cancer Institute, P30CA138292. The funders had no role in the study design, data collection, analysis, and interpretation of data and in writing the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the CDC. The authors thank the members of the Emory Prevention Research Center’s Community Advisory Board for their many contributions to this project. They thank the organizations and participants that participated in this qualitative study.

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Department of Behavioral, Social, and Health Education Sciences, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA

Courtney N. Petagna, Stephen Perez, Erica Hsu, Ionie Banner & Cam Escoffery

Southwest Health District, 8-2, Division of Public Health, Georgia Department of Public Health, Albany, GA, 31710, USA

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Contributions

C.E. and C.N.P. are responsible for the study design and oversight of the study. B.M.G. contributed to recruiting participants and to the conception of the study. C.N.P. and S.P. interviewed participants, and C.N.P., S.P., and I.B. coded transcripts. S.P., C.N.P., and C.E. contributed to the data analysis and the results. C.N.P., E.H., and C.E. contributed to the discussion. E.H. and C.N.P. prepared Fig.  1 and Table  2 . C.N.P. and I.B. prepared Tables 3. and 4 . S.P. prepared Tables 5 and 6 . C.N.P. prepared Table  1 and the Supplemental Table 1. R.A.B. contributed his model, expertise, and design of the study. All authors read, edited, and approved the final manuscript.

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Correspondence to Courtney N. Petagna .

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Informed consent was obtained by all participants who enrolled in the study.

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Petagna, C.N., Perez, S., Hsu, E. et al. Facilitators and barriers of HPV vaccination: a qualitative study in rural Georgia. BMC Cancer 24 , 592 (2024). https://doi.org/10.1186/s12885-024-12351-1

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Received : 22 February 2024

Accepted : 07 May 2024

Published : 15 May 2024

DOI : https://doi.org/10.1186/s12885-024-12351-1

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