The “Affiliated” list emerged from the process described in this methods article. All other lists on this table already existed and were used for consultation in arriving at the “Affiliated” list in the far right-hand column.
The authors compiled a complete and accurate list of 108 CTSC-affiliated faculty members following several months of investigation using the process outlined above. The authors were highly confident that the list included all faculty members who belonged in the study population, while excluding all others. This “Affiliated” list was stratified by (1) clinical, (2) basic science, (3) pharmacy, or (4) nursing and other faculty members, before a random selection process began for the subsequent study process.
Plato and Aristotle, as well as many early physical scientists in more recent centuries such as Bacon and Mill, pioneered inductive approaches [ 5 ]. In its purest form, an inductivist approach begins “with a collection of data, empirical observations or measurements of some kind, and build[s] theoretical categories and propositions from [the] relationships discovered among the data” [ 6 ]. Pure inductivism tends to introduce biases, however [ 7 ]. More modern forms of inductivism have emerged in the form of techniques for developing clearly delineated categorizations and classifications that have high fidelity with describing logical groupings in the real world.
Induction contrasts with the other major form of logical reasoning known as deduction. Whereas induction assembles more and more specific observations in order to form a generalization, deduction begins with a generalization and then applies this generalization to specific instances [ 8 ]. Deduction assumes that the generalization of interest consists of an agreed-upon fact, principle, or theory [ 9 ].
Inductive approaches are more common than might be recognized immediately. While rarely noted explicitly, investigators often employ inductive approaches at different junctures of the research process. Investigators might use inductive processes, for example, to generate hypotheses or research questions. Investigators also apply inductive approaches to define study populations. Clinicians use inductive techniques when formulating patient diagnoses. Librarians and information science professionals, moreover, use inductive processes while engaged in classification processes, such as indexing or meta-tagging.
The analytic induction approach “involves an iterative testing and retesting of theoretical ideas using the data” [ 10 ]. Investigators use analytic induction to develop more clearly defined concepts or categories by examining individual cases against emerging concepts [ 11 ]. It seeks to remain faithful to empirical observations of a limited number of cases as any categorizations take shape, while ensuring that investigators avoid jumping to premature conclusions. It abstracts from some detailed observations and then, in a quasi-deductive way, it tests those abstractions against more and more cases. The abstractions are revised if they prove too inflexible or too narrow to incorporate any apparent deviant cases [ 12 ].
Analytic inductive approaches orient one unfamiliar with a new phenomenon to make sense out of it. In a way, analytic inductivism creates order out of disorder, or at least order amidst relative confusion. A zoologist, for example, upon encountering a new species, might initially categorize the animal by color. Eventually the zoologist finds this narrow color categorization scheme to be insufficient over successive cases to accurately describe additional examples of the new species, which are otherwise similar, except for the different colors. The zoologist instead shifts focus to the presence of vertebrae in the new species as a possibly more precise categorization system for uniquely defining the species [ 13 ].
Becker illustrated the similar use of analytic inductivism in the social sciences when he wrote: “Once we have isolated … a generic feature of some social relation or process and given it a name, and thus created a concept, we can look for the same phenomenon in places other than where we found it” [ 14 ]. Inductive analysis in a broader sense offers great flexibility in defining previously nebulous categories and classifications, whether in the physical sciences, in the social sciences, or in clinical settings [ 15 ]. Inductive analysis can only serve as a starting point that initiates further exploration to validate concepts or systems in the real world. Neither inductive nor deductive logic approaches alone will suffice in the research process [ 16 ]. Yet, inductivism offers the advantage of viewing even a familiar research problem from a fresh perspective [ 17 ].
In this study, the analytic inductive aspect of defining a study population relied on reacting to existing lists in order to more precisely define the final criteria for identifying all members of the study population. Questions arose about the current employment status of certain faculty members who had left the university when their names still appeared on a list of faculty members funded through the CTSC, to cite one example. As noted already, the simplified truth table displayed in Table 1 guided the dual definitional and identification processes.
The study population description needs to offer a clear definition as to who belongs to the study population and who should be excluded from the study population. The Uniform Requirements for Manuscripts Submitted to Biomedical Journals remind investigators to be explicit about the selection criteria for inclusion and exclusion that define a study population, adding that “The guiding principle should be clarity about how and why a study was done in a particular way” [ 18 ]. Some researchers recommend including a table that delineates inclusion criteria [ 19 ]. The left-hand column in Table 1 provides an example of such inclusion criteria.
Investigators should employ standardized, commonly understood definitions of groups whenever possible in developing a study population description. The population of affiliated faculty members described in this article adhered to common conceptions of faculty members. Imagine the potential complexity of defining a study population associated with more nebulous concepts. Comstock et al. reveal, in their analysis of multiple epidemiological studies, how different investigators employed their own operational definitions for the concepts of race and ethnicity, rather than standardized definitions of these concepts [ 20 ]. The net result of these multiple definitions undermined the utility of comparing the studies. Gobbens et al. similarly discovered that the commonly referenced phrase “medical frailty in the elderly” did not have a standard definition, despite some investigators’ misconceptions to the contrary [ 21 ]. The research community in such circumstances might need to convene a consensus conference to arrive at a commonly understood definition [ 22 ].
Investigators need to be alert to (and, if necessary, to correct for) the possibility that their inclusion and exclusion criteria reflect gender, racial, ethnic, economic, or information disparities biases [ 23 ]. The first author made such a mistake, years ago while conducting a study of rural health care providers. The author assumed that most rural inhabitants had access to the same high-quality Internet connections as urban residents. Not much later, the field informants for the study population fortunately corrected this misunderstanding [ 24 ]. Attitudes, behaviors, or skills within a study population might change over time in other circumstances. If that change itself serves as a focus of the study, this dynamic needs to be addressed in the research strategy. For example, if a research study wishes to gauge the effectiveness of library or informatics training on a defined population, but some members of the initial study population acquire the training via different venues, then the investigators need to identify and exclude these individuals from the study population.
Who applies the stated inclusion criteria to determine eligibility? The authors defined the study population and then recruited participants directly from that defined population in every study that they have conducted. One experiment involving first-year medical students used enrollment lists of students in good academic standing to define the study population [ 25 ]. What if a study recruits individuals by asking prospective participants to self-identify? In such circumstances, study population members’ own self-definitions might not align with investigators’ definitions. One study found that immigrant and refugee youth self-defined themselves in ways within the contexts of their own experiences [ 26 ]. Investigators must clearly define and promulgate their definition of a study population early in the study to avoid enrollment of ineligible individuals either later in the study or at other study sites [ 27 ].
Validity refers to the close proximity of a defined concept and what the investigators are measuring [ 28 ]. For the central example in this article, a poorly defined or recruited study population of putatively CTSC-affiliated faculty members that includes ineligible individuals such as staff members who are not engaged in the same types of roles will jeopardize the credibility of the study. The inadvertent inclusion of faculty not affiliated with the CTSC or retired faculty members would have similarly threatened the integrity of the study. Critics could justifiably deem this, or any other study, to be flawed due to sloppiness in the assembling of the study population.
The authors implemented this study in a highly controlled university environment where many resources were available to verify the accuracy of individual members of the defined study population. The authors had an online employee directory, a faculty contracts office, and departmental administrators who often could quickly resolve identity or status issues. The authors additionally had their own extensive 17 years of cumulated experience with the CTSC to aid in the resolution of identity issues with a high degree of fidelity. Many research studies occur in less-controlled “field” contexts that lack such verification resources or expert knowledge.
Simpson et al. have noted that “Although it is recommended that study eligibility criteria should be clear, objective and precise, they are often complex and open to interpretation” [ 29 ]. The authors hope that the recommendations in this article will help others to streamline and lend greater credibility to their own studies involving human populations. The authors could not locate a study to serve as an exemplar of a perfectly defined and identified study population. Instead, they identified a series of large scale cohort studies in the International Journal of Epidemiology that do offer definitions of study populations that are both concise and precise. The cohorts involve study populations as diverse as Japanese diabetics, Danish nurses, secondary school students in Amsterdam, twins, German uranium miners, and Thai university students [ 30 – 35 ]. Readers might find these examples helpful for their own research studies.
Institutions and organizations often function and build decisions on definitions of groups that are not well defined. Such definitions will serve episodic needs connected with an initiative or program in the short-run, rather than as master lists capable of serving long-term institutional needs. In the example at the University of New Mexico, many official CTSC communications contain the words “affiliated” or “affiliation” when referring to faculty with an institutional relationship, when no definition exists to guide one in interpreting what these words mean. All organizations reflect at least some ambiguity when defining affiliated faculty groups. Still, many investigators wanting to conduct organizationally related research often assume, at least initially, that the organizations have rigorously defined all group definitions.
Real world experience reveals wide gaps between aspirational goals and actual practices when defining populations. Motivating organizations to construct well-thought-out definitions poses challenges, since doing so might force these organizations to confront very difficult structural, hierarchal, political, or cultural conflicts. Some examples of these myriad conflicts may involve resolving ambiguities about budgeting, organizational authority, or political problems. As an example, defining who is affiliated with a CTSA at an institution may impact the authority academic departments or other research centers have over their own primarily affiliated faculty members. These kinds of probing discussions are often difficult to pursue smoothly and might be politically charged.
Despite these challenges, organizations that invest the time and resources to more rigorously define these group definitions will reap the previously described long-term benefits. Perhaps analogous to swallowing bad-tasting medicine, working out important group definitions will improve organizational health by helping the organization to engage in difficult, complex, or otherwise unpleasant issues.
Defining a study population early in the design stages of a research project will help to facilitate a smooth implementation phase. Clear definitions inform the value of applying research results to relevant populations for real world purposes. Importantly, a carefully and accurately defined study population enhances the completed study’s validity. This article describes the many challenges in defining a research study population and their potential solutions.
Funding Support
Health Sciences Library and Informatics Center, University of New Mexico, Albuquerque and Clinical and Translational Sciences Center, University of New Mexico. NIH Award # UL1TR000041.
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Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem. ... An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe ...
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 ...
Definition: Significance of the study in research refers to the potential importance, relevance, or impact of the research findings. It outlines how the research contributes to the existing body of knowledge, what gaps it fills, or what new understanding it brings to a particular field of study. In general, the significance of a study can be ...
Research conducted for the purpose of contributing towards science by the systematic collection, interpretation and evaluation of data and that, too, in a planned manner is called scientific research: a researcher is the one who conducts this research. The results obtained from a small group through scientific studies are socialised, and new ...
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
Here are the steps to write the background of the study in a research paper: Identify the research problem: Start by identifying the research problem that your study aims to address. This can be a particular issue, a gap in the literature, or a need for further investigation. Conduct a literature review: Conduct a thorough literature review to ...
As we mentioned, research methodology refers to the collection of practical decisions regarding what data you'll collect, from who, how you'll collect it and how you'll analyse it. Research design, on the other hand, is more about the overall strategy you'll adopt in your study. For example, whether you'll use an experimental design ...
Advantages. Limitations. Examples. A pilot study, also known as a feasibility study, is a small-scale preliminary study conducted before the main research to check the feasibility or improve the research design. Pilot studies can be very important before conducting a full-scale research project, helping design the research methods and protocol.
Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, "research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.".
Research Definition. Research is a careful and detailed study into a specific problem, concern, or issue using the scientific method. It's the adult form of the science fair projects back in ...
The background of the study is the first section of a research paper and gives context surrounding the research topic. The background explains to the reader where your research journey started, why you got interested in the topic, and how you developed the research question that you will later specify. That means that you first establish the ...
Abstractspiepr Abs1. Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain ...
The background of a study in a research paper helps to establish the research problem or gap in knowledge that the study aims to address, sets the stage for the research question and objectives, and highlights the significance of the research. The background of a study also includes a review of relevant literature, which helps researchers ...
There are various types of scientific studies such as experiments and comparative analyses, observational studies, surveys, or interviews. The choice of study type will mainly depend on the research question being asked. When making decisions, patients and doctors need reliable answers to a number of questions.
Significance of a study is established by making a case for it, not by simply choosing hypotheses everyone already thinks are important. Although you might believe the significance of your study is obvious, readers will need to be convinced. Significance is something you develop in your evolving research paper.
research study. (REE-serch STUH-dee) A scientific study of nature that sometimes includes processes involved in health and disease. For example, clinical trials are research studies that involve people. These studies may be related to new ways to screen, prevent, diagnose, and treat disease. They may also study certain outcomes and certain ...
Definition, Types, and Examples. Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of ...
the concepts that will be used throughout your whole research, allowing readers to have a better understanding of the concepts, factors, and context in your study. • It makes sure that readers will grasp the fundamental concepts of your study in the specific way you are using them. Quite often, people have different and various
The Common Rule defines research as "systematic investigation, including research development, testing and evaluation, designed to develop or contribute to generalizable knowledge" (§ 46.102 (d), see box, next page, for full definition). This means that a project or study is research if it: uses a commonly accepted scientific method.
Study Design: Researchers decide on the overall approach to the study, whether it's a case study, ethnography, grounded theory, phenomenology, or another qualitative design. The choice depends on the research question and the nature of the phenomenon under investigation.
Types of study design. Medical research is classified into primary and secondary research. Clinical/experimental studies are performed in primary research, whereas secondary research consolidates available studies as reviews, systematic reviews and meta-analyses. Three main areas in primary research are basic medical research, clinical research ...
Research is a systematic endeavor to acquire understanding, broaden knowledge, or find answers to unanswered questions. It is a methodical and structured undertaking to investigate the natural and ...
What is Quantitative Research? Quantitative methodology is the dominant research framework in the social sciences. It refers to a set of strategies, techniques and assumptions used to study psychological, social and economic processes through the exploration of numeric patterns.Quantitative research gathers a range of numeric data.
Ranganathan P. Understanding Research Study Designs. Indian J Crit Care Med 2019;23 (Suppl 4):S305-S307. Keywords: Clinical trials as topic, Observational studies as topic, Research designs. We use a variety of research study designs in biomedical research. In this article, the main features of each of these designs are summarized. Go to:
The Walton Family Foundation and Gallup's new 2024 Voices of Gen Z Study builds on one of the largest and most comprehensive national research surveys tracking the sentiments and attitudes of Gen Z. The latest research focuses on Gen Z's views about themselves, their schools, and their future prospects.
Moreover, a standardised definition allows meaningful comparisons to be made across health services and ensures consistency in future research. This study has shown recognition of OOH clinical deterioration to be a complex issue requiring further research.
An early step in research projects that involve humans consists of composing a clear and detailed definition of the study population. All experimental, observational, and qualitative research designs involving human subjects should define the study population in order to determine the eligibility of individuals for a study.