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Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis

Affiliations.

• 1 a Faculty of Health Care, Research Centre Autonomy and Participation of Chronically Ill People , Zuyd University of Applied Sciences , Heerlen , The Netherlands.
• 2 b Faculty of Health, Medicine and Life Sciences, Department of Family Medicine , Maastricht University , Maastricht , The Netherlands.
• 3 c Faculty of Health Care, Research Centre for Midwifery Science , Zuyd University of Applied Sciences , Maastricht , The Netherlands.
• PMID: 29199486
• PMCID: PMC5774281
• DOI: 10.1080/13814788.2017.1375091

In the course of our supervisory work over the years, we have noticed that qualitative research tends to evoke a lot of questions and worries, so-called frequently asked questions (FAQs). This series of four articles intends to provide novice researchers with practical guidance for conducting high-quality qualitative research in primary care. By 'novice' we mean Master's students and junior researchers, as well as experienced quantitative researchers who are engaging in qualitative research for the first time. This series addresses their questions and provides researchers, readers, reviewers and editors with references to criteria and tools for judging the quality of qualitative research papers. The second article focused on context, research questions and designs, and referred to publications for further reading. This third article addresses FAQs about sampling, data collection and analysis. The data collection plan needs to be broadly defined and open at first, and become flexible during data collection. Sampling strategies should be chosen in such a way that they yield rich information and are consistent with the methodological approach used. Data saturation determines sample size and will be different for each study. The most commonly used data collection methods are participant observation, face-to-face in-depth interviews and focus group discussions. Analyses in ethnographic, phenomenological, grounded theory, and content analysis studies yield different narrative findings: a detailed description of a culture, the essence of the lived experience, a theory, and a descriptive summary, respectively. The fourth and final article will focus on trustworthiness and publishing qualitative research.

Keywords: General practice/family medicine; analysis; data collection; general qualitative designs and methods; sampling.

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Conflict of interest statement

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

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The Ultimate Guide to Qualitative Research - Part 3: Presenting Qualitative Data

• Introduction

How do you present qualitative data?

Data visualization.

• Research paper writing
• Transparency and rigor in research
• How to publish a research paper

Table of contents

• Transparency and rigor

Navigate to other guide parts:

Part 1: The Basics or Part 2: Handling Qualitative Data

• Presenting qualitative data

In the end, presenting qualitative research findings is just as important a skill as mastery of qualitative research methods for the data collection and data analysis process . Simply uncovering insights is insufficient to the research process; presenting a qualitative analysis holds the challenge of persuading your audience of the value of your research. As a result, it's worth spending some time considering how best to report your research to facilitate its contribution to scientific knowledge.

When it comes to research, presenting data in a meaningful and accessible way is as important as gathering it. This is particularly true for qualitative research , where the richness and complexity of the data demand careful and thoughtful presentation. Poorly written research is taken less seriously and left undiscussed by the greater scholarly community; quality research reporting that persuades its audience stands a greater chance of being incorporated in discussions of scientific knowledge.

Qualitative data presentation differs fundamentally from that found in quantitative research. While quantitative data tend to be numerical and easily lend themselves to statistical analysis and graphical representation, qualitative data are often textual and unstructured, requiring an interpretive approach to bring out their inherent meanings. Regardless of the methodological approach , the ultimate goal of data presentation is to communicate research findings effectively to an audience so they can incorporate the generated knowledge into their research inquiry.

As the section on research rigor will suggest, an effective presentation of your research depends on a thorough scientific process that organizes raw data into a structure that allows for a thorough analysis for scientific understanding.

Preparing the data

The first step in presenting qualitative data is preparing the data. This preparation process often begins with cleaning and organizing the data. Cleaning involves checking the data for accuracy and completeness, removing any irrelevant information, and making corrections as needed. Organizing the data often entails arranging the data into categories or groups that make sense for your research framework.

Coding the data

Once the data are cleaned and organized, the next step is coding , a crucial part of qualitative data analysis. Coding involves assigning labels to segments of the data to summarize or categorize them. This process helps to identify patterns and themes in the data, laying the groundwork for subsequent data interpretation and presentation. Qualitative research often involves multiple iterations of coding, creating new and meaningful codes while discarding unnecessary ones , to generate a rich structure through which data analysis can occur.

Uncovering insights

As you navigate through these initial steps, keep in mind the broader aim of qualitative research, which is to provide rich, detailed, and nuanced understandings of people's experiences, behaviors, and social realities. These guiding principles will help to ensure that your data presentation is not only accurate and comprehensive but also meaningful and impactful.

While this process might seem intimidating at first, it's an essential part of any qualitative research project. It's also a skill that can be learned and refined over time, so don't be discouraged if you find it challenging at first. Remember, the goal of presenting qualitative data is to make your research findings accessible and understandable to others. This requires careful preparation, a clear understanding of your data, and a commitment to presenting your findings in a way that respects and honors the complexity of the phenomena you're studying.

In the following sections, we'll delve deeper into how to create a comprehensive narrative from your data, the visualization of qualitative data , and the writing and publication processes . Let's briefly excerpt some of the content in the articles in this part of the guide.

ATLAS.ti helps you make sense of your data

Find out how with a free trial of our powerful data analysis interface.

How often do you read a research article and skip straight to the tables and figures? That's because data visualizations representing qualitative and quantitative data have the power to make large and complex research projects with thousands of data points comprehensible when authors present data to research audiences. Researchers create visual representations to help summarize the data generated from their study and make clear the pathways for actionable insights.

In everyday situations, a picture is always worth a thousand words. Illustrations, figures, and charts convey messages that words alone cannot. In research, data visualization can help explain scientific knowledge, evidence for data insights, and key performance indicators in an orderly manner based on data that is otherwise unstructured.

For all of the various data formats available to researchers, a significant portion of qualitative and social science research is still text-based. Essays, reports, and research articles still rely on writing practices aimed at repackaging research in prose form. This can create the impression that simply writing more will persuade research audiences. Instead, framing research in terms that are easy for your target readers to understand makes it easier for your research to become published in peer-reviewed scholarly journals or find engagement at scholarly conferences. Even in market or professional settings, data visualization is an essential concept when you need to convince others about the insights of your research and the recommendations you make based on the data.

Importance of data visualization

Data visualization is important because it makes it easy for your research audience to understand your data sets and your findings. Also, data visualization helps you organize your data more efficiently. As the explanation of ATLAS.ti's tools will illustrate in this section, data visualization might point you to research inquiries that you might not even be aware of, helping you get the most out of your data. Strictly speaking, the primary role of data visualization is to make the analysis of your data , if not the data itself, clear. Especially in social science research, data visualization makes it easy to see how data scientists collect and analyze data.

Prerequisites for generating data visualizations

Data visualization is effective in explaining research to others only if the researcher or data scientist can make sense of the data in front of them. Traditional research with unstructured data usually calls for coding the data with short, descriptive codes that can be analyzed later, whether statistically or thematically. These codes form the basic data points of a meaningful qualitative analysis . They represent the structure of qualitative data sets, without which a scientific visualization with research rigor would be extremely difficult to achieve. In most respects, data visualization of a qualitative research project requires coding the entire data set so that the codes adequately represent the collected data.

A successfully crafted research study culminates in the writing of the research paper . While a pilot study or preliminary research might guide the research design , a full research study leads to discussion that highlights avenues for further research. As such, the importance of the research paper cannot be overestimated in the overall generation of scientific knowledge.

The physical and natural sciences tend to have a clinical structure for a research paper that mirrors the scientific method: outline the background research, explain the materials and methods of the study, outline the research findings generated from data analysis, and discuss the implications. Qualitative research tends to preserve much of this structure, but there are notable and numerous variations from a traditional research paper that it's worth emphasizing the flexibility in the social sciences with respect to the writing process.

Requirements for research writing

While there aren't any hard and fast rules regarding what belongs in a qualitative research paper , readers expect to find a number of pieces of relevant information in a rigorously-written report. The best way to know what belongs in a full research paper is to look at articles in your target journal or articles that share a particular topic similar to yours and examine how successfully published papers are written.

It's important to emphasize the more mundane but equally important concerns of proofreading and formatting guidelines commonly found when you write a research paper. Research publication shouldn't strictly be a test of one's writing skills, but acknowledging the importance of convincing peer reviewers of the credibility of your research means accepting the responsibility of preparing your research manuscript to commonly accepted standards in research.

As a result, seemingly insignificant things such as spelling mistakes, page numbers, and proper grammar can make a difference with a particularly strict reviewer. Even when you expect to develop a paper through reviewer comments and peer feedback, your manuscript should be as close to a polished final draft as you can make it prior to submission.

Qualitative researchers face particular challenges in convincing their target audience of the value and credibility of their subsequent analysis. Numbers and quantifiable concepts in quantitative studies are relatively easier to understand than their counterparts associated with qualitative methods . Think about how easy it is to make conclusions about the value of items at a store based on their prices, then imagine trying to compare those items based on their design, function, and effectiveness.

Qualitative research involves and requires these sorts of discussions. The goal of qualitative data analysis is to allow a qualitative researcher and their audience to make such determinations, but before the audience can accept these determinations, the process of conducting research that produces the qualitative analysis must first be seen as trustworthy. As a result, it is on the researcher to persuade their audience that their data collection process and subsequent analysis is rigorous.

Qualitative rigor refers to the meticulousness, consistency, and transparency of the research. It is the application of systematic, disciplined, and stringent methods to ensure the credibility, dependability, confirmability, and transferability of research findings. In qualitative inquiry, these attributes ensure the research accurately reflects the phenomenon it is intended to represent, that its findings can be understood or used by others, and that its processes and results are open to scrutiny and validation.

Transparency

It is easier to believe the information presented to you if there is a rigorous analysis process behind that information, and if that process is explicitly detailed. The same is true for qualitative research results, making transparency a key element in qualitative research methodologies. Transparency is a fundamental aspect of rigor in qualitative research. It involves the clear, detailed, and explicit documentation of all stages of the research process. This allows other researchers to understand, evaluate, replicate, and build upon the study. Transparency in qualitative research is essential for maintaining rigor, trustworthiness, and ethical integrity. By being transparent, researchers allow their work to be scrutinized, critiqued, and improved upon, contributing to the ongoing development and refinement of knowledge in their field.

Research papers are only as useful as their audience in the scientific community is wide. To reach that audience, a paper needs to pass the peer review process of an academic journal. However, the idea of having research published in peer-reviewed journals may seem daunting to newer researchers, so it's important to provide a guide on how an academic journal looks at your research paper as well as how to determine what is the right journal for your research.

In simple terms, a research article is good if it is accepted as credible and rigorous by the scientific community. A study that isn't seen as a valid contribution to scientific knowledge shouldn't be published; ultimately, it is up to peers within the field in which the study is being considered to determine the study's value. In established academic research, this determination is manifest in the peer review process. Journal editors at a peer-reviewed journal assign papers to reviewers who will determine the credibility of the research. A peer-reviewed article that completed this process and is published in a reputable journal can be seen as credible with novel research that can make a profound contribution to scientific knowledge.

The process of research publication

The process has been codified and standardized within the scholarly community to include three main stages. These stages include the initial submission stage where the editor reviews the relevance of the paper, the review stage where experts in your field offer feedback, and, if reviewers approve your paper, the copyediting stage where you work with the journal to prepare the paper for inclusion in their journal.

Publishing a research paper may seem like an opaque process where those involved with academic journals make arbitrary decisions about the worthiness of research manuscripts. In reality, reputable publications assign a rubric or a set of guidelines that reviewers need to keep in mind when they review a submission. These guidelines will most likely differ depending on the journal, but they fall into a number of typical categories that are applicable regardless of the research area or the type of methods employed in a research study, including the strength of the literature review , rigor in research methodology , and novelty of findings.

Choosing the right journal isn't simply a matter of which journal is the most famous or has the broadest reach. Many universities keep lists of prominent journals where graduate students and faculty members should publish a research paper , but oftentimes this list is determined by a journal's impact factor and their inclusion in major academic databases.

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This section is part of an entire guide. Use this table of contents to jump to any page in the guide.

Part 1: The Basics

• What is qualitative data?
• 10 examples of qualitative data
• Qualitative vs. quantitative research
• What is mixed methods research?
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research questions
• Conceptual framework
• Conceptual vs. theoretical framework
• Focus groups
• Observational research
• Case studies
• Survey research
• What is ethnographic research?
• Confidentiality and privacy in research
• Bias in research
• Power dynamics in research
• Reflexivity

Part 2: Handling Qualitative Data

• Research transcripts
• Field notes in research
• Research memos
• Survey data
• Images, audio, and video in qualitative research
• Coding qualitative data
• Coding frame
• Auto-coding and smart coding
• Organizing codes
• Content analysis
• Thematic analysis
• Thematic analysis vs. content analysis
• Narrative research
• Phenomenological research
• Discourse analysis
• Grounded theory
• Deductive reasoning
• What is inductive reasoning?
• Inductive vs. deductive reasoning
• What is data interpretation?
• Qualitative analysis software

Part 3: Presenting Qualitative Data

• Data visualization - What is it and why is it important?

• DOI: 10.1080/13814788.2017.1375091
• Corpus ID: 3632826

Series: Practical guidance to qualitative research. Part 3: Sampling, data collection and analysis

• Albine Moser , I. Korstjens
• Published in European Journal of General… 4 December 2017
• Education, Sociology

1,305 Citations

Series: practical guidance to qualitative research. part 2: context, research questions and designs, series: practical guidance to qualitative research. part 4: trustworthiness and publishing, research and scholarly methods: semi‐structured interviews, determining the sample in qualitative research, how to use and assess qualitative research methods, qualitative research: defining features and guiding principles, sensitivities and ethics in qualitative research.

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Types of Study in Medical Research

Bernd röhrig.

1 MDK Rheinland-Pfalz, Referat Rehabilitation/Biometrie, Alzey

Jean-Baptist du Prel

2 Zentrum für Präventive Pädiatrie, Zentrum für Kinder- und Jugendmedizin, Mainz

Daniel Wachtlin

3 Interdisziplinäres Zentrum Klinische Studien (IZKS), Fachbereich Medizin der Universität Mainz

Maria Blettner

4 Institut für Medizinische Biometrie, Epidemiologie und Informatik (IMBEI), Johannes Gutenberg Universität Mainz

The choice of study type is an important aspect of the design of medical studies. The study design and consequent study type are major determinants of a study’s scientific quality and clinical value.

This article describes the structured classification of studies into two types, primary and secondary, as well as a further subclassification of studies of primary type. This is done on the basis of a selective literature search concerning study types in medical research, in addition to the authors’ own experience.

Three main areas of medical research can be distinguished by study type: basic (experimental), clinical, and epidemiological research. Furthermore, clinical and epidemiological studies can be further subclassified as either interventional or noninterventional.

Conclusions

The study type that can best answer the particular research question at hand must be determined not only on a purely scientific basis, but also in view of the available financial resources, staffing, and practical feasibility (organization, medical prerequisites, number of patients, etc.).

The quality, reliability and possibility of publishing a study are decisively influenced by the selection of a proper study design. The study type is a component of the study design (see the article "Study Design in Medical Research") and must be specified before the study starts. The study type is determined by the question to be answered and decides how useful a scientific study is and how well it can be interpreted. If the wrong study type has been selected, this cannot be rectified once the study has started.

After an earlier publication dealing with aspects of study design, the present article deals with study types in primary and secondary research. The article focuses on study types in primary research. A special article will be devoted to study types in secondary research, such as meta-analyses and reviews. This article covers the classification of individual study types. The conception, implementation, advantages, disadvantages and possibilities of using the different study types are illustrated by examples. The article is based on a selective literature research on study types in medical research, as well as the authors’ own experience.

Classification of study types

In principle, medical research is classified into primary and secondary research. While secondary research summarizes available studies in the form of reviews and meta-analyses, the actual studies are performed in primary research. Three main areas are distinguished: basic medical research, clinical research, and epidemiological research. In individual cases, it may be difficult to classify individual studies to one of these three main categories or to the subcategories. In the interests of clarity and to avoid excessive length, the authors will dispense with discussing special areas of research, such as health services research, quality assurance, or clinical epidemiology. Figure 1 gives an overview of the different study types in medical research.

Classification of different study types

*1 , sometimes known as experimental research; *2 , analogous term: interventional; *3 , analogous term: noninterventional or nonexperimental

This scheme is intended to classify the study types as clearly as possible. In the interests of clarity, we have excluded clinical epidemiology — a subject which borders on both clinical and epidemiological research ( 3 ). The study types in this area can be found under clinical research and epidemiology.

Basic research

Basic medical research (otherwise known as experimental research) includes animal experiments, cell studies, biochemical, genetic and physiological investigations, and studies on the properties of drugs and materials. In almost all experiments, at least one independent variable is varied and the effects on the dependent variable are investigated. The procedure and the experimental design can be precisely specified and implemented ( 1 ). For example, the population, number of groups, case numbers, treatments and dosages can be exactly specified. It is also important that confounding factors should be specifically controlled or reduced. In experiments, specific hypotheses are investigated and causal statements are made. High internal validity (= unambiguity) is achieved by setting up standardized experimental conditions, with low variability in the units of observation (for example, cells, animals or materials). External validity is a more difficult issue. Laboratory conditions cannot always be directly transferred to normal clinical practice and processes in isolated cells or in animals are not equivalent to those in man (= generalizability) ( 2 ).

Basic research also includes the development and improvement of analytical procedures—such as analytical determination of enzymes, markers or genes—, imaging procedures—such as computed tomography or magnetic resonance imaging—, and gene sequencing—such as the link between eye color and specific gene sequences. The development of biometric procedures—such as statistical test procedures, modeling and statistical evaluation strategies—also belongs here.

Clinical studies

Clinical studies include both interventional (or experimental) studies and noninterventional (or observational) studies. A clinical drug study is an interventional clinical study, defined according to §4 Paragraph 23 of the Medicines Act [Arzneimittelgesetz; AMG] as "any study performed on man with the purpose of studying or demonstrating the clinical or pharmacological effects of drugs, to establish side effects, or to investigate absorption, distribution, metabolism or elimination, with the aim of providing clear evidence of the efficacy or safety of the drug."

Interventional studies also include studies on medical devices and studies in which surgical, physical or psychotherapeutic procedures are examined. In contrast to clinical studies, §4 Paragraph 23 of the AMG describes noninterventional studies as follows: "A noninterventional study is a study in the context of which knowledge from the treatment of persons with drugs in accordance with the instructions for use specified in their registration is analyzed using epidemiological methods. The diagnosis, treatment and monitoring are not performed according to a previously specified study protocol, but exclusively according to medical practice."

The aim of an interventional clinical study is to compare treatment procedures within a patient population, which should exhibit as few as possible internal differences, apart from the treatment ( 4 , e1 ). This is to be achieved by appropriate measures, particularly by random allocation of the patients to the groups, thus avoiding bias in the result. Possible therapies include a drug, an operation, the therapeutic use of a medical device such as a stent, or physiotherapy, acupuncture, psychosocial intervention, rehabilitation measures, training or diet. Vaccine studies also count as interventional studies in Germany and are performed as clinical studies according to the AMG.

Interventional clinical studies are subject to a variety of legal and ethical requirements, including the Medicines Act and the Law on Medical Devices. Studies with medical devices must be registered by the responsible authorities, who must also approve studies with drugs. Drug studies also require a favorable ruling from the responsible ethics committee. A study must be performed in accordance with the binding rules of Good Clinical Practice (GCP) ( 5 , e2 – e4 ). For clinical studies on persons capable of giving consent, it is absolutely essential that the patient should sign a declaration of consent (informed consent) ( e2 ). A control group is included in most clinical studies. This group receives another treatment regimen and/or placebo—a therapy without substantial efficacy. The selection of the control group must not only be ethically defensible, but also be suitable for answering the most important questions in the study ( e5 ).

Clinical studies should ideally include randomization, in which the patients are allocated by chance to the therapy arms. This procedure is performed with random numbers or computer algorithms ( 6 – 8 ). Randomization ensures that the patients will be allocated to the different groups in a balanced manner and that possible confounding factors—such as risk factors, comorbidities and genetic variabilities—will be distributed by chance between the groups (structural equivalence) ( 9 , 10 ). Randomization is intended to maximize homogeneity between the groups and prevent, for example, a specific therapy being reserved for patients with a particularly favorable prognosis (such as young patients in good physical condition) ( 11 ).

Blinding is another suitable method to avoid bias. A distinction is made between single and double blinding. With single blinding, the patient is unaware which treatment he is receiving, while, with double blinding, neither the patient nor the investigator knows which treatment is planned. Blinding the patient and investigator excludes possible subjective (even subconscious) influences on the evaluation of a specific therapy (e.g. drug administration versus placebo). Thus, double blinding ensures that the patient or therapy groups are both handled and observed in the same manner. The highest possible degree of blinding should always be selected. The study statistician should also remain blinded until the details of the evaluation have finally been specified.

A well designed clinical study must also include case number planning. This ensures that the assumed therapeutic effect can be recognized as such, with a previously specified statistical probability (statistical power) ( 4 , 6 , 12 ).

It is important for the performance of a clinical trial that it should be carefully planned and that the exact clinical details and methods should be specified in the study protocol ( 13 ). It is, however, also important that the implementation of the study according to the protocol, as well as data collection, must be monitored. For a first class study, data quality must be ensured by double data entry, programming plausibility tests, and evaluation by a biometrician. International recommendations for the reporting of randomized clinical studies can be found in the CONSORT statement (Consolidated Standards of Reporting Trials, www.consort-statement.org ) ( 14 ). Many journals make this an essential condition for publication.

For all the methodological reasons mentioned above and for ethical reasons, the randomized controlled and blinded clinical trial with case number planning is accepted as the gold standard for testing the efficacy and safety of therapies or drugs ( 4 , e1 , 15 ).

In contrast, noninterventional clinical studies (NIS) are patient-related observational studies, in which patients are given an individually specified therapy. The responsible physician specifies the therapy on the basis of the medical diagnosis and the patient’s wishes. NIS include noninterventional therapeutic studies, prognostic studies, observational drug studies, secondary data analyses, case series and single case analyses ( 13 , 16 ). Similarly to clinical studies, noninterventional therapy studies include comparison between therapies; however, the treatment is exclusively according to the physician’s discretion. The evaluation is often retrospective. Prognostic studies examine the influence of prognostic factors (such as tumor stage, functional state, or body mass index) on the further course of a disease. Diagnostic studies are another class of observational studies, in which either the quality of a diagnostic method is compared to an established method (ideally a gold standard), or an investigator is compared with one or several other investigators (inter-rater comparison) or with himself at different time points (intra-rater comparison) ( e1 ). If an event is very rare (such as a rare disease or an individual course of treatment), a single-case study, or a case series, are possibilities. A case series is a study on a larger patient group with a specific disease. For example, after the discovery of the AIDS virus, the Center for Disease Control (CDC) in the USA collected a case series of 1000 patients, in order to study frequent complications of this infection. The lack of a control group is a disadvantage of case series. For this reason, case series are primarily used for descriptive purposes ( 3 ).

Epidemiological studies

The main point of interest in epidemiological studies is to investigate the distribution and historical changes in the frequency of diseases and the causes for these. Analogously to clinical studies, a distinction is made between experimental and observational epidemiological studies ( 16 , 17 ).

Interventional studies are experimental in character and are further subdivided into field studies (sample from an area, such as a large region or a country) and group studies (sample from a specific group, such as a specific social or ethnic group). One example was the investigation of the iodine supplementation of cooking salt to prevent cretinism in a region with iodine deficiency. On the other hand, many interventions are unsuitable for randomized intervention studies, for ethical, social or political reasons, as the exposure may be harmful to the subjects ( 17 ).

Observational epidemiological studies can be further subdivided into cohort studies (follow-up studies), case control studies, cross-sectional studies (prevalence studies), and ecological studies (correlation studies or studies with aggregated data).

In contrast, studies with only descriptive evaluation are restricted to a simple depiction of the frequency (incidence and prevalence) and distribution of a disease within a population. The objective of the description may also be the regular recording of information (monitoring, surveillance). Registry data are also suited for the description of prevalence and incidence; for example, they are used for national health reports in Germany.

In the simplest case, cohort studies involve the observation of two healthy groups of subjects over time. One group is exposed to a specific substance (for example, workers in a chemical factory) and the other is not exposed. It is recorded prospectively (into the future) how often a specific disease (such as lung cancer) occurs in the two groups ( figure 2a ). The incidence for the occurrence of the disease can be determined for both groups. Moreover, the relative risk (quotient of the incidence rates) is a very important statistical parameter which can be calculated in cohort studies. For rare types of exposure, the general population can be used as controls ( e6 ). All evaluations naturally consider the age and gender distributions in the corresponding cohorts. The objective of cohort studies is to record detailed information on the exposure and on confounding factors, such as the duration of employment, the maximum and the cumulated exposure. One well known cohort study is the British Doctors Study, which prospectively examined the effect of smoking on mortality among British doctors over a period of decades ( e7 ). Cohort studies are well suited for detecting causal connections between exposure and the development of disease. On the other hand, cohort studies often demand a great deal of time, organization, and money. So-called historical cohort studies represent a special case. In this case, all data on exposure and effect (illness) are already available at the start of the study and are analyzed retrospectively. For example, studies of this sort are used to investigate occupational forms of cancer. They are usually cheaper ( 16 ).

Graphical depiction of a prospective cohort study (simplest case [2a]) and a retrospective case control study (2b)

In case control studies, cases are compared with controls. Cases are persons who fall ill from the disease in question. Controls are persons who are not ill, but are otherwise comparable to the cases. A retrospective analysis is performed to establish to what extent persons in the case and control groups were exposed ( figure 2b ). Possible exposure factors include smoking, nutrition and pollutant load. Care should be taken that the intensity and duration of the exposure is analyzed as carefully and in as detailed a manner as possible. If it is observed that ill people are more often exposed than healthy people, it may be concluded that there is a link between the illness and the risk factor. In case control studies, the most important statistical parameter is the odds ratio. Case control studies usually require less time and fewer resources than cohort studies ( 16 ). The disadvantage of case control studies is that the incidence rate (rate of new cases) cannot be calculated. There is also a great risk of bias from the selection of the study population ("selection bias") and from faulty recall ("recall bias") (see too the article "Avoiding Bias in Observational Studies"). Table 1 presents an overview of possible types of epidemiological study ( e8 ). Table 2 summarizes the advantages and disadvantages of observational studies ( 16 ).

1 = slight; 2 = moderate; 3 = high; N/A, not applicable.

*Individual cases may deviate from this pattern.

Selecting the correct study type is an important aspect of study design (see "Study Design in Medical Research" in volume 11/2009). However, the scientific questions can only be correctly answered if the study is planned and performed at a qualitatively high level ( e9 ). It is very important to consider or even eliminate possible interfering factors (or confounders), as otherwise the result cannot be adequately interpreted. Confounders are characteristics which influence the target parameters. Although this influence is not of primary interest, it can interfere with the connection between the target parameter and the factors that are of interest. The influence of confounders can be minimized or eliminated by standardizing the procedure, stratification ( 18 ), or adjustment ( 19 ).

The decision as to which study type is suitable to answer a specific primary research question must be based not only on scientific considerations, but also on issues related to resources (personnel and finances), hospital capacity, and practicability. Many epidemiological studies can only be implemented if there is access to registry data. The demands for planning, implementation, and statistical evaluation for observational studies should be just as high for observational studies as for experimental studies. There are particularly strict requirements, with legally based regulations (such as the Medicines Act and Good Clinical Practice), for the planning, implementation, and evaluation of clinical studies. A study protocol must be prepared for both interventional and noninterventional studies ( 6 , 13 ). The study protocol must contain information on the conditions, question to be answered (objective), the methods of measurement, the implementation, organization, study population, data management, case number planning, the biometric evaluation, and the clinical relevance of the question to be answered ( 13 ).

Important and justified ethical considerations may restrict studies with optimal scientific and statistical features. A randomized intervention study under strictly controlled conditions of the effect of exposure to harmful factors (such as smoking, radiation, or a fatty diet) is not possible and not permissible for ethical reasons. Observational studies are a possible alternative to interventional studies, even though observational studies are less reliable and less easy to control ( 17 ).

A medical study should always be published in a peer reviewed journal. Depending on the study type, there are recommendations and checklists for presenting the results. For example, these may include a description of the population, the procedure for missing values and confounders, and information on statistical parameters. Recommendations and guidelines are available for clinical studies ( 14 , 20 , e10 , e11 ), for diagnostic studies ( 21 , 22 , e12 ), and for epidemiological studies ( 23 , e13 ). Since 2004, the WHO has demanded that studies should be registered in a public registry, such as www.controlled-trials.com or www.clinicaltrials.gov . This demand is supported by the International Committee of Medical Journal Editors (ICMJE) ( 24 ), which specifies that the registration of the study before inclusion of the first subject is an essential condition for the publication of the study results ( e14 ).

When specifying the study type and study design for medical studies, it is essential to collaborate with an experienced biometrician. The quality and reliability of the study can be decisively improved if all important details are planned together ( 12 , 25 ).

Acknowledgments

Translated from the original German by Rodney A. Yeates, M.A., Ph.D.

Conflict of interest statement

The authors declare that there is no conflict of interest in the sense of the International Committee of Medical Journal Editors.