In part 2, the problem statement, describe the nature and importance of the problem you studied.
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Identify the effects or implications of the problem, its scope and severity, and the populations affected. Convince readers that your research addresses an important issue. This information is often missing in scientific articles because authors assume—incorrectly—that readers will know what the research sought to do and why it was done.
In part 3, the activity statement, tell what you did to address the problem. Describe your hypothesis, how you studied the problem, and why you approached the problem the way you did. Convince readers that your research will adequately answer the questions associated with the problem. In part 4, the forecasting statement, tell readers what to expect if they continue to read your article. By the end of the introduction, readers should be able to determine whether your article is likely to interest them. One of the most common problems in writing introductions is that parts 1, 2, and 4 are incomplete or missing.
Using the above introduction, an incomplete introduction might read as follows:. A good introduction should be long enough to do what it is supposed to do, as described above.
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Many introductions are too short because authors do not appreciate how important a good introduction can be. Length can also be affected by the conventions in a particular field of science: journals in the social sciences including some nursing and public health journals often include the literature review in the introduction, whereas those in the clinical sciences usually include the literature review in the discussion, for example.
The purpose of the methods section is to tell readers how you went about answering your research question. Although many authors have been taught that the methods section should allow others to repeat their research, the reality is that the typical 3,word scientific article is usually not long enough to include this amount of detail.
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Instead, give readers enough information to understand what you did and to persuade them that what you did was adequate and that you knew what you were doing. Many journals also allow or require research protocols or more detailed information on the methods to be included in online supplemental information. The methods section is usually the easiest part of the article to write simply because you describe what you did in your research. For this reason, many authors write this section first.
Here, I briefly describe what to consider when describing the study design, the sample selection, and the variables and how they were measured. The purpose of the study must be clear.
The reasons for doing the study must also be strong. In the original introduction, the authors used the LIKA justification, which hides the fact that the study actually addressed a compelling problem: what should surgeons tell the parents of premature infants born with severe heart defects? Which conditions are associated with survival and so would favor corrective surgery, and which are associated with death and so would favor palliative care?
To identify valid biological relationships, studies have to be as free as possible from error random error, mistakes, or incomplete processes , confounding the failure to rule out alternative explanations , and bias systematic as opposed to random error. There are hundreds of sources of error, confounding, and bias. Minimizing these sources is the purpose of specific research design features and activities. For example, measurement error can be minimized by duplicating a measurement; sampling error error caused by studying only a sample of a population can be minimized by using adequate sample sizes and assessed with confidence intervals; and random error can be assessed with P values.
Selection bias can be minimized with random assignment, and expectation and surveillance biases can be minimized with blinding. Finally, confounding can be minimized by studying the literature on the problem to identify important covariates and relationships in advance. Identify the study design Figure 1. Follow any evidence-based reporting standards that are associated with your research.
These guidelines are a minimum set of standards for reporting case-control, cross-sectional, and cohort studies. Give the dates of the data collection period and say why you choose those dates Reporting the dates places your study in time relative to other, possibly relevant events and can provide additional information, such as how long it took to reach the targeted sample size. When reading studies, you should look for these dates.
A study published in with a data collection period from to might be outdated or it might be a solid study with a 5-year follow up. The nature and size of the sample and how it was selected are critical in any research study. Ideally, the sample will be representative: it will reflect exactly the characteristics of the population to which the results are to be applied. Wine drinkers pay lots of money for a bottle of wine after tasting only a sip because that sip is supposed to be representative of the taste for the rest of the bottle. Sample selection is not so simple in public health, but the principle is the same.
The sample also has to be large enough to provide estimates with enough accuracy to be useful. Because studies are usually conducted with samples, the effect size can only estimate the likely effect of in intervention. Larger samples provide more precise estimates in the form of narrower confidence intervals. Confidence intervals, in turn, keep the interpretation of the results focused on the biological implications of the effect size and away from P values, which are essentially measures of chance as an explanation for the outcome and that have no biological meaning.
Clearly identify your explanatory and response variables.
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Explanatory or independent variables are those related to the treatment or exposure under study, and response or dependent variables are the outcomes or end points of interest. Primary and secondary end points should be specifically identified because they can determine the study design, the sample size, the order in which the results are presented, and the interpretation of the study. Science depends on measurement.
Some measurements are obvious: hypertension defined as a systolic blood pressure of mm Hg or more on 3 consecutive days, for example. Others measurements may not be obvious. This definition may not be appropriate for answering some questions about smoking, but at least we know how the variable was defined. However, different groups have proposed different case definitions for the same disease Table 1 , and case definitions for the same disease can change over time Table 2 , so it is important to specify which case definition is being used and when it was last updated.
Sometimes, the outcomes of a study are judgements, rather than more objective measurements, such as blood pressure or weight. Whether a blood sample contains evidence of a pathogen is a judgment made by a microbiologist. In such cases, it is often useful to know the qualifications of the microbiologist, how many microbiologists were judging the same slides, how often they agreed on their judgments, and what they did and did not know about the slides before they reviewed them.
A standard subheading in the methods is Statistical Methods. Here, you should identify the comparisons you made and the statistical analyses used to make them In the results, report your data, but also tell readers what happened during the study. Explain if and why the research did not go as planned. In many studies, including a visual summary or flow chart of the sample selection process can be enormously useful Figure 2.
Present the results for the primary endpoint first, whether or not they are clinically important, statistically significant, or interesting. You designed the study to answer a specific question, and the results section should focus on that question. Other results can be presented, but only after the primary results have been reported.
When reporting their results, authors often put many numerical and statistical values in the text and then repeat these values in the tables and graphs. Instead, describe the most important results and refer readers to the tables or graphs for the supporting data Figure 3. Common writing errors include using the table or figure number as the subject of the sentence and then telling readers what the title or caption already says. A good title or caption will identify the data and perhaps key aspects of it, such as how or when the data were collected or any qualifications needed to understand them.
The goal is to allow readers to understand the data without having to refer back to the text, which is inefficient and annoying.
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It is often a good idea to express results in terms of patients or people and not just the study end point. For example, instead of just reporting a mean change, tell the number of patients who improved and, if appropriate, how many crossed any thresholds in the measurement scale:. The discussion is usually the weakest part of the article because you have to determine the meaning and implications of your results and integrate them with what else is and is not known about the topic.
Summarize the study and the main findings in a few sentences.
pacentsucki.cf Instead of repeating individual results, however, describe the overall findings and relate them to the reason for the study. Interpret your results and suggest an explanation s for them. Describe why you think you found what you found or did not find. Here, consider only those explanations related to the study: patients declined to participate because…; survey questions were misinterpreted because…; or the results were better than expected because….
Compare your results with what else is known about the problem; that is, review the literature. Here, explain why you think you found what you found or did not find, considering what other studies have found. Did your sample include more factory workers than were included in those of other studies? Did you measure quality of life with a different instrument? Was your vaccine not the same as the one tested in other studies? Generalize your results; say how they might relate to other patient populations or settings. If you studied adults, can the results be generalized to children? If you studied rural health care in China, can the results be generalized to rural health care in Venezuela?
If your study was done in a desert climate, can the results by generalized to a tropical one?