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Analytical Research: What is it, Importance + Examples

Analytical research is a type of research that requires critical thinking skills and the examination of relevant facts and information.

Finding knowledge is a loose translation of the word “research.” It’s a systematic and scientific way of researching a particular subject. As a result, research is a form of scientific investigation that seeks to learn more. Analytical research is one of them.

Any kind of research is a way to learn new things. In this research, data and other pertinent information about a project are assembled; after the information is gathered and assessed, the sources are used to support a notion or prove a hypothesis.

An individual can successfully draw out minor facts to make more significant conclusions about the subject matter by using critical thinking abilities (a technique of thinking that entails identifying a claim or assumption and determining whether it is accurate or untrue).

What is analytical research?

This particular kind of research calls for using critical thinking abilities and assessing data and information pertinent to the project at hand.

Determines the causal connections between two or more variables. The analytical study aims to identify the causes and mechanisms underlying the trade deficit’s movement throughout a given period.

It is used by various professionals, including psychologists, doctors, and students, to identify the most pertinent material during investigations. One learns crucial information from analytical research that helps them contribute fresh concepts to the work they are producing.

Some researchers perform it to uncover information that supports ongoing research to strengthen the validity of their findings. Other scholars engage in analytical research to generate fresh perspectives on the subject.

Various approaches to performing research include literary analysis, Gap analysis , general public surveys, clinical trials, and meta-analysis.

Importance of analytical research

The goal of analytical research is to develop new ideas that are more believable by combining numerous minute details.

The analytical investigation is what explains why a claim should be trusted. Finding out why something occurs is complex. You need to be able to evaluate information critically and think critically. 

This kind of information aids in proving the validity of a theory or supporting a hypothesis. It assists in recognizing a claim and determining whether it is true.

Analytical kind of research is valuable to many people, including students, psychologists, marketers, and others. It aids in determining which advertising initiatives within a firm perform best. In the meantime, medical research and research design determine how well a particular treatment does.

Thus, analytical research can help people achieve their goals while saving lives and money.

Methods of Conducting Analytical Research

Analytical research is the process of gathering, analyzing, and interpreting information to make inferences and reach conclusions. Depending on the purpose of the research and the data you have access to, you can conduct analytical research using a variety of methods. Here are a few typical approaches:

Quantitative research

Numerical data are gathered and analyzed using this method. Statistical methods are then used to analyze the information, which is often collected using surveys, experiments, or pre-existing datasets. Results from quantitative research can be measured, compared, and generalized numerically.

Qualitative research

In contrast to quantitative research, qualitative research focuses on collecting non-numerical information. It gathers detailed information using techniques like interviews, focus groups, observations, or content research. Understanding social phenomena, exploring experiences, and revealing underlying meanings and motivations are all goals of qualitative research.

Mixed methods research

This strategy combines quantitative and qualitative methodologies to grasp a research problem thoroughly. Mixed methods research often entails gathering and evaluating both numerical and non-numerical data, integrating the results, and offering a more comprehensive viewpoint on the research issue.

Experimental research

Experimental research is frequently employed in scientific trials and investigations to establish causal links between variables. This approach entails modifying variables in a controlled environment to identify cause-and-effect connections. Researchers randomly divide volunteers into several groups, provide various interventions or treatments, and track the results.

Observational research

With this approach, behaviors or occurrences are observed and methodically recorded without any outside interference or variable data manipulation . Both controlled surroundings and naturalistic settings can be used for observational research . It offers useful insights into behaviors that occur in the actual world and enables researchers to explore events as they naturally occur.

Case study research

This approach entails thorough research of a single case or a small group of related cases. Case-control studies frequently include a variety of information sources, including observations, records, and interviews. They offer rich, in-depth insights and are particularly helpful for researching complex phenomena in practical settings.

Secondary data analysis

Examining secondary information is time and money-efficient, enabling researchers to explore new research issues or confirm prior findings. With this approach, researchers examine previously gathered information for a different reason. Information from earlier cohort studies, accessible databases, or corporate documents may be included in this.

Content analysis

Content research is frequently employed in social sciences, media observational studies, and cross-sectional studies. This approach systematically examines the content of texts, including media, speeches, and written documents. Themes, patterns, or keywords are found and categorized by researchers to make inferences about the content.

Depending on your research objectives, the resources at your disposal, and the type of data you wish to analyze, selecting the most appropriate approach or combination of methodologies is crucial to conducting analytical research.

Examples of analytical research

Analytical research takes a unique measurement. Instead, you would consider the causes and changes to the trade imbalance. Detailed statistics and statistical checks help guarantee that the results are significant.

For example, it can look into why the value of the Japanese Yen has decreased. This is so that an analytical study can consider “how” and “why” questions.

Another example is that someone might conduct analytical research to identify a study’s gap. It presents a fresh perspective on your data. Therefore, it aids in supporting or refuting notions.

Descriptive vs analytical research

Here are the key differences between descriptive research and analytical research:

The study of cause and effect makes extensive use of analytical research. It benefits from numerous academic disciplines, including marketing, health, and psychology, because it offers more conclusive information for addressing research issues.

QuestionPro offers solutions for every issue and industry, making it more than just survey software. For handling data, we also have systems like our InsightsHub research library.

You may make crucial decisions quickly while using QuestionPro to understand your clients and other study subjects better. Make use of the possibilities of the enterprise-grade research suite right away!



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Overview of Analytic Studies


We search for the determinants of health outcomes, first, by relying on descriptive epidemiology to generate hypotheses about associations between exposures and outcomes. Analytic studies are then undertaken to test specific hypotheses. Samples of subjects are identified and information about exposure status and outcome is collected. The essence of an analytic study is that groups of subjects are compared in order to estimate the magnitude of association between exposures and outcomes.

In their book entitled "Epidemiology Matters" Katherine Keyes and Sandro Galea discuss three fundamental options for studying samples from a population as illustrated in the video below (duration 8:30).

Learning Objectives

After successfully completing this section, the student will be able to:

  • Describe the difference between descriptive and scientific/analytic epidemiologic studies in terms of information/evidence provided for medicine and public health.
  • Define and explain the distinguishing features of a cohort study.
  • Describe and identify the types of epidemiologic questions that can be addressed by cohort studies.
  • Define and distinguish among prospective and retrospective cohort studies using the investigator as the point of reference.  
  • Define and explain the distinguishing features of a case-control study.
  • Explain the distinguishing features of an intervention study.
  • Identify the study design when reading an article or abstract.

Cohort Type Studies

A cohort is a "group." In epidemiology a cohort is a group of individuals who are followed over a period of time, primarily to assess what happens to them, i.e., their health outcomes. In cohort type studies one identifies individuals who do not have the outcome of interest initially, and groups them in subsets that differ in their exposure to some factor, e.g., smokers and non-smokers. The different exposure groups are then followed over time in order to compare the incidence of health outcomes, such as lung cancer or heart disease. As an example, the Framingham Heart Study enrolled a cohort of 5,209 residents of Framingham, MA who were between the ages of 30-62 and who did not have cardiovascular disease when they were enrolled. These subjects differed from one another in many ways: whether they smoked, how much they smoked, body mass index, eating habits, exercise habits, gender, family history of heart disease, etc. The researchers assessed these and many other characteristics or "exposures" soon after the subjects had been enrolled and before any of them had developed cardiovascular disease. The many "baseline characteristics" were assessed in a number of ways including questionnaires, physical exams, laboratory tests, and imaging studies (e.g., x-rays). They then began "following" the cohort, meaning that they kept in contact with the subjects by phone, mail, or clinic visits in order to determine if and when any of the subjects developed any of the "outcomes of interest," such as myocardial infarction (heart attack), angina, congestive heart failure, stroke, diabetes and many other cardiovascular outcomes.

Over time some subjects eventually began to develop some of the outcomes of interest. Having followed the cohort in this fashion, it was eventually possible to use the information collected to evaluate many hypotheses about what characteristics were associated with an increased risk of heart disease. For example, if one hypothesized that smoking increased the risk of heart attacks, the subjects in the cohort could be sorted based on their smoking habits, and one could compare the subset of the cohort that smoked to the subset who had never smoked. For each such comparison that one wanted to make the cohort could be grouped according to whether they had a given exposure or not, and one could measure and compare the frequency of heart attacks (i.e., the incidence) between the groups. Incidence provides an estimate of risk, so if the incidence of heart attacks is 3 times greater in smokers compared to non-smokers, it suggests an association between smoking and risk of developing a heart attack. (Various biases might also be an explanation for an apparent association. We will learn about these later in the course.) The hallmark of analytical studies, then, is that they collect information about both exposure status and outcome status, and they compare groups to identify whether there appears to be an association or a link.

The Population "At Risk"

From the discussion above, it should be obvious that one of the basic requirements of a cohort type study is that none of the subjects have the outcome of interest at the beginning of the follow-up period, and time must pass in order to determine the frequency of developing the outcome.

  • For example, if one wanted to compare the risk of developing uterine cancer between postmenopausal women receiving hormone-replacement therapy and those not receiving hormones, one would consider certain eligibility criteria for the members prior to the start of the study: 1) they should be female, 2) they should be post-menopausal, and 3) they should have a uterus. Among post-menopausal women there might be a number who had had a hysterectomy already, perhaps for persistent bleeding problems or endometriosis. Since these women no longer have a uterus, one would want to exclude them from the cohort, because they are no longer at risk of developing this particular type of cancer.
  • Similarly, if one wanted to compare the risk of developing diabetes among nursing home residents who exercised and those who did not, it would be important to test the subjects for diabetes at the beginning of the follow-up period in order to exclude all subjects who already had diabetes and therefore were not "at risk" of developing diabetes.

Eligible subjects have to meet certain criteria to be included as subjects in a study (inclusion criteria). One of these would be that they did not have any of the diseases or conditions that the investigators want to study, i.e., the subjects must be "at risk," of developing the outcome of interest, and the members of the cohort to be followed are sometimes referred to as "the population at risk."

However, at times decisions about who is "at risk" and eligible get complicated.

Example #1: Suppose the outcome of interest is development of measles. There may be subjects who:

  • Already were known to have had clinically apparent measles and are immune to subsequent measles infection
  • Had sub-clinical cases of measles that went undetected (but the subject may still be immune)
  • Had a measles vaccination that conferred immunity
  • Had a measles vaccination that failed to confer immunity

In this case the eligibility criteria would be shaped by the specific scientific questions being asked. One might want to compare subjects known to have had clinically apparent measles to those who had not had clinical measles and had not had a measles vaccination. Or, one could take blood sample from all potential subjects in order to measure their antibody titers (levels) to the measles virus.

Example #2: Suppose you are studying an event that can occur more that once, such as a heart attack. Again, the eligibility criteria should be shaped to fit the scientific questions that are being answered. If one were interested in the risk of a first myocardial infarction, then obviously subjects who had already had a heart attack would not be eligible for study. On the other hand, if one were interested in tertiary prevention of heart attacks, the study cohort would include people who had had heart attacks or other clinical manifestations of heart disease, and the outcome of interest would be subsequent significant cardiac events or death. 

Prospective and Retrospective Cohort Studies

Cohort studies can be classified as prospective or retrospective based on when outcomes occurred in relation to the enrollment of the cohort.

Prospective Cohort Studies

Summary of sequence of events in a hypothetical prospective cohort study from The Nurses Health Study

In a prospective study like the Nurses Health Study baseline information is collected from all subjects in the same way using exactly the same questions and data collection methods for all subjects. The investigators design the questions and data collection procedures carefully in order to obtain accurate information about exposures before disease develops in any of the subjects. After baseline information is collected, subjects in a prospective cohort study are then followed "longitudinally," i.e. over a period of time, usually for years, to determine if and when they become diseased and whether their exposure status changes. In this way, investigators can eventually use the data to answer many questions about the associations between "risk factors" and disease outcomes. For example, one could identify smokers and non-smokers at baseline and compare their subsequent incidence of developing heart disease. Alternatively, one could group subjects based on their body mass index (BMI) and compare their risk of developing heart disease or cancer.

 Examples of Prospective Cohort Studies

  • The Framingham Heart Study Home Page
  • The Nurses Health Study Home Page

Pitfall icon sigifying a potential pitfall to be avoided

Pitfall: Note that in these prospective cohort studies a comparison of incidence between the groups can only take place after enough time has elapsed so that some subjects developed the outcomes of interest. Since the data analysis occurs after some outcomes have occurred, some students mistakenly would call this a retrospective study, but this is incorrect. The analysis always occurs after a certain number of events have taken place. The characteristic that distinguishes a study as prospective is that the subjects were enrolled, and baseline data was collected before any subjects developed an outcome of interest.

Retrospective Cohort Studies

In contrast, retrospective studies are conceived after some people have already developed the outcomes of interest. The investigators jump back in time to identify a cohort of individuals at a point in time before they have developed the outcomes of interest, and they try to establish their exposure status at that point in time. They then determine whether the subject subsequently developed the outcome of interest.

Summary of a retrospective cohort study in which the investigator initiates the study after the outcome of interest has already taken place in some subjects.

Suppose investigators wanted to test the hypothesis that working with the chemicals involved in tire manufacturing increases the risk of death. Since this is a fairly rare exposure, it would be advantageous to use a special exposure cohort such as employees of a large tire manufacturing factory. The employees who actually worked with chemicals used in the manufacturing process would be the exposed group, while clerical workers and management might constitute the "unexposed" group. However, rather than following these subjects for decades, it would be more efficient to use employee health and employment records over the past two or three decades as a source of data. In essence, the investigators are jumping back in time to identify the study cohort at a point in time before the outcome of interest (death) occurred. They can classify them as "exposed" or "unexposed" based on their employment records, and they can use a number of sources to determine subsequent outcome status, such as death (e.g., using health records, next of kin, National Death Index, etc.).

Retrospective cohort studies like the one described above are very efficient for studying rare or unusual exposures, but there are many potential problems here. Sometimes exposure status is not clear when it is necessary to go back in time and use whatever data is available, especially because the data being used was not designed to answer a health question. Even if it was clear who was exposed to tire manufacturing chemicals based on employee records, it would also be important to take into account (or adjust for) other differences that could have influenced mortality, i.e., confounding factors. For example, it might be important to know whether the subjects smoked, or drank, or what kind of diet they ate. However, it is unlikely that a retrospective cohort study would have accurate information on these many other risk factors.

The video below provides a brief (7:31) explanation of the distinction between retrospective and prospective cohort studies.

Link to a transcript of the video

Intervention Studies (Clinical Trials)

Intervention studies (clinical trials) are experimental research studies that compare the effectiveness of medical treatments, management strategies, prevention strategies, and other medical or public health interventions. Their design is very similar to that of a prospective cohort study. However, in cohort studies exposure status is determined by genetics, self-selection, or life circumstances, and the investigators just observe differences in outcome between those who have a given exposure and those who do not. In clinical trials  exposure status  (the treatment type)  is assigned by the investigators . Ideally, assignment of subjects to one of the comparison groups should be done randomly in order to produce equal distributions of potentially confounding factors. Sometimes a group receiving a new treatment is compared to an untreated group, or a group receiving a placebo or a sham treatment. Sometimes, a new treatment is compared to an untreated group or to a group receiving an established treatment. For more on this topic see the module on Intervention Studies.

In summary, the characteristic that distinguishes a clinical trial from a cohort study is that the investigator assigns the exposure status in a clinical trial, while subjects' genetics, behaviors, and life circumstances determine their exposures in a cohort study.

Summarizing Data in a Cohort Study

Investigators often use contingency tables to summarize data. In essence, the table is a matrix that displays the combinations of exposure and outcome status. If one were summarizing the results of a study with two possible exposure categories and two possible outcomes, one would use a "two by two" table in which the numbers in the four cells indicate the number of subjects within each of the 4 possible categories of risk and disease status.

For example, consider data from a retrospective cohort study conducted by the Massachusetts Department of Public Health (MDPH) during an investigation of an outbreak of Giardia lamblia in Milton, MA in 2003. The descriptive epidemiology indicated that almost all of the cases belonged to a country club in Milton. The club had an adult swimming pool and a wading pool for toddlers, and the investigators suspected that the outbreak may have occurred when an infected child with a dirty diaper contaminated the water in the kiddy pool. This hypothesis was tested by conducting a retrospective cohort study. The cases of Giardia lamblia had already occurred and had been reported to MDPH via the infectious disease surveillance system (for more information on surveillance, see the Surveillance module). The investigation focused on an obvious cohort - 479 members of the country club who agreed to answer the MDPH questionnaire. The questionnaire asked, among many other things, whether the subject had been exposed to the kiddy pool. The incidence of subsequent Giardia infection was then compared between subjects who been exposed to the kiddy pool and those who had not.

The table below summarizes the findings. A total of 479 subjects completed the questionnaire, and 124 of them indicated that they had been exposed to the kiddy pool. Of these, 16 subsequently developed Giardia infection, but 108 did not. Among the 355 subjects who denied kiddy pool exposure, 14 developed Giardia infection, and the other 341 did not.

 Organization of the data this way makes it easier to compute the cumulative incidence in each group (12.9% and 3.9% respectively). The incidence in each group provides an estimate of risk, and the groups can be compared in order to estimate the magnitude of association. (This will be addressed in much greater detail in the module on Measures of Association.) One way of quantifying the association is to calculate the relative risk, i.e., dividing the incidence in the exposed group by the incidence in the unexposed group). In this case, the risk ratio is (12.9% / 3.9%) = 3.3. This suggest that subjects who swam in the kiddy pool had 3.3 times the risk of getting Giardia infections compared to those who did not, suggesting that the kiddy pool was the source.

Unanswered Questions

If the kiddy pool was the source of contamination responsible for this outbreak, why was it that:

  • Only 16 people exposed to the kiddy pool developed the infection?
  • There were 14 Giardia cases among people who denied exposure to the kiddy pool?

Before you look at the answer, think about it and try to come up with a possible explanation.

Likely Explanation

Optional Links of Potential Interest

Link to the 2003 Giardia outbreak

Link to CDC page on Organizing Data

analytical research is the

Possible Pitfall: Contingency tables can be oriented in several ways, and this can cause confusion when calculating measures of association.

There is no standard rule about how to set up contingency tables, and you will see them set up in different ways.

  • With exposure status in rows and outcome status in columns
  • With exposure status in columns and outcome status in rows
  • With exposed group first followed by non-exposed group
  • With non-exposed group first followed by exposed group

If you aren't careful, these different orientations can result in errors in calculating measures of association. One way to avoid confusion is to always set up your contingency tables in the same way. For example, in these learning modules the contingency tables almost always indicate outcome status in columns listing subjects who have the outcome of interest to the left of subjects who do not have the outcome, and exposure status of the exposed (or most exposed) group is listed in a row above those who are unexposed (or have less exposure).

The table below illustrates this arrangement.

Case-Control Studies

Cohort studies have an intuitive logic to them, but they can be very problematic when:

  • The outcomes being investigated are rare;
  • There is a long time period between the exposure of interest and the development of the disease; or
  • It is expensive or very difficult to obtain exposure information from a cohort.

In the first case, the rarity of the disease requires enrollment of very large numbers of people. In the second case, the long period of follow-up requires efforts to keep contact with and collect outcome information from individuals. In all three situations, cost and feasibility become an important concern.

A case-control design offers an alternative that is much more efficient. The goal of a case-control study is the same as that of cohort studies, i.e. to estimate the magnitude of association between an exposure and an outcome. However, case-control studies employ a different sampling strategy that gives them greater efficiency.   As with a cohort study, a case-control study attempts to identify all people who have developed the disease of interest in the defined population. This is not because they are inherently more important to estimating an association, but because they are almost always rarer than non-diseased individuals, and one of the requirements of accurate estimation of the association is that there are reasonable numbers of people in both the numerators (cases) and denominators (people or person-time) in the measures of disease frequency for both exposed and reference groups. However, because most of the denominator is made up of people who do not develop disease, the case-control design avoids the need to collect information on the entire population by selecting a sample of the underlying population.

Rothman describes the case-control strategy as follows: 

To illustrate this consider the following hypothetical scenario in which the source population is Plymouth County in Massachusetts, which has a total population of 6,647 (hypothetical). Thirteen people in the county have been diagnosed with an unusual disease and seven of them have a particular exposure that is suspected of being an important contributing factor. The chief problem here is that the disease is quite rare.

Map of Plymouth County showing red icons of people who developed hepatitis A in the outbreak

If I somehow had exposure and outcome information on all of the subjects in the source population and looked at the association using a cohort design, it might look like this:

Therefore, the incidence in the exposed individuals would be 7/1,007 = 0.70%, and the incidence in the non-exposed individuals would be 6/5,640 = 0.11%. Consequently, the risk ratio would be 0.70/0.11=6.52, suggesting that those who had the risk factor (exposure) had 6.5 times the risk of getting the disease compared to those without the risk factor. This is a strong association.

In this hypothetical example, I had data on all 6,647 people in the source population, and I could compute the probability of disease (i.e., the risk or incidence) in both the exposed group and the non-exposed group, because I had the denominators for both the exposed and non-exposed groups.

The problem , of course, is that I usually don't have the resources to get the data on all subjects in the population. If I took a random sample of even 5-10% of the population, I might not have any diseased people in my sample.

An alternative approach would be to use surveillance databases or administrative databases to find most or all 13 of the cases in the source population and determine their exposure status. However, instead of enrolling all of the other 5,634 residents, suppose I were to just take a sample of the non-diseased population. In fact, suppose I only took a sample of 1% of the non-diseased people and I then determined their exposure status. The data might look something like this:

With this sampling approach I can no longer compute the probability of disease in each exposure group, because I no longer have the denominators in the last column. In other words, I don't know the exposure distribution for the entire source population. However, the small control sample of non-diseased subjects gives me a way to estimate the exposure distribution in the source population. So, I can't compute the probability of disease in each exposure group, but I can compute the odds of disease in the case-control sample.

The Odds Ratio

The odds of disease among the exposed sample are 7/10, and the odds of disease in the non-exposed sample are 6/56. If I compute the odds ratio, I get (7/10) / (5/56) = 6.56, very close to the risk ratio that I computed from data for the entire population. We will consider odds ratios and case-control studies in much greater depth in a later module. However, for the time being the key things to remember are that:

  • The sampling strategy for a case-control study is very different from that of cohort studies, despite the fact that both have the goal of estimating the magnitude of association between the exposure and the outcome.
  • In a case-control study there is no "follow-up" period. One starts by identifying diseased subjects and determines their exposure distribution; one then takes a sample of the source population that produced those cases in order to estimate the exposure distribution in the overall source population that produced the cases. [In cohort studies none of the subjects have the outcome at the beginning of the follow-up period.]
  • In a case-control study, you cannot measure incidence, because you start with diseased people and non-diseased people, so you cannot calculate relative risk.
  • The case-control design is very efficient. In the example above the case-control study of only 79 subjects produced an odds ratio (6.56) that was a very close approximation to the risk ratio (6.52) that was obtained from the data in the entire population.
  • Case-control studies are particularly useful when the outcome is rare is uncommon in both exposed and non-exposed people.

The Difference Between "Probability" and "Odds"?

analytical research is the

  • The odds are defined as the probability that the event will occur divided by the probability that the event will not occur.

If the probability of an event occurring is Y, then the probability of the event not occurring is 1-Y. (Example: If the probability of an event is 0.80 (80%), then the probability that the event will not occur is 1-0.80 = 0.20, or 20%.

The odds of an event represent the ratio of the (probability that the event will occur) / (probability that the event will not occur). This could be expressed as follows:

Odds of event = Y / (1-Y)

So, in this example, if the probability of the event occurring = 0.80, then the odds are 0.80 / (1-0.80) = 0.80/0.20 = 4 (i.e., 4 to 1).

  • If a race horse runs 100 races and wins 25 times and loses the other 75 times, the probability of winning is 25/100 = 0.25 or 25%, but the odds of the horse winning are 25/75 = 0.333 or 1 win to 3 loses.
  • If the horse runs 100 races and wins 5 and loses the other 95 times, the probability of winning is 0.05 or 5%, and the odds of the horse winning are 5/95 = 0.0526.
  • If the horse runs 100 races and wins 50, the probability of winning is 50/100 = 0.50 or 50%, and the odds of winning are 50/50 = 1 (even odds).
  • If the horse runs 100 races and wins 80, the probability of winning is 80/100 = 0.80 or 80%, and the odds of winning are 80/20 = 4 to 1.

NOTE that when the probability is low, the odds and the probability are very similar.

On Sept. 8, 2011 the New York Times ran an article on the economy in which the writer began by saying "If history is a guide, the odds that the American economy is falling into a double-dip recession have risen sharply in recent weeks and may even have reached 50 percent." Further down in the article the author quoted the economist who had been interviewed for the story. What the economist had actually said was, "Whether we reach the technical definition [of a double-dip recession] I think is probably close to 50-50."

Question: Was the author correct in saying that the "odds" of a double-dip recession may have reached 50 percent?

Which Study Design Is Best?

Decisions regarding which study design to use rest on a number of factors including::

  • Uncommon Outcome: If the outcome of interest is uncommon or rare, a case-control study would usually be best.
  • Uncommon Exposure: When studying an uncommon exposure, the investigators need to enroll an adequate number of subjects who have that exposure. In this situation a cohort study is best.
  • Ethics of Assigning Subjects to an Exposure: If you wanted to study the association between smoking and lung cancer, It wouldn't be ethical to conduct a clinical trial in which you randomly assigned half of the subjects to smoking.
  • Resources: If you have limited time, money, and personnel to gather data, it is unlikely that you will be able to conduct a prospective cohort study. A case-control study or a retrospective cohort study would be better options. The best one to choose would be dictated by whether the outcome was rare or the exposure of interest was rare.

There are some situations in which more than one study design could be used.

Smoking and Lung Cancer: For example, when investigators first sought to establish whether there was a link between smoking and lung cancer, they did a study by finding hospital subjects who had lung cancer and a comparison group of hospital patients who had diseases other than cancer. They then compared the prior exposure histories with respect to smoking and many other factors. They found that past smoking was much more common in the lung cancer cases, and they concluded that there was an association. The advantages to this approach were that they were able to collect the data they wanted relatively quickly and inexpensively, because they started with people who already had the disease of interest.

The short video below provides a nice overview of epidemiological studies.

analytical research is the

However, there were several limitations to the study they had done. The study design did not allow them to measure the incidence of lung cancer in smokers and non-smokers, so they couldn't measure the absolute risk of smoking. They also didn't know what other diseases smoking might be associated with, and, finally, they were concerned about some of the biases that can creep into this type of study.

As a result, these investigators then initiated another study. They invited all of the male physicians in the United Kingdom to fill out questionnaires regarding their health status and their smoking status. They then focused on the healthy physicians who were willing to participate, and the investigators mailed follow-up questionnaires to them every few years. They also had a way of finding out the cause of death for any subjects who became ill and died. The study continued for about 50 years. Along the way the investigators periodically compared the incidence of death among non-smoking physicians and physicians who smoked small, moderate or heavy amounts of tobacco.

These studies were useful, because they were able to demonstrate that smokers had an increased risk of over 20 different causes of death. They were also able to measure the incidence of death in different categories, so they knew the absolute risk for each cause of death. Of course, the downside to this approach was that it took a long time, and it was very costly. So, both a case-control study and a prospective cohort study provided useful information about the association between smoking and lung cancer and other diseases, but there were distinct advantages and limitations to each approach. 

Hepatitis Outbreak in Marshfield, MA

In 2004 there was an outbreak of hepatitis A on the South Shore of Massachusetts. Over a period of a few weeks there were 20 cases of hepatitis A that were reported to the MDPH, and most of the infected persons were residents of Marshfield, MA. Marshfield's health department requested help in identifying the source from MDPH. The investigators quickly performed descriptive epidemiology. The epidemic curve indicated a point source epidemic, and most of the cases lived in the Marshfield area, although some lived as far away as Boston. They conducted hypothesis-generating interviews, and taken together, the descriptive epidemiology suggested that the source was one of five or six food establishments in the Marshfield area, but it wasn't clear which one. Consequently, the investigators wanted to conduct an analytic study to determine which restaurant was the source. Which study design should have been conducted? Think about the scenario, and then open the "Quiz Me" below and choose your answer.

Link to more on the hepatitis outbreak

Case-control studies are particularly efficient for rare diseases because they begin by identifying a sufficient number of diseased people (or people have some "outcome" of interest) to enable you to do an analysis that tests associations. Case-control studies can be done in just about any circumstance, but they are particularly useful when you are dealing with rare diseases or disease for which there is a very long latent period, i.e. a long time between the causative exposure and the eventual development of disease. 

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Research Methods | Definitions, Types, Examples

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 :

  • Qualitative vs. quantitative : Will your data take the form of words or numbers?
  • Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
  • Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

  • For quantitative data, you can use statistical analysis methods to test relationships between variables.
  • For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

  • From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
  • Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

  • During an experiment .
  • Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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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.

  • Chi square test of independence
  • Statistical power
  • Descriptive statistics
  • Degrees of freedom
  • Pearson correlation
  • Null hypothesis
  • Double-blind study
  • Case-control study
  • Research ethics
  • Data collection
  • Hypothesis testing
  • Structured interviews

Research bias

  • Hawthorne effect
  • Unconscious bias
  • Recall bias
  • Halo effect
  • Self-serving bias
  • Information bias

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.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

  • If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
  • If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
  • If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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What is Scientific Research and How Can it be Done?

Scientific researches are studies that should be systematically planned before performing them. In this review, classification and description of scientific studies, planning stage randomisation and bias are explained.

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 information is revealed with respect to diagnosis, treatment and reliability of applications. The purpose of this review is to provide information about the definition, classification and methodology of scientific research.

Before beginning the scientific research, the researcher should determine the subject, do planning and specify the methodology. In the Declaration of Helsinki, it is stated that ‘the primary purpose of medical researches on volunteers is to understand the reasons, development and effects of diseases and develop protective, diagnostic and therapeutic interventions (method, operation and therapies). Even the best proven interventions should be evaluated continuously by investigations with regard to reliability, effectiveness, efficiency, accessibility and quality’ ( 1 ).

The questions, methods of response to questions and difficulties in scientific research may vary, but the design and structure are generally the same ( 2 ).

Classification of Scientific Research

Scientific research can be classified in several ways. Classification can be made according to the data collection techniques based on causality, relationship with time and the medium through which they are applied.

  • Observational
  • Experimental
  • Descriptive
  • Retrospective
  • Prospective
  • Cross-sectional
  • Social descriptive research ( 3 )

Another method is to classify the research according to its descriptive or analytical features. This review is written according to this classification method.

I. Descriptive research

  • Case series
  • Surveillance studies

II. Analytical research

  • Observational studies: cohort, case control and cross- sectional research
  • Interventional research: quasi-experimental and clinical research
  • Case Report: it is the most common type of descriptive study. It is the examination of a single case having a different quality in the society, e.g. conducting general anaesthesia in a pregnant patient with mucopolysaccharidosis.
  • Case Series: it is the description of repetitive cases having common features. For instance; case series involving interscapular pain related to neuraxial labour analgesia. Interestingly, malignant hyperthermia cases are not accepted as case series since they are rarely seen during historical development.
  • Surveillance Studies: these are the results obtained from the databases that follow and record a health problem for a certain time, e.g. the surveillance of cross-infections during anaesthesia in the intensive care unit.

Moreover, some studies may be experimental. After the researcher intervenes, the researcher waits for the result, observes and obtains data. Experimental studies are, more often, in the form of clinical trials or laboratory animal trials ( 2 ).

Analytical observational research can be classified as cohort, case-control and cross-sectional studies.

Firstly, the participants are controlled with regard to the disease under investigation. Patients are excluded from the study. Healthy participants are evaluated with regard to the exposure to the effect. Then, the group (cohort) is followed-up for a sufficient period of time with respect to the occurrence of disease, and the progress of disease is studied. The risk of the healthy participants getting sick is considered an incident. In cohort studies, the risk of disease between the groups exposed and not exposed to the effect is calculated and rated. This rate is called relative risk. Relative risk indicates the strength of exposure to the effect on the disease.

Cohort research may be observational and experimental. The follow-up of patients prospectively is called a prospective cohort study . The results are obtained after the research starts. The researcher’s following-up of cohort subjects from a certain point towards the past is called a retrospective cohort study . Prospective cohort studies are more valuable than retrospective cohort studies: this is because in the former, the researcher observes and records the data. The researcher plans the study before the research and determines what data will be used. On the other hand, in retrospective studies, the research is made on recorded data: no new data can be added.

In fact, retrospective and prospective studies are not observational. They determine the relationship between the date on which the researcher has begun the study and the disease development period. The most critical disadvantage of this type of research is that if the follow-up period is long, participants may leave the study at their own behest or due to physical conditions. Cohort studies that begin after exposure and before disease development are called ambidirectional studies . Public healthcare studies generally fall within this group, e.g. lung cancer development in smokers.

  • Case-Control Studies: these studies are retrospective cohort studies. They examine the cause and effect relationship from the effect to the cause. The detection or determination of data depends on the information recorded in the past. The researcher has no control over the data ( 2 ).

Cross-sectional studies are advantageous since they can be concluded relatively quickly. It may be difficult to obtain a reliable result from such studies for rare diseases ( 2 ).

Cross-sectional studies are characterised by timing. In such studies, the exposure and result are simultaneously evaluated. While cross-sectional studies are restrictedly used in studies involving anaesthesia (since the process of exposure is limited), they can be used in studies conducted in intensive care units.

  • Quasi-Experimental Research: they are conducted in cases in which a quick result is requested and the participants or research areas cannot be randomised, e.g. giving hand-wash training and comparing the frequency of nosocomial infections before and after hand wash.
  • Clinical Research: they are prospective studies carried out with a control group for the purpose of comparing the effect and value of an intervention in a clinical case. Clinical study and research have the same meaning. Drugs, invasive interventions, medical devices and operations, diets, physical therapy and diagnostic tools are relevant in this context ( 6 ).

Clinical studies are conducted by a responsible researcher, generally a physician. In the research team, there may be other healthcare staff besides physicians. Clinical studies may be financed by healthcare institutes, drug companies, academic medical centres, volunteer groups, physicians, healthcare service providers and other individuals. They may be conducted in several places including hospitals, universities, physicians’ offices and community clinics based on the researcher’s requirements. The participants are made aware of the duration of the study before their inclusion. Clinical studies should include the evaluation of recommendations (drug, device and surgical) for the treatment of a disease, syndrome or a comparison of one or more applications; finding different ways for recognition of a disease or case and prevention of their recurrence ( 7 ).

Clinical Research

In this review, clinical research is explained in more detail since it is the most valuable study in scientific research.

Clinical research starts with forming a hypothesis. A hypothesis can be defined as a claim put forward about the value of a population parameter based on sampling. There are two types of hypotheses in statistics.

  • H 0 hypothesis is called a control or null hypothesis. It is the hypothesis put forward in research, which implies that there is no difference between the groups under consideration. If this hypothesis is rejected at the end of the study, it indicates that a difference exists between the two treatments under consideration.
  • H 1 hypothesis is called an alternative hypothesis. It is hypothesised against a null hypothesis, which implies that a difference exists between the groups under consideration. For example, consider the following hypothesis: drug A has an analgesic effect. Control or null hypothesis (H 0 ): there is no difference between drug A and placebo with regard to the analgesic effect. The alternative hypothesis (H 1 ) is applicable if a difference exists between drug A and placebo with regard to the analgesic effect.

The planning phase comes after the determination of a hypothesis. A clinical research plan is called a protocol . In a protocol, the reasons for research, number and qualities of participants, tests to be applied, study duration and what information to be gathered from the participants should be found and conformity criteria should be developed.

The selection of participant groups to be included in the study is important. Inclusion and exclusion criteria of the study for the participants should be determined. Inclusion criteria should be defined in the form of demographic characteristics (age, gender, etc.) of the participant group and the exclusion criteria as the diseases that may influence the study, age ranges, cases involving pregnancy and lactation, continuously used drugs and participants’ cooperation.

The next stage is methodology. Methodology can be grouped under subheadings, namely, the calculation of number of subjects, blinding (masking), randomisation, selection of operation to be applied, use of placebo and criteria for stopping and changing the treatment.

I. Calculation of the Number of Subjects

The entire source from which the data are obtained is called a universe or population . A small group selected from a certain universe based on certain rules and which is accepted to highly represent the universe from which it is selected is called a sample and the characteristics of the population from which the data are collected are called variables. If data is collected from the entire population, such an instance is called a parameter . Conducting a study on the sample rather than the entire population is easier and less costly. Many factors influence the determination of the sample size. Firstly, the type of variable should be determined. Variables are classified as categorical (qualitative, non-numerical) or numerical (quantitative). Individuals in categorical variables are classified according to their characteristics. Categorical variables are indicated as nominal and ordinal (ordered). In nominal variables, the application of a category depends on the researcher’s preference. For instance, a female participant can be considered first and then the male participant, or vice versa. An ordinal (ordered) variable is ordered from small to large or vice versa (e.g. ordering obese patients based on their weights-from the lightest to the heaviest or vice versa). A categorical variable may have more than one characteristic: such variables are called binary or dichotomous (e.g. a participant may be both female and obese).

If the variable has numerical (quantitative) characteristics and these characteristics cannot be categorised, then it is called a numerical variable. Numerical variables are either discrete or continuous. For example, the number of operations with spinal anaesthesia represents a discrete variable. The haemoglobin value or height represents a continuous variable.

Statistical analyses that need to be employed depend on the type of variable. The determination of variables is necessary for selecting the statistical method as well as software in SPSS. While categorical variables are presented as numbers and percentages, numerical variables are represented using measures such as mean and standard deviation. It may be necessary to use mean in categorising some cases such as the following: even though the variable is categorical (qualitative, non-numerical) when Visual Analogue Scale (VAS) is used (since a numerical value is obtained), it is classified as a numerical variable: such variables are averaged.

Clinical research is carried out on the sample and generalised to the population. Accordingly, the number of samples should be correctly determined. Different sample size formulas are used on the basis of the statistical method to be used. When the sample size increases, error probability decreases. The sample size is calculated based on the primary hypothesis. The determination of a sample size before beginning the research specifies the power of the study. Power analysis enables the acquisition of realistic results in the research, and it is used for comparing two or more clinical research methods.

Because of the difference in the formulas used in calculating power analysis and number of samples for clinical research, it facilitates the use of computer programs for making calculations.

It is necessary to know certain parameters in order to calculate the number of samples by power analysis.

  • Type-I (α) and type-II (β) error levels
  • Difference between groups (d-difference) and effect size (ES)
  • Distribution ratio of groups
  • Direction of research hypothesis (H1)

a. Type-I (α) and Type-II (β) Error (β) Levels

Two types of errors can be made while accepting or rejecting H 0 hypothesis in a hypothesis test. Type-I error (α) level is the probability of finding a difference at the end of the research when there is no difference between the two applications. In other words, it is the rejection of the hypothesis when H 0 is actually correct and it is known as α error or p value. For instance, when the size is determined, type-I error level is accepted as 0.05 or 0.01.

Another error that can be made during a hypothesis test is a type-II error. It is the acceptance of a wrongly hypothesised H 0 hypothesis. In fact, it is the probability of failing to find a difference when there is a difference between the two applications. The power of a test is the ability of that test to find a difference that actually exists. Therefore, it is related to the type-II error level.

Since the type-II error risk is expressed as β, the power of the test is defined as 1–β. When a type-II error is 0.20, the power of the test is 0.80. Type-I (α) and type-II (β) errors can be intentional. The reason to intentionally make such an error is the necessity to look at the events from the opposite perspective.

b. Difference between Groups and ES

ES is defined as the state in which statistical difference also has clinically significance: ES≥0.5 is desirable. The difference between groups is the absolute difference between the groups compared in clinical research.

c. Allocation Ratio of Groups

The allocation ratio of groups is effective in determining the number of samples. If the number of samples is desired to be determined at the lowest level, the rate should be kept as 1/1.

d. Direction of Hypothesis (H1)

The direction of hypothesis in clinical research may be one-sided or two-sided. While one-sided hypotheses hypothesis test differences in the direction of size, two-sided hypotheses hypothesis test differences without direction. The power of the test in two-sided hypotheses is lower than one-sided hypotheses.

After these four variables are determined, they are entered in the appropriate computer program and the number of samples is calculated. Statistical packaged software programs such as Statistica, NCSS and G-Power may be used for power analysis and calculating the number of samples. When the samples size is calculated, if there is a decrease in α, difference between groups, ES and number of samples, then the standard deviation increases and power decreases. The power in two-sided hypothesis is lower. It is ethically appropriate to consider the determination of sample size, particularly in animal experiments, at the beginning of the study. The phase of the study is also important in the determination of number of subjects to be included in drug studies. Usually, phase-I studies are used to determine the safety profile of a drug or product, and they are generally conducted on a few healthy volunteers. If no unacceptable toxicity is detected during phase-I studies, phase-II studies may be carried out. Phase-II studies are proof-of-concept studies conducted on a larger number (100–500) of volunteer patients. When the effectiveness of the drug or product is evident in phase-II studies, phase-III studies can be initiated. These are randomised, double-blinded, placebo or standard treatment-controlled studies. Volunteer patients are periodically followed-up with respect to the effectiveness and side effects of the drug. It can generally last 1–4 years and is valuable during licensing and releasing the drug to the general market. Then, phase-IV studies begin in which long-term safety is investigated (indication, dose, mode of application, safety, effectiveness, etc.) on thousands of volunteer patients.

II. Blinding (Masking) and Randomisation Methods

When the methodology of clinical research is prepared, precautions should be taken to prevent taking sides. For this reason, techniques such as randomisation and blinding (masking) are used. Comparative studies are the most ideal ones in clinical research.

Blinding Method

A case in which the treatments applied to participants of clinical research should be kept unknown is called the blinding method . If the participant does not know what it receives, it is called a single-blind study; if even the researcher does not know, it is called a double-blind study. When there is a probability of knowing which drug is given in the order of application, when uninformed staff administers the drug, it is called in-house blinding. In case the study drug is known in its pharmaceutical form, a double-dummy blinding test is conducted. Intravenous drug is given to one group and a placebo tablet is given to the comparison group; then, the placebo tablet is given to the group that received the intravenous drug and intravenous drug in addition to placebo tablet is given to the comparison group. In this manner, each group receives both the intravenous and tablet forms of the drug. In case a third party interested in the study is involved and it also does not know about the drug (along with the statistician), it is called third-party blinding.

Randomisation Method

The selection of patients for the study groups should be random. Randomisation methods are used for such selection, which prevent conscious or unconscious manipulations in the selection of patients ( 8 ).

No factor pertaining to the patient should provide preference of one treatment to the other during randomisation. This characteristic is the most important difference separating randomised clinical studies from prospective and synchronous studies with experimental groups. Randomisation strengthens the study design and enables the determination of reliable scientific knowledge ( 2 ).

The easiest method is simple randomisation, e.g. determination of the type of anaesthesia to be administered to a patient by tossing a coin. In this method, when the number of samples is kept high, a balanced distribution is created. When the number of samples is low, there will be an imbalance between the groups. In this case, stratification and blocking have to be added to randomisation. Stratification is the classification of patients one or more times according to prognostic features determined by the researcher and blocking is the selection of a certain number of patients for each stratification process. The number of stratification processes should be determined at the beginning of the study.

As the number of stratification processes increases, performing the study and balancing the groups become difficult. For this reason, stratification characteristics and limitations should be effectively determined at the beginning of the study. It is not mandatory for the stratifications to have equal intervals. Despite all the precautions, an imbalance might occur between the groups before beginning the research. In such circumstances, post-stratification or restandardisation may be conducted according to the prognostic factors.

The main characteristic of applying blinding (masking) and randomisation is the prevention of bias. Therefore, it is worthwhile to comprehensively examine bias at this stage.

Bias and Chicanery

While conducting clinical research, errors can be introduced voluntarily or involuntarily at a number of stages, such as design, population selection, calculating the number of samples, non-compliance with study protocol, data entry and selection of statistical method. Bias is taking sides of individuals in line with their own decisions, views and ideological preferences ( 9 ). In order for an error to lead to bias, it has to be a systematic error. Systematic errors in controlled studies generally cause the results of one group to move in a different direction as compared to the other. It has to be understood that scientific research is generally prone to errors. However, random errors (or, in other words, ‘the luck factor’-in which bias is unintended-do not lead to bias ( 10 ).

Another issue, which is different from bias, is chicanery. It is defined as voluntarily changing the interventions, results and data of patients in an unethical manner or copying data from other studies. Comparatively, bias may not be done consciously.

In case unexpected results or outliers are found while the study is analysed, if possible, such data should be re-included into the study since the complete exclusion of data from a study endangers its reliability. In such a case, evaluation needs to be made with and without outliers. It is insignificant if no difference is found. However, if there is a difference, the results with outliers are re-evaluated. If there is no error, then the outlier is included in the study (as the outlier may be a result). It should be noted that re-evaluation of data in anaesthesiology is not possible.

Statistical evaluation methods should be determined at the design stage so as not to encounter unexpected results in clinical research. The data should be evaluated before the end of the study and without entering into details in research that are time-consuming and involve several samples. This is called an interim analysis . The date of interim analysis should be determined at the beginning of the study. The purpose of making interim analysis is to prevent unnecessary cost and effort since it may be necessary to conclude the research after the interim analysis, e.g. studies in which there is no possibility to validate the hypothesis at the end or the occurrence of different side effects of the drug to be used. The accuracy of the hypothesis and number of samples are compared. Statistical significance levels in interim analysis are very important. If the data level is significant, the hypothesis is validated even if the result turns out to be insignificant after the date of the analysis.

Another important point to be considered is the necessity to conclude the participants’ treatment within the period specified in the study protocol. When the result of the study is achieved earlier and unexpected situations develop, the treatment is concluded earlier. Moreover, the participant may quit the study at its own behest, may die or unpredictable situations (e.g. pregnancy) may develop. The participant can also quit the study whenever it wants, even if the study has not ended ( 7 ).

In case the results of a study are contrary to already known or expected results, the expected quality level of the study suggesting the contradiction may be higher than the studies supporting what is known in that subject. This type of bias is called confirmation bias. The presence of well-known mechanisms and logical inference from them may create problems in the evaluation of data. This is called plausibility bias.

Another type of bias is expectation bias. If a result different from the known results has been achieved and it is against the editor’s will, it can be challenged. Bias may be introduced during the publication of studies, such as publishing only positive results, selection of study results in a way to support a view or prevention of their publication. Some editors may only publish research that extols only the positive results or results that they desire.

Bias may be introduced for advertisement or economic reasons. Economic pressure may be applied on the editor, particularly in the cases of studies involving drugs and new medical devices. This is called commercial bias.

In recent years, before beginning a study, it has been recommended to record it on the Web site www.clinicaltrials.gov for the purpose of facilitating systematic interpretation and analysis in scientific research, informing other researchers, preventing bias, provision of writing in a standard format, enhancing contribution of research results to the general literature and enabling early intervention of an institution for support. This Web site is a service of the US National Institutes of Health.

The last stage in the methodology of clinical studies is the selection of intervention to be conducted. Placebo use assumes an important place in interventions. In Latin, placebo means ‘I will be fine’. In medical literature, it refers to substances that are not curative, do not have active ingredients and have various pharmaceutical forms. Although placebos do not have active drug characteristic, they have shown effective analgesic characteristics, particularly in algology applications; further, its use prevents bias in comparative studies. If a placebo has a positive impact on a participant, it is called the placebo effect ; on the contrary, if it has a negative impact, it is called the nocebo effect . Another type of therapy that can be used in clinical research is sham application. Although a researcher does not cure the patient, the researcher may compare those who receive therapy and undergo sham. It has been seen that sham therapies also exhibit a placebo effect. In particular, sham therapies are used in acupuncture applications ( 11 ). While placebo is a substance, sham is a type of clinical application.

Ethically, the patient has to receive appropriate therapy. For this reason, if its use prevents effective treatment, it causes great problem with regard to patient health and legalities.

Before medical research is conducted with human subjects, predictable risks, drawbacks and benefits must be evaluated for individuals or groups participating in the study. Precautions must be taken for reducing the risk to a minimum level. The risks during the study should be followed, evaluated and recorded by the researcher ( 1 ).

After the methodology for a clinical study is determined, dealing with the ‘Ethics Committee’ forms the next stage. The purpose of the ethics committee is to protect the rights, safety and well-being of volunteers taking part in the clinical research, considering the scientific method and concerns of society. The ethics committee examines the studies presented in time, comprehensively and independently, with regard to ethics and science; in line with the Declaration of Helsinki and following national and international standards concerning ‘Good Clinical Practice’. The method to be followed in the formation of the ethics committee should be developed without any kind of prejudice and to examine the applications with regard to ethics and science within the framework of the ethics committee, Regulation on Clinical Trials and Good Clinical Practice ( www.iku.com ). The necessary documents to be presented to the ethics committee are research protocol, volunteer consent form, budget contract, Declaration of Helsinki, curriculum vitae of researchers, similar or explanatory literature samples, supporting institution approval certificate and patient follow-up form.

Only one sister/brother, mother, father, son/daughter and wife/husband can take charge in the same ethics committee. A rector, vice rector, dean, deputy dean, provincial healthcare director and chief physician cannot be members of the ethics committee.

Members of the ethics committee can work as researchers or coordinators in clinical research. However, during research meetings in which members of the ethics committee are researchers or coordinators, they must leave the session and they cannot sign-off on decisions. If the number of members in the ethics committee for a particular research is so high that it is impossible to take a decision, the clinical research is presented to another ethics committee in the same province. If there is no ethics committee in the same province, an ethics committee in the closest settlement is found.

Thereafter, researchers need to inform the participants using an informed consent form. This form should explain the content of clinical study, potential benefits of the study, alternatives and risks (if any). It should be easy, comprehensible, conforming to spelling rules and written in plain language understandable by the participant.

This form assists the participants in taking a decision regarding participation in the study. It should aim to protect the participants. The participant should be included in the study only after it signs the informed consent form; the participant can quit the study whenever required, even when the study has not ended ( 7 ).

Peer-review: Externally peer-reviewed.

Author Contributions: Concept - C.Ö.Ç., A.D.; Design - C.Ö.Ç.; Supervision - A.D.; Resource - C.Ö.Ç., A.D.; Materials - C.Ö.Ç., A.D.; Analysis and/or Interpretation - C.Ö.Ç., A.D.; Literature Search - C.Ö.Ç.; Writing Manuscript - C.Ö.Ç.; Critical Review - A.D.; Other - C.Ö.Ç., A.D.

Conflict of Interest: No conflict of interest was declared by the authors.

Financial Disclosure: The authors declared that this study has received no financial support.


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5 Essential Principles for Understanding Analytics

  • Thomas H. Davenport

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Where to start learning if you don’t know the basics.

I’m convinced that the ingredient for the effective use of data and analytics that is in shortest supply is managers’ understanding of what is possible. Data, hardware, and software are available in droves, but human comprehension of the possibilities they enable is much less common. Given that problem, there is a great need for more education on this topic. And u nfortunately, there aren’t a lot of other good options out there for non-quantitative managers who want to learn about analytics. MOOCs and traditional academic courses mostly focus on methods. And while there are lots of executive programs in “Accounting and Finance for Nonfinancial Managers,” there aren’t any that I know of on “Analytics for Non-Quantitative Managers.”

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  • Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, a visiting scholar at the MIT Initiative on the Digital Economy, and a senior adviser to Deloitte’s AI practice. He is a coauthor of All-in on AI: How Smart Companies Win Big with Artificial Intelligence (Harvard Business Review Press, 2023).

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  • > General Analytics

Different Types of Research Methods

  • Mallika Rangaiah
  • Dec 22, 2021
  • Updated on: Nov 21, 2023

Different Types of Research Methods title banner

Unlike what a layman generally presumes, Research is not just about determining a hypothesis and unraveling a conclusion for that hypothesis. Every research approach that we take up falls under the category of a type of methodology and every methodology is exclusive and intricate in its depth. 

So what are these research methodologies and how do the researchers make use of them? This is what we are going to explore through this blog. Before we attempt to understand these methods, let us understand what research methodology actually means. 

What are Research Methods ?

Firstly, let's understand why we undertake research? What exactly is the point of it? 

Research is mainly done to gain knowledge to support a survey or quest regarding a particular conception or theory and to reach a resolute conclusion regarding the same.  Research is generally an approach for gaining knowledge which is required to interpret, write, delve further and to distribute data. 

For ensuring that a fulfilling experience is delivered, it is essential that the Research is premium in its quality and that’s where Research Methods come to the rescue. 

(Recommended blog - Research Market Analysis )

Types of Research Methods

An area is selected, a specific hypothesis is determined and a defined conclusion is required to be achieved. But how is this conclusion reached? What is the approach that can be taken up? As per CR Kothari’s book “Research Methodology Methods and Techniques” (The Second Revised Edition),  the basic types of Research Methods are the following : 

The image depicts the Types of Research Methods and has the following points :1. Descriptive Research2. Analytical Research3. Applied Research4. Fundamental Research5. Quantitative Research6. Qualitative Research7. Conceptual Research8. Empirical Research

Descriptive Research

Descriptive Research is a form of research that incorporates surveys as well as different varieties of fact-finding investigations. This form of research is focused on describing the prevailing state of affairs as they are. Descriptive Research is also termed as Ex post facto research. 

This research form emphasises on factual reporting, the researcher cannot control the involved variables and can only report the details as they took place or as they are taking place. 

Researchers mainly make use of a descriptive research approach for purposes such as when the research is aimed at deciphering characteristics, frequencies or trends. 

Ex post facto studies also include attempts by researchers to discover causes even when they cannot control the variables. The descriptive research methods are mainly, observations, surveys as well as case studies. 

(Speaking of variables, have you ever wondered - What are confounding variables? )

Analytical Research

Analytical Research is a form of research where the researcher has to make do with the data and factual information available at their behest and interpret this information to undertake an acute evaluation of the data. 

This form of research is often undertaken by researchers to uncover some evidence that supports their present research and which makes it more authentic. It is also undertaken for concocting fresh ideas relating to the topic on which the research is based. 

From conducting meta analysis, literary research or scientific trials and learning public opinion, there are many methods through which this research is done. 

Applied Research

When a business or say, the society is faced with an issue that needs an immediate solution or resolution, Applied Research is the research type that comes to the rescue. 

We primarily make use of Applied Research when it comes to resolving the issues plaguing our daily lives, impacting our work, health or welfare. This research type is undertaken to uncover solutions for issues relating to varying sectors like education, engineering, psychology or business. 

For instance, a company might employ an applied researcher for concluding the best possible approach of selecting employees that would be the best fit for specific positions in the company. 

The crux of Applied Research is to figure out the solution to a certain growing practical issue. 

The 3 Types of Applied Research are mainly 

Evaluation Research - Research where prevailing data regarding the topic is interpreted to arrive at proper decisions

Research and Development - Where the focus is on setting up fresh products or services which focus on the target market requirements

Action Research - Which aims at offering practical solutions for certain business issues by giving them proper direction, are the 3 types of Applied Research. 

(Related blog - Target Marketing using AI )

Fundamental Research

This is a Research type that is primarily concerned with formulating a theory or understanding a particular natural phenomenon. Fundamental Research aims to discover information with an extensive application base, supplementing the existing concepts in a certain field or industry. 

Research on pure mathematics or research regarding generalisation of the behavior of humans are also examples of Fundamental Research. This form of research is mainly carried out in sectors like Education, Psychology and Science. 

For instance, in Psychology fundamental research assists the individual or the company in gaining better insights regarding certain behaviors such as deciphering how consumption of caffeine can possibly impact the attention span of a student or how culture stereotypes can possibly trigger depression. 

Quantitative Research

Quantitative Research, as the name suggests, is based on the measurement of a particular amount or quantity of a particular phenomenon. It focuses on gathering and interpreting numerical data and can be adopted for discovering any averages or patterns or for making predictions.

This form of Research is number based and it lies under the two main Research Types. It makes use of tables, data and graphs to reach a conclusion. The outcomes generated from this research are measurable and can be repeated unlike the outcomes of qualitative research. This research type is mainly adopted for scientific and field based research.

Quantitative research generally involves a large number of people and a huge section of data and has a lot of scope for accuracy in it. 

These research methods can be adopted for approaches like descriptive, correlational or experimental research.

Descriptive research - The study variables are analyzed and a summary of the same is seeked.

Correlational Research - The relationship between the study variables is analyzed. 

Experimental Research - It is deciphered to analyse whether a cause and effect relationship between the variables exists. 

Quantitative research methods

  • Experiment Research - This method controls or manages independent variables for calculating the effect it has on dependent variables. 
  • Survey - Surveys involve inquiring questions from a certain specified number or set of people either online, face to face or over the phone. 
  • (Systematic) observation - This method involves detecting any occurrence and monitoring it in a natural setting. 
  • Secondary research : This research focuses on making use of data which has been previously collected for other purposes such as for say, a national survey. 

(Related blog - Hypothesis Testing )

Qualitative Research

As the name suggests, this form of Research is more considered with the quality of a certain phenomenon, it dives into the “why” alongside the “what”. For instance, let’s consider a gender neutral clothing store which has more women visiting it than men. 

Qualitative research would be determining why men are not visiting the store by carrying out an in-depth interview of some potential customers in this category.

This form of research is interested in getting to the bottom of the reasons for human behaviour, i.e understanding why certain actions are taken by people or why they think certain thoughts. 

Through this research the factors influencing people into behaving in a certain way or which control their preferences towards a certain thing can be interpreted.

An example of Qualitative Research would be Motivation Research . This research focuses on deciphering the rooted motives or desires through intricate methods like in depth interviews. It involves several tests like story completion or word association. 

Another example would be Opinion Research . This type of research is carried out to discover the opinion and perspective of people regarding a certain subject or phenomenon.

This is a theory based form of research and it works by describing an issue by taking into account the prior concepts, ideas and studies. The experience of the researcher plays an integral role here.

The Types of Qualitative Research includes the following methods :

Qualitative research methods

  • Observations: In this method what the researcher sees, hears of or encounters is recorded in detail.
  • Interviews: Personally asking people questions in one-on-one conversations.
  • Focus groups: This involves asking questions and discussions among a group of people to generate conclusions from the same. 
  • Surveys: In these surveys unlike the quantitative research surveys, the questionnaires involve extensive open ended questions that require elaborate answers. 
  • Secondary research: Gathering the existing data such as images, texts or audio or video recordings. This can involve a text analysis, a research of a case study, or an In-depth interview.

Conceptual Research

This research is related to an abstract idea or a theory. It is adopted by thinkers and philosophers with the aim of developing a new concept or to re-examine the existing concepts. 

Conceptual Research is mainly defined as a methodology in which the research is conducted by observing and interpreting the already present information on a present topic. It does not include carrying out any practical experiments. 

This methodology has often been adopted by famous Philosophers like Aristotle, Copernicus, Einstein and Newton for developing fresh theories and insights regarding the working of the world and for examining the existing ones from a different perspective. 

The concepts were set up by philosophers to observe their environment and to sort, study, and summarise the information available. 

Empirical Research

This is a research method that focuses solely on aspects like observation and experience, without focusing on the theory or system. It is based on data and it can churn conclusions that can be confirmed or verified through observation and experiment. Empirical Research is mainly undertaken to determine proof that certain variables are affecting the others in a particular way.   

This kind of research can also be termed as Experimental Research. In this research it is essential that all the facts are received firsthand, directly from the source so that the researcher can actively go and carry out the actions and manipulate the concerned materials to gain the information he requires.

In this research a hypothesis is generated and then a path is undertaken to confirm or invalidate this hypothesis. The control that the researcher holds over the involved variables defines this research. The researcher can manipulate one of these variables to examine its effect.

(Recommended blog - Data Analysis )

Other Types of Research

All research types apart from the ones stated above are mainly variations of them, either in terms of research purpose or in the terms of the time that is required for accomplishing the research, or say, the research environment. 

If we take the perspective of time, research can be considered as either One-time research or Longitudinal Research. 

One time Research : The research is restricted to a single time period. 

Longitudinal Research : The research is executed over multiple time periods. 

A research can also be set in a field or a laboratory or be a simulation, it depends on the environment that the research is based on. 

We’ve also got Historical Research which makes use of historical sources such as documents and remains for examining past events and ideas. This also includes the philosophy of an individual and groups at a particular time. 

Research may be clinical or diagnostic . These kinds of research generally carry out case study or in-depth interview approaches to determine basic causal relationships. 

Research can also be Exploratory or Formalized. 

Exploratory Research: This is a research that is more focused on establishing hypotheses than on deriving the result. This form of Research focuses on understanding the prevailing issue but it doesn’t really offer defining results. 

Formalized research: This is a research that has a solid structure and which also has specific hypotheses for testing. 

We can also classify Research as conclusion-oriented and decision-oriented. 

Conclusion Oriented Research: In this form of research, the researcher can select an issue, revamp the enquiry as he continues and visualize it as per his requirements. 

Decision-oriented research: This research depends on the requirement of the decision maker and offers less freedom to the research to conduct it as he pleases. 

The common and well known research methods have been listed in this blog. Hopefully this blog will give the readers and present and future researchers proper knowledge regarding important methods they can adopt to conduct their Research.

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  • 8.5 Writing Process: Creating an Analytical Report
  • 1 Unit Introduction


  • 1.1 "Reading" to Understand and Respond
  • 1.2 Social Media Trailblazer: Selena Gomez
  • 1.3 Glance at Critical Response: Rhetoric and Critical Thinking
  • 1.4 Annotated Student Sample: Social Media Post and Responses on Voter Suppression
  • 1.5 Writing Process: Thinking Critically About a “Text”
  • 1.6 Evaluation: Intention vs. Execution
  • 1.7 Spotlight on … Academia
  • 1.8 Portfolio: Tracing Writing Development
  • Further Reading
  • Works Cited
  • 2.1 Seeds of Self
  • 2.2 Identity Trailblazer: Cathy Park Hong
  • 2.3 Glance at the Issues: Oppression and Reclamation
  • 2.4 Annotated Sample Reading from The Souls of Black Folk by W. E. B. Du Bois
  • 2.5 Writing Process: Thinking Critically about How Identity Is Constructed Through Writing
  • 2.6 Evaluation: Antiracism and Inclusivity
  • 2.7 Spotlight on … Variations of English
  • 2.8 Portfolio: Decolonizing Self
  • 3.1 Identity and Expression
  • 3.2 Literacy Narrative Trailblazer: Tara Westover
  • 3.3 Glance at Genre: The Literacy Narrative
  • 3.4 Annotated Sample Reading: from Narrative of the Life of Frederick Douglass by Frederick Douglass
  • 3.5 Writing Process: Tracing the Beginnings of Literacy
  • 3.6 Editing Focus: Sentence Structure
  • 3.7 Evaluation: Self-Evaluating
  • 3.8 Spotlight on … The Digital Archive of Literacy Narratives (DALN)
  • 3.9 Portfolio: A Literacy Artifact
  • Works Consulted
  • 2 Unit Introduction
  • 4.1 Exploring the Past to Understand the Present
  • 4.2 Memoir Trailblazer: Ta-Nehisi Coates
  • 4.3 Glance at Genre: Conflict, Detail, and Revelation
  • 4.4 Annotated Sample Reading: from Life on the Mississippi by Mark Twain
  • 4.5 Writing Process: Making the Personal Public
  • 4.6 Editing Focus: More on Characterization and Point of View
  • 4.7 Evaluation: Structure and Organization
  • 4.8 Spotlight on … Multilingual Writers
  • 4.9 Portfolio: Filtered Memories
  • 5.1 Profiles as Inspiration
  • 5.2 Profile Trailblazer: Veronica Chambers
  • 5.3 Glance at Genre: Subject, Angle, Background, and Description
  • 5.4 Annotated Sample Reading: “Remembering John Lewis” by Carla D. Hayden
  • 5.5 Writing Process: Focusing on the Angle of Your Subject
  • 5.6 Editing Focus: Verb Tense Consistency
  • 5.7 Evaluation: Text as Personal Introduction
  • 5.8 Spotlight on … Profiling a Cultural Artifact
  • 5.9 Portfolio: Subject as a Reflection of Self
  • 6.1 Proposing Change: Thinking Critically About Problems and Solutions
  • 6.2 Proposal Trailblazer: Atul Gawande
  • 6.3 Glance at Genre: Features of Proposals
  • 6.4 Annotated Student Sample: “Slowing Climate Change” by Shawn Krukowski
  • 6.5 Writing Process: Creating a Proposal
  • 6.6 Editing Focus: Subject-Verb Agreement
  • 6.7 Evaluation: Conventions, Clarity, and Coherence
  • 6.8 Spotlight on … Technical Writing as a Career
  • 6.9 Portfolio: Reflecting on Problems and Solutions
  • 7.1 Thumbs Up or Down?
  • 7.2 Review Trailblazer: Michiko Kakutani
  • 7.3 Glance at Genre: Criteria, Evidence, Evaluation
  • 7.4 Annotated Student Sample: "Black Representation in Film" by Caelia Marshall
  • 7.5 Writing Process: Thinking Critically About Entertainment
  • 7.6 Editing Focus: Quotations
  • 7.7 Evaluation: Effect on Audience
  • 7.8 Spotlight on … Language and Culture
  • 7.9 Portfolio: What the Arts Say About You
  • 8.1 Information and Critical Thinking
  • 8.2 Analytical Report Trailblazer: Barbara Ehrenreich
  • 8.3 Glance at Genre: Informal and Formal Analytical Reports
  • 8.4 Annotated Student Sample: "U.S. Response to COVID-19" by Trevor Garcia
  • 8.6 Editing Focus: Commas with Nonessential and Essential Information
  • 8.7 Evaluation: Reviewing the Final Draft
  • 8.8 Spotlight on … Discipline-Specific and Technical Language
  • 8.9 Portfolio: Evidence and Objectivity
  • 9.1 Breaking the Whole into Its Parts
  • 9.2 Rhetorical Analysis Trailblazer: Jamil Smith
  • 9.3 Glance at Genre: Rhetorical Strategies
  • 9.4 Annotated Student Sample: “Rhetorical Analysis: Evicted by Matthew Desmond” by Eliana Evans
  • 9.5 Writing Process: Thinking Critically about Rhetoric
  • 9.6 Editing Focus: Mixed Sentence Constructions
  • 9.7 Evaluation: Rhetorical Analysis
  • 9.8 Spotlight on … Business and Law
  • 9.9 Portfolio: How Thinking Critically about Rhetoric Affects Intellectual Growth
  • 10.1 Making a Case: Defining a Position Argument
  • 10.2 Position Argument Trailblazer: Charles Blow
  • 10.3 Glance at Genre: Thesis, Reasoning, and Evidence
  • 10.4 Annotated Sample Reading: "Remarks at the University of Michigan" by Lyndon B. Johnson
  • 10.5 Writing Process: Creating a Position Argument
  • 10.6 Editing Focus: Paragraphs and Transitions
  • 10.7 Evaluation: Varied Appeals
  • 10.8 Spotlight on … Citation
  • 10.9 Portfolio: Growth in the Development of Argument
  • 11.1 Developing Your Sense of Logic
  • 11.2 Reasoning Trailblazer: Paul D. N. Hebert
  • 11.3 Glance at Genre: Reasoning Strategies and Signal Words
  • 11.4 Annotated Sample Reading: from Book VII of The Republic by Plato
  • 11.5 Writing Process: Reasoning Supported by Evidence
  • 12.1 Introducing Research and Research Evidence
  • 12.2 Argumentative Research Trailblazer: Samin Nosrat
  • 12.3 Glance at Genre: Introducing Research as Evidence
  • 12.4 Annotated Student Sample: "Healthy Diets from Sustainable Sources Can Save the Earth" by Lily Tran
  • 12.5 Writing Process: Integrating Research
  • 12.6 Editing Focus: Integrating Sources and Quotations
  • 12.7 Evaluation: Effectiveness of Research Paper
  • 12.8 Spotlight on … Bias in Language and Research
  • 12.9 Portfolio: Why Facts Matter in Research Argumentation
  • 13.1 The Research Process: Where to Look for Existing Sources
  • 13.2 The Research Process: How to Create Sources
  • 13.3 Glance at the Research Process: Key Skills
  • 13.4 Annotated Student Sample: Research Log
  • 13.5 Research Process: Making Notes, Synthesizing Information, and Keeping a Research Log
  • 13.6 Spotlight on … Ethical Research
  • 14.1 Compiling Sources for an Annotated Bibliography
  • 14.2 Glance at Form: Citation Style, Purpose, and Formatting
  • 14.3 Annotated Student Sample: “Healthy Diets from Sustainable Sources Can Save the Earth” by Lily Tran
  • 14.4 Writing Process: Informing and Analyzing
  • 15.1 Tracing a Broad Issue in the Individual
  • 15.2 Case Study Trailblazer: Vilayanur S. Ramachandran
  • 15.3 Glance at Genre: Observation, Description, and Analysis
  • 15.4 Annotated Sample Reading: Case Study on Louis Victor "Tan" Leborgne
  • 15.5 Writing Process: Thinking Critically About How People and Language Interact
  • 15.6 Editing Focus: Words Often Confused
  • 15.7 Evaluation: Presentation and Analysis of Case Study
  • 15.8 Spotlight on … Applied Linguistics
  • 15.9 Portfolio: Your Own Uses of Language
  • 3 Unit Introduction
  • 16.1 An Author’s Choices: What Text Says and How It Says It
  • 16.2 Textual Analysis Trailblazer: bell hooks
  • 16.3 Glance at Genre: Print or Textual Analysis
  • 16.4 Annotated Student Sample: "Artists at Work" by Gwyn Garrison
  • 16.5 Writing Process: Thinking Critically About Text
  • 16.6 Editing Focus: Literary Works Live in the Present
  • 16.7 Evaluation: Self-Directed Assessment
  • 16.8 Spotlight on … Humanities
  • 16.9 Portfolio: The Academic and the Personal
  • 17.1 “Reading” Images
  • 17.2 Image Trailblazer: Sara Ludy
  • 17.3 Glance at Genre: Relationship Between Image and Rhetoric
  • 17.4 Annotated Student Sample: “Hints of the Homoerotic” by Leo Davis
  • 17.5 Writing Process: Thinking Critically and Writing Persuasively About Images
  • 17.6 Editing Focus: Descriptive Diction
  • 17.7 Evaluation: Relationship Between Analysis and Image
  • 17.8 Spotlight on … Video and Film
  • 17.9 Portfolio: Interplay Between Text and Image
  • 18.1 Mixing Genres and Modes
  • 18.2 Multimodal Trailblazer: Torika Bolatagici
  • 18.3 Glance at Genre: Genre, Audience, Purpose, Organization
  • 18.4 Annotated Sample Reading: “Celebrating a Win-Win” by Alexandra Dapolito Dunn
  • 18.5 Writing Process: Create a Multimodal Advocacy Project
  • 18.6 Evaluation: Transitions
  • 18.7 Spotlight on . . . Technology
  • 18.8 Portfolio: Multimodalism
  • 19.1 Writing, Speaking, and Activism
  • 19.2 Podcast Trailblazer: Alice Wong
  • 19.3 Glance at Genre: Language Performance and Visuals
  • 19.4 Annotated Student Sample: “Are New DOT Regulations Discriminatory?” by Zain A. Kumar
  • 19.5 Writing Process: Writing to Speak
  • 19.6 Evaluation: Bridging Writing and Speaking
  • 19.7 Spotlight on … Delivery/Public Speaking
  • 19.8 Portfolio: Everyday Rhetoric, Rhetoric Every Day
  • 20.1 Thinking Critically about Your Semester
  • 20.2 Reflection Trailblazer: Sandra Cisneros
  • 20.3 Glance at Genre: Purpose and Structure
  • 20.4 Annotated Sample Reading: “Don’t Expect Congrats” by Dale Trumbore
  • 20.5 Writing Process: Looking Back, Looking Forward
  • 20.6 Editing Focus: Pronouns
  • 20.7 Evaluation: Evaluating Self-Reflection
  • 20.8 Spotlight on … Pronouns in Context

Learning Outcomes

By the end of this section, you will be able to:

  • Identify the elements of the rhetorical situation for your report.
  • Find and focus a topic to write about.
  • Gather and analyze information from appropriate sources.
  • Distinguish among different kinds of evidence.
  • Draft a thesis and create an organizational plan.
  • Compose a report that develops ideas and integrates evidence from sources.
  • Give and act on productive feedback to works in progress.

You might think that writing comes easily to experienced writers—that they draft stories and college papers all at once, sitting down at the computer and having sentences flow from their fingers like water from a faucet. In reality, most writers engage in a recursive process, pushing forward, stepping back, and repeating steps multiple times as their ideas develop and change. In broad strokes, the steps most writers go through are these:

  • Planning and Organization . You will have an easier time drafting if you devote time at the beginning to consider the rhetorical situation for your report, understand your assignment, gather ideas and information, draft a thesis statement, and create an organizational plan.
  • Drafting . When you have an idea of what you want to say and the order in which you want to say it, you’re ready to draft. As much as possible, keep going until you have a complete first draft of your report, resisting the urge to go back and rewrite. Save that for after you have completed a first draft.
  • Review . Now is the time to get feedback from others, whether from your instructor, your classmates, a tutor in the writing center, your roommate, someone in your family, or someone else you trust to read your writing critically and give you honest feedback.
  • Revising . With feedback on your draft, you are ready to revise. You may need to return to an earlier step and make large-scale revisions that involve planning, organizing, and rewriting, or you may need to work mostly on ensuring that your sentences are clear and correct.

Considering the Rhetorical Situation

Like other kinds of writing projects, a report starts with assessing the rhetorical situation —the circumstance in which a writer communicates with an audience of readers about a subject. As the writer of a report, you make choices based on the purpose of your writing, the audience who will read it, the genre of the report, and the expectations of the community and culture in which you are working. A graphic organizer like Table 8.1 can help you begin.

Summary of Assignment

Write an analytical report on a topic that interests you and that you want to know more about. The topic can be contemporary or historical, but it must be one that you can analyze and support with evidence from sources.

The following questions can help you think about a topic suitable for analysis:

  • Why or how did ________ happen?
  • What are the results or effects of ________?
  • Is ________ a problem? If so, why?
  • What are examples of ________ or reasons for ________?
  • How does ________ compare to or contrast with other issues, concerns, or things?

Consult and cite three to five reliable sources. The sources do not have to be scholarly for this assignment, but they must be credible, trustworthy, and unbiased. Possible sources include academic journals, newspapers, magazines, reputable websites, government publications or agency websites, and visual sources such as TED Talks. You may also use the results of an experiment or survey, and you may want to conduct interviews.

Consider whether visuals and media will enhance your report. Can you present data you collect visually? Would a map, photograph, chart, or other graphic provide interesting and relevant support? Would video or audio allow you to present evidence that you would otherwise need to describe in words?

Another Lens. To gain another analytic view on the topic of your report, consider different people affected by it. Say, for example, that you have decided to report on recent high school graduates and the effect of the COVID-19 pandemic on the final months of their senior year. If you are a recent high school graduate, you might naturally gravitate toward writing about yourself and your peers. But you might also consider the adults in the lives of recent high school graduates—for example, teachers, parents, or grandparents—and how they view the same period. Or you might consider the same topic from the perspective of a college admissions department looking at their incoming freshman class.

Quick Launch: Finding and Focusing a Topic

Coming up with a topic for a report can be daunting because you can report on nearly anything. The topic can easily get too broad, trapping you in the realm of generalizations. The trick is to find a topic that interests you and focus on an angle you can analyze in order to say something significant about it. You can use a graphic organizer to generate ideas, or you can use a concept map similar to the one featured in Writing Process: Thinking Critically About a “Text.”

Asking the Journalist’s Questions

One way to generate ideas about a topic is to ask the five W (and one H) questions, also called the journalist’s questions : Who? What? When? Where? Why? How? Try answering the following questions to explore a topic:

Who was or is involved in ________?

What happened/is happening with ________? What were/are the results of ________?

When did ________ happen? Is ________ happening now?

Where did ________ happen, or where is ________ happening?

Why did ________ happen, or why is ________ happening now?

How did ________ happen?

For example, imagine that you have decided to write your analytical report on the effect of the COVID-19 shutdown on high-school students by interviewing students on your college campus. Your questions and answers might look something like those in Table 8.2 :

Asking Focused Questions

Another way to find a topic is to ask focused questions about it. For example, you might ask the following questions about the effect of the 2020 pandemic shutdown on recent high school graduates:

  • How did the shutdown change students’ feelings about their senior year?
  • How did the shutdown affect their decisions about post-graduation plans, such as work or going to college?
  • How did the shutdown affect their academic performance in high school or in college?
  • How did/do they feel about continuing their education?
  • How did the shutdown affect their social relationships?

Any of these questions might be developed into a thesis for an analytical report. Table 8.3 shows more examples of broad topics and focusing questions.

Gathering Information

Because they are based on information and evidence, most analytical reports require you to do at least some research. Depending on your assignment, you may be able to find reliable information online, or you may need to do primary research by conducting an experiment, a survey, or interviews. For example, if you live among students in their late teens and early twenties, consider what they can tell you about their lives that you might be able to analyze. Returning to or graduating from high school, starting college, or returning to college in the midst of a global pandemic has provided them, for better or worse, with educational and social experiences that are shared widely by people their age and very different from the experiences older adults had at the same age.

Some report assignments will require you to do formal research, an activity that involves finding sources and evaluating them for reliability, reading them carefully, taking notes, and citing all words you quote and ideas you borrow. See Research Process: Accessing and Recording Information and Annotated Bibliography: Gathering, Evaluating, and Documenting Sources for detailed instruction on conducting research.

Whether you conduct in-depth research or not, keep track of the ideas that come to you and the information you learn. You can write or dictate notes using an app on your phone or computer, or you can jot notes in a journal if you prefer pen and paper. Then, when you are ready to begin organizing your report, you will have a record of your thoughts and information. Always track the sources of information you gather, whether from printed or digital material or from a person you interviewed, so that you can return to the sources if you need more information. And always credit the sources in your report.

Kinds of Evidence

Depending on your assignment and the topic of your report, certain kinds of evidence may be more effective than others. Other kinds of evidence may even be required. As a general rule, choose evidence that is rooted in verifiable facts and experience. In addition, select the evidence that best supports the topic and your approach to the topic, be sure the evidence meets your instructor’s requirements, and cite any evidence you use that comes from a source. The following list contains different kinds of frequently used evidence and an example of each.

Definition : An explanation of a key word, idea, or concept.

The U.S. Census Bureau refers to a “young adult” as a person between 18 and 34 years old.

Example : An illustration of an idea or concept.

The college experience in the fall of 2020 was starkly different from that of previous years. Students who lived in residence halls were assigned to small pods. On-campus dining services were limited. Classes were small and physically distanced or conducted online. Parties were banned.

Expert opinion : A statement by a professional in the field whose opinion is respected.

According to Louise Aronson, MD, geriatrician and author of Elderhood , people over the age of 65 are the happiest of any age group, reporting “less stress, depression, worry, and anger, and more enjoyment, happiness, and satisfaction” (255).

Fact : Information that can be proven correct or accurate.

According to data collected by the NCAA, the academic success of Division I college athletes between 2015 and 2019 was consistently high (Hosick).

Interview : An in-person, phone, or remote conversation that involves an interviewer posing questions to another person or people.

During our interview, I asked Betty about living without a cell phone during the pandemic. She said that before the pandemic, she hadn’t needed a cell phone in her daily activities, but she soon realized that she, and people like her, were increasingly at a disadvantage.

Quotation : The exact words of an author or a speaker.

In response to whether she thought she needed a cell phone, Betty said, “I got along just fine without a cell phone when I could go everywhere in person. The shift to needing a phone came suddenly, and I don’t have extra money in my budget to get one.”

Statistics : A numerical fact or item of data.

The Pew Research Center reported that approximately 25 percent of Hispanic Americans and 17 percent of Black Americans relied on smartphones for online access, compared with 12 percent of White people.

Survey : A structured interview in which respondents (the people who answer the survey questions) are all asked the same questions, either in person or through print or electronic means, and their answers tabulated and interpreted. Surveys discover attitudes, beliefs, or habits of the general public or segments of the population.

A survey of 3,000 mobile phone users in October 2020 showed that 54 percent of respondents used their phones for messaging, while 40 percent used their phones for calls (Steele).

  • Visuals : Graphs, figures, tables, photographs and other images, diagrams, charts, maps, videos, and audio recordings, among others.

Thesis and Organization

Drafting a thesis.

When you have a grasp of your topic, move on to the next phase: drafting a thesis. The thesis is the central idea that you will explore and support in your report; all paragraphs in your report should relate to it. In an essay-style analytical report, you will likely express this main idea in a thesis statement of one or two sentences toward the end of the introduction.

For example, if you found that the academic performance of student athletes was higher than that of non-athletes, you might write the following thesis statement:

student sample text Although a common stereotype is that college athletes barely pass their classes, an analysis of athletes’ academic performance indicates that athletes drop fewer classes, earn higher grades, and are more likely to be on track to graduate in four years when compared with their non-athlete peers. end student sample text

The thesis statement often previews the organization of your writing. For example, in his report on the U.S. response to the COVID-19 pandemic in 2020, Trevor Garcia wrote the following thesis statement, which detailed the central idea of his report:

student sample text An examination of the U.S. response shows that a reduction of experts in key positions and programs, inaction that led to equipment shortages, and inconsistent policies were three major causes of the spread of the virus and the resulting deaths. end student sample text

After you draft a thesis statement, ask these questions, and examine your thesis as you answer them. Revise your draft as needed.

  • Is it interesting? A thesis for a report should answer a question that is worth asking and piques curiosity.
  • Is it precise and specific? If you are interested in reducing pollution in a nearby lake, explain how to stop the zebra mussel infestation or reduce the frequent algae blooms.
  • Is it manageable? Try to split the difference between having too much information and not having enough.

Organizing Your Ideas

As a next step, organize the points you want to make in your report and the evidence to support them. Use an outline, a diagram, or another organizational tool, such as Table 8.4 .

Drafting an Analytical Report

With a tentative thesis, an organization plan, and evidence, you are ready to begin drafting. For this assignment, you will report information, analyze it, and draw conclusions about the cause of something, the effect of something, or the similarities and differences between two different things.

Some students write the introduction first; others save it for last. Whenever you choose to write the introduction, use it to draw readers into your report. Make the topic of your report clear, and be concise and sincere. End the introduction with your thesis statement. Depending on your topic and the type of report, you can write an effective introduction in several ways. Opening a report with an overview is a tried-and-true strategy, as shown in the following example on the U.S. response to COVID-19 by Trevor Garcia. Notice how he opens the introduction with statistics and a comparison and follows it with a question that leads to the thesis statement (underlined).

student sample text With more than 83 million cases and 1.8 million deaths at the end of 2020, COVID-19 has turned the world upside down. By the end of 2020, the United States led the world in the number of cases, at more than 20 million infections and nearly 350,000 deaths. In comparison, the second-highest number of cases was in India, which at the end of 2020 had less than half the number of COVID-19 cases despite having a population four times greater than the U.S. (“COVID-19 Coronavirus Pandemic,” 2021). How did the United States come to have the world’s worst record in this pandemic? underline An examination of the U.S. response shows that a reduction of experts in key positions and programs, inaction that led to equipment shortages, and inconsistent policies were three major causes of the spread of the virus and the resulting deaths end underline . end student sample text

For a less formal report, you might want to open with a question, quotation, or brief story. The following example opens with an anecdote that leads to the thesis statement (underlined).

student sample text Betty stood outside the salon, wondering how to get in. It was June of 2020, and the door was locked. A sign posted on the door provided a phone number for her to call to be let in, but at 81, Betty had lived her life without a cell phone. Betty’s day-to-day life had been hard during the pandemic, but she had planned for this haircut and was looking forward to it; she had a mask on and hand sanitizer in her car. Now she couldn’t get in the door, and she was discouraged. In that moment, Betty realized how much Americans’ dependence on cell phones had grown in the months since the pandemic began. underline Betty and thousands of other senior citizens who could not afford cell phones or did not have the technological skills and support they needed were being left behind in a society that was increasingly reliant on technology end underline . end student sample text

Body Paragraphs: Point, Evidence, Analysis

Use the body paragraphs of your report to present evidence that supports your thesis. A reliable pattern to keep in mind for developing the body paragraphs of a report is point , evidence , and analysis :

  • The point is the central idea of the paragraph, usually given in a topic sentence stated in your own words at or toward the beginning of the paragraph. Each topic sentence should relate to the thesis.
  • The evidence you provide develops the paragraph and supports the point made in the topic sentence. Include details, examples, quotations, paraphrases, and summaries from sources if you conducted formal research. Synthesize the evidence you include by showing in your sentences the connections between sources.
  • The analysis comes at the end of the paragraph. In your own words, draw a conclusion about the evidence you have provided and how it relates to the topic sentence.

The paragraph below illustrates the point, evidence, and analysis pattern. Drawn from a report about concussions among football players, the paragraph opens with a topic sentence about the NCAA and NFL and their responses to studies about concussions. The paragraph is developed with evidence from three sources. It concludes with a statement about helmets and players’ safety.

student sample text The NCAA and NFL have taken steps forward and backward to respond to studies about the danger of concussions among players. Responding to the deaths of athletes, documented brain damage, lawsuits, and public outcry (Buckley et al., 2017), the NCAA instituted protocols to reduce potentially dangerous hits during football games and to diagnose traumatic head injuries more quickly and effectively. Still, it has allowed players to wear more than one style of helmet during a season, raising the risk of injury because of imperfect fit. At the professional level, the NFL developed a helmet-rating system in 2011 in an effort to reduce concussions, but it continued to allow players to wear helmets with a wide range of safety ratings. The NFL’s decision created an opportunity for researchers to look at the relationship between helmet safety ratings and concussions. Cocello et al. (2016) reported that players who wore helmets with a lower safety rating had more concussions than players who wore helmets with a higher safety rating, and they concluded that safer helmets are a key factor in reducing concussions. end student sample text

Developing Paragraph Content

In the body paragraphs of your report, you will likely use examples, draw comparisons, show contrasts, or analyze causes and effects to develop your topic.

Paragraphs developed with Example are common in reports. The paragraph below, adapted from a report by student John Zwick on the mental health of soldiers deployed during wartime, draws examples from three sources.

student sample text Throughout the Vietnam War, military leaders claimed that the mental health of soldiers was stable and that men who suffered from combat fatigue, now known as PTSD, were getting the help they needed. For example, the New York Times (1966) quoted military leaders who claimed that mental fatigue among enlisted men had “virtually ceased to be a problem,” occurring at a rate far below that of World War II. Ayres (1969) reported that Brigadier General Spurgeon Neel, chief American medical officer in Vietnam, explained that soldiers experiencing combat fatigue were admitted to the psychiatric ward, sedated for up to 36 hours, and given a counseling session with a doctor who reassured them that the rest was well deserved and that they were ready to return to their units. Although experts outside the military saw profound damage to soldiers’ psyches when they returned home (Halloran, 1970), the military stayed the course, treating acute cases expediently and showing little concern for the cumulative effect of combat stress on individual soldiers. end student sample text

When you analyze causes and effects , you explain the reasons that certain things happened and/or their results. The report by Trevor Garcia on the U.S. response to the COVID-19 pandemic in 2020 is an example: his report examines the reasons the United States failed to control the coronavirus. The paragraph below, adapted from another student’s report written for an environmental policy course, explains the effect of white settlers’ views of forest management on New England.

student sample text The early colonists’ European ideas about forest management dramatically changed the New England landscape. White settlers saw the New World as virgin, unused land, even though indigenous people had been drawing on its resources for generations by using fire subtly to improve hunting, employing construction techniques that left ancient trees intact, and farming small, efficient fields that left the surrounding landscape largely unaltered. White settlers’ desire to develop wood-built and wood-burning homesteads surrounded by large farm fields led to forestry practices and techniques that resulted in the removal of old-growth trees. These practices defined the way the forests look today. end student sample text

Compare and contrast paragraphs are useful when you wish to examine similarities and differences. You can use both comparison and contrast in a single paragraph, or you can use one or the other. The paragraph below, adapted from a student report on the rise of populist politicians, compares the rhetorical styles of populist politicians Huey Long and Donald Trump.

student sample text A key similarity among populist politicians is their rejection of carefully crafted sound bites and erudite vocabulary typically associated with candidates for high office. Huey Long and Donald Trump are two examples. When he ran for president, Long captured attention through his wild gesticulations on almost every word, dramatically varying volume, and heavily accented, folksy expressions, such as “The only way to be able to feed the balance of the people is to make that man come back and bring back some of that grub that he ain’t got no business with!” In addition, Long’s down-home persona made him a credible voice to represent the common people against the country’s rich, and his buffoonish style allowed him to express his radical ideas without sounding anti-communist alarm bells. Similarly, Donald Trump chose to speak informally in his campaign appearances, but the persona he projected was that of a fast-talking, domineering salesman. His frequent use of personal anecdotes, rhetorical questions, brief asides, jokes, personal attacks, and false claims made his speeches disjointed, but they gave the feeling of a running conversation between him and his audience. For example, in a 2015 speech, Trump said, “They just built a hotel in Syria. Can you believe this? They built a hotel. When I have to build a hotel, I pay interest. They don’t have to pay interest, because they took the oil that, when we left Iraq, I said we should’ve taken” (“Our Country Needs” 2020). While very different in substance, Long and Trump adopted similar styles that positioned them as the antithesis of typical politicians and their worldviews. end student sample text

The conclusion should draw the threads of your report together and make its significance clear to readers. You may wish to review the introduction, restate the thesis, recommend a course of action, point to the future, or use some combination of these. Whichever way you approach it, the conclusion should not head in a new direction. The following example is the conclusion from a student’s report on the effect of a book about environmental movements in the United States.

student sample text Since its publication in 1949, environmental activists of various movements have found wisdom and inspiration in Aldo Leopold’s A Sand County Almanac . These audiences included Leopold’s conservationist contemporaries, environmentalists of the 1960s and 1970s, and the environmental justice activists who rose in the 1980s and continue to make their voices heard today. These audiences have read the work differently: conservationists looked to the author as a leader, environmentalists applied his wisdom to their movement, and environmental justice advocates have pointed out the flaws in Leopold’s thinking. Even so, like those before them, environmental justice activists recognize the book’s value as a testament to taking the long view and eliminating biases that may cloud an objective assessment of humanity’s interdependent relationship with the environment. end student sample text

Citing Sources

You must cite the sources of information and data included in your report. Citations must appear in both the text and a bibliography at the end of the report.

The sample paragraphs in the previous section include examples of in-text citation using APA documentation style. Trevor Garcia’s report on the U.S. response to COVID-19 in 2020 also uses APA documentation style for citations in the text of the report and the list of references at the end. Your instructor may require another documentation style, such as MLA or Chicago.

Peer Review: Getting Feedback from Readers

You will likely engage in peer review with other students in your class by sharing drafts and providing feedback to help spot strengths and weaknesses in your reports. For peer review within a class, your instructor may provide assignment-specific questions or a form for you to complete as you work together.

If you have a writing center on your campus, it is well worth your time to make an online or in-person appointment with a tutor. You’ll receive valuable feedback and improve your ability to review not only your report but your overall writing.

Another way to receive feedback on your report is to ask a friend or family member to read your draft. Provide a list of questions or a form such as the one in Table 8.5 for them to complete as they read.

Revising: Using Reviewers’ Responses to Revise your Work

When you receive comments from readers, including your instructor, read each comment carefully to understand what is being asked. Try not to get defensive, even though this response is completely natural. Remember that readers are like coaches who want you to succeed. They are looking at your writing from outside your own head, and they can identify strengths and weaknesses that you may not have noticed. Keep track of the strengths and weaknesses your readers point out. Pay special attention to those that more than one reader identifies, and use this information to improve your report and later assignments.

As you analyze each response, be open to suggestions for improvement, and be willing to make significant revisions to improve your writing. Perhaps you need to revise your thesis statement to better reflect the content of your draft. Maybe you need to return to your sources to better understand a point you’re trying to make in order to develop a paragraph more fully. Perhaps you need to rethink the organization, move paragraphs around, and add transition sentences.

Below is an early draft of part of Trevor Garcia’s report with comments from a peer reviewer:

student sample text To truly understand what happened, it’s important first to look back to the years leading up to the pandemic. Epidemiologists and public health officials had long known that a global pandemic was possible. In 2016, the U.S. National Security Council (NSC) published a 69-page document with the intimidating title Playbook for Early Response to High-Consequence Emerging Infectious Disease Threats and Biological Incidents . The document’s two sections address responses to “emerging disease threats that start or are circulating in another country but not yet confirmed within U.S. territorial borders” and to “emerging disease threats within our nation’s borders.” On 13 January 2017, the joint Obama-Trump transition teams performed a pandemic preparedness exercise; however, the playbook was never adopted by the incoming administration. end student sample text

annotated text Peer Review Comment: Do the words in quotation marks need to be a direct quotation? It seems like a paraphrase would work here. end annotated text

annotated text Peer Review Comment: I’m getting lost in the details about the playbook. What’s the Obama-Trump transition team? end annotated text

student sample text In February 2018, the administration began to cut funding for the Prevention and Public Health Fund at the Centers for Disease Control and Prevention; cuts to other health agencies continued throughout 2018, with funds diverted to unrelated projects such as housing for detained immigrant children. end student sample text

annotated text Peer Review Comment: This paragraph has only one sentence, and it’s more like an example. It needs a topic sentence and more development. end annotated text

student sample text Three months later, Luciana Borio, director of medical and biodefense preparedness at the NSC, spoke at a symposium marking the centennial of the 1918 influenza pandemic. “The threat of pandemic flu is the number one health security concern,” she said. “Are we ready to respond? I fear the answer is no.” end student sample text

annotated text Peer Review Comment: This paragraph is very short and a lot like the previous paragraph in that it’s a single example. It needs a topic sentence. Maybe you can combine them? end annotated text

annotated text Peer Review Comment: Be sure to cite the quotation. end annotated text

Reading these comments and those of others, Trevor decided to combine the three short paragraphs into one paragraph focusing on the fact that the United States knew a pandemic was possible but was unprepared for it. He developed the paragraph, using the short paragraphs as evidence and connecting the sentences and evidence with transitional words and phrases. Finally, he added in-text citations in APA documentation style to credit his sources. The revised paragraph is below:

student sample text Epidemiologists and public health officials in the United States had long known that a global pandemic was possible. In 2016, the National Security Council (NSC) published Playbook for Early Response to High-Consequence Emerging Infectious Disease Threats and Biological Incidents , a 69-page document on responding to diseases spreading within and outside of the United States. On January 13, 2017, the joint transition teams of outgoing president Barack Obama and then president-elect Donald Trump performed a pandemic preparedness exercise based on the playbook; however, it was never adopted by the incoming administration (Goodman & Schulkin, 2020). A year later, in February 2018, the Trump administration began to cut funding for the Prevention and Public Health Fund at the Centers for Disease Control and Prevention, leaving key positions unfilled. Other individuals who were fired or resigned in 2018 were the homeland security adviser, whose portfolio included global pandemics; the director for medical and biodefense preparedness; and the top official in charge of a pandemic response. None of them were replaced, leaving the White House with no senior person who had experience in public health (Goodman & Schulkin, 2020). Experts voiced concerns, among them Luciana Borio, director of medical and biodefense preparedness at the NSC, who spoke at a symposium marking the centennial of the 1918 influenza pandemic in May 2018: “The threat of pandemic flu is the number one health security concern,” she said. “Are we ready to respond? I fear the answer is no” (Sun, 2018, final para.). end student sample text

A final word on working with reviewers’ comments: as you consider your readers’ suggestions, remember, too, that you remain the author. You are free to disregard suggestions that you think will not improve your writing. If you choose to disregard comments from your instructor, consider submitting a note explaining your reasons with the final draft of your report.

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What are Analytical Study Designs?

  • Research Process
  • Peer Review

Analytical study designs can be experimental or observational and each type has its own features. In this article, you'll learn the main types of designs and how to figure out which one you'll need for your study.

Updated on September 19, 2022

word cloud highlighting research, results, and analysis

A study design is critical to your research study because it determines exactly how you will collect and analyze your data. If your study aims to study the relationship between two variables, then an analytical study design is the right choice.

But how do you know which type of analytical study design is best for your specific research question? It's necessary to have a clear plan before you begin data collection. Lots of researchers, sadly, speed through this or don't do it at all.

When are analytical study designs used?

A study design is a systematic plan, developed so you can carry out your research study effectively and efficiently. Having a design is important because it will determine the right methodologies for your study. Using the right study design makes your results more credible, valid, and coherent.

Descriptive vs. analytical studies

Study designs can be broadly divided into either descriptive or analytical.

Descriptive studies describe characteristics such as patterns or trends. They answer the questions of what, who, where, and when, and they generate hypotheses. They include case reports and qualitative studies.

Analytical study designs quantify a relationship between different variables. They answer the questions of why and how. They're used to test hypotheses and make predictions.

Experimental and observational

Analytical study designs can be either experimental or observational. In experimental studies, researchers manipulate something in a population of interest and examine its effects. These designs are used to establish a causal link between two variables.

In observational studies, in contrast, researchers observe the effects of a treatment or intervention without manipulating anything. Observational studies are most often used to study larger patterns over longer periods.

analytical study design infographic

Experimental study designs

Experimental study designs are when a researcher introduces a change in one group and not in another. Typically, these are used when researchers are interested in the effects of this change on some outcome. It's important to try to ensure that both groups are equivalent at baseline to make sure that any differences that arise are from any introduced change.

In one study, Reiner and colleagues studied the effects of a mindfulness intervention on pain perception . The researchers randomly assigned participants into an experimental group that received a mindfulness training program for two weeks. The rest of the participants were placed in a control group that did not receive the intervention.

Experimental studies help us establish causality. This is critical in science because we want to know whether one variable leads to a change, or causes another. Establishing causality leads to higher internal validity and makes results reproducible.

Experimental designs include randomized control trials (RCTs), nonrandomized control trials (non-RCTs), and crossover designs. Read on to learn the differences.

Randomized control trials

In an RCT, one group of individuals receives an intervention or a treatment, while another does not. It's then possible to investigate what happens to the participants in each group.

Another important feature of RCTs is that participants are randomly assigned to study groups. This helps to limit certain biases and retain better control. Randomization also lets researchers pinpoint any differences in outcomes to the intervention received during the trial. RTCs are considered the gold standard in biomedical research and are considered to provide the best kind of evidence.

For example, one RCT looked at whether an exercise intervention impacts depression . Researchers randomly placed patients with depressive symptoms into intervention groups containing different types of exercise (i.e., light, moderate, or strong). Another group received usual medications or no exercise interventions.

Results showed that after the 12-week trial, patients in all exercise groups had decreased depression levels compared to the control group. This means that by using an RCT design, researchers can now safely assume that the exercise variable has a positive impact on depression.

However, RCTs are not without drawbacks. In the example above, we don't know if exercise still has a positive impact on depression in the long term. This is because it's not feasible to keep people under these controlled settings for a long time.

Advantages of RCTs

  • It is possible to infer causality
  • Everything is properly controlled, so very little is left to chance or bias
  • Can be certain that any difference is coming from the intervention

Disadvantages of RCTs

  • Expensive and can be time-consuming
  • Can take years for results to be available
  • Cannot be done for certain types of questions due to ethical reasons, such as asking participants to undergo harmful treatment
  • Limited in how many participants researchers can adequately manage in one study or trial
  • Not feasible for people to live under controlled conditions for a long time

Nonrandomized controlled trials

Nonrandomized controlled trials are a type of nonrandomized controlled studies (NRS) where the allocation of participants to intervention groups is not done randomly . Here, researchers purposely assign some participants to one group and others to another group based on certain features. Alternatively, participants can sometimes also decide which group they want to be in.

For example, in one study, clinicians were interested in the impact of stroke recovery after being in an enriched versus non-enriched hospital environment . Patients were selected for the trial if they fulfilled certain requirements common to stroke recovery. Then, the intervention group was given access to an enriched environment (i.e. internet access, reading, going outside), and another group was not. Results showed that the enriched group performed better on cognitive tasks.

NRS are useful in medical research because they help study phenomena that would be difficult to measure with an RCT. However, one of their major drawbacks is that we cannot be sure if the intervention leads to the outcome. In the above example, we can't say for certain whether those patients improved after stroke because they were in the enriched environment or whether there were other variables at play.

Advantages of NRS's

  • Good option when randomized control trials are not feasible
  • More flexible than RCTs

Disadvantages of NRS's

  • Can't be sure if the groups have underlying differences
  • Introduces risk of bias and confounds

Crossover study

In a crossover design, each participant receives a sequence of different treatments. Crossover designs can be applied to RCTs, in which each participant is randomly assigned to different study groups.

For example, one study looked at the effects of replacing butter with margarine on lipoproteins levels in individuals with cholesterol . Patients were randomly assigned to a 6-week butter diet, followed by a 6-week margarine diet. In between both diets, participants ate a normal diet for 5 weeks.

These designs are helpful because they reduce bias. In the example above, each participant completed both interventions, making them serve as their own control. However, we don't know if eating butter or margarine first leads to certain results in some subjects.

Advantages of crossover studies

  • Each participant serves as their own control, reducing confounding variables
  • Require fewer participants, so they have better statistical power

Disadvantages of crossover studies

  • Susceptible to order effects, meaning the order in which a treatment was given may have an effect
  • Carry-over effects between treatments

Observational studies

In observational studies, researchers watch (observe) the effects of a treatment or intervention without trying to change anything in the population. Observational studies help us establish broad trends and patterns in large-scale datasets or populations. They are also a great alternative when an experimental study is not an option.

Unlike experimental research, observational studies do not help us establish causality. This is because researchers do not actively control any variables. Rather, they investigate statistical relationships between them. Often this is done using a correlational approach.

For example, researchers would like to examine the effects of daily fiber intake on bone density . They conduct a large-scale survey of thousands of individuals to examine correlations of fiber intake with different health measures.

The main observational studies are case-control, cohort, and cross-sectional. Let's take a closer look at each one below.

Case-control study

A case-control is a type of observational design in which researchers identify individuals with an existing health situation (cases) and a similar group without the health issue (controls). The cases and the controls are then compared based on some measurements.

Frequently, data collection in a case-control study is retroactive (i.e., backwards in time). This is because participants have already been exposed to the event in question. Additionally, researchers must go through records and patient files to obtain the records for this study design.

For example, a group of researchers examined whether using sleeping pills puts people at risk of Alzheimer's disease . They selected 1976 individuals that received a dementia diagnosis (“cases”) with 7184 other individuals (“controls”). Cases and controls were matched on specific measures such as sex and age. Patient data was consulted to find out how much sleeping pills were consumed over the course of a certain time.

Case-control is ideal for situations where cases are easy to pick out and compare. For instance, in studying rare diseases or outbreaks.

Advantages of case-control studies

  • Feasible for rare diseases
  • Cheaper and easier to do than an RCT

Disadvantages of case-control studies

  • Relies on patient records, which could be lost or damaged
  • Potential recall and selection bias

Cohort study (longitudinal)

A cohort is a group of people who are linked in some way. For instance, a birth year cohort is all people born in a specific year. In cohort studies, researchers compare what happens to individuals in the cohort that have been exposed to some variable compared with those that haven't on different variables. They're also called longitudinal studies.

The cohort is then repeatedly assessed on variables of interest over a period of time. There is no set amount of time required for cohort studies. They can range from a few weeks to many years.

Cohort studies can be prospective. In this case, individuals are followed for some time into the future. They can also be retrospective, where data is collected on a cohort from records.

One of the longest cohort studies today is The Harvard Study of Adult Development . This cohort study has been tracking various health outcomes of 268 Harvard graduates and 456 poor individuals in Boston from 1939 to 2014. Physical screenings, blood samples, brain scans and surveys were collected on this cohort for over 70 years. This study has produced a wealth of knowledge on outcomes throughout life.

A cohort study design is a good option when you have a specific group of people you want to study over time. However, a major drawback is that they take a long time and lack control.

Advantages of cohort studies

  • Ethically safe
  • Allows you to study multiple outcome variables
  • Establish trends and patterns

Disadvantages of cohort studies

  • Time consuming and expensive
  • Can take many years for results to be revealed
  • Too many variables to manage
  • Depending on length of study, can have many changes in research personnel

Cross-sectional study

Cross-sectional studies are also known as prevalence studies. They look at the relationship of specific variables in a population in one given time. In cross-sectional studies, the researcher does not try to manipulate any of the variables, just study them using statistical analyses. Cross-sectional studies are also called snapshots of a certain variable or time.

For example, researchers wanted to determine the prevalence of inappropriate antibiotic use to study the growing concern about antibiotic resistance. Participants completed a self-administered questionnaire assessing their knowledge and attitude toward antibiotic use. Then, researchers performed statistical analyses on their responses to determine the relationship between the variables.

Cross-sectional study designs are ideal when gathering initial data on a research question. This data can then be analyzed again later. By knowing the public's general attitudes towards antibiotics, this information can then be relayed to physicians or public health authorities. However, it's often difficult to determine how long these results stay true for.

Advantages of cross-sectional studies

  • Fast and inexpensive
  • Provides a great deal of information for a given time point
  • Leaves room for secondary analysis

Disadvantages of cross-sectional studies

  • Requires a large sample to be accurate
  • Not clear how long results remain true for
  • Do not provide information on causality
  • Cannot be used to establish long-term trends because data is only for a given time

So, how about your next study?

Whether it's an RCT, a case-control, or even a qualitative study, AJE has services to help you at every step of the publication process. Get expert guidance and publish your work for the world to see.

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  • Open access
  • Published: 13 March 2022

The role of analytic direction in qualitative research

  • Joanna E. M. Sale 1 , 2 , 3  

BMC Medical Research Methodology volume  22 , Article number:  66 ( 2022 ) Cite this article

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The literature on qualitative data analysis mostly concerns analyses pertaining to an individual research question and the organization of data within that research question. Few authors have written about the entire qualitative dataset from which multiple and separate analyses could be conducted and reported. The concept of analytic direction is a strategy that can assist qualitative researchers in deciding which findings to highlight within a dataset. The objectives of this paper were to: 1) describe the importance of analytic direction in qualitative research, and 2) provide a working example of the concept of analytic direction.

A qualitative dataset from one of the author’s research programs was selected for review. Ten potential analytic directions were identified after the initial phenomenological analysis was conducted. Three analytic directions based on the same coding template but different content areas of the data were further developed using phenomenological analysis ( n  = 2) and qualitative description ( n  = 1) and are the focus of this paper. Development and selection of these three analytic directions was determined partially relying on methodological criteria to promote rigour including a comprehensive examination of the data, the use of multiple analysts, direct quotations to support claims, negative case analysis, and reflexivity.

The three analytic directions addressed topics within the scope of the overall research question. Each analytic direction had its own central point or story line and each highlighted a different perspective or voice. The use of an inductive and deductive approach to analysis and how the role of theory was integrated varied in each analytic direction.


The concept of analytic direction enables researchers to organize their qualitative datasets in order to tell different and unique “stories”. The concept relies upon, and promotes, the conduct of rigourous qualitative research.

Peer Review reports

Reports on data analysis in qualitative research are well documented. Procedural steps have been described [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ] and authors have made distinctions between the concepts of coding, analysis, and interpretation [ 1 , 2 , 8 , 9 ]. Authors have written about different researchers accessing different representations of a topic or phenomenon [ 2 , 10 ] or multiple interpretations being applied to the same transcript [ 11 ]. The literature on data analysis mostly concerns analyses pertaining to an individual research question and the organization of data within that research question. Few authors have written about the entire qualitative dataset from which multiple and separate analyses could be conducted and reported.

The data collected by qualitative researchers can be voluminous and often surpass the data pertaining to objectives outlined in grant proposals. These data may be compelling but analyses of some data are often given lower priority if they do not align directly with the stated objectives.

There comes a point during data collection and analysis where qualitative researchers must choose “which story, of the many stories available to them in a data set, to tell” (p. 376) [ 12 ]. According to Arthur Frank, “[a] fter the methods, there has to be a story” (p. 431) [ 13 ]. “Stories” should have a central point or storyline [ 12 ]. The final report can be told from the perspective of different voices [ 12 ] and organized by time such as emphasizing key turning points and milestones in the sequence of events studied [ 12 , 14 ] or by using other forms of representation such as metaphors [ 2 , 12 ]. Theory can be central or more peripheral in the account [ 15 ]. The question remains, what “story”, or “stories”, do we tell?

The concept of analytic direction

The concept of analytic direction is a strategy that can assist qualitative researchers in deciding which “stories” to highlight within a dataset. Sandelowski reports that researchers account for their data and then determine the different “paths” [ 1 ] or “analytic paths” [ 16 ] they can pursue. Others have proposed that decision-making throughout analysis implies analytic ideas at every stage of the coding process [ 8 ] and that researchers define for themselves what analytic issues are to be explored and what ideas are important [ 8 ]. Charmaz [ 17 ] reports that grounded theory researchers pursue more than one analytic direction by focusing on certain ideas first and then returning to the data to address an unfinished analysis in another area later. While the concept of analytic direction has been referenced, or alluded to, by these and other authors [ 1 , 8 , 16 , 18 , 19 ], operationalization of this concept is not well articulated. In this paper, the term analytic direction refers to a message developed by the researchers about the data that may or may not require further substantiation. An analytic direction can be presented as a single message or theme, and can stand alone or be supported by multiple sub-messages or sub-themes. Analytic directions can be developed during the coding process, in later stages of analysis, or possibly during analyses of new datasets. Relying on strategies to promote rigour can assist with the development, substantiation, and selection of analytic directions. If substantiated, each analytic direction could be the focus of an individual publication. The objectives of this paper were to: 1) describe the importance of analytic direction in qualitative research; and 2) provide a working example of the concept of analytic direction.

Why analytic direction is important

The concept of analytic direction is important because it has implications for methodological rigour. We have an obligation to conduct methodological rigourous studies [ 20 ], especially when studies require primary data collection that involves a burden to participants [ 21 ]. The author proposes that methodological rigour is embedded within, and contributes to, the concept of analytic direction. Several strategies to promote rigour that are universal to many qualitative approaches, including phenomenology, are discussed. These strategies include, but are not limited to, a comprehensive examination of the data, the use of multiple analysts, direct quotations to support claims, negative case analysis, and reflexivity. It is important to support the quality of analytic directions so that researchers can then determine which analytic directions may or may not require further substantiation. The quality of the analytic direction will also assist in determining which directions may be selected for reporting.

The relationship between analytic direction and methodological rigour

This paper focuses on the stage where data collection is considered to be complete and does not directly address how data collection, and methodological rigour related to data collection, contributes to the concept of analytic direction. The assumption is that data collection and analysis were conducted iteratively [ 22 , 23 ] and that the team decided when data collection was complete, perhaps relying upon one of the various conceptualizations of saturation discussed by Saunders and colleagues [ 24 ]. A decision about saturation would not necessarily apply to any, or all, analytic directions being developed.

The author proposes that several strategies for promoting rigour assist with the development and selection of analytic directions. One aspect of methodological rigour is that authors carry out a comprehensive examination of their data [ 5 , 25 ]. By thinking about, and engaging in, analytic direction, researchers are encouraged to attend to all of their data rather than attending only to data that interests them initially.

The use of multiple analysts promotes a comprehensive examination of the data [ 2 , 26 ] and thus, contributes to the concept of analytic direction. Different viewpoints lead to an enrichment of the analysis and can lead to a conceptual clarification of the interpretations [ 2 ]. Multiple viewpoints can be used at the level of coding but also at the level of the larger team as data collection and analysis proceeds. Discussions about the novelty, clinical significance, and relevance [ 27 ] of the analytic directions may occur at this time and continue through to the writing of the respective manuscripts. Analytic directions are relevant if they add knowledge, or increase the confidence with which existing knowledge is regarded [ 28 ]. According to Malterud [ 26 ], engaging multiple researchers in a qualitative study strengthens the design of the study, not for the purpose of consensus or identical readings of the data but to supplement and contest each others’ statements.

The use of direct quotations to support the claims made about the analytic directions (and/or themes within) is another strategy to promote rigour [ 29 ]. Not only do quotations illustrate and clarify the results but they also demonstrate whether there is substantive evidence to support the analytic directions being proposed. In contrast, data that do not support the analytic directions (and/or themes within) should be accounted for and their exclusion justified when promoting methodological rigour [ 30 ]. Authors may refer to this as attending to negative cases [ 28 ] or deviant case analysis [ 25 , 31 ]. This strategy promotes that “deviant cases” or “outliers” are not forced into categories or ignored but used instead to aid understanding or theory development [ 25 ]. For example, these cases may explain why the patterns developed from the data or the more normative behaviours are not always found in the researchers’ interpretations [ 25 , 31 ].

Reflexivity is an essential component of methodological rigour [ 26 ]. Reflexivity has been described as “an attitude of attending systematically to the context of knowledge construction, especially to the effect of the researcher, at every step of the research process” [ 26 ] (p. 484). Being reflexive means being aware of your own position in producing partial knowledge [ 32 ]. The qualitative researcher acknowledges his or her personal influence on what that partial knowledge is (for example, the data collected are dependent on the interviewer’s questions and prompts). According to Eakin and Gladstone [ 33 ], knowing one’s standpoint helps one to recognize the forces that might drive certain interpretations and stifle other conceptualizations of the data. Knowledge production is also partial because it is not possible to report all interpretations of the data and therefore, the research team has to decide what to report. Researchers engaging in the concept of analytic direction are more likely to be reflexive about what they are, and are not, reporting from their datasets.

Rationale for the chosen example

The dataset chosen for this example was from a study where the author and her team identified 10 potential analytic directions based on a compilation of the memos and team discussions pertaining to analysis and interpretation of the data. The publications developed from this dataset reflected the selection of three analytic directions that focused on different content areas [ 34 , 35 , 36 ]. The same coding template was the foundation for the three publications and the timing of the reporting was ordered based on the author’s interests. The author chose the dataset as an example primarily because it was not heavily theory-laden and therefore accessible to novice qualitative researchers. The resulting publications have practical implications for clinical and health services research and the process of developing these publications could inform graduate students who are embarking on a qualitative program of research for their thesis work.

Original research funded

The goal of the original research project was to reduce the burden of illness due to fracture through improved bone health investigation and treatment. Specifically, the aim was to examine what researchers could learn from members of a patient group. The study was approved by the Research Ethics Board at Unity Health Toronto (REB# 10–371). The study team consisted of scientists, clinicians, a policy maker, and a patient representative with expertise related to bone health. Informed by the Theory of Planned Behaviour [ 37 , 38 ], the team set out to examine members of a patient group to ask them about their intentions and actions toward bone health diagnosis and treatment and their experiences with diagnostic tests and treatment recommendations. All individuals ( n  = 28) were 50+ years old and had sustained a fragility fracture. The overall project relied on a phenomenological approach conceptualized by Giorgi and Wertz [ 30 , 39 , 40 , 41 ].

We developed a master coding template of 27 broad codes that were designed to organize the data with minimal reliance on theory. The coding template was revised four times as data collection and analysis proceeded. The codes were developed from a combination of inductive and deductive codes. More specifically, inductive codes were developed from topics discussed in the interviews. Other codes were pre-specified from the overall aim of the original funded study and from the domains of the Theory of Planned Behaviour.

Development of analytic directions from the dataset

Qualitative researchers can use several strategies to develop analytic directions. The author started the organization process early in order to think about how best to maximize the data collected. Coding began after the first couple of interviews had been conducted; this is conventional advice for analysis in qualitative research [ 1 , 2 , 23 , 42 ]. As soon as the coding process began, a document specific to analysis was created. Miles and colleagues have referred to this as “analytic memoing” [ 6 ]. This document is different from other documents in which the team discusses design features, decisions, and interview logistics related to the study. Analytic ideas were added to this document after coding and discussing each transcript. The author engaged two individuals in the coding/analysis process, as multiple analysts promote a comprehensive examination of the data [ 2 , 26 ]. The author met regularly with members of the team during the process of data collection and analysis to discuss the data, interpretations of them, and different lines of inquiry. These discussions were recorded in the analysis document. Table  1 outlines the potential analytic directions considered for this paper. The 10 analytic directions were developed prior to publication of analytic direction #1. Some of these directions were posed as questions that required further analysis and substantiation. Tables were then created to help us to visualize patterns during analysis. As an example, for analytic direction #2, a table was created in which each participant was assigned a row and perceived messages from the various health care providers (for example, primary care providers and specialists) were placed in columns. Perceived messages were presented as quotations from participants. We examined the columns to compare perceived messages across provider groups for each participant and then examined the columns to compare the perceived messages within each provider group. For analytic direction #3, a table was created with each participant assigned a row and the domains of the Theory of Planned Behaviour assigned to columns. The table was populated with data in the form of quotations from each participant that we believed corresponded to each of the domains. Strategies such as matrices [ 5 , 6 ] or thematic maps [ 42 ] can also be used to visualize developing patterns when presenting or organizing data.

Selection of the three analytic directions

The number of analytic directions selected likely depends on circumstances including the quality of the data, the quality of the analysis, and available resources. The research team considered the multiple analytic directions, discussing their relevance [ 27 ], novelty, and clinical significance and also the interests of the team in order to incorporate the perspectives of the different stakeholders. It was important to the author that the content of each analytic direction was bounded in that it did not overlap with the content of the other analytic directions. For example, analytic directions #2 and #3 discuss the potential influence of others in participants’ lives. However, analytic direction #2 focused on health care providers while analytic direction #3 focused on family members, friends, and colleagues of participants and specifically excluded health care providers from the analysis based on the Theory of Planned Behaviour domain “subjective norm”. In narrowing down the list of analytic directions, the author ensured there were sufficient data (quotations) to support the claims. Cases that did not fit the general results were acknowledged in order to justify their exclusion or explain why they did not fit. For example, in analytic direction #3, we examined instances where the data did not appear to fit with the Theory of Planned Behaviour and explained what happened in these instances where the model did not appear to be predictive of intentions.

The master coding template was important as it assisted with the organization of evidence for each analytic direction. The master coding template also assisted the team with the creation of tables for each analytic direction discussed. Table  2 demonstrates the relationship between the master coding template and the three selected analytic directions.

The impetus for analytic direction #1 [ 34 ] was based on an assumption held by the author as she was working on the research proposal. Her expectation was that members of a patient group would be patient advocates who were experts in navigating for care. She was interested in what patients could learn from members of this patient group. The analytic direction for the paper came from surprise, and subsequent disappointment, that those assumptions were not supported by the data and that members of the patient group did not all appear to be advocates and experts in navigating for care. One commonality that defined the patient group was that members appeared to be in favour of taking prescribed medication.

Analytic direction #1 included elements of both inductive and deductive analysis in that codes were developed for the master coding template from the data (inductive) but the author’s expectations also influenced how those codes were combined and how the team interpreted the data (deductive). Drawing from the literature, the term “advocacy” was equated with the theoretical concept of “effective” or “activated” consumer [ 43 , 44 ]. The code “effective consumer” did not exist in the original master template, partly because we preferred to not apply theoretical labels prematurely to the data. Based on the coding template, we drew from six codes to create a table about “effective consumer” behaviours (see Table 2 ). Participants were then coded along a continuum between what was referred to as “few effective consumer behaviours” (patients who followed orders with minimal involvement in their care and demonstrating the least amount of advocacy) to “many effective consumer behaviours” (individuals demonstrating significant involvement in their care, those who demanded diagnostic testing and requested specific medications).

Analytic direction #2 [ 35 ] was developed concurrently with analytic direction #1. The role of theory was minimal in analytic direction #2 and perhaps implicit in the methodology of phenomenology which focuses on individuals’ experiences [ 23 , 39 ]. The impetus for analytic direction #2 was our proposal that messages from health care providers might determine individuals’ strategies or behaviours that were the focus of analytic direction #1. The analysis was more inductive than that of analytic direction #1 as the team had no pre-contemplated plan to examine how messages from health care providers might determine individuals’ behaviours. In conducting the analysis, the team wondered whether conflicts about what individuals did with the recommendations they received (their actions) appeared to be due to messages perceived across, and within, health care provider groups. Health care providers discussed in the interviews included clinic staff, primary care providers, specialists, nurses, physiotherapists, and chiropractors.

For analytic direction #2, we used seven of the codes in the master coding template (see Table 2 ). Five of these seven codes were also used in analytic direction #1 but for very different reasons and drawing from different data within these codes. We were interested in individuals’ understanding or interpretation of recommendations by health care providers, not how individuals interacted with health care providers or what they did with information received from health care providers. In other words, we were interested in the meaning of what health care providers reportedly said to participants and not what participants did with that information.

The publication for analytic direction #3 [ 36 ] was written 3 years after that for analytic direction #1. This was the author’s least preferred paper, despite the Theory of Planned Behaviour being the theoretical framework guiding the original funded research. Analytic direction #3 involved a primarily deductive analysis where the Theory of Planned Behaviour guided the coding and analysis. Because of the restrictions of forcing exploratory data from open-ended questions into pre-defined domains, the author selected a qualitative description approach for the research design.

Contrary to memos and reflexive notes documented by the author about the potential value of this analysis and whether the team had learned anything about the application of the Theory of Planned Behaviour in the context of our study, the pursuit of analytic direction #3 became an interesting methodological exercise for a number of reasons. We collected data on several behaviours including receiving diagnostic tests, taking supplements, exercising, attending falls prevention classes, and initiating medication. The author believed that one particular behaviour had to be selected for analysis which entailed examining the data for each of the behaviours in depth. The author chose to focus on medication initiation and/or medication use because of a longstanding interest in medication use. Also, there was sufficient data to substantiate the Theory of Planned Behaviour domains in relation to medication initiation and/or medication use. The Theory of Planned Behaviour did not appear to be particularly relevant to intentions to attend a bone mineral density test and there did not appear to be sufficient data to support any one of the non-pharmacological treatment strategies mentioned. The team also had to make decisions about what counted as “perceived behavioural control”, “subjective norms”, and “attitudes” which were the three domains of the Theory of Planned Behaviour [ 37 , 38 ]. In particular, participants’ discussions about medication side effects were problematic to conceptualize in reference to these domains. The team decided to code “ experiences with side effects” as “perceived behavioural control” but “ anticipated side effects” as an “attitude”.

For analytic direction #3, the team drew from five codes, three of which were pre-specified prior to analyzing the interviews and meant to capture the domains of the Theory of Planned Behaviour. The code “attitude to BMD testing” and “attitude to bone health treatment” were existing codes based on the Theory of Planned Behaviour. The code “subjective norm” was not part of the coding template because the team believed it was too specific. We instead examined the code “social influence” which captured a broader array of information about peers such as family members and friends. Similarly, “perceived behavioural control” was not part of the coding template because we found it too specific. Information for this domain was taken from another code labelled “bone health treatment” which captured data pertaining to participants’ medications, including past behaviour with medication and how difficult it was, or not, to take the medication. The code “intentions” was an existing code.

The three selected analytic directions varied in how the team used an inductive and deductive approach to analysis [ 15 , 45 ] and how the role of theory was integrated (“central” vs. more “peripheral”) [ 15 ]. Each publication was within the scope of the overall research goal or question. As proposed by Agee [ 46 ], this overall question offered the potential for more specific questions during analysis. Finally, each publication had its own central point [ 12 ] and highlighted a different perspective or voice [ 12 ].

The following is a summary of the three analytic directions labelled with the first few words of the titles of each publication (see Table  3 ).

Analytic direction #1 (Strategies used by a patient group; inductive and deductive-driven)

In this publication, we examined the strategies described by three groups of individuals: individuals demonstrating few effective consumer behaviours, individuals demonstrating many effective consumer behaviours, and individuals demonstrating both types of behaviours. We discussed how the continuum was contrary to our expectations of what behaviours members of a patient group would exhibit. Having acknowledged this finding, we reported that more than half of the participants described effective consumer behaviours including making requests of health care providers for referral to specialists, bone mineral density tests, and prescription medications. Our overall message was that members of a patient group described a range of effective consumer behaviours that could be incorporated as skill sets in post-fracture interventions.

Analytic direction #2 (Perceived messages about bone health; inductive-driven)

In this publication, we described the perceived messages across the different provider groups and then the perceived messages within each provider group. We reported that participants perceived that specialists were more interested in their bone health than general practitioners and that very few messages about bone health were perceived from other health care providers. We also reported that perceived messages about one’s bone health and recommendations for management across provider groups were inconsistent (for example, with regard to medication initiation). The message for analytic direction #2 was that patients perceived inconsistent messages within, and across, various healthcare providers, suggesting a need to raise awareness of bone health management guidelines to providers.

Analytic direction #3 (Theory of Planned Behaviour explains intentions to use medication; deductive-driven)

In this publication, we described the data in each domain of the Theory of Planned Behaviour and the apparent relationship between these domains and participants’ intentions with regard to medication use. Our message was that the Theory of Planned Behaviour appeared to be predictive of intentions to take prescribed medication in approximately three-quarters of participants and when it was not predictive, a positive attitude to medication was the most important domain in determining participants’ intentions.

This working example of analytic direction resulted in three publications highlighting distinct "stories”. The publications differed in a number of ways. Each publication had its own central point or story line [ 12 ]. The role of theory [ 15 ] was minimal in analytic direction #2 but was more central in analytic directions #1 and #3 with the concept of “effective” or “activated” consumer and the Theory of Planned Behaviour dominating the analyses, respectively. Acknowledging that the authentic voices of participants may always be manufactured by the authorial account [ 32 , 47 ], all papers were written from the perspective of “I” or “we”. However, we focused on participants at the forefront for analytic direction #1 and we focused on participants’ perceptions of their providers’ voices for analytic direction #2. For analytic direction #3, the voice of the research team dominated as we struggled with methodological decisions. It is proposed that the voice of the model (Theory of Planned Behaviour) also dominated in analytic direction #3.

One implication related to analytic direction is that the research team may need to modify elements of the original research design to better suit the analytic direction selected. If such a modification is made, the team should ensure theoretical consistency in how the methods and methodologies are integrated [ 48 , 49 ]. For example, Crotty [ 49 ] proposes that theoretical consistency is needed between methods, methodology, theoretical perspective, and epistemology because these four elements inform one another. Similarly, Carter and Little [ 48 ] argue that consistency between methods, methodology, and epistemology contribute to the rigour of a qualitative study. Authors should demonstrate that elements of their theoretical perspectives and research design are compatible if they are applying another methodological approach to the data. Carter and Little [ 48 ] suggest that methodologies can be combined or altered if the researcher retains a coherent epistemological position and justifies the choices made. In the funded grant, a phenomenological program of research was proposed and the data were collected through in-depth interviews conducted from a phenomenological perspective. Analytic direction #3 was not purely consistent with a phenomenological approach because of the restriction to force exploratory data into domains of a theoretical framework and so we pursued this analytic direction with a different approach (qualitative description). As pointed out by Sandelowski [ 50 ], using phenomenology and qualitative description in this way is not to be confused with misuses of methods or techniques. Unlike quantitative research, qualitative research is not produced from any “pure” use of a method, but from the use of methods that are variously textured, toned, and hued [ 50 ]. According to Sandelowski [ 50 ], qualitative description can be used in conjunction with phenomenological research in a number of ways. For example, phenomenological analyses can be applied to qualitative descriptive studies [ 50 ]. However, the pursuit of other approaches to analysis, such as grounded theory or a participatory action approach, might lead to epistemological tensions if the original study design and data collection was guided by a phenomenological approach. Future discussion about the concept of analytic direction when considering theoretical and methodological positions that differ epistemologically from the original design and conduct of the study is needed.

There are a number of other implications related to the concept of analytic direction. Practically, it is advised that researchers start to think about analytic directions early so that they are aware of the potential analytic directions being developed as soon as data collection and analysis begin. By thinking about the “larger picture” at this early stage in the research, the team is better equipped to make the most of the data collected. Having said this, one will likely never use the entire dataset. As researchers, we rarely have sufficient funds or personnel to pursue all analytic directions. Data are often set aside because researchers are eager to analyze data collected for new projects or pressured to seek future funding opportunities. Analytic directions that are not pursued can be transferred to student projects. Alternatively, it is possible to draw on a sub-set of the transcripts/observations to carry out a secondary analysis. The author has developed subsequent analytic directions that span across studies and draw from a subset of transcripts for several secondary analyses [ 51 , 52 , 53 ]. Analytic directions can also contribute to ideas for new grant proposals that enable the researcher to generate more data on analytic directions that need further substantiation and further exploration.

This paper demonstrates some guidance about how to bound each analytic direction. Bounding the analytic direction is necessary so one does not re-use the data or produce multiple, yet quite similar, papers on the same topic. Researchers are encouraged to be open and transparent and acknowledge related publications so reviewers and other audiences reading the work are able to determine for themselves that the analyses are different.

There are ethical considerations in developing an analytic direction or framing the analytic direction in a way that might be different or supplementary to the original design. It is not always feasible to obtain subsequent consent from participants for use of the data if this use differs from that of the original goal of the study. As a result, analytic directions pursued should be within the scope of the approved research ethics application. One strategy is to keep the study goal or aim broad in the research ethics submission so that it encompasses many topics that might be discussed during data collection. Another consideration is to not prematurely close a research ethics application because researchers may be able to use the data for a secondary analysis at a later date.

This paper makes novel contributions to qualitative research methodology by demonstrating how the process of analytic direction works, by operationalizing the concept and providing an example, and by describing the connection between analytic direction and rigour. This paper further contributes to the advancement of rigour by demonstrating how the development and selection of analytic directions relies on several strategies to promote rigour, such as a comprehensive examination of the data, the use of multiple analysts, providing quotations to support claims made, checking for negative cases, and reflexivity.

In conclusion, the concept of analytic direction enables researchers to organize their qualitative datasets in order to tell different and unique “stories”. The concept relies upon, and promotes, the conduct of rigourous qualitative research. As with all elements of qualitative analysis, researchers are encouraged to think about the role of analytic direction as soon as data collection commences.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available due to participants not consenting to having their data deposited in a public dataset but are available from the corresponding author on reasonable request.

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Funding for the work described in this paper was provided by the Canadian Institutes of Health Research (Funding Reference Number: CBO-109629). The Canadian Institutes of Health Research had no involvement in the design of the study and collection, analysis, and interpretation of the data and in the writing the manuscript.

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Sale, J.E.M. The role of analytic direction in qualitative research. BMC Med Res Methodol 22 , 66 (2022). https://doi.org/10.1186/s12874-022-01546-4

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  • Analytic direction
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What Does an Analytical Research Chemist Do?

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An Analytical Research Chemist works on analyzing and interpreting data generated from experiments to aid other research. They organize and produce reports and findings, which scientists will use to create principles, ideas, and strategies in the scientific field. Included in a research chemist's primary responsibilities are the maintenance of the laboratory, organizing documents, and inventory of supplies and equipment.

Analytical research chemist responsibilities

Here are examples of responsibilities from real analytical research chemist resumes:

  • Manage an elemental analytical laboratory that include operating, maintaining and troubleshooting an ICP-OES, ICPMS, MXRF, and IC.
  • Maintain laboratory instrumentation (HPLC).
  • Gain experience of working in GLP environment.
  • Perform hands-on HPLC methods, cleaning validations, and finish product testing
  • Maintain equipment logbooks for laboratory instruments toward compliance of cGMP and GLP.
  • Develop numerous LC and GC analytical methods for drug product components and formulations.
  • Process impurity analysis/CHO host cell protein contamination using ELISA.
  • Operate GC, potentiometric analysis and other wet chemical methods routinely.
  • Develop new FTIR imaging methodology for the analysis of friction material components.
  • Perform calibration, troubleshooting, and repairs on laboratory instrumentation using ASTM and ISO methodologies.
  • Perform analysis for mercury determination by AA mercury analyzer including maintenance of instrument and preparation of reagents.
  • Provide technical consultation/service/training regarding NMR analysis and data interpretation.
  • Full knowledge of wet chemistry and inorganic chemistry techniques including EPA-600, SW846, ASTM methods for testing environmental samples.
  • Develop structure-activity relationships (SAR) for lead structures!
  • Develop and modify new and/or existing products according to customer/sales & marketing requirements while ensuring they meet require ASTM standards.


Analytical research chemist skills and personality traits

We calculated that 12 % of Analytical Research Chemists are proficient in Liquid Chromatography , R , and HPLC . They’re also known for soft skills such as Analytical skills , Interpersonal skills , and Math skills .

We break down the percentage of Analytical Research Chemists that have these skills listed on their resume here:

Developed methods for separation of complex mixtures and purification of a large catalog of compounds by high pressure liquid chromatography.

Participate in the preparation of deviation reports as necessary for submission to R &D Management.

Performed hands-on HPLC methods, cleaning validations, and finished product testing

Operated GC, potentiometric analysis and other wet chemical methods routinely.

Develop an MS ACCESS database to list all laboratory chemicals/standards/equipment/MSDS, and monitor inventory.

Task-led and coordinated multiple analytical techniques to solve challenges in manufacturing and drive new product innovation.

Common skills that an analytical research chemist uses to do their job include "liquid chromatography," "r," and "hplc." You can find details on the most important analytical research chemist responsibilities below.

  • Analytical skills. One of the key soft skills for an analytical research chemist to have is analytical skills. You can see how this relates to what analytical research chemists do because "chemists and materials scientists need to evaluate the results of experiments to ensure accuracy in their research." Additionally, an analytical research chemist resume shows how analytical research chemists use analytical skills: "organized scientific data and files for fda review. "
  • Interpersonal skills. Another essential skill to perform analytical research chemist duties is interpersonal skills. Analytical research chemists responsibilities require that "chemists and materials scientists typically work on teams and need to be cooperative." Analytical research chemists also use interpersonal skills in their role according to a real resume snippet: "demonstrated interpersonal skills by working with medicinal chemists to solve chemistry problems inherent in scale-up of organic reactions. "
  • Math skills. analytical research chemists are also known for math skills, which are critical to their duties. You can see how this skill relates to analytical research chemist responsibilities, because "chemists and materials scientists regularly use calculus, algebra, statistics, and other math for calculations." An analytical research chemist resume example shows how math skills is used in the workplace: "performed quantitative organic extractions and structure determination. "
  • Organizational skills. A big part of what analytical research chemists do relies on "organizational skills." You can see how essential it is to analytical research chemist responsibilities because "chemists and materials scientists must document processes carefully when conducting experiments, tracking outcomes, and analyzing results." Here's an example of how this skill is used from a resume that represents typical analytical research chemist tasks: "prioritized chemistry research and gave scientific advice in order to improve organizational efficiency. "
  • Problem-solving skills. Another crucial skill for an analytical research chemist to carry out their responsibilities is "problem-solving skills." A big part of what analytical research chemists relies on this skill, since "chemists’ and materials scientists’ work involves posing questions during research and finding answers through results." How this skill relates to analytical research chemist duties can be seen in an example from an analytical research chemist resume snippet: "acted on a usp request to generate a dissolution procedure for an alternative dosage form. "
  • Time-management skills. While "time-management skills" is last on this skills list, don't underestimate its importance to analytical research chemist responsibilities. Much of what an analytical research chemist does relies on this skill, seeing as "chemists and materials scientists usually need to meet deadlines and must be able to prioritize tasks while maintaining quality." Here is a resume example of how this skill is used in the everyday duties of analytical research chemists: "performed and maintained 98 % on time, turn around for pretest sample preparation prior to analysis. "

See the full list of analytical research chemist skills

The three companies that hire the most analytical research chemists are:

  • Sasol Chemicals (USA) LLC 2 analytical research chemists jobs

1 analytical research chemists jobs

  • Shell Trading 1 analytical research chemists jobs

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Compare different analytical research chemists

Analytical research chemist vs. chemist, development.

A development chemist is responsible for delivering products that are specially created for a customer. You will be responsible for conducting qualitative and quantitative chemical analyses for process or quality control. Other tasks that you will perform include researching chemical substances, conducting laboratory experiments, and evaluating the effects of chemical compounds in different situations. Additionally, you will be responsible for determining chemical properties by analyzing compounds and helping resolve quality issues. As a development chemist, you are also responsible for evaluating safety procedures in laboratories and maintaining laboratory equipment.

If we compare the average analytical research chemist annual salary with that of a chemist, development, we find that chemists, development typically earn a $10,192 lower salary than analytical research chemists make annually.

While the two careers have a salary gap, they share some of the same responsibilities. Employees in both analytical research chemist and chemist, development positions are skilled in liquid chromatography, r, and hplc.

These skill sets are where the common ground ends though. The responsibilities of an analytical research chemist are more likely to require skills like "gc," "ms," "analytical techniques," and "lc-ms." On the other hand, a job as a chemist, development requires skills like "method development," "technical support," "analytical procedures," and "gc-ms." As you can see, what employees do in each career varies considerably.

Chemists, development tend to reach similar levels of education than analytical research chemists. In fact, chemists, development are 3.7% less likely to graduate with a Master's Degree and 0.7% less likely to have a Doctoral Degree.

Analytical research chemist vs. Product development chemist

Chemists are generally responsible for observing and investigating chemical substances to create new and safe compounds essential for practical applications. They are often grouped depending on a particular area of specialization to focus and understand the complexities of the matter. A chemist usually takes time performing research and conducting experiments to test and further improve the quality and usage of a specific chemical substance. Chemists are expected to write on their observations and findings to establish scientific results.

On average, product development chemists earn a $8,679 lower salary than analytical research chemists a year.

While the salary may differ for these jobs, they share a few skills needed to perform their duties. Based on resume data, both analytical research chemists and product development chemists have skills such as "liquid chromatography," "r," and "hplc. "

Each career also uses different skills, according to real analytical research chemist resumes. While analytical research chemist responsibilities can utilize skills like "gc," "ms," "analytical techniques," and "analytical laboratory," product development chemists use skills like "lab equipment," "analytical methods," "qc," and "iso."

Average education levels between the two professions vary. Product development chemists tend to reach similar levels of education than analytical research chemists. In fact, they're 3.3% less likely to graduate with a Master's Degree and 0.7% less likely to earn a Doctoral Degree.

Analytical research chemist vs. Chemist

Laboratory Chemists are licensed scientists who work with chemicals. They are usually found in academic institutions or research facilities. Laboratory Chemists create experiments based on the needs of the institution or company. They mix different chemicals to make the desired results. They would also analyze compounds borne out of the different mixtures and record their observations. They tabulate the results and present these to the body. Laboratory Chemists should have good organization skills. They should be able to document their experiments properly. This would help the company and future researchers.

On average scale, chemists bring in lower salaries than analytical research chemists. In fact, they earn a $16,802 lower salary per year.

Using the responsibilities included on analytical research chemists and chemists resumes, we found that both professions have similar skill requirements, such as "liquid chromatography," "r," and "hplc.rdquo;

There are many key differences between these two careers, including some of the skills required to perform responsibilities within each role. For example, an analytical research chemist is likely to be skilled in "analytical laboratory," "lc-ms," "laboratory equipment," and "analytical instrumentation," while a typical chemist is skilled in "chemistry," "lab equipment," "test results," and "method development."

Chemists typically earn lower educational levels compared to analytical research chemists. Specifically, they're 6.3% less likely to graduate with a Master's Degree, and 1.1% less likely to earn a Doctoral Degree.

Analytical research chemist vs. Laboratory chemist

Laboratory chemists tend to earn a lower pay than analytical research chemists by an average of $19,061 per year.

While their salaries may vary, analytical research chemists and laboratory chemists both use similar skills to perform their duties. Resumes from both professions include skills like "liquid chromatography," "r," and "hplc. "

While some skills are required in each professionacirc;euro;trade;s responsibilities, there are some differences to note. "analytical techniques," "analytical laboratory," "product development," and "lc-ms" are skills that commonly show up on analytical research chemist resumes. On the other hand, laboratory chemists use skills like lab equipment, chromatography, analytical methods, and laboratory procedures on their resumes.

Laboratory chemists reach lower levels of education compared to analytical research chemists, in general. The difference is that they're 6.0% more likely to earn a Master's Degree, and 2.9% less likely to graduate with a Doctoral Degree.

Types of analytical research chemist

How to become a chemist, research scientist, how to become a research scientist.

  • Analytical Chemist

How To Become an Analytical Chemist

  • Quality Control Chemist

How To Become a Quality Control Chemist

Research and development scientist, how to become a research and development scientist, research chemist, how to become a research chemist, what similar roles do.

  • What an Analytical Chemist Does
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  • What a Chemist Does
  • What a Chemist, Development Does
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  • What a Production Chemist Does
  • What a Quality Assurance Chemist Does
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  • Analytical Chemist Job Description
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  • Chemist Job Description
  • Chemist, Development Job Description
  • Environmental Chemist Job Description
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Analytical Research Chemist Related Jobs

  • Associate Chemist
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Written By : Pitch N Hire

Mon Sep 18 2023

Everything You Need To Know About Analytical Research Its Essential Uses


Research is vital in any field. It helps in finding out information about various subjects. It is a systematic process of collecting data, documenting critical information, analyzing data, and interpreting it. It employs different methodologies to perform various tasks. Its main task is to collect, compose and analyze data on the subject matter. It can be defined as the process of creating new knowledge or applying existing knowledge to invent new concepts.

Research methods are classified into different categories based on the methods, nature of the study, purpose, and research design. Based on the nature of the study, research is classified into two parts- descriptive research and analytical research. This article will cover the subject matter of analytical research. Now, you must be thinking about what is analytical research. It is that kind of research in which secondary data are used to critically examine the study. Researchers used already existing information for research analysis. Different types of analytical research designs are used for critically evaluating the information extracted from the data of the existing research.

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Effect of Analytical Studies on Education Trails

Students, research scholars, doctors, psychologists, etc. take the help of analytical research for taking out important information for their research studies. It helps in adding new concepts and ideas to the already produced material. Various kinds of analytical research designs are used to add value to the study material. It is conducted using various methods such as literary research, public opinion, meta-analysis, scientific trials, etc.

When you come across a question of what is analytical research, you can define it as a tool that is used to add reliability to the work. This is generally conducted to provide support to an idea or hypothesis. It employs critical thinking to extract the small details. This helps in building big assumptions about the subject matter or the material of the study. It emphasizes comprehending the cause-effect relationship between variables. 

Analytical Research Designs

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Analytical Research includes critical assessment and critical thinking and hence, it is important. It creates new ideas about the data and proves or disproves the hypothesis. If the question comes of what analytical research is used for, it can be said that it is used to create an association between exposure and the outcome. This association is based on two types of analytical research design. The first is cohort studies and the second is a case-control study. In cohort studies, people of different groups with different levels of exposure are observed over time to analyze the occurrence of an outcome. It is a forward-direction and prospective kind of study. It is easier to determine the outcome risk among unexposed and exposed groups. 

It resembles the experimental design. Whereas in case-control studies, researchers enlist two groups, cases, and controls, and then bring out the history of exposure of each group. It is a backward-direction and retrospective study. It consumes less time and is comparatively cheaper than cohort studies. It is the primary study design that is used to examine the relationship between a particular exposure and an outcome.

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Methods of Conducting Analytical Research 

Analytical Research saves time, money, and lives and helps in achieving objectives effectively. It can be conducted using the following methods:

Literary Research 

Literary Research is one of the methods of conducting analytical research. It means finding new ideas and concepts from already existing literary work. It requires you to invent something new, a new way of interpreting the already available information to discuss it. It is the backbone of various research studies. Its function is to find out all the literary information, preserve them with different methodologies and analyze them. It provides hypotheses in the already existing research and also helps in analyzing modern-day research. It helps in analyzing unsolved or doubtful theories.

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Meta-Analysis Research

Meta-Analysis is an epidemiological, formal, and quantitative research design that helps in the systematic assessment of previous research results to develop a conclusion about the body of research. It is a subset of systematic reviews. It analyzes the strength of the evidence. It helps in examining the variability or heterogeneity. It includes a quantitative review of the body of literature. It is PRISMA and its aim is to identify the existence of effects, finding the negative and positive kinds of effects. Its results can improve the accuracy of the estimates of effects.

Scientific Trials

Scientific Trials research is conducted on people. It is of two types, observational studies and the second is clinical traits. It finds new possibilities for clinical traits. It aims to find out medical strategies. It also helps in determining whether medical treatment and devices are safe it not. It searches for a better way of treating, diagnosing, screening, and treatment of the disease. It is a scientific study that involves 4 stages. It is conducted to find if a new treatment method is safe, effective, and efficient in people or not.

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It aims to examine or analyze surgical, medical, and behavioral interventions. There are different types of scientific trials such as cohort studies, case-control studies, treatment trials, cross-sectional studies, screening trials, pilot trials, prevention trials, etc. 

Analytical Research is that kind of research that utilizes the already available data for extracting information. Its main aim is to divide a topic or a concept into smaller pieces to understand it in a better way and then assemble those parts in a way that is understandable by you. You can conduct analytical research by using the methods discussed in the article. It involves ex-ante research. It means analyzing the phenomenon. 

It is of different types such as historical research, philosophical research, research synthesis, and reviews. Also, it intends to comprehend the causal relation between phenomena. It works within the limited variables and involves in-depth research and analysis of the available data. Therefore, it is crucial for any data because it adds relevance to it and makes it authentic. It supports and validates a hypothesis. It helps companies in making quick and effective decision-making about the product and services provided by them.

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The needs of healthcare personnel who provide home-based pediatric palliative care: a mixed method systematic review

  • Judith Schröder   ORCID: orcid.org/0000-0001-5397-0260 1 ,
  • Kirsti Riiser 2 &
  • Heidi Holmen 1 , 3  

BMC Health Services Research volume  24 , Article number:  45 ( 2024 ) Cite this article

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Families with children who have life-limiting or life-threatening illnesses often prefer to receive care at home to maintain a sense of normalcy. However, caring for children at home is different from caring for them in a hospital, and we do not know enough about the needs of healthcare personnel who provide home-based pediatric palliative care.

The aim of this review was to systematically summarize, appraise and synthesize available quantitative, qualitative, and mixed methods research to identify the needs of healthcare personnel in home-based pediatric palliative care.

We used the Joanna Briggs Institute methodology for mixed method systematic reviews and searched systematically in Medline, Embase, PsycINFO, CINAHL, Web of Science, AMED, and the Cochrane Library. Quantitative, qualitative and mixed methods studies from 2012 to 2021 reporting on healthcare personnel’s needs, experiences, perspectives, coping strategies, and/or challenges related to home-based pediatric palliative care were eligible for inclusion. The screening was conducted independently in pairs. The quantitative data were transformed into qualitative data and analyzed using thematic synthesis.

Overall, 9285 citations were identified, and 21 studies were eligible for review. Most of the studies were qualitative and interview-based. Few studies included healthcare personnel other than doctors and nurses. Three analytical themes were developed: (1) being connected and engaged with the child and family , (2) being part of a dedicated team , and (3) ensuring the quality of home-based pediatric palliative care services. Healthcare personnel strived to deliver high-quality, home-based pediatric palliative care. Establishing a relationship with the child and their parents, collaborating within a committed team, and having sufficient resources were identified as important needs influencing healthcare personnel when providing home-based pediatric palliative care.

The findings underscore the importance of building trusting relationships among healthcare personnel, children, and families. It also emphasizes the significance of interdisciplinary collaboration that is effective, along with the presence of enough skilled personnel to ensure high-quality home-based pediatric palliative care. Further research is necessary to include healthcare personnel beyond doctors and nurses, as palliative care requires a team of professionals from various disciplines. Addressing the needs of healthcare personnel can ensure safe and professional palliative care for children at home.

Peer Review reports

Palliative care for children with life-limiting or life-threatening diseases aims to ease suffering and enhance the quality of life of these children and their families [ 1 ]. Home-based services, which are often more common than inpatient services, especially in developed countries play a significant role in pediatric palliative care [ 2 ]. These services can allow the families to maintain a sense of normalcy in their homes, which is often preferred when caring for children with severe illness [ 3 , 4 , 5 ]. Healthcare personnel can provide specialized and professional care in a home environment, which can positively impact a family’s care experience and reduce costs [ 1 , 3 , 4 , 5 , 6 ]. International standards in pediatric palliative care recommend that healthcare personnel are specialized in pediatric palliative care [ 6 ], but research indicates that there are vast variations in the competencies, content, and quality of services between countries and between urban and rural regions [ 4 , 5 ]. In home-based pediatric palliative care, healthcare personnel experience more professional isolation because they predominantly work alone with limited professional collaboration and medical equipment [ 4 , 5 , 7 , 8 ]. Furthermore, the emotional impact of providing pediatric palliative care is burdensome as healthcare personnel are confronted with children with unnaturally shortened lifespans [ 5 , 7 ]. Palliative care services are often provided over a long period, which allows healthcare personnel to establish close relationships with the affected families, often leading to a strong emotional impact and making it challenging to maintain professional boundaries [ 5 , 7 ]. Previous research on home-based services indicate that healthcare personnel struggle with organizational deficits, such as a lack of staff and equipment, which can be exhausting [ 9 ]. These organizational deficits can cause emotional reactions in healthcare personnel, such as anxiety and frustration and the feeling of not being able to deliver adequate pediatric palliative care [ 5 , 9 ]. In addition to resources, it is important that organizations facilitate systematic and regular training in pediatric palliative care to promote the quality of home-based pediatric palliative care [ 4 ].

Home-based pediatric palliative care is a complex and challenging practice and few reviews have assessed and synthesized the existing research on this topic from different perspectives. Previous reviews have examined various aspects of home-based pediatric palliative care, such as community-based care in the U.S. [ 4 ], end-of-life care for children at home [ 5 ], or work-related stress among registered nurses [ 7 ]. However, none of these reviews have focused in identifying healthcare personnel`s needs in this setting. The aim of our mixed method systematic review is to identify the specific needs of healthcare personnel when providing home-based pediatric palliative care. We consider that understanding these needs of healthcare personnel is an appropriate approach to explore their roles and responsibilities, ultimately contributing to the improvement of practices in this field. Mixed method systematic reviews are widely recognized as the most robust method for addressing clinical questions and generating evidence that directly informs best practice [ 10 ]. To achieve a broad understanding of healthcare personnel`s needs, when they provided home-based pediatric palliative care, we conducted a mixed methods systematic review, which involved summarizing, appraising, and synthesizing available research studies that employed quantitative, qualitative, and mixed methods approaches.

In this mixed method systematic review, we applied a convergent integrated approach transforming quantitative data to text, following the guidance of the Joanna Briggs Institute [ 10 ], to enable the use of the same synthesis method for both qualitative and quantitative data. We selected a mixed method review as this has been recommended when attempting to understand complex phenomena [ 10 ]. Needs can be conceptualized through expressed and felt needs [ 11 ]. These categories of needs were considered appropriate to examine the characteristics of healthcare personnel experiences when providing home-based pediatric palliative care. The protocol was registered in PROSPERO (CRD42021292865) a priori.

Inclusion criteria

We used the sample, phenomenon of interest, design, evaluation, and research type (SPIDER) framework [ 12 ] to ensure a comprehensive search and to determine explicitly whether a study was eligible for inclusion in the review (Table  1 ). This review centers on contemporary research regarding practices in home-based pediatric palliative care and encompasses studies published between 2012 and 2021, reflecting the evolution of pediatric palliative care research over the past decade [ 13 ]. The articles that were not explicit about whether palliative care was provided in a child’s home or if healthcare personnel provided palliative care to adults or children were excluded. The review only included articles published in English, German, or Scandinavian languages due to limited funding, time, and resources. Articles in other languages were excluded during the full-text screening to assess the potential impact of language exclusion on the review’s internal validity [ 14 ].

Search strategy and information sources

To find relevant evidence that explored the aim of our review, we collaborated with university librarians to identify relevant keywords and index terms to develop the search strategy. In October 2021, an initial test of the search strategy was conducted by the university librarians using the Medline database. This enabled us to evaluate the relevance of the retrieved literature and refine our strategy with additional keywords and index terms. The purpose of this preliminary test search was to develop a systematic and comprehensive search strategy. Based on this testing process, the university librarians performed the final systematic search in seven databases, namely, Medline, Embase, PsycInfo, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, Allied and Complementary Medicine (AMED), and the Cochrane Library, on December 9, 2021. An updated search was conducted by the librarian on December 5, 2023, utilizing the same search strategy and databases as before.

In each database, the keywords, synonyms, and subject headings to palliative care (1/ Phenomenon of interest), child/adolescent (2/ Phenomenon of interest), homecare services (3/ Phenomenon of interest), and healthcare personnel (4/ Sample) were used to compose the search strategies. The complete search strategies are available in Additional file 1 . The phenomenon of interest was divided into four separate terms, which were combined (AND).

Study selection

All the identified citations were uploaded to Covidence [ 15 ]. Covidence identifies duplicate citations by checking the title, year, and volume. However, any remaining duplicates were identified manually by the first author. Pairs of reviewers independently screened the titles, abstracts, and full text based on the preestablished eligibility criteria. JS screened all the studies, whereas HH and KR split the screening of the titles, abstracts, and full text, and each assessed one-half. The citations were discussed and disagreements regarding abstract or full text were resolved by consensus between all three authors.

Assessment of methodological quality

The methodological quality of the included studies was assessed independently in Covidence by JS and HH using the mixed methods appraisal tool (MMAT) version 2018 [ 16 ]. MMAT is a critical appraisal tool for five different study designs in qualitative, quantitative, and mixed methods studies [ 16 ]. In this review, MMAT was used to independently assess qualitative research (MMAT 1 ), quantitative descriptive studies (MMAT 4 ), and mixed methods studies (MMAT 5 ). Questions about the quality domains of the chosen study design were answered with “Yes,” “No,” and “Can’t tell.” All the quality domains were considered important, and no studies were excluded from the analysis based on their methodological quality.

Data extraction and transformation

The characteristics of the included studies were extracted in Covidence using a tailored data collection form (Additional file 2 ), which was inspired by the SPIDER framework [ 12 ], and developed a priori. JS extracted specific details about the author(s), year, country, sample, phenomena of interest, research design, evaluation, and results. HH checked all the collected data. JS reviewed the full texts of all the studies included, and all the authors agreed to extract the text, tables, and data of all the figures labeled as “results” or “findings” for the thematic synthesis. Both quantitative and qualitative data can address our research question and can be combined once transformed into the same format [ 10 ]. We transformed quantitative data from included studies into textual descriptions, enabling their integration with qualitative data through a process known as “qualitizing” [ 10 ]. In our review this approach involved narrative interpretation of quantitative results, which primarily consisted of descriptive statistics like mean values or percentages. Additional file 4 provides examples of transformed quantitative data into qualitative data. JS extracted all the text and imported it into NVivo 12.

Data synthesis and integration

To ensure that our research question guided the synthesis, we explicitly focused on data from the included studies that told something about expressed and felt needs in relation to the scope, responsibilities, and tasks of healthcare personnel in home-based pediatric palliative care [ 11 ]. JS and HH independently read the document with the result sections of all the included studies to determine their relevance in relation to the research question. As described by Thomas and Harden [ 17 ], in the first stage of the thematic synthesis, JS coded each line of the extracted data according to its meaning and content. To identify relevant results describing the needs of healthcare personnel, the data were carefully examined, and results that could be coded as needs were included. Examples of coded results are presented in additional file 4 . This process generated 79 codes, which were closely linked to the content and meaning of the text. In stage two, JS read and reread all 79 codes to summarize the codes with similar meanings and developed descriptive themes that were close to the content. These descriptive themes were presented to HH to facilitate a discussion on the understanding and interpretation of the themes and to consider the meaning of all the findings. In stage three, the descriptive themes were interpreted and discussed by all authors in the context of the review question. Additional file 5 presents examples of all stages of our thematic synthesis.

Characteristics of the included studies

The search identified 9285 citations. After deduplication, the titles, and abstracts of 6066 unique citations were screened for eligibility according to the inclusion criteria. Altogether, the full text of 175 citations were evaluated, which resulted in 21 studies [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ] that were eligible for review (Fig.  1 ).

figure 1

PRISMA flow diagram [ 39 ] of the citations that were screened and included in the systematic review

The included studies originated from Europe (n = 16) [ 18 , 20 , 22 , 24 , 25 , 26 , 28 , 29 , 30 , 31 , 32 , 33 , 35 , 36 , 37 , 38 ], North America (n = 2) [ 27 , 34 ], Australia (n = 1) [ 19 ], Africa (n = 1) [ 21 ], and Asia (n = 1) [ 23 ]. Thirteen studies were qualitative and took the form of interview studies [ 19 , 20 , 21 , 22 , 23 , 26 , 27 , 28 , 29 , 34 , 35 , 37 , 38 ]. Seven studies were quantitative and presented survey data [ 18 , 24 , 30 , 31 , 32 , 33 , 36 ], and study had a mixed methods approach [ 25 ]. Physicians (n = 333), including pediatricians [ 22 , 24 , 25 , 30 , 35 , 37 , 38 ], were the largest participant group, followed by nurses (n = 219) [ 18 , 19 , 21 , 22 , 23 , 27 , 28 , 29 , 34 , 35 , 36 , 37 , 38 ]. Some studies involved other healthcare personnel, such as counsellors, assistant nurses, occupational therapists, physiotherapists, dietitians [ 22 , 35 , 37 ], paramedics, psychosocial professionals, and other professionals [ 31 , 35 , 37 , 38 ], and home-based care workers [ 21 ]. Two studies presented participants as external stakeholders, in-patient hospital staff, and hospice at home teams [ 20 ] or as allied health clinicians [ 19 ]. Two studies did not report the exact sample size [ 26 , 32 ].

Methodological quality

The appraisal of the included studies’ methodological quality is presented in full in Additional file 3 . The majority of the included qualitative studies clearly described the collection of the data and analyses and provided representative quotations to justify the findings. In one of the studies [ 20 ], the findings were less obvious because of insufficient quotes, while another study [ 21 ] had insufficient details to determine the method of data collection. These two studies could therefore not be evaluated in relation to coherence between the data sources and interpretation.

The quality of the quantitative studies included in this review varied, and many lacked transparency. The categorial differences between the respondents and nonrespondents were only possible to evaluate in one of the studies [ 30 ]. In three studies [ 30 , 31 , 32 ], the variables were defined, and the measurements were appropriated to answer the research question. Four studies [ 18 , 24 , 30 , 32 ] reported a description of the target population and sample and the inclusion and exclusion criteria. None of the studies used validated questionnaires.

One mixed methods study [ 25 ] was included and reviewed. However, the main focus of the study was on the quantitative findings, and the qualitative elements were not described in detail. As a result, it was challenging to evaluate the methodological quality of the qualitative aspects of the study.

Overview of themes

Our thematic analysis provides a summary and synthesis of the needs of healthcare personnel. Three analytical themes were developed: (1) being connected and engaged with the child and family , (2) being part of a dedicated team , and (3) ensuring the quality of home-based pediatric palliative care (Fig.  2 ). The themes and corresponding descriptive subthemes are presented below.

figure 2

Overview of the analytical and descriptive themes

Theme 1: being connected and engaged with the child and family

The healthcare personnel described the children’s homes as unfamiliar places but acknowledged that families preferred to receive care at home. Sometimes healthcare personnel did not know the child before the first home visit [ 29 ]. To ensure that the care they provided met the needs of the child and family at home and to make decision-making a shared responsibility between healthcare personnel and parents, healthcare personnel stressed the need to build a trusting relationship with the child and their family [ 18 , 19 , 22 , 23 , 25 , 26 , 28 , 29 , 34 , 35 , 37 , 38 ]. Healthcare personnel found that establishing a trusting relationship made families feel safe and protected and had the potential to alleviate the burdens experienced as their children’s illnesses progressed [ 22 , 23 , 25 , 26 , 35 , 37 ]. They also reported that gaining the trust of a child can be challenging [ 22 , 23 ], but trust is needed to ensure open discussions and truth-telling and to involve the child in their care [ 21 , 22 , 28 ]. Healthcare personnel expressed that knowing and understanding the family helps clarify the responsibilities between the healthcare personnel and the family [ 27 , 35 ] and makes it easier to assess if the family is coping at home [ 22 , 23 ]. Telehealth can be useful in facilitating this relationship if large geographical distances are involved, but this was experienced by healthcare personnel as a less personal communication form compared to physical home visits [ 19 , 34 ].

According to the perspective of healthcare personnel, the family’s perception of palliative care solely as end-of-life care may pose a challenge in establishing a trusting relationship early in a child’s disease [ 30 , 36 , 37 ]. Late referrals from inpatient to outpatient services constituted another barrier for healthcare personnel when trying to establish a trusting relationship with a child and their family [ 22 , 23 , 24 , 29 ]. Maintaining professional boundaries was mentioned as an important aspect of a trusting relationship, but balancing familiarity and emotional involvement with a level of detachment could be challenging [ 18 , 22 , 25 , 26 , 28 , 29 , 35 , 37 ]. Healthcare personnel who provide home-based pediatric palliative care needed to be aware of the emotional impact of their work on themselves [ 35 ].

Home-based pediatric palliative care was described as a type of health care that was provided in the individual home where the child and family lived. According to healthcare personnel, this made home care an unpredictable environment, with limited resources and the need to adapt to different families [ 37 ]. Therefor healthcare personnel emphasized the importance of standing out as skilled professionals when working alone in a family’s home [ 20 , 22 , 23 , 25 , 26 , 27 , 29 , 37 ]. Appearing less knowledgeable or unready was found to cause distress [ 18 , 20 , 22 , 23 , 24 , 25 , 26 , 27 , 29 , 30 , 31 , 33 , 36 ]. This distress emphasized the need for being prepared when caring for a child at home . A need for being prepared was particularly evident among the healthcare personnel who mainly provided palliative care for adults and were, therefore, less experienced with young patients [ 22 , 23 , 27 ]. This included being prepared to manage medical devices, assess symptoms, adjust medication dosages [ 24 , 26 , 30 , 33 ], and handle ethical dilemmas related to life-prolonging interventions and alternative medicine [ 21 , 28 ]. Additionally, healthcare personnel needed to adopt an attitude of accepting death as a natural occurrence rather than a professional failure; this shift had to happen for their emotional well-being [ 37 ]. Once home-based pediatric palliative care was established, healthcare personnel understood that care in the home favored a holistic approach to the needs of the children and their families [ 37 , 38 ]. Age-appropriate communication with the children and open communication with families experiencing emotional stress were also identified as crucial factors [ 21 , 23 , 28 , 29 , 37 ].

To maintain a family’s trust and provide safe care, healthcare personnel needed to respond to the family’s needs in time . Home-based pediatric palliative care was perceived as more time-consuming and less predictable than home-based palliative care for adults [ 18 , 22 , 23 , 24 , 26 ]. According to healthcare personnel, their busy workloads made it difficult for them to manage children’s medical procedures and emergency calls from families and to have time to be present for children and families experiencing emotional stress [ 18 , 22 , 23 ]. On the other hand, providing home-based pediatric palliative care was deemed to offer a unique experience that justified allocating sufficient time, even outside of working hours [ 18 , 22 , 24 , 25 , 29 ].

Theme 2. Being part of a dedicated team

The healthcare personnel in several studies reported that optimal home-based pediatric palliative care should involve different professionals from within and across healthcare services [ 19 , 20 , 22 , 23 , 24 , 26 , 31 , 34 , 37 ]. Services like psychosocial support, play therapists, respite services, and specialized pediatric palliative care services helped to ensure that families could fulfill their wish to care for their children at home. However, effective working relationships and communication across different healthcare services for joint patient care were experienced as challenging [ 22 , 23 , 24 , 31 , 33 ]. This was partly due to the rarity of palliative care for children in community services, which meant that personnel from different healthcare services often found themselves working together for the first and only time [ 26 ]. Accordingly, the need for effective collaboration within and across services was essential. For healthcare personnel effective collaboration between healthcare services was entailed as a clear understanding of each other’s roles and responsibilities and required functional routines for the exchange of information [ 19 , 20 , 22 , 24 , 26 , 28 , 30 , 31 , 33 , 36 , 37 , 38 ]. In addition, healthcare personnel needed to change their mentality to be flexible, proactive, decisive, and willing to work as a team [ 37 ]. Healthcare personnel can become confused, frustrated, or even feel threatened when collaboration is poorly planned and managed [ 22 , 24 , 28 , 29 , 30 , 31 ]. Introductory meetings between specialized pediatric palliative care teams and the home care services could make healthcare personnel feel more prepared for home-based pediatric palliative care [ 36 ]. Three studies [ 19 , 34 , 36 ] examined the use of telehealth, like video conferences, in home-based pediatric palliative care, and it was reported to be an efficient method for maintaining working relationships and enhanced communication. Video conferences permitted personnel from different healthcare services to be brought up to date and included in discussions regardless of the geographical location of the services [ 19 , 34 ]. Collaboration seemed to cause in-hospital healthcare personnel to hand more responsibility over to local healthcare personnel [ 19 ], and children could thus be transferred home earlier because the hospital staff knew the local healthcare personnel from different services well [ 22 ].

Simultaneously, close collaboration enhanced the transfer of knowledge and skills from specialized hospital personnel to homecare personnel with less competence in pediatric palliative care [ 18 , 19 , 22 , 24 , 25 , 27 , 30 , 31 , 32 ]. The need for guidance and support from other healthcare personnel were echoed in the included studies. The healthcare personnel who collaborated with pediatric palliative care teams considered these specialized teams to be the most useful resources when providing palliative care at the homes of children [ 18 , 22 , 24 , 25 , 27 , 31 , 32 , 36 ]. Through that collaboration, the healthcare personnel received guidance and clarity regarding the symptoms to be expected, clinical assessments, and medication dosages and were able to discuss dilemmas. Notwithstanding, support from colleagues in the organization was also highlighted as important [ 23 , 24 , 25 , 27 , 29 , 35 ]. When healthcare personnel worked alone with a child in their home and dealt with occasionally stressful work tasks, colleagues served as a source of emotional support. Reflective sharing, debriefing sessions, joint visits, or just having had the opportunity to reach out to a colleague by phone promoted coping and made healthcare personnel feel more supported at work [ 22 , 23 , 24 , 25 , 27 , 29 , 30 , 35 ].

Theme 3. Ensuring the quality of home-based pediatric palliative care services

The findings of our review reflected that healthcare personnel had the need to establish equitable and sustainable services to ensure optimal care for children at home.

Home-based pediatric palliative care services seemed to consist of small local teams and pediatric patients spread over wide geographical areas [ 19 , 22 , 23 , 29 , 34 ]. The limited availability of appropriately skilled healthcare personnel was a barrier to provide flexible home-based pediatric palliative care, and especially out-of-hours care [ 18 , 20 , 26 , 28 , 38 ]. Small teams did not provide healthcare personnel with opportunities to rest and relinquish their practical and emotional responsibilities [ 26 , 29 ]. Healthcare personnel requested clear guidelines and protocols to ensure that all involved personnel provide equal care to children in need of home-based pediatric palliative care as well as their families [ 20 , 23 , 28 ].

The scarcity of pediatric patients compared to adults and the fundamental differences between caring for children versus adults were highlighted as reasons for healthcare personnel’s lack of knowledge and skills in pediatric palliative care [ 20 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 32 , 33 ]. Healthcare personnel, therefore, expressed a need for continuous professional development in pediatric palliative care . As healthcare personnel shared their experiences, they emphasized the importance of the workplace having supported them with time, resources, and access to relevant courses and in-service training. This included not only theoretical courses but also practical guidance and hands-on learning, all tailored to their specific needs [ 20 , 22 , 24 , 27 , 37 ]. The topics specifically mentioned for training were clinical assessments, communication, and strategies for self-care (Table  2 ) [ 18 , 19 , 20 , 22 , 23 , 24 , 25 , 26 , 27 , 29 , 30 , 31 , 33 , 37 , 38 ].

The emotional toll of providing home-based pediatric palliative care became manageable for healthcare personnel when time is dedicated to debriefing, or routines for supervision were established [ 22 , 23 , 32 , 35 ]. Healthcare personnel recounted that workplace managers would demonstrate their appreciation for professional development by facilitating and funding courses and educational opportunities [ 22 , 27 ].

This systematic review presents a summary of studies and a synthesis of healthcare personnel’s needs when providing home-based pediatric palliative care. Most of the studies investigated the needs of doctors and nurses more than those of other healthcare personnel. The methodological quality of the included primary studies was varying. The difficulties with feasible and rigorous quantitative research are a well-known phenomenon in pediatric palliative care research [ 40 ]. The small population, heterogeneity of the illnesses, heterogeneity among providers, low response rates, and ethical concerns are some of the challenges. Despite these issues, previous research can serve as a foundation for future systematic research [ 6 , 40 , 41 ].

The reviewed studies illustrated that home-based pediatric palliative care can be provided by various services, from hospital- to community-based services. These different models of care demonstrate that home-based pediatric palliative care depends on the country and region, local regulations, and access to resources [ 5 , 42 ]. To better inform future service development in home-based pediatric palliative care, researchers should provide more details on characteristics of the home-based service and how it is delivered in practice [ 5 , 7 , 42 , 43 ].

The need to establish trusting relationships was highlighted as important but challenging when providing home-based pediatric palliative care. This aligns with previous research, which has noted that trust is an important prerequisite in care [ 4 , 5 , 9 , 43 ]. A trusting relationship also contributes to the assessment of the individual needs of children and their families and gives healthcare personnel the opportunity to respond proactively [ 4 , 5 , 43 , 44 ]. Previous research in home-based care has shown that healthcare personnel adapt from being a person outside of the family to becoming part of the family [ 4 , 5 , 7 , 43 ]. A recent systematic integrative review described nurses’ competences in collaborating with patients and families and how patients value nurses’ personal involvement [ 43 ]. Findings indicates that being personally involved is not contradictory to being professional, especially when providing care in a patient’s home [ 43 ]. Healthcare personnel who become a part of the family and show personal involvement can help normalize the family’s situation when they rely on professional help for their child. However, on the other hand research shows that a close relationship between healthcare personnel and the family can blur professional boundaries, leading to work-related stress [ 5 , 7 ]. Caring for children with palliative needs can also impact healthcare personnel emotionally, potentially making them feel unprofessional [ 7 , 9 , 45 ]. Implementing mental health support interventions, such as training and supervision, is of paramount importance in the workplace. These interventions not only encourage reflective clinical practice and professional progression, but they also help alleviate the stress that healthcare personnel frequently face in challenging work situations over long periods, such as those encountered in pediatric palliative care [ 7 , 9 , 45 ].

Standards for pediatric palliative care describe care as an interdisciplinary and complex service, and the reviewed studies showed that working in a team affected the needs of healthcare personnel [ 6 ]. A lack of collaboration across healthcare services can be a source of stress [ 7 ], and this was echoed throughout the studies included in our review. A recent systematic integrative review that investigated the distinct levels of palliative care provision (palliative approach, generalized palliative care, and specialized palliative care) and the associated competence showed how the provision of different levels of palliative care is not sufficiently defined [ 43 ]. Clarifying roles and responsibilities can help ensure that all involved healthcare personnel take responsibility for and contribute to achieving common care goals [ 4 , 41 , 44 ]. Coordinated collaboration also contributes by providing a source of guidance from more experienced colleagues to healthcare personnel who are less experienced in providing pediatric palliative care [ 4 , 41 , 46 ]. The advantage of interprofessional collaboration was also discussed in the included studies. Understanding and appreciating each other’s roles between different providers can secure continuity of care [ 4 , 5 , 7 , 41 , 44 ].

Establishing a service with competent professionals and overarching collaboration to provide home-based care for children and their families was identified as difficult in most of the studies included in this review. As confirmed in previous research, staff shortages are a key stressor for healthcare personnel [ 5 , 7 ]. In addition, workplaces must enable healthcare personnel to acquire basic levels of knowledge and skills in pediatric palliative care via education, affiliations with professional organizations, and networking opportunities [ 4 , 7 , 46 ]. However, the included studies illustrated that healthcare personnel struggle to acquire sufficient and relevant palliative care competencies because the number of children who would benefit from palliative care is low, and the opportunities for education and training are thus limited. Mentoring from colleagues was highlighted as an effective measure that requires few resources, as mentoring can take place in the interdisciplinary groups established around children [ 4 , 7 ]. Digital collaboration can provide more flexible care for children and their families, but also to a greater extent, cover the needs of healthcare personnel in terms of collaboration, coordination, and competence enhancement [ 47 , 48 , 49 ]. Only two of the reviewed studies investigated digital solutions in home-based pediatric palliative care.


Although we strived to conduct our review rigorously according to the guidelines of the Joanna Briggs Institute, it has some limitations. The use of various terms to describe home-based pediatric palliative care based on different services in different countries may have resulted in missed studies that were not indexed with the keywords, synonyms, or subject headings used in our search strategy. To reduce the risk of missing out eligible studies, we accepted a high number of records to review; consequently, leading to the exclusion of a high number of records at the title and abstract screening. These records mainly did not agree with our inclusion criteria, such as phenomenon of interest, or the records were not reporting research. The inclusion process may have been affected by the individual reviewers’ interpretations of “needs”. While there have been studies exploring different aspects of home-based pediatric palliative care, these studies did not explicitly aim to identify the needs of healthcare personnel. As reviewers, we had to interpret the findings of these studies to determine what they reveal about the needs of healthcare personnel in this context. Finally, many of the studies were from high-income countries, which made it difficult to generalize the results to low- and middle-income countries.


Healthcare personnel have various needs that must be met for them to provide professional care for children and their families at home. These needs include being connected with the child and their family, being part of a dedicated team, and ensuring service quality. This review confirmed that providing home-based palliative care services varies from country to country. We found few studies on healthcare personnel other than doctors and nurses, even though palliative care is intended to be an interdisciplinary service. Trusting relationships between children, families, and healthcare personnel are fundamental when providing palliative care for children at home. Close relationships give healthcare personnel the opportunity to identify and meet families’ needs and provide proactive, personalized care. Coordinated interdisciplinary collaboration between healthcare personnel within and across services contributes to the provision of holistic care for families as well as emotional and professional support. Sufficient competent healthcare personnel and adequate training and guidance are essential for providing home-based pediatric palliative care. Healthcare services should ensure that their personnel are well-trained and supported to provide high-quality care in the home setting. Further research is required to determine how healthcare services can be organized to meet the needs of the healthcare personnel who provide palliative care for children in their homes.

Data Availability

All the data generated and analyzed during this study have been included in this published article in line with the search strategy presented in the PRISMA flow diagram in Fig.  1 . Characteristics of the included articles on which this review was based are attached as Additional file 2 .

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The authors are grateful for the assistance of Camilla Thorvik and Elisabeth Karlsen, both of whom are head librarians at the Division for Education and Library, Oslo Metropolitan University, for providing their expertise and support in the development of a sound search strategy for this review. A poster with preliminary findings from this review was presented at the national children’s palliative care conference in March 2023, in Norway.

This work was funded by the Research Council of Norway, grant nr. 314850.

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Schröder, J., Riiser, K. & Holmen, H. The needs of healthcare personnel who provide home-based pediatric palliative care: a mixed method systematic review. BMC Health Serv Res 24 , 45 (2024). https://doi.org/10.1186/s12913-023-10495-7

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