how to develop hypotheses for qualitative research

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• v.37(16); 2022 Apr 25

A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

• Manuscript Preparation

What is and How to Write a Good Hypothesis in Research?

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Table of Contents

One of the most important aspects of conducting research is constructing a strong hypothesis. But what makes a hypothesis in research effective? In this article, we’ll look at the difference between a hypothesis and a research question, as well as the elements of a good hypothesis in research. We’ll also include some examples of effective hypotheses, and what pitfalls to avoid.

What is a Hypothesis in Research?

Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper. Unless your hypothesis is clearly and carefully constructed, any flaw can have an adverse, and even grave, effect on the quality of your experiment and its subsequent results.

Research Question vs Hypothesis

It’s easy to confuse research questions with hypotheses, and vice versa. While they’re both critical to the Scientific Method, they have very specific differences. Primarily, a research question, just like a hypothesis, is focused and concise. But a hypothesis includes a prediction based on the proposed research, and is designed to forecast the relationship of and between two (or more) variables. Research questions are open-ended, and invite debate and discussion, while hypotheses are closed, e.g. “The relationship between A and B will be C.”

A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment. The research question is typically used for new topics that have not yet been researched extensively. Here, the relationship between different variables is less known. There is no prediction made, but there may be variables explored. The research question can be casual in nature, simply trying to understand if a relationship even exists, descriptive or comparative.

How to Write Hypothesis in Research

Writing an effective hypothesis starts before you even begin to type. Like any task, preparation is key, so you start first by conducting research yourself, and reading all you can about the topic that you plan to research. From there, you’ll gain the knowledge you need to understand where your focus within the topic will lie.

Remember that a hypothesis is a prediction of the relationship that exists between two or more variables. Your job is to write a hypothesis, and design the research, to “prove” whether or not your prediction is correct. A common pitfall is to use judgments that are subjective and inappropriate for the construction of a hypothesis. It’s important to keep the focus and language of your hypothesis objective.

An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions.

Use the following points as a checklist to evaluate the effectiveness of your research hypothesis:

• Predicts the relationship and outcome
• Simple and concise – avoid wordiness
• Clear with no ambiguity or assumptions about the readers’ knowledge
• Observable and testable results
• Relevant and specific to the research question or problem

Research Hypothesis Example

Perhaps the best way to evaluate whether or not your hypothesis is effective is to compare it to those of your colleagues in the field. There is no need to reinvent the wheel when it comes to writing a powerful research hypothesis. As you’re reading and preparing your hypothesis, you’ll also read other hypotheses. These can help guide you on what works, and what doesn’t, when it comes to writing a strong research hypothesis.

Here are a few generic examples to get you started.

Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits.

Budget airlines are more likely to receive more customer complaints. A budget airline is defined as an airline that offers lower fares and fewer amenities than a traditional full-service airline. (Note that the term “budget airline” is included in the hypothesis.

Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.

Each of the above examples are specific, observable and measurable, and the statement of prediction can be verified or shown to be false by utilizing standard experimental practices. It should be noted, however, that often your hypothesis will change as your research progresses.

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How to Develop a Good Research Hypothesis

The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.

This blog will help you understand what is a research hypothesis, its characteristics and, how to formulate a research hypothesis

Table of Contents

What is Hypothesis?

Hypothesis is an assumption or an idea proposed for the sake of argument so that it can be tested. It is a precise, testable statement of what the researchers predict will be outcome of the study.  Hypothesis usually involves proposing a relationship between two variables: the independent variable (what the researchers change) and the dependent variable (what the research measures).

What is a Research Hypothesis?

Research hypothesis is a statement that introduces a research question and proposes an expected result. It is an integral part of the scientific method that forms the basis of scientific experiments. Therefore, you need to be careful and thorough when building your research hypothesis. A minor flaw in the construction of your hypothesis could have an adverse effect on your experiment. In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment).

Essential Characteristics of a Good Research Hypothesis

As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory.

A good research hypothesis involves more effort than just a guess. In particular, your hypothesis may begin with a question that could be further explored through background research.

To help you formulate a promising research hypothesis, you should ask yourself the following questions:

• Is the language clear and focused?
• What is the relationship between your hypothesis and your research topic?
• Is your hypothesis testable? If yes, then how?
• What are the possible explanations that you might want to explore?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate your variables without hampering the ethical standards?
• Does your research predict the relationship and outcome?
• Is your research simple and concise (avoids wordiness)?
• Is it clear with no ambiguity or assumptions about the readers’ knowledge
• Is your research observable and testable results?
• Is it relevant and specific to the research question or problem?

The questions listed above can be used as a checklist to make sure your hypothesis is based on a solid foundation. Furthermore, it can help you identify weaknesses in your hypothesis and revise it if necessary.

Source: Educational Hub

How to formulate an effective research hypothesis.

A testable hypothesis is not a simple statement. It is rather an intricate statement that needs to offer a clear introduction to a scientific experiment, its intentions, and the possible outcomes. However, there are some important things to consider when building a compelling hypothesis.

1. State the problem that you are trying to solve.

Make sure that the hypothesis clearly defines the topic and the focus of the experiment.

2. Try to write the hypothesis as an if-then statement.

Follow this template: If a specific action is taken, then a certain outcome is expected.

3. Define the variables

Independent variables are the ones that are manipulated, controlled, or changed. Independent variables are isolated from other factors of the study.

Dependent variables , as the name suggests are dependent on other factors of the study. They are influenced by the change in independent variable.

4. Scrutinize the hypothesis

The types of research hypothesis are stated below:

1. Simple Hypothesis

It predicts the relationship between a single dependent variable and a single independent variable.

2. Complex Hypothesis

It predicts the relationship between two or more independent and dependent variables.

3. Directional Hypothesis

It specifies the expected direction to be followed to determine the relationship between variables and is derived from theory. Furthermore, it implies the researcher’s intellectual commitment to a particular outcome.

4. Non-directional Hypothesis

It does not predict the exact direction or nature of the relationship between the two variables. The non-directional hypothesis is used when there is no theory involved or when findings contradict previous research.

5. Associative and Causal Hypothesis

The associative hypothesis defines interdependency between variables. A change in one variable results in the change of the other variable. On the other hand, the causal hypothesis proposes an effect on the dependent due to manipulation of the independent variable.

6. Null Hypothesis

Null hypothesis states a negative statement to support the researcher’s findings that there is no relationship between two variables. There will be no changes in the dependent variable due the manipulation of the independent variable. Furthermore, it states results are due to chance and are not significant in terms of supporting the idea being investigated.

7. Alternative Hypothesis

It states that there is a relationship between the two variables of the study and that the results are significant to the research topic. An experimental hypothesis predicts what changes will take place in the dependent variable when the independent variable is manipulated. Also, it states that the results are not due to chance and that they are significant in terms of supporting the theory being investigated.

Research Hypothesis Examples of Independent and Dependent Variables:

Research Hypothesis Example 1 The greater number of coal plants in a region (independent variable) increases water pollution (dependent variable). If you change the independent variable (building more coal factories), it will change the dependent variable (amount of water pollution).

Research Hypothesis Example 2 What is the effect of diet or regular soda (independent variable) on blood sugar levels (dependent variable)? If you change the independent variable (the type of soda you consume), it will change the dependent variable (blood sugar levels)

You should not ignore the importance of the above steps. The validity of your experiment and its results rely on a robust testable hypothesis. Developing a strong testable hypothesis has few advantages, it compels us to think intensely and specifically about the outcomes of a study. Consequently, it enables us to understand the implication of the question and the different variables involved in the study. Furthermore, it helps us to make precise predictions based on prior research. Hence, forming a hypothesis would be of great value to the research. Here are some good examples of testable hypotheses.

More importantly, you need to build a robust testable research hypothesis for your scientific experiments. A testable hypothesis is a hypothesis that can be proved or disproved as a result of experimentation.

Importance of a Testable Hypothesis

To devise and perform an experiment using scientific method, you need to make sure that your hypothesis is testable. To be considered testable, some essential criteria must be met:

• There must be a possibility to prove that the hypothesis is true.
• There must be a possibility to prove that the hypothesis is false.
• The results of the hypothesis must be reproducible.

Without these criteria, the hypothesis and the results will be vague. As a result, the experiment will not prove or disprove anything significant.

What are your experiences with building hypotheses for scientific experiments? What challenges did you face? How did you overcome these challenges? Please share your thoughts with us in the comments section.

Frequently Asked Questions

The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a ‘if-then’ structure. 3. Defining the variables: Define the variables as Dependent or Independent based on their dependency to other factors. 4. Scrutinizing the hypothesis: Identify the type of your hypothesis

Hypothesis testing is a statistical tool which is used to make inferences about a population data to draw conclusions for a particular hypothesis.

Hypothesis in statistics is a formal statement about the nature of a population within a structured framework of a statistical model. It is used to test an existing hypothesis by studying a population.

Research hypothesis is a statement that introduces a research question and proposes an expected result. It forms the basis of scientific experiments.

The different types of hypothesis in research are: • Null hypothesis: Null hypothesis is a negative statement to support the researcher’s findings that there is no relationship between two variables. • Alternate hypothesis: Alternate hypothesis predicts the relationship between the two variables of the study. • Directional hypothesis: Directional hypothesis specifies the expected direction to be followed to determine the relationship between variables. • Non-directional hypothesis: Non-directional hypothesis does not predict the exact direction or nature of the relationship between the two variables. • Simple hypothesis: Simple hypothesis predicts the relationship between a single dependent variable and a single independent variable. • Complex hypothesis: Complex hypothesis predicts the relationship between two or more independent and dependent variables. • Associative and casual hypothesis: Associative and casual hypothesis predicts the relationship between two or more independent and dependent variables. • Empirical hypothesis: Empirical hypothesis can be tested via experiments and observation. • Statistical hypothesis: A statistical hypothesis utilizes statistical models to draw conclusions about broader populations.

Wow! You really simplified your explanation that even dummies would find it easy to comprehend. Thank you so much.

Thanks a lot for your valuable guidance.

I enjoy reading the post. Hypotheses are actually an intrinsic part in a study. It bridges the research question and the methodology of the study.

Useful piece!

This is awesome.Wow.

It very interesting to read the topic, can you guide me any specific example of hypothesis process establish throw the Demand and supply of the specific product in market

Nicely explained

It is really a useful for me Kindly give some examples of hypothesis

It was a well explained content ,can you please give me an example with the null and alternative hypothesis illustrated

clear and concise. thanks.

So Good so Amazing

Good to learn

Thanks a lot for explaining to my level of understanding

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2.4 Developing a Hypothesis

Learning objectives.

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.2  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be  logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be  positive.  That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

• A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
• Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
• Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
• Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

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Qualitative Study

Affiliations.

• 1 University of Nebraska Medical Center
• 2 GDB Research and Statistical Consulting
• 3 GDB Research and Statistical Consulting/McLaren Macomb Hospital
• PMID: 29262162
• Bookshelf ID: NBK470395

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a stand-alone study, purely relying on qualitative data or it could be part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and application of qualitative research.

Qualitative research at its core, ask open-ended questions whose answers are not easily put into numbers such as ‘how’ and ‘why’. Due to the open-ended nature of the research questions at hand, qualitative research design is often not linear in the same way quantitative design is. One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. Phenomena such as experiences, attitudes, and behaviors can be difficult to accurately capture quantitatively, whereas a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a certain time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify and it is important to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore ‘compete’ against each other and the philosophical paradigms associated with each, qualitative and quantitative work are not necessarily opposites nor are they incompatible. While qualitative and quantitative approaches are different, they are not necessarily opposites, and they are certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined that there is a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated together.

Examples of Qualitative Research Approaches

Ethnography

Ethnography as a research design has its origins in social and cultural anthropology, and involves the researcher being directly immersed in the participant’s environment. Through this immersion, the ethnographer can use a variety of data collection techniques with the aim of being able to produce a comprehensive account of the social phenomena that occurred during the research period. That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc. through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded Theory

Grounded Theory is the “generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior.” As opposed to quantitative research which is deductive and tests or verifies an existing theory, grounded theory research is inductive and therefore lends itself to research that is aiming to study social interactions or experiences. In essence, Grounded Theory’s goal is to explain for example how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is defined as the “study of the meaning of phenomena or the study of the particular”. At first glance, it might seem that Grounded Theory and Phenomenology are quite similar, but upon careful examination, the differences can be seen. At its core, phenomenology looks to investigate experiences from the perspective of the individual. Phenomenology is essentially looking into the ‘lived experiences’ of the participants and aims to examine how and why participants behaved a certain way, from their perspective . Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources whereas Phenomenology focuses on describing and explaining an event or phenomena from the perspective of those who have experienced it.

Narrative Research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called ‘thick’ or ‘rich’ description and is a strength of qualitative research. Narrative research is rife with the possibilities of ‘thick’ description as this approach weaves together a sequence of events, usually from just one or two individuals, in the hopes of creating a cohesive story, or narrative. While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be “opportunities for innovation”.

Research Paradigm

Research paradigms are the assumptions, norms, and standards that underpin different approaches to research. Essentially, research paradigms are the ‘worldview’ that inform research. It is valuable for researchers, both qualitative and quantitative, to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontology and epistemologies . Ontology is defined as the "assumptions about the nature of reality” whereas epistemology is defined as the “assumptions about the nature of knowledge” that inform the work researchers do. It is important to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a full understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, it is crucial that researchers understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist vs Postpositivist

To further understand qualitative research, we need to discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social as well as natural sciences. Essentially, positivist thinking insists that the social sciences should use natural science methods in its research which stems from positivist ontology that there is an objective reality that exists that is fully independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained but it could be approximated. Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world” and therefore postpositivist philosophy is often associated with qualitative research. An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are constructivist as well, meaning they think there is no objective external reality that exists but rather that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. “Constructivism contends that individuals’ views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality”. Essentially, Constructivist thought focuses on how ‘reality’ is not a fixed certainty and experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike in positivist views, that there is not necessarily an ‘objective’ reality we all experience. This is the ‘relativist’ ontological view that reality and the world we live in are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.”

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have and can even change the role of the researcher themselves. For example, is the researcher an ‘objective’ observer such as in positivist quantitative work? Or is the researcher an active participant in the research itself, as in postpositivist qualitative work? Understanding the philosophical base of the research undertaken allows researchers to fully understand the implications of their work and their role within the research, as well as reflect on their own positionality and bias as it pertains to the research they are conducting.

Data Sampling

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors at play. The following are examples of participant sampling and selection:

Purposive sampling- selection based on the researcher’s rationale in terms of being the most informative.

Criterion sampling-selection based on pre-identified factors.

Convenience sampling- selection based on availability.

Snowball sampling- the selection is by referral from other participants or people who know potential participants.

Extreme case sampling- targeted selection of rare cases.

Typical case sampling-selection based on regular or average participants.

Data Collection and Analysis

Qualitative research uses several techniques including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one on one and is appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be a participant-observer to share the experiences of the subject or a non-participant or detached observer.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or in the environment of the participants, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed which may then be coded manually or with the use of Computer Assisted Qualitative Data Analysis Software or CAQDAS such as ATLAS.ti or NVivo.

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. Results also could be in the form of themes and theory or model development.

Dissemination

To standardize and facilitate the dissemination of qualitative research outcomes, the healthcare team can use two reporting standards. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a wider range of qualitative research.

Examples of Application

Many times a research question will start with qualitative research. The qualitative research will help generate the research hypothesis which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data for a better understanding of what the numbers truly mean and their implications. The qualitative methods can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research researchers can explore subjects that are poorly studied with quantitative methods. These include opinions, individual's actions, and social science research.

A good qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure there are no omissions of part of the target population. A proper collection method should be selected which will help obtain the desired information without overly limiting the collected data because many times, the information sought is not well compartmentalized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of both why teens start to smoke as well as factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered “cool,” and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current non-smokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the results of the survey to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the major factor that keeps teens from starting to smoke, and peer pressure was the major factor that contributed to teens to start smoking. The researcher can go back to qualitative research methods to dive deeper into each of these for more information. The researcher wants to focus on how to keep teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and/or focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking first starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure of smoking. The researcher finds a local park where many local teenagers hang out and see that a shady, overgrown area of the park is where the smokers tend to hang out. The researcher notes the smoking teenagers buy their cigarettes from a local convenience store adjacent to the park where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region of the park, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to the smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk population their perceptions of the changes, what factors are still at play, as well as quantitative research that includes teen smoking rates in the community, the incidence of new teen smokers, among others.

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• Issues of Concern
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• Enhancing Healthcare Team Outcomes
• Review Questions

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How to Write a Great Hypothesis

Hypothesis Format, Examples, and Tips

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis, operational definitions, types of hypotheses, hypotheses examples.

• Collecting Data

Frequently Asked Questions

A hypothesis is a tentative statement about the relationship between two or more  variables. It is a specific, testable prediction about what you expect to happen in a study.

One hypothesis example would be a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. It is only at this point that researchers begin to develop a testable hypothesis. Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore a number of factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk wisdom that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis.   In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in a number of different ways. One of the basic principles of any type of scientific research is that the results must be replicable.   By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. How would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

In order to measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming other people. In this situation, the researcher might utilize a simulated task to measure aggressiveness.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests that there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type of hypothesis suggests a relationship between three or more variables, such as two independent variables and a dependent variable.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative sample of the population and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• Complex hypothesis: "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "Children who receive a new reading intervention will have scores different than students who do not receive the intervention."
• "There will be no difference in scores on a memory recall task between children and adults."

Examples of an alternative hypothesis:

• "Children who receive a new reading intervention will perform better than students who did not receive the intervention."
• "Adults will perform better on a memory task than children." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when it would be impossible or difficult to  conduct an experiment . These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a correlational study can then be used to look at how the variables are related. This type of research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

A Word From Verywell

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Some examples of how to write a hypothesis include:

• "Staying up late will lead to worse test performance the next day."
• "People who consume one apple each day will visit the doctor fewer times each year."
• "Breaking study sessions up into three 20-minute sessions will lead to better test results than a single 60-minute study session."

The four parts of a hypothesis are:

• The research question
• The independent variable (IV)
• The dependent variable (DV)
• The proposed relationship between the IV and DV

Castillo M. The scientific method: a need for something better? . AJNR Am J Neuroradiol. 2013;34(9):1669-71. doi:10.3174/ajnr.A3401

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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How to Do Qualitative Research

Last Updated: October 26, 2022 References Approved

This article was co-authored by Jeremiah Kaplan . Jeremiah Kaplan is a Research and Training Specialist at the Center for Applied Behavioral Health Policy at Arizona State University. He has extensive knowledge and experience in motivational interviewing. In addition, Jeremiah has worked in the mental health, youth engagement, and trauma-informed care fields. Using his expertise, Jeremiah supervises Arizona State University’s Motivational Interviewing Coding Lab. Jeremiah has also been internationally selected to participate in the Motivational Interviewing International Network of Trainers sponsored Train the Trainer event. Jeremiah holds a BS in Human Services with a concentration in Family and Children from The University of Phoenix. There are 10 references cited in this article, which can be found at the bottom of the page. wikiHow marks an article as reader-approved once it receives enough positive feedback. This article received 27 testimonials and 96% of readers who voted found it helpful, earning it our reader-approved status. This article has been viewed 732,487 times.

Qualitative research is a broad field of inquiry that uses unstructured data collections methods, such as observations, interviews, surveys and documents, to find themes and meanings to inform our understanding of the world. [1] X Trustworthy Source PubMed Central Journal archive from the U.S. National Institutes of Health Go to source Qualitative research tends to try to cover the reasons for behaviors, attitudes and motivations, instead of just the details of what, where and when. Qualitative research can be done across many disciplines, such as social sciences, healthcare and businesses, and it is a common feature of nearly every single workplace and educational environment.

Preparing Your Research

• The research questions is one of the most important pieces of your research design. It determines what you want to learn or understand and also helps to focus the study, since you can't investigate everything at once. Your research question will also shape how you conduct your study since different questions require different methods of inquiry.
• You should start with a burning question and then narrow it down more to make it manageable enough to be researched effectively. For example, "what is the meaning of teachers' work to teachers" is too broad for a single research endeavor, but if that's what you're interested you could narrow it by limiting the type of teacher or focusing on one level of education. For example, "what is the meaning of teachers' work to second career teachers?" or "what is the meaning of teachers' work to junior high teachers?"

Tip: Find the balance between a burning question and a researchable question. The former is something you really want to know about and is often quite broad. The latter is one that can be directly investigated using available research methods and tools.

• For example, if your research question focuses on how second career teachers attribute meaning to their work, you would want to examine the literature on second career teaching - what motivates people to turn to teaching as a second career? How many teachers are in their second career? Where do most second career teachers work? Doing this reading and review of existing literature and research will help you refine your question and give you the base you need for your own research. It will also give you a sense of the variables that might impact your research (e.g., age, gender, class, etc.) and that you will need to take into consideration in your own study.
• A literature review will also help you to determine whether you are really interested and committed to the topic and research question and that there is a gap in the existing research that you want to fill by conducting your own investigation.

For example, if your research question is "what is the meaning of teachers' work to second career teachers?" , that is not a question that can be answered with a 'yes' or 'no'. Nor is there likely to be a single overarching answer. This means that qualitative research is the best route.

• Consider the possible outcomes. Because qualitative methodologies are generally quite broad, there is almost always the possibility that some useful data will come out of the research. This is different than in a quantitative experiment, where an unproven hypothesis can mean that a lot of time has been wasted.
• Your research budget and available financial resources should also be considered. Qualitative research is often cheaper and easier to plan and execute. For example, it is usually easier and cost-saving to gather a small number of people for interviews than it is to purchase a computer program that can do statistical analysis and hire the appropriate statisticians.

• Action Research – Action research focuses on solving an immediate problem or working with others to solve problem and address particular issues. [7] X Research source
• Ethnography – Ethnography is the study of human interaction in communities through direct participation and observation within the community you wish to study. Ethnographic research comes from the discipline of social and cultural anthropology but is now becoming more widely used. [8] X Research source
• Phenomenology – Phenomenology is the study of the subjective experiences of others. It researches the world through the eyes of another person by discovering how they interpret their experiences. [9] X Research source
• Grounded Theory – The purpose of grounded theory is to develop theory based on the data systematically collected and analyzed. It looks at specific information and derives theories and reasons for the phenomena.
• Case Study Research – This method of qualitative study is an in-depth study of a specific individual or phenomena in its existing context. [10] X Research source

Collecting and Analyzing Your Data

• Direct observation – Direct observation of a situation or your research subjects can occur through video tape playback or through live observation. In direct observation, you are making specific observations of a situation without influencing or participating in any way. [12] X Research source For example, perhaps you want to see how second career teachers go about their routines in and outside the classrooms and so you decide to observe them for a few days, being sure to get the requisite permission from the school, students and the teacher and taking careful notes along the way.
• Participant observation – Participant observation is the immersion of the researcher in the community or situation being studied. This form of data collection tends to be more time consuming, as you need to participate fully in the community in order to know whether your observations are valid. [13] X Research source
• Interviews – Qualitative interviewing is basically the process of gathering data by asking people questions. Interviewing can be very flexible - they can be on-on-one, but can also take place over the phone or Internet or in small groups called "focus groups". There are also different types of interviews. Structured interviews use pre-set questions, whereas unstructured interviews are more free-flowing conversations where the interviewer can probe and explore topics as they come up. Interviews are particularly useful if you want to know how people feel or react to something. For example, it would be very useful to sit down with second career teachers in either a structured or unstructured interview to gain information about how they represent and discuss their teaching careers.
• Surveys – Written questionnaires and open ended surveys about ideas, perceptions, and thoughts are other ways by which you can collect data for your qualitative research. For example, in your study of second career schoolteachers, perhaps you decide to do an anonymous survey of 100 teachers in the area because you're concerned that they may be less forthright in an interview situation than in a survey where their identity was anonymous.
• "Document analysis" – This involves examining written, visual, and audio documents that exist without any involvement of or instigation by the researcher. There are lots of different kinds of documents, including "official" documents produced by institutions and personal documents, like letters, memoirs, diaries and, in the 21st century, social media accounts and online blogs. For example, if studying education, institutions like public schools produce many different kinds of documents, including reports, flyers, handbooks, websites, curricula, etc. Maybe you can also see if any second career teachers have an online meet group or blog. Document analysis can often be useful to use in conjunction with another method, like interviewing.

• Coding – In coding, you assign a word, phrase, or number to each category. Start out with a pre-set list of codes that you derived from your prior knowledge of the subject. For example, "financial issues" or "community involvement" might be two codes you think of after having done your literature review of second career teachers. You then go through all of your data in a systematic way and "code" ideas, concepts and themes as they fit categories. You will also develop another set of codes that emerge from reading and analyzing the data. For example, you may see while coding your interviews, that "divorce" comes up frequently. You can add a code for this. Coding helps you organize your data and identify patterns and commonalities. [15] X Research source tobaccoeval.ucdavis.edu/analysis-reporting/.../CodingQualitativeData.pdf
• Descriptive Statistics – You can analyze your data using statistics. Descriptive statistics help describe, show or summarize the data to highlight patterns. For example, if you had 100 principal evaluations of teachers, you might be interested in the overall performance of those students. Descriptive statistics allow you to do that. Keep in mind, however, that descriptive statistics cannot be used to make conclusions and confirm/disprove hypotheses. [16] X Research source
• Narrative analysis – Narrative analysis focuses on speech and content, such as grammar, word usage, metaphors, story themes, meanings of situations, the social, cultural and political context of the narrative. [17] X Research source
• Hermeneutic Analysis – Hermeneutic analysis focuses on the meaning of a written or oral text. Essentially, you are trying to make sense of the object of study and bring to light some sort of underlying coherence. [18] X Research source
• Content analysis / Semiotic analysis – Content or semiotic analysis looks at texts or series of texts and looks for themes and meanings by looking at frequencies of words. Put differently, you try to identify structures and patterned regularities in the verbal or written text and then make inferences on the basis of these regularities. [19] X Research source For example, maybe you find the same words or phrases, like "second chance" or "make a difference," coming up in different interviews with second career teachers and decide to explore what this frequency might signify.

Community Q&A

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• Qualitative research is often regarded as a precursor to quantitative research, which is a more logical and data-led approach which statistical, mathematical and/or computational techniques. Qualitative research is often used to generate possible leads and formulate a workable hypothesis that is then tested with quantitative methods. [20] X Research source Thanks Helpful 0 Not Helpful 0
• Try to remember the difference between qualitative and quantitative as each will give different data. Thanks Helpful 4 Not Helpful 0

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• ↑ https://www.ncbi.nlm.nih.gov/books/NBK470395/
• ↑ https://owl.purdue.edu/owl/research_and_citation/conducting_research/writing_a_literature_review.html
• ↑ https://academic.oup.com/humrep/article/31/3/498/2384737?login=false
• ↑ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4275140/
• ↑ http://www.qual.auckland.ac.nz/
• ↑ http://www.socialresearchmethods.net/kb/qualapp.php
• ↑ http://www.socialresearchmethods.net/kb/qualdata.php
• ↑ tobaccoeval.ucdavis.edu/analysis-reporting/.../CodingQualitativeData.pdf
• ↑ https://statistics.laerd.com/statistical-guides/descriptive-inferential-statistics.php
• ↑ https://explorable.com/qualitative-research-design

About This Article

To do qualitative research, start by deciding on a clear, specific question that you want to answer. Then, do a literature review to see what other experts are saying about the topic, and evaluate how you will best be able to answer your question. Choose an appropriate qualitative research method, such as action research, ethnology, phenomenology, grounded theory, or case study research. Collect and analyze data according to your chosen method, determine the answer to your question. For tips on performing a literature review and picking a method for collecting data, read on! Did this summary help you? Yes No

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Methodology

• Exploratory Research | Definition, Guide, & Examples

Exploratory Research | Definition, Guide, & Examples

Published on December 6, 2021 by Tegan George . Revised on June 22, 2023.

Exploratory research is a methodology approach that investigates research questions that have not previously been studied in depth.

Exploratory research is often qualitative and primary in nature. However, a study with a large sample conducted in an exploratory manner can be quantitative as well. It is also often referred to as interpretive research or a grounded theory approach due to its flexible and open-ended nature.

Table of contents

When to use exploratory research, exploratory research questions, exploratory research data collection, step-by-step example of exploratory research, exploratory vs. explanatory research, advantages and disadvantages of exploratory research, other interesting articles, frequently asked questions about exploratory research.

Exploratory research is often used when the issue you’re studying is new or when the data collection process is challenging for some reason.

You can use this type of research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

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Exploratory research questions are designed to help you understand more about a particular topic of interest. They can help you connect ideas to understand the groundwork of your analysis without adding any preconceived notions or assumptions yet.

Here are some examples:

• What effect does using a digital notebook have on the attention span of middle schoolers?
• What factors influence mental health in undergraduates?
• What outcomes are associated with an authoritative parenting style?
• In what ways does the presence of a non-native accent affect intelligibility?
• How can the use of a grocery delivery service reduce food waste in single-person households?

Collecting information on a previously unexplored topic can be challenging. Exploratory research can help you narrow down your topic and formulate a clear hypothesis and problem statement , as well as giving you the “lay of the land” on your topic.

Data collection using exploratory research is often divided into primary and secondary research methods, with data analysis following the same model.

Primary research

In primary research, your data is collected directly from primary sources : your participants. There is a variety of ways to collect primary data.

Some examples include:

• Survey methodology: Sending a survey out to the student body asking them if they would eat vegan meals
• Focus groups: Compiling groups of 8–10 students and discussing what they think of vegan options for dining hall food
• Interviews: Interviewing students entering and exiting the dining hall, asking if they would eat vegan meals

Secondary research

In secondary research, your data is collected from preexisting primary research, such as experiments or surveys.

Some other examples include:

• Case studies : Health of an all-vegan diet
• Literature reviews : Preexisting research about students’ eating habits and how they have changed over time
• Online polls, surveys, blog posts, or interviews; social media: Have other schools done something similar?

For some subjects, it’s possible to use large- n government data, such as the decennial census or yearly American Community Survey (ACS) open-source data.

How you proceed with your exploratory research design depends on the research method you choose to collect your data. In most cases, you will follow five steps.

We’ll walk you through the steps using the following example.

Therefore, you would like to focus on improving intelligibility instead of reducing the learner’s accent.

Step 1: Identify your problem

The first step in conducting exploratory research is identifying what the problem is and whether this type of research is the right avenue for you to pursue. Remember that exploratory research is most advantageous when you are investigating a previously unexplored problem.

Step 2: Hypothesize a solution

The next step is to come up with a solution to the problem you’re investigating. Formulate a hypothetical statement to guide your research.

Step 3. Design your methodology

Next, conceptualize your data collection and data analysis methods and write them up in a research design.

Step 4: Collect and analyze data

Next, you proceed with collecting and analyzing your data so you can determine whether your preliminary results are in line with your hypothesis.

In most types of research, you should formulate your hypotheses a priori and refrain from changing them due to the increased risk of Type I errors and data integrity issues. However, in exploratory research, you are allowed to change your hypothesis based on your findings, since you are exploring a previously unexplained phenomenon that could have many explanations.

Step 5: Avenues for future research

Decide if you would like to continue studying your topic. If so, it is likely that you will need to change to another type of research. As exploratory research is often qualitative in nature, you may need to conduct quantitative research with a larger sample size to achieve more generalizable results.

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It can be easy to confuse exploratory research with explanatory research. To understand the relationship, it can help to remember that exploratory research lays the groundwork for later explanatory research.

Exploratory research investigates research questions that have not been studied in depth. The preliminary results often lay the groundwork for future analysis.

Explanatory research questions tend to start with “why” or “how”, and the goal is to explain why or how a previously studied phenomenon takes place.

Like any other research design , exploratory studies have their trade-offs: they provide a unique set of benefits but also come with downsides.

• It can be very helpful in narrowing down a challenging or nebulous problem that has not been previously studied.
• It can serve as a great guide for future research, whether your own or another researcher’s. With new and challenging research problems, adding to the body of research in the early stages can be very fulfilling.
• It is very flexible, cost-effective, and open-ended. You are free to proceed however you think is best.

Disadvantages

• It usually lacks conclusive results, and results can be biased or subjective due to a lack of preexisting knowledge on your topic.
• It’s typically not externally valid and generalizable, and it suffers from many of the challenges of qualitative research .
• Since you are not operating within an existing research paradigm, this type of research can be very labor-intensive.

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

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

Research bias

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

Exploratory research is a methodology approach that explores research questions that have not previously been studied in depth. It is often used when the issue you’re studying is new, or the data collection process is challenging in some way.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

You can use exploratory research if you have a general idea or a specific question that you want to study but there is no preexisting knowledge or paradigm with which to study it.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

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How do you write a hypothesis for qualitative research?

How to Formulate an Effective Research Hypothesis

• State the problem that you are trying to solve. Make sure that the hypothesis clearly defines the topic and the focus of the experiment.
• Try to write the hypothesis as an if-then statement. …
• Define the variables.

Why is a hypothesis inappropriate for a qualitative study?

People who argue that a hypothesis is inappropriate for a qualitative study do so because they believe that a hypothesis leads a researcher to approach the subject in a biased way. … This is because the researcher will get numerical data that will prove or disprove the hypothesis.

Does qualitative research require hypothesis?

What is Qualitative Research? In Qualitative Research: We do not test hypothesis or previous theories. happens in specific situations.

Is it possible to set hypothesis for qualitative theories?

It is certainly possible to start with a hypothesis and then design a way to test that hypothesis via qualitative methods — for example by predicting patterns that will or will not be present in the data.

What is a research hypothesis example?

Examples of Hypotheses Students who eat breakfast will perform better on a math exam than students who do not eat breakfast. Students who experience test anxiety prior to an English exam will get higher scores than students who do not experience test anxiety.

What is hypothesis example?

Examples of Hypothesis:

• If I replace the battery in my car, then my car will get better gas mileage.
• If I eat more vegetables, then I will lose weight faster.
• If I add fertilizer to my garden, then my plants will grow faster.
• If I brush my teeth every day, then I will not develop cavities.

What is used in qualitative research instead of hypothesis?

A qualitative research study would most likely employ the use of focus group discussions, case studies and in-depth interviews to find the answers to these questions; whereas in quantitative studies (especially the experimental ones), there is a tendency to control observations or assume them to be stable.

What is qualitative research examples?

A good example of a qualitative research method would be unstructured interviews which generate qualitative data through the use of open questions. This allows the respondent to talk in some depth, choosing their own words. … Photographs, videos, sound recordings and so on, can be considered qualitative data.

Is my research qualitative or quantitative?

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions. … The differences between quantitative and qualitative research.

Is there a theoretical framework in qualitative research?

Qualitative research designs may begin with a structured, or perhaps less structured theoretical framework to keep the researcher from forcing preconceptions on the findings. In the latter case, the theoretical framework often emerges in the data analysis phase.

Does qualitative research have variables?

Qualitative research has no variables. In qualitative research raw data are transformed into final description, or themes and categories.

How do we write a hypothesis?

Tips for Writing a Hypothesis

• Don’t just choose a topic randomly. Find something that interests you.
• Keep it clear and to the point.
• Use your research to guide you.
• Always clearly define your variables.
• Write it as an if-then statement. If this, then that is the expected outcome.

What are the three pillars of qualitative research?

Again, the three pillars of qualitative research are observation, you know, actually observing people behave. Focus groups, you know, and you’ve heard of what focus groups are.

Is qualitative research predictive?

Qualitative research is designed to reveal a target audience’s range of behavior and the perceptions that drive it with reference to specific topics or issues. … The results of qualitative research are descriptive rather than predictive.

Which is a method that is commonly used in qualitative research?

However, the three most commonly used qualitative research methods are in-depth interviews, focus group discussions (FGDs) and observation.

What is a good hypothesis example?

Here’s an example of a hypothesis: If you increase the duration of light, (then) corn plants will grow more each day. The hypothesis establishes two variables, length of light exposure, and the rate of plant growth. An experiment could be designed to test whether the rate of growth depends on the duration of light.

How do you write a research hypothesis example?

Which of the following is the best example of a hypothesis.

Answer: Dear if plants receives air, water, sunlight then it grows. FOR hypothesis, if a plant receives water, then it will grow.

What is simple hypothesis?

Simple hypotheses are ones which give probabilities to potential observations. The contrast here is with complex hypotheses, also known as models, which are sets of simple hypotheses such that knowing that some member of the set is true (but not which) is insufficient to specify probabilities of data points.

What makes a good hypothesis?

A good hypothesis posits an expected relationship between variables and clearly states a relationship between variables. … A hypothesis should be brief and to the point. You want the research hypothesis to describe the relationship between variables and to be as direct and explicit as possible.

How do you write a correlation hypothesis?

State the null hypothesis. The null hypothesis gives an exact value that implies there is no correlation between the two variables. If the results show a percentage equal to or lower than the value of the null hypothesis, then the variables are not proven to correlate.

What is the most commonly used instrument in research?

Questionnaire The two most commonly used research instruments in quantitative research studies include Questionnaire and Tests.

Does qualitative research use inductive or deductive reasoning?

Qualitative research is often said to employ inductive thinking or induction reasoning since it moves from specific observations about individual occurrences to broader generalizations and theories.

What is conceptual framework in qualitative research?

Your conceptual framework is the theory-based collection of principles that are relevant to your particular study. … The conceptual framework represents those research-based theories that 1) you used in creating your methods and 2) those that were relevant to your data analysis.

What are 5 examples of qualitative research?

5 Types of Qualitative Research Methods

• Ethnography. Ethnography, one of the most popular methods of qualitative research, involves the researcher embedding himself or herself into the daily life and routine of the subject or subjects. …
• Narrative. …
• Phenomenology. …
• Grounded Theory. …
• Case study.

What are examples of qualitative?

The hair colors of players on a football team, the color of cars in a parking lot, the letter grades of students in a classroom, the types of coins in a jar, and the shape of candies in a variety pack are all examples of qualitative data so long as a particular number is not assigned to any of these descriptions.

What are the 5 qualitative approaches?

The Five Qualitative approach is a method to framing Qualitative Research, focusing on the methodologies of five of the major traditions in qualitative research: biography, ethnography, phenomenology, grounded theory, and case study.

What are the two major types of research?

Types of research methods can be broadly divided into two quantitative and qualitative categories.

• Quantitative research describes, infers, and resolves problems using numbers. …
• Qualitative research, on the other hand, is based on words, feelings, emotions, sounds and other non-numerical and unquantifiable elements.

Why is qualitative research better than quantitative research?

Formulating hypotheses: Qualitative research helps you gather detailed information on a topic. … Finding general answers: Quantitative research usually has more respondents than qualitative research because it is easier to conduct a multiple-choice survey than a series of interviews or focus groups.

Why is qualitative research better?

Here are some of the main benefits: Qualitative techniques give you a unique depth of understanding which is difficult to gain from a closed question survey. Respondents are able to freely disclose their experiences, thoughts and feelings without constraint.

Graduated from ENSAT (national agronomic school of Toulouse) in plant sciences in 2018, I pursued a CIFRE doctorate under contract with Sun’Agri and INRAE ​​in Avignon between 2019 and 2022. My thesis aimed to study dynamic agrivoltaic systems, in my case in arboriculture. I love to write and share science related Stuff Here on my Website. I am currently continuing at Sun’Agri as an R&D engineer.

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This paper is in the following e-collection/theme issue:

Published on 30.8.2023 in Vol 25 (2023)

Trust and Health Information Exchanges: Qualitative Analysis of the Intent to Share Personal Health Information

Authors of this article:

Original Paper

• Julia Busch-Casler * , Dr   ; 
• Marija Radic * , Dr  

Fraunhofer Center for International Management and Knowledge Economy IMW, Leipzig, Germany

*all authors contributed equally

Corresponding Author:

Julia Busch-Casler, Dr

Fraunhofer Center for International Management and Knowledge Economy IMW

Neumarkt 9-19

Leipzig, 04109

Phone: 49 341231039249

Email: [email protected]

Background: Digital health has the potential to improve the quality of care, reduce health care costs, and increase patient satisfaction. Patient acceptance and consent are a prerequisite for effective sharing of personal health information (PHI) through health information exchanges (HIEs). Patients need to form and retain trust in the system(s) they use to leverage the full potential of digital health. Germany is at the forefront of approving digital treatment options with cost coverage through statutory health insurance. However, the German population has a high level of technology skepticism and a low level of trust, providing a good basis to illuminate various facets of eHealth trust formation.

Objective: In a German setting, we aimed to answer the question, How does an individual form a behavioral intent to share PHI with an HIE platform? We discussed trust and informed consent through (1) synthesizing the main influence factor models into a complex model of trust in HIE, (2) providing initial validation of influence factors based on a qualitative study with patient interviews, and (3) developing a model of trust formation for digital health apps.

Methods: We developed a complex model of the formation of trust and the intent to share PHI. We provided initial validation of the influence factors through 20 qualitative, semistructured interviews in the German health care setting and used a deductive coding approach to analyze the data.

Results: We found that German patients show a positive intent to share their PHI with HIEs under certain conditions. These include (perceived) information security and a noncommercial organization as the recipient of the PHI. Technology experience, age, policy and regulation, and a disposition to trust play an important role in an individual’s privacy concern, which, combined with social influence, affects trust formation on a cognitive and emotional level. We found a high level of cognitive trust in health care and noncommercial research institutions but distrust in commercial entities. We further found that in-person interactions with physicians increase trust in digital health apps and PHI sharing. Patients’ emotional trust depends on disposition and social influences. To form their intent to share, patients undergo a privacy calculus. Hereby, the individual’s benefit (eg, convenience), benefits for the individual’s own health, and the benefits for public welfare often outweigh the perceived risks of sharing PHI.

Conclusions: With the higher demand for timely PHI, HIE providers will need to clearly communicate the benefits of their solutions and their information security measures to health care providers (physicians, nursing and administrative staff) and patients and include them as key partners to increase trust. Offering easy access and educational measures as well as the option for specific consent may increase patients’ trust and their intention to share PHI.

Introduction

Data-driven medicine promises better care and more efficient health care processes. Digital health information exchanges (HIEs), electronic health records (EHRs), and eHealth and mobile health (mHealth) apps have become increasingly relevant for sharing personal health information (PHI) in the past years. Countries aim to adopt and implement HIEs to improve the quality of care, reduce health care costs, und increase patient outcomes and satisfaction [ 1 ]. Germany is no exception. In 2019, Germany passed a law approving the prescription of mHealth apps by doctors whereby the costs are covered by the German statutory health insurance. All insured people are eligible to use registered mHealth apps as part of standard care [ 2 ]. However, uptake has been slow because of restraints from both patients and providers [ 3 , 4 ]. A recent study among German citizens [ 5 ] found that almost 25% of the respondents believe that technology creates more problems than it solves, thus indicating that Germans are highly skeptical toward technology overall. This is in line with prior research on country-specific trust levels [ 6 , 7 ], where Germany is associated with rather low levels of trust compared to other countries.

Patient acceptance and opt-in are crucial for efficient use of HIEs (we subsume EHRs, mHealth apps, and eHealth apps under the term “HIEs” for purposes of readability). Patients need to trust that the information security measures and privacy policies of the HIE provider are sufficient to protect their PHI [ 8 , 9 ]. Providers must explain these policies to the patient and show that they are upheld. Several studies have found that most patients have a positive attitude toward EHRs for reasons of convenience, completeness, and ease of communication [ 4 , 10 - 13 ]. However, PHI is considered highly sensitive. Data breaches can potentially have significant negative consequences for the patients involved [ 14 ]. Patients, although excited about the possibilities of EHRs [ 10 , 15 - 17 ], might not fully understand the impact their sharing decisions may have. They may even be reluctant to share their PHI digitally after witnessing data breaches [ 18 ]. Privacy concerns are the largest barrier to sharing PHI [ 16 , 19 ]. Trust in the safety and soundness of technological solutions has a strong impact on user opt-in [ 9 , 19 - 21 ]. Backhaus [ 22 ] described the trust of a user in a technical system as the expectation that the system will perform certain tasks based on the user’s wishes and assumptions without misusing their vulnerability caused by the execution of the process. Trust in digital health apps is strongly linked to trust in the respective health care provider [ 23 ]. Buhr et al [ 23 ], for example, found that Germans trust governmental institutions, such as the statutory health insurance, more than private institutions. Dhopeshwarkar et al [ 20 ] found that patients trust physicians regarding accessing health care files. Considering these developments, patients need to become the sovereign of their own PHI [ 24 ]. They need to be able to provide informed consent on what should be shared through HIEs and who can use PHI stored in their EHRs.

There have been multiple calls for more research on the subject, followed by an upswing in recent years [ 25 ]. Looking at the specific case of Germany in the context of regulatory initiatives [ 26 ] and a comparatively low trust level [ 27 ], however, may provide additional insight into patients’ behavioral intentions [ 28 , 29 ] and measures that HIE providers can undertake to increase the level of trust in their solutions and processes. Our research aimed to answer the following research question: How does an individual form a behavioral intent to share PHI with an HIE platform? We contributed to the discussion of trust and informed consent in digital health in the following ways: (1) We derived a complex model of trust in an eHealth app and intent formation to share PHI based on the belief-attitude-intention framework, (2) provided initial exploratory validation of influence factors through a qualitative analysis process with interviews of German patients, and (3) developed a model of trust formation for eHealth apps.

Initial Model

Trust in and acceptance of eHealth apps have become a more prevalent research area in recent years due to the increasing uptake of HIEs and the rise in virtual interactions in the COVID-19 pandemic years [ 23 , 25 , 28 ]. Different approaches try to assess trust in and user acceptance of (health) information technology and the sharing of PHI. Consumer acceptance and use of technology is often assessed based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and its extensions and adaptations [ 4 , 30 , 31 ]. The model has been applied to the health care context [ 4 , 12 , 13 , 21 ] and has also been enhanced with health behavior theories, such as the Health Belief Model, protection motivation theory, and social cognitive theory [ 24 ]. Abdelhamid [ 31 ], for example, adapted the UTAUT model to PHI sharing with HIEs and used privacy concerns, social influence, trust in health care professionals, health concerns, and perceived usefulness as the main variables for his quantitative study. He found that all factors except for privacy concerns have a positive impact on the sharing intention. More customized sharing choices may mitigate the negative effect of privacy concerns on PHI-sharing intention.

Privacy concerns are often stated as the main barrier to sharing of PHI. They are, however, not always part of the (adapted) Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) models. The Antecedent-Privacy Concern-Outcome (APCO) model presents 1 approach for the analysis of privacy concerns [ 32 - 35 ]. Shen et al [ 35 ], for example, developed an eHealth trust model based on the APCO approach and suggested personality, tech-savviness, eHealth awareness, health care perception, privacy experience, demographics, and culture as antecedents of privacy concerns. The authors described trusting belief as well as policy and regulation as moderating factors. In the outcome stage, they distinguished between a privacy calculus and the final behavioral reaction.

Privacy concerns are often associated with the privacy calculus model [ 16 , 35 - 38 ]. Abdelhamid et al [ 16 ], for example, presented a model with the following variables: patient activation, issue involvement, privacy concerns, trust in providers, and patient-physician relationship. They found that privacy concerns negatively affect the intention to share PHI. This can only partially be mitigated by the other variables.

Trust is another factor in assessing the acceptance of sharing PHI, which is sometimes covered in the UTAUT2 adaptions [ 21 ] but also analyzed separately [ 39 ]. Trust is often distinguished into a personal disposition, cognitive trust (in systems, people, etc) and emotional trust [ 19 , 39 ]. Esmaeilzadeh [ 8 ], for example, examined the acceptance of HIEs using a complex model of trust formation. The main variables analyzed included trust in health care providers and perceived transparency of the HIE privacy policy, leading to cognitive trust in, first, the integrity of the HIEs and, second, the competency of the HIEs. The latter factors influence emotional trust, which then translates into opt-in and willingness to disclose information.

Given the multifaceted nature of the concepts presented, in this study, we aimed to integrate the previous findings into a comprehensive model.

Scientific Basis of the Initial Model

Our initial model is depicted in Figure 1 . An overall definition of the constructs used in the initial model can be found in Multimedia Appendix 1 . We transferred the common belief-attitude-intention framework based on the theory of reasoned action [ 8 , 40 ] to a PHI-sharing setting. We divided our model into 3 stages: (1) belief formation, (2) attitude formation, and (3) information-sharing intent. We defined privacy concern as a belief referring to “the information the individual has about the object,” in this case the HIE [ 41 ]. We defined trust as an attitude, that is, the “favorable or unfavorable evaluation of an attitude object” [ 41 ]. We defined intent as “the subjective probability that the person will behave in a particular way vis-à-vis the attitude object” [ 41 ]. Beliefs are formed based on preconditions and previous experiences [ 40 , 41 ], which are sometimes referred to as values [ 41 ] or antecedents [ 32 , 35 ]. The antecedents of privacy concerns are depicted in the APCO model [ 32 , 35 , 42 ]. We added the antecedent component to the belief-attitude-intention framework to enhance the understanding of the belief formation. Behavioral intentions are influenced not only by trust but also by an individual’s privacy calculus [ 32 , 36 , 37 ], defined as the “cost-benefit analysis” of disclosing information [ 32 , 43 ]. We defined privacy calculus as an attitude, following the attitude definition of Stone et al [ 41 ].

Stage 1: Belief Formation

In the belief formation stage , an individual forms a belief about privacy and related risks of sharing PHI (privacy concern) based on antecedents. An individual has certain previous experiences with sharing information and (eHealth) technology, which we subsumed under technology experience [ 4 ]. We included an individual’s general experience with technology in the variable tech-savviness [ 35 ]. Since eHealth is a comparatively new topic for most individuals, we can only assess the awareness of eHealth of an individual [ 35 ]. We synthesized Shen et al’s [ 35 ] privacy perspective and related findings of Abdelhamid [ 31 ] and Hassandoust et al [ 37 ] into the item information security knowledge to cover potential experiences with data breaches and measures taken to protect an individual’s PHI in the light of the discussions of data sovereignty. We followed the following definition of information security: “Information security is the protection of information from a wide range of threats. This is achieved by managing a suitable set of security controls, policies and procedures within an Information Security Management System. The goal of general InfoSec is the ‘preservation of confidentiality, integrity and availability of information’ and includes such terms as the accountability of users, authentication, non-repudiation and reliability” [ 44 ]. Esmailzadeh [ 8 ] and Shen et al [ 35 ] have shown that an individual’s perception of policy and regulation influences their privacy beliefs. We included demographic factors as antecedents because studies have shown clear differences between the privacy beliefs of diverse demographic groups [ 4 , 5 , 45 , 46 ]. Finally, an individual has personality traits and dispositions, particularly a disposition to trust, that influence all interactions with the individual’s environment [ 19 , 47 , 48 ], including privacy concerns, attitudes, and the intent to share or withhold PHI [ 32 , 49 ]. In the eHealth setting, we argued in line with Abdelhamid et al [ 16 , 31 ] and added health concerns and patient activation into the initial dispositions. All the aforementioned factors lead to the formation of privacy concerns related to sharing an individual’s PHI. Individuals continuously interact in their specific social environment. Studies [ 31 , 37 , 50 ] have shown that social influence affects the intent to share via trust formation. We included social influence as an additional antecedent to trust and the privacy calculus, influencing the perceived utility of sharing PHI [ 31 , 37 ], and the emotional trust of an individual in an HIE.

Stage 2: Attitude Formation

In the attitude formation stage , an individual forms attitudes toward sharing PHI. These attitudes can be divided into the privacy calculus (see the previous section) and trust. The concept of trust has a composite definition [ 49 , 51 , 52 ]. The thoughts and decisions of an individual include both cognition and emotion [ 52 ], leading to a distinction between cognitive trust and emotional trust [ 52 , 53 ]. Cognitive trust in eHealth has different dimensions: First, patients develop a level of cognitive trust in their health care providers, which is necessary for the initial treatment. Based on trust transfer theory, individuals may transfer this established trust to the HIE [ 23 , 49 , 54 ]. To form a sharing intent through an HIE, however, individuals not only need to trust the health care institution but also have thoughts about the expertise and integrity of the HIE provider [ 8 ]. This is often not directly associated with the health care provider. Research shows that an individual’s privacy concern influences the risk associated with sharing PHI and well as the cognitive judgment whether to trust an entity in a digital setting [ 32 , 37 ]. The privacy calculus assesses the perceived utility of the sharing decision and compares it to the perceived risks associated with this decision [ 32 , 37 ]. Individuals value sharing data if they have a perceived benefit. In the case of PHI, patients may, for example, experience better or faster treatment. They may perceive a benefit because a certain health topic has personal relevance due to a particular health concern, which we captured as issue involvement [ 31 ]. The perceived risk refers to the loss or misuse of PHI because of, for example, data breaches and the associated perceived damages the individual incurs because of the data incident [ 35 ].

Stage 3: Information-Sharing Intent

Finally, in the information-sharing intent stage , the individual forms a behavioral intent to share PHI with the HIE. Contrary to Esmaeilzadeh [ 8 , 53 ], we did not differentiate between the opt-in intention and the willingness-to-share intention. Patients often do not actually have an option to opt in or out of an HIE [ 8 ], but rather, they have choices on what to share with an HIE. We regarded the willingness to share information with an HIE as the outcome of the trust formation model. We defined the willingness to share (health) information as the intention to voluntarily disclose information about one’s (health) status to others [ 55 ]. The complete theoretical model is depicted in Figure 1 .

Methodology

We described the methodology along the Consolidated Criteria for Reporting Qualitative Research (COREQ) domains ( Multimedia Appendix 2 ) [ 56 ].

Domain 1: Research Team and Reflexivity

Personal characteristics.

Interviews were conducted by 3 different interviewers who were part of the joint research project funding this research. All researchers had postgraduate degrees, while 1 also had a PhD; 1 of the researchers was female, while 2 were male. All interviewers had received prior training in conducting qualitative interviews, and all were employed at a research institution or university when conducting the interviews.

Relationship With Participants

The researchers conducting the interviews had no previous relationship with the interviewees. The participants received a 1-page introduction of the research project and its goals before agreeing to take part in the interviews. They did not have any further knowledge of the researchers other than project involvement. The researchers were involved in a common research project with the objective of developing a virtual consent assistant for informed and sovereign patient consent.

Domain 2: Study Design

Theoretical framework.

The study was based on a qualitative content analysis and followed a deductive category application [ 57 , 58 ]. Due to the exploratory nature of our study for the German system, we performed qualitative, semistructured interviews [ 59 ] to provide a starting point for our empirical assessment.

Participant Selection

Due to the COVID19 pandemic and associated contact restrictions, we were unable to proceed with our initial plan to recruit a variety of participants onsite at a large German university clinic. We evaluated different data-gathering strategies for their viability. We eventually recruited targeted interview candidates using a combination of purposive and convenience sampling. We selected candidates who (1) had signed a consent form for a medical procedure in the past 6 months and (2) met the rough replication of the demographics of potential app users from the existing personal networks of the researchers. The participants were approached via phone calls. Overall, we approached 25 people, of which 20 (80%) agreed to be interviewed. All participants were offered a small financial compensation (€25, or US $27) for their time. One person asked for the compensation to be donated to a worthy cause.

The interviews were conducted in German language and over the phone, whereby the participants answered the phone in their own homes. We could not assess whether there was anyone else present with them. The researchers worked out of their own offices and were by themselves. Overall, we conducted 20 qualitative user interviews. Table 1 shows the details of the participants.

Data Collection

We developed a questionnaire for the semistructured interviews, focusing on experiences with PHI consent, digital and consent literacy, trust, and individual data-sharing preferences. The translated questionnaire can be found in Multimedia Appendix 3 . The questionnaire was developed by the researchers conducting the interviews and discussed in the project consortium. As shown in the provided questionnaire, we added a vignette as a final question in order to elicit the participants’ intent on sharing PHI based on a specific situation in line with Barter and Renold [ 60 ]. Before the interview, the participants were asked to fill out a short questionnaire for demographic data. No repeat interviews were carried out. One interview had to be paused because the participant had to take a call, and was continued soon after. All interviews were audio-recorded, and the research team took limited field notes during the interviews. The average interview time was about 60 minutes.

Metathemes, as defined by Guest [ 61 ], presented themselves after coding about half the interviews, and we could assume data saturation after analyzing all 20 interviews. The transcripts were not returned to the participants for comment.

Domain 3: Analysis and Findings

Data analysis.

The interviews were transcribed using a transcription service via commissioned data processing following all stipulated information security measures. The transcripts were imported into MAXQDA [ 62 ] for coding. Coding was performed independently by 2 researchers with postgraduate degrees, 1 of whom had a PhD. We revised the coding agenda and coding rules before final coding and then compared results after final coding using Cohen κ. We reached a Cohen κ value of 0.88, indicating solid interrater agreement [ 63 ]. An overview of the constructs and definitions of the coding agenda, key illustrative quotations per code, and the number of statements coded per interview can be found in Multimedia Appendix 1 .

In this paper, we presented quotations to illustrate our findings. All interview quotations presented in the results were translated from German to English. The interviews were numbered for identification, and the position (denoted as “pos.” in the quotations) of each quotation in the transcript was marked accordingly. We presented major and minor themes in the results, and we adapted our initial model according to our exploratory findings.

Ethical Considerations

We obtained a positive ethics vote from the University of Cologne (review number: 21-1271). The survey was conducted in accordance with the applicable provisions of the Data Protection Act (Art.9 para.2 letter b DSGVO). The interviewers are subject to the obligation of secrecy and are also bound to data secrecy.

Prior to conducting the interviews, the participants obtained information about study participation and a consent form. The signed consent forms are kept separately from the short questionnaire and interview results in the university clinic so that no connection can be made between the information in the short questionnaire and the consent forms. The interviews were recorded with the help of a recorder. The recorded interviews were transcribed and pseudonymized. They were processed in written and pseudonymized form only so that it is no longer possible to draw conclusions about the person or third parties. In contrast to the transcripts, the audio files created could not be sufficiently pseudonymized for technical reasons, which is why they were not further processed after the interviews. They will, however, be stored until the end of the project in October 2023 and then deleted.

The participants were thoroughly informed that their participation in the study is completely voluntary. This means that at any time and without giving reasons, they had the right to refuse to answer individual questions. They could also terminate participation in the study or withdraw their consent to participate at any time without incurring any disadvantages. In this case, all data collected up to that point (questionnaires, transcripts, audio recordings) were completely deleted. All data collected in the context of the interview study were treated confidentially, stored exclusively for scientific purposes, and used exclusively by the scientists in the project team.

Participant Details

The average age of the participants was 48.5 (median 43.0) years. The youngest participant was 26 years old, and the oldest was 83 years old. About 70% (n=14) of the participants were women. About 80% (n=16) had an academic degree. The sample was skewed and may have overrepresented women with higher education. We did, however, postulate that the gained insights were relevant, given the articulated need to improve the understanding of female health perceptions and behaviors [ 64 , 65 ]. Of the 20 participants, 12 (60%; n=10, 83%, female) suffered from chronic illnesses and were more frequently in contact with health care institutions. Most participants dealt with 2-5 medical consent forms annually. In addition, 5 (42%) participants, who all reported 1 or more chronic illnesses, stated they would be confronted with 6-11 consent forms, indicating a multitude of interactions with the health care system. All participants said they use technology, mainly smartphones and laptops, for personal communication and information purposes. The most used features are search engines (n=20, 100%), email (n=19, 95%), online shopping (n=19, 95%), and online banking (n=18, 90%). Only 12 (60%) participants reported the usage of social media, and only 10 (83%) participants reported using online education formats. Furthermore, 12 (60%) participants reported their digital aptitude with a 3.5-4 score on a 5-point Likert scale ranging from little to no knowledge to expert knowledge. In contrast, participants over the age of 70 years reported their digital aptitude with an average score of 2.6.

Conceptual Model Validation

To validate the conceptual model of eHealth trust formation in Figure 1 , we analyzed our results along 3 stages: belief formation, attitude formation, and information-sharing intent. Generally, we found that most people show a behavioral intent to share their PHI with health care professionals digitally. One participant stated:

I am 100% convinced that the pros outweigh the cons. [Interview 2, pos. 121]

Another stated:

If you can judge the risk [associated with data breaches when sharing], then, generally yes, I would share it. [Interview 20, pos. 201]

Given the fulfillment of certain conditions, such as anonymity, participants would be willing to share their PHI with (noncommercial) medical research institutions for advancement in medical science. One participant stated:

And I think it is very important that everything that is related to [human health] is made available to science. [Interview 11, pos. 11]

Technology Experience

In the belief formation stage, we found that an individual’s privacy concern is indeed influenced by previous experiences with technology (tech-savviness) and their knowledge on information security. As previously mentioned, all participants used digital technology, mainly smartphones and laptops. The median self-reported tech-savviness was 3.5 on a 5-point Likert-Scale, with 1 being low and 5 being high. A notable statement was:

I would check the possibilities suggested on my computer or phone, and then I would check settings to see what I want and don’t want, and if I don’t understand it, then [the app] I would delete it. [Interview 16, pos. 77]

People with low tech-savviness (mostly over the age of 70 years in our sample) adopt strategies to help interact with digital technology. This was indicated in this statement:

If I need a new app or want to delete one, then someone has to do this for me. [Interview 11, pos. 55]

The overall knowledge on information security can be classified as low to medium and heavily relies on what has been communicated by the provider and preinstalled in the used system. One participant mentioned:

Something like this is already on my phone, an antivirus program. [Interview 10, pos. 67]

Often, people do not seem to be aware of or interested in the subject, as indicated by, for example, participant 2 (pos. 53), who “never looked into it.” People seem to assume that:

As soon as you are digital or you are transferring information, then you can’t control where it ends up and who uses it. [Interview 12, pos. 71]

Participants with a higher level of digital competency stated:

Not every app gets a right to access things where I don’t think the app needs them. [Interview 1, pos. 71]

There is no security measure that cannot be hacked…Because otherwise you would not be able to operate it, if it was completely secure. [Interview 14, pos. 107]

eHealth awareness does not seem to play a predominant role.

Policy and Regulation

Information security policies and the regulatory framework for data sharing pose another antecedent to privacy concerns. Participants statements included “if it is encrypted […] then I don’t see a problem” (interview 9, pos. 137), “always using the latest standard of anonymization […] and ensure transparency” (interview 14, pos. 171), and “so that no third party can access the data, but only the person that one has consented to” (interview 16, pos. 47). When sharing data for medical research, most participants wanted to stay completely anonymous. One participant, however, stated:

I would share my data with the condition that I get informed when they find something. That would be useful for me as a prophylactic measure. [Interview 12, pos. 145]

This indicates that complete anonymity may not always be beneficial for the data owner. We analyzed data from a German health care system, implying strict regulation on information sharing and information usage, which aids participants in feeling secure when sharing PHI. A notable statement was made by a participant who is an immigrant:

If you are here in Germany and know that everything is checked and done meticulously, then I don’t have a problem [with sharing my data]. In [the country] where I am from, you don’t know what they do with the [data]. There, I would think twice about it. [Interview 2, pos. 17]

Participants said they are comfortable sharing PHI within Germany or the European Union (EU) but are wary about sharing PHI with institutions outside the EU, as indicated, for example, by participant 16:

I would trust [institutions] within Europe. [Interview 16, pos. 173]

Demographics

Considering demographics, age was found to be the most predominant factor influencing privacy concerns. Participants stated:

So if I was 75, then I would say, I don’t care, take my data. Because I think, ok, then hackers have my data, but what are they going to do with it? But not in my current age. [Interview 7, pos. 133]

If you would ask someone who is 20, 30, or 40, they would give a different answer because everything happens digitally for them. [Interview 12, pos. 39]

All other factors were barely found in the interviews.

Personality and Disposition

The final antecedent was personality and disposition. In our sample, we found evidence for the importance of disposition to trust when sharing PHI. Most participants exhibited a tendency to trust and mentioned that a base level of trust is needed in all social and digital interactions. This was indicated by statements such as:

You need to have a level of trust these days, both in technology and in relation to the digital possibilities we have today. [Interview 15, pos. 105]

Then I have to give them the benefit of the doubt, that the information is important, and that’s what you need to have in general towards a doctor and a hospital. [Interview 3, pos. 23]

One participant, however, stated:

This is difficult. I trust myself...I don’t trust anyone. This is based on my experience. [Interview 10, pos. 81]

Participants were aware of the benefits of actively pursuing a healthy lifestyle (patient activation), and most stated that they try to do so, succeeding to a varying extent. Some participants mentioned a (brief) use of step counters or sleep trackers. They did not relate these statements to privacy concerns. We found evidence that showed an influence of patient activation on privacy concern formation, as indicated by the statement:

Yes, you see. Then we have a yes if I am affected myself. [Interview 10, pos. 149]

Regarding the impact of health status on privacy concerns, we found ambivalent results. Some people with chronic illnesses were skeptical about sharing their PHI or believed it is not important, while others said they would happily share their PHI. There was no evidence that health concerns such as chronic illnesses have an influence on privacy concerns.

Privacy Concerns

All participants expressed some level of privacy concern. Participants had “the feeling, that my data already is everywhere anyway” (interview 5, pos. 51) and a feeling of “overstimulation due to too much information” (interview 17, pos. 105) and being unable to control it in the first place. This was fittingly expressed by participant 13:

Yes, because I always have this remaining risk that the data could be misused. [Interview 13, pos. 143]

This was often mentioned in relation to a level of acceptance of the matter. Most participants stated the risk of “sensitive data in the wrong hands” (interview 3, pos. 151) through data leaks or hacking. Some worried about leaking illness-related PHI to employers and the resulting discrimination due to health concerns, as mentioned by participant 7:

If someone has an illness and she applies somewhere, [then] the potential employer could find that [the person] has an illness and not hire her. [Interview 7, pos. 13]

Other participants mentioned unwanted targeted ads or discrimination.

Social Influence

In addition to privacy concerns, social influence also affects trust formation. Particularly, older participants actively rely on their children and grandchildren for support in IT and sharing decisions and involve them in their decision process. Participant 12, for example, stated:

Then I would ask the younger generation. [Interview 12, pos. 57]

In the attitude formation stage, we differentiated between cognitive and emotional trust as well as the privacy calculus calculation.

Cognitive Trust

Overall, there was a high level of cognitive trust in medical institutions, such as hospitals, and other health care and health insurance providers:

You are willing to share your data as long as you trust the institution. [Interview 3, pos. 117]

Because I have a base level of trust in our health care system. [Interview 8, pos. 21]

We also found that a base level of trust is created through an in-person interaction with the treating physician or the health insurance provider, as indicated by participant 2:

If my family doctors said you need to monitor your blood pressure and I would like you to use this [app]…then I would use it. [Interview 2, pos. 79]

However, trust in Big Pharma and the general intentions of companies using health-related data was low. Participants used large platforms, such as Facebook and Google, but tended to have reservations about their data collection and usage policies. One participant stated:

The motivation of the companies to get data is high…They surely get more information than they deserve. [Interview 1, pos. 89]

This statement displays the influence of the person’s privacy concern on trust formation. Participants did, however, trust the expertise and integrity of, for example, the apps provided by their health insurance providers, as indicated by the following statements:

If you talk about expertise, they are all competent. Generally, I would say that everything related to health insurances and sport universities would have the highest level of expertise. [Interview 4, pos. 107]

The health insurance app is competent because I can upload my bills. [Interview 6, pos. 151]

Emotional Trust

Participants based their emotional trust in HIEs on their general disposition to trust and previous experiences expressed within privacy concerns, as indicated by this statement:

When the health insurance said we have this app and we would like you to use it…I thought I can do that for them…I have blind faith that the [health insurance] makes sure it is safe. [Interview 2, pos. 71]

Depending on their dispositions and experiences, some participants showed an emotional mistrust in HIEs, such as 1 participant:

I would have a feeling, I don’t know, what types of data go where, what they can tell someone about me…I think this is too risky. [Interview 7, pos. 149]

Privacy Calculus

In addition to forming a trust attitude at this stage, participants underwent a privacy calculus, comparing the utility and associated risks of sharing PHI. Participants were more likely to opt into sharing PHI if they perceived it to be beneficial (1) for their own convenience and usability of the app, (2) for their own health, or (3) if it aids a common good of advancing medical diagnosis and treatment. One participant stated clearly, “Yes, if I benefit from it,” (interview 5, pos. 123), while mentioning the common good, “So if I share this data for research purposes, then I definitely see the benefit that you can perform research with it. And that is somehow a priority for me” (interview 5, pos. 173).

Another one said:

[I’d] have a good feeling [that] things will be a bit easier…if the data is already saved. [Interview 8, pos. 117]

… Because then you have your whole health history and all the relevant information in one spot…I believe this has a lot of potential. [Interview 8, pos. 41]

Yet another participant stated:

I think it is very important that data is shared between the [medical] professionals. [Interview 11, pos. 23]

Perceived risk is mainly associated with misuse of data by third parties, as indicated by participant 3:

…Sensitive data gets into the wrong hands…That would be bad for the user. [Interview 3, pos. 151]

Stage 3: Intent Formation

In the information-sharing intent stage, participants formed their final intent toward sharing their PHI with an HIE. To assess intent, participants received a vignette (see Multimedia Appendix 1 ) and were asked for their recommendation. Most participants (n=19, 95%) displayed a positive intent to share their PHI, even given the special circumstances:

Because it will be beneficial for research on this illness […] I think you should do it, even if data is stolen. [Interview 6, pos. 227]

Additional Themes

In addition to the deductive themes from the model, we found that participants preferred apps that are easy to use in daily life. Further, participants preferred a specific consent solution [ 66 ] compared to a broad consent solution.

Figure 2 shows the updated eHealth trust formation model based on our interview results. The figure shows which constructs are supported by evidence within our data set and which ones are currently not supported. These results should certainly be validated with further qualitative and quantitative cross-country studies.

Principal Findings

The objective of our study was to gain deeper insight into the issue of trust in HIEs and answer the following question: How does an individual form a behavioral intent to share PHI with an HIE platform? We contributed to the discussion of trust and informed consent in digital health in the following ways: First, we synthesized the main influence factors into a complex model of trust in HIEs. Next, we verified the influence factors through a qualitative analysis using patient interviews in the German health care setting. We showed which constructs are supported by evidence within our data set and which ones are not. Since this was an exploratory study, we did not adapt the model based on our current findings.

Our results showed that most patients generally have a positive attitude toward sharing their PHI digitally through an HIE. Our model provides a new point of view on the formation of a behavioral intention to share PHI by combining key concepts of the APCO model with a belief-attitude-intention framework and research on trust and the privacy calculus. Based on the interviews, we found that patients form a privacy concern in the belief formation stage based on antecedents, which can be divided into 4 categories: (1) demographics, (2) policy and regulation, (3) previous experiences with technology and information security, and (4) an individual’s own personality and disposition to trust. We also highlighted which factors appear more important in influencing the information-sharing intent. All participants in our sample use technology and gather their own experiences with it.

In the attitude formation stage, privacy concerns and social influence lead to the formation of trust in both cognitive and emotional terms. A base level of trust is created through in-person interactions with the treating physician or the health insurance provider. Trust is then transferred to the suggested HIE for sharing PHI. This is a crucial difference compared to trust formation in an e-commerce setting, for example, without contact with a physical party in the process. In the German health care setting, patients can choose their health care provider (within time and location restrictions). They can already develop a level of cognitive trust in the health care provider before interacting with the HIE. In addition to trust, the privacy calculus influences the intent to (not) share PHI with an HIE.

Limitations

We based our model on previous empirical and theoretical research. Regarding the representativeness of our results, our sample was slightly skewed and may have overrepresented women with higher education. This may be due to increased digital health literacy [ 67 , 68 ] and an increased interest shown by this demographic in the topic [ 23 ]. The insights gained are relevant, given the articulated need to improve the understanding of female health perceptions and behaviors [ 64 , 65 ].

We did not interview people under the age of 25 years, which may have impacted the results. We did, however, capture some secondary insights into their attitudes through conversations with a younger age group mentioned by the participants. All participants displayed some level of tech-savviness, which may be due to low interest of non-tech-savvy people in the research and an unwillingness to participate.

The study did not capture real-life PHI-sharing decisions but, rather, analyzed the behavioral intent. Participants may have provided socially desirable answers, which may not be in line with their final action of sharing PHI. We did, however, assume that a positive intent will eventually lead to a positive action, that is, sharing PHI for the majority of participants [ 69 ].

The validation was conducted based on the results of 20 interviews with patients in Germany. To generalize the results, further qualitative and quantitative cross-country validations of the model are needed.

Comparison With Prior Work

Our model provides a new point of view on the formation of a behavioral intention to share PHI by combining key concepts of research on privacy, user acceptance, and trust. With this, we address calls for a more nuanced view on the patient perspectives concerning privacy and trust [ 42 ]. We collected data from a country that is a front-runner for approving digital treatment options with cost coverage through statutory health insurance but at the same time has a comparatively rather low level of trust and high level of technology skepticism [ 4 ].

With our data, we confirmed previous findings [ 46 , 68 ] that most patients generally have a positive attitude toward sharing their PHI digitally through an HIE, even in the German health care setting [ 23 , 70 ]. Our data indicate that age is a predictor of privacy concerns [ 4 ]. Older participants stated that they are happy to share their PHI, which is in line with previous findings [ 71 ]. This could relate to a lack of understanding of what the shared PHI could be used for, which is in line with studies on digital (health) literacy [ 5 , 72 , 73 ]. However, it could also reflect the need to share information in order to enable a better understanding of the information for oneself [ 71 ]. The middle-aged participants in our study exhibited a higher level of privacy concerns. Studies [ 45 , 74 ] have found that adolescents and young adults exhibit fewer privacy concerns, possibly due to a limited understanding of the consequences as well. Further studies are needed to better understand the impact of age on privacy concerns, particularly for the older generation.

Our results further indicated that knowledge of and previous experiences with information security and technology might play an ambivalent role in forming privacy concerns: A higher level of knowledge could, on the one hand, decrease privacy concerns, as the individual knows which measures to take to mitigate the risk of a data breach. On the other hand, it may increase the level of privacy concerns, as the individual understands how easily data breaches can occur, even with measures in place. The latter is in line with the findings of Baruh et al [ 75 ].

In addition, a base level of trust is created through in-person interaction with the treating physician or the health insurance provider, which is in line with previous studies [ 8 , 31 , 50 , 76 ] and poses a stark difference to non-health-related information sharing, where there is rarely an in-person interaction required.

Sharing PHI through or with HIEs has the potential to significantly improve the quality of care, patient outcomes, and satisfaction and to raise efficiencies in the health care sector. Privacy concerns and trust formation are a main pillar of successful and patient-centered introduction and usage of HIEs and EHRs. In terms of the practical implications of our study, patients generally have a high level of trust toward medical institutions and tend to be willing to share their PHI, given the fulfillment of certain antecedent conditions by HIEs providers, such as information security, risk mitigation, transparency, anonymity, and a defined group of (noncommercial) users.

Offering educational measures as well as the option for specific consent [ 66 ] may increase patients’ trust and their intention to share PHI. Increasing patients’ knowledge appears essential in facilitating empowerment and awareness of data sovereignty, despite the potential effect on privacy concerns. Developers of HIE solutions should, along the lines of General Data Protection Regulation (GDPR) requirements, aim to educate users (both medical and nonmedical staff as well as patients) on the implications of their choices. They should enable patients to choose sharing options based on their personal knowledge and preferences. Arguably, however, implementing privacy by design and security by design in old implementations of systems proves to be more difficult compared to new applications.

HIE providers need to clearly communicate the benefits of their solutions and information security measures to both health care providers (physicians, nursing and administrative staff) and patients in terms of convenience, health benefits, and public welfare. Health care providers are key partners of HIE developers with regard to sharing PHI. This entails that creating trusting relationships with physicians and health care staff, as well as national health organizations, is essential to increase patients’ PHI sharing. Medical professionals need to be convinced that the technology provides benefits, not only for the patient and related care activities, but also for internal service provision processes entailing time and cost savings for the practitioners. Implementing digital services must facilitate care delivery rather than producing additional work for the care provider. HIE developers should integrate care providers into their service development to better adapt their product to user needs. Another strategy may be to aim for a national rollout through a governmental organization to create a base level of trust.

In terms of usability, HIE providers should aim at making it easy for health care providers and patients to access, use, and navigate their apps. This could be done by, for example, performing early usability testing and offering access through multiple operating systems. Offering (non)monetary compensation for sharing certain types of PHI with commercial parties could create an additional incentive for partaking in commercial research, which is needed to bring medication and treatments to market.

Acknowledgments

This research was funded by the German Federal Ministry of Education and Research (BMBF) within the scope of the research project Virtual Consent Assistant for Informed and Data-Sovereign Patient Consent (ViCon; funding reference number 16SV8497). We thank Ms Carla Riese, Mr Peter Haberland, and Mr Patrick Casler for their support for this paper.

Conflicts of Interest

None declared.

Overview of constructs, definitions, illustrative quotes, and frequencies.

Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist.

Questionaire (translated).

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Abbreviations

Edited by T Leung; submitted 03.08.22; peer-reviewed by N Shen, R Cochran, AS Alaqra, RM Åhlfeldt; comments to author 19.12.22; revised version received 12.02.23; accepted 31.07.23; published 30.08.23

©Julia Busch-Casler, Marija Radic. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.08.2023.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Showerheads Market Size 2023 Research Report by Global Growth Rate, Development Strategy, Recent Trends, and Regional Demand till 2029

The qualitative report published by market intelligence data research on the " Showerheads Market offers an in-depth examination of the current trends, latest expansions, conditions, market size, various drivers, limitations, and key players along with their profile details. The Showerheads market report offers the historical data for 2017 to 2022 and also makes available the forecast data from the year 2023 to 2029 which is based on revenue. With the help of all this information research report helps the Market contributors to expand their market positions. With the benefit of all these explanations, this market research report recommends a business strategy for present market participants to strengthen their role in the market. This report analyzes the impact of the Covid 19 pandemic on the Showerheads Market from a Global and Regional perspective.

The global Showerheads market is expected to grow at a CAGR of 5.5% between 2023 and 2029.

Click Here to Download Sample Copy:

https://www.marketintelligencedata.com/reports/7321580/global-showerheads-market-research-report-2023/inquiry?Mode=Ragini_14

Top Key Players are covered in the Showerheads Market Report:

Aqualisa (UK), Zoe Industries, Inc. (US), Dornbracht (Germany), Grohe AG (Germany), Jacuzzi Group Worldwide (US), Jaquar & Company Private Limited, Kohler Co. (US), Masco Corporation (US), Hansgrohe AG (Germany), Moen, Inc. (US), MX Group (UK), ROHL LLC (US), TRITON SHOWERS (UK), Vigo Industries LLC (US), Vola A/S (Denmark)

Regional Analysis:

The region-wise coverage of the market is mentioned in the report, mainly focusing on the regions:

North America (NA) - US, Canada, and Mexico

Europe (EU) - UK, Germany, France, Italy, Russia, Spain & Rest of Europe

Asia-Pacific (APAC) - China, India, Japan, South Korea, Australia & Rest of APAC

Latin America (LA) - Brazil, Argentina, Peru, Chile & Rest of Latin America

The Middle East and Africa (MEA) - Saudi Arabia, UAE, Israel, South Africa

Market Segment Analysis :

The Showerheads Market Report provides a preliminary review of the industry, definitions, classifications, and enterprise chain shape. Market analysis is furnished for the worldwide markets which include improvement tendencies, hostile view evaluation, and key regions development. Development policies and plans are discussed, and manufacturing strategies and fee systems are also analyzed.

Showerheads Market Segmentation by Types:

Fixed Showerhead

Handheld Showerhead

Showerheads Market Segmentation by Applications:

Public Bath & Spa Club

Gym & Swimming Pool

For The Full Report Click here:

https://www.marketintelligencedata.com/reports/7321580/global-showerheads-market-research-report-2023?Mode=Ragini_14

Considered in this report

• Historic year: 2017
• Base year: 2022
• Estimated year: 2023
• Forecast year: 2029

Aspects covered in this report

• Showerheads market with its value and forecast along with its segments
• Country-wise Showerheads market analysis
• Various drivers and challenges
• On-going trends and developments
• Top profiled companies
• Strategic recommendation

Significant Features and Key Highlights of the Showerheads Market Reports :

- Detailed overview of The Showerheads market.

- Changing market dynamics of the industry.

- In-depth market breakdown by Type, Application, etc.

- Historic, existing, and predictable market size in terms of extent and worth.

- Recent manufacturing trends and developments.

- Competitive landscape of The Showerheads market.

- Approaches to significant performers and product help.

- Prospective and niche sectors/regions exhibiting promising growth.

Detailed TOC of Showerheads Market Research Report 2023 - 2029

Chapter 1 Showerheads Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers and, Market data

Chapter 4 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 5 Global Production, Revenue (Value), Price Trend by Type and Region

Chapter 6 Global Market Analysis by Application

Chapter 7 Manufacturing Cost and, Gross profit Analysis

Chapter 8 Industrial Chain, Sourcing Strategy, and Downstream Buyers

Chapter 9 Marketing Strategy and, Status Analysis, Distributors/Traders

Chapter 10 Market Driving Effect Factors Analysis

Chapter 11 Global Showerheads Market Trends and, Forecast

Chapter 12 Research Methodology

COVID-19 and Russia-Ukraine War Influence Analysis

The readers in the section will recognize how the Showerheads market scenario changed across the globe during the pandemic, post-pandemic, and Russia-Ukraine War. The study is done keeping in view the changes in aspects such as demand, consumption, transference, consumer behavior, supply chain management, export and import, and production. The industry experts have also highlighted the key factors that will help create opportunities for players and stabilize the overall industry in the years to come. These factors negatively affected the market during the war.

The Showerheads Market report gives answers to the following:

• What guidelines are followed by key performers to contest this Covid-19 condition?
• What are the important matters drivers, opportunities, challenges, and dangers of the market?
• will face surviving?
• Which are the essential market players in the Showerheads industry?
• What is the forecast compound annual growth rate (CAGR) of the global market for the duration of the forecast period (2023-2029)?
• What could be the anticipated value of the Showerheads marketplace during the forecast period?

Read Our Other Reports:

https://www.marketintelligencedata.com/reports/4785203/global-and-united-states-tributyl-phosphate-market-report-forecast-2022-2028/inquiry?Mode=Ragini

https://www.marketintelligencedata.com/reports/5957359/global-blood-thawing-system-industry-research-report-growth-trends-and-competitive-analysis-2022-2028/inquiry?Mode=Ragini

https://www.marketintelligencedata.com/reports/4337417/global-technology-business-management-tbm-tools-market-insights-forecast-to-2028/inquiry?Mode=Ragini

Customization:

The Showerheads Market Report may well be modified to meet your detailed business essentials. Because we understand what our clients want, we provide up to 20% customization for any of our market intelligence data reports at no added cost to all of our Users.

Thanks for reading this article!! you can also customize this report to get select chapters or region-wise coverage with regions such as Asia, North America, and Europe.

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