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Some nurses feel that they lack the necessary skills to read a research paper and to then decide if they should implement the findings into their practice. This is particularly the case when considering the results of quantitative research, which often contains the results of statistical testing. However, nurses have a professional responsibility to critique research to improve their practice, care and patient safety.1 This article provides a step by step guide on how to critically appraise a quantitative paper.
Title, keywords and the authors
The title of a paper should be clear and give a good idea of the subject area. The title should not normally exceed 15 words2 and should attract the attention of the reader.3 The next step is to review the key words. These should provide information on both the ideas or concepts discussed in the paper and the subject area addressed in the article. These first steps can often influence your decision whether to read the entire paper.
The authors’ names may not mean much, but knowing the following will be helpful:
Their position, for example, academic, researcher or healthcare practitioner.
Their qualification, both professional, for example, a nurse or physiotherapist and academic (eg, degree, masters, doctorate).
This can indicate how the research has been conducted and the authors’ competence on the subject. Basically, do you want to read a paper on quantum physics written by a plumber?
The abstract is a resume of the article and should contain:
Methods including sample design, tests used and the statistical analysis (of course! Remember we love numbers).
The subheadings in the abstract will vary depending on the journal. An abstract should not usually be more than 300 words but this varies depending on specific journal requirements. If the above information is contained in the abstract, it can give you an idea about whether the study is relevant to your area of practice. However, before deciding if the results of a research paper are relevant to your practice, it is important to review the overall quality of the article. This can only be done by reading and critically appraising the entire article.
A well-structured introduction should gain the attention of the reader by making the subject area interesting.4 The choice of subject should be clearly explained. The introduction should arouse your interest and curiosity and answer the question why should I bother reading this paper? Normally, the research question, aim, hypothesis and null hypothesis will be clearly stated in the introduction. An example of a hypothesis and null hypothesis can be seen in box 1.
Example: the effect of paracetamol on levels of pain
My hypothesis is that A has an effect on B, for example, paracetamol has an effect on levels of pain.
My null hypothesis is that A has no effect on B, for example, paracetamol has no effect on pain.
My study will test the null hypothesis and if the null hypothesis is validated then the hypothesis is false (A has no effect on B). This means paracetamol has no effect on the level of pain. If the null hypothesis is rejected then the hypothesis is true (A has an effect on B). This means that paracetamol has an effect on the level of pain.
The literature review should include reference to recent and relevant research in the area. It should summarise what is already known about the topic and why the research study is needed and state what the study will contribute to new knowledge.5 The literature review should be up to date, usually 5–8 years, but it will depend on the topic and sometimes it is acceptable to include older (seminal) studies.
In quantitative studies, the data analysis varies between studies depending on the type of design used. For example, descriptive, correlative or experimental studies all vary. A descriptive study will describe the pattern of a topic related to one or more variable.6 A correlational study examines the link (correlation) between two variables7 and focuses on how a variable will react to a change of another variable. In experimental studies, the researchers manipulate variables looking at outcomes8 and the sample is commonly assigned into different groups (known as randomisation) to determine the effect (causal) of a condition (independent variable) on a certain outcome. This is a common method used in clinical trials.
There should be sufficient detail provided in the methods section for you to replicate the study (should you want to). To enable you to do this, the following sections are normally included:
Overview and rationale for the methodology.
Participants or sample.
Data collection tools.
Methods of data analysis.
Data collection should be clearly explained and the article should discuss how this process was undertaken. Data collection should be systematic, objective, precise, repeatable, valid and reliable. Any tool (eg, a questionnaire) used for data collection should have been piloted (or pretested and/or adjusted) to ensure the quality, validity and reliability of the tool.9 The participants (the sample) and any randomisation technique used should be identified. The sample size is central in quantitative research, as the findings should be able to be generalised for the wider population.10 The data analysis can be done manually or more complex analyses performed using computer software sometimes with advice of a statistician. From this analysis, results like mode, mean, median, p value, CI and so on are always presented in a numerical format.
The author(s) should present the results clearly. These may be presented in graphs, charts or tables alongside some text. You should perform your own critique of the data analysis process; just because a paper has been published, it does not mean it is perfect. Your findings may be different from the author’s. Through critical analysis the reader may find an error in the study process that authors have not seen or highlighted. These errors can change the study result or change a study you thought was strong to weak. To help you critique a quantitative research paper, some guidance on understanding statistical terminology is provided in table 1.
Quantitative studies examine the relationship between variables, and the p value illustrates this objectively. 11 If the p value is less than 0.5, the null hypothesis is rejected and the hypothesis is accepted and the study will say there is a significant difference. If the p value is more than 0.5, the null hypothesis is accepted then the hypothesis is rejected. The study will say there is no significant difference. As a general rule, a p value of less than 0.5 means, the hypothesis is accepted and if it is more than 0.5 the hypothesis is rejected.
The CI is a number between 0 and 1 or is written as a per cent, demonstrating the level of confidence the reader can have in the result.12 The CI is calculated by subtracting the p value to 1 (1–p). If there is a p value of 0.05, the CI will be 1–0.05=0.95=95%. A CI over 95% means, we can be confident the result is statistically significant. A CI below 95% means, the result is not statistically significant. The p values and CI highlight the confidence and robustness of a result.
Discussion, recommendations and conclusion
The final section of the paper is where the authors discuss their results and link them to other literature in the area (some of which may have been included in the literature review at the start of the paper). This reminds the reader of what is already known, what the study has found and what new information it adds. The discussion should demonstrate how the authors interpreted their results and how they contribute to new knowledge in the area. Implications for practice and future research should also be highlighted in this section of the paper.
A few other areas you may find helpful are:
Limitations of the study.
Conflicts of interest.
Table 2 provides a useful tool to help you apply the learning in this paper to the critiquing of quantitative research papers.
Competing interests None declared.
Patient consent Not required.
Provenance and peer review Commissioned; internally peer reviewed.
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