Write Background Study Research Paper


This article is written in the form of an original-research paper for the journal Sportscience. A few of the requirements for form and content are unique to Sportscience, but most are common to all good scientific journals. You can therefore use this article to help you write a research paper for any journal.

This article also exists in slightly modified form as a template for a Sportscience research article. If you intend to submit a paper to Sportscience, you should download the template from the Information for Authors page at the Sportscience site.

Whether you are submitting your article to Sportscience or to another journal, you should read my guidelines on scientific writing (Hopkins, 1999). Here are the main points from that article:

  • Avoid technical terms.
  • Avoid abbreviations.
  • Use simple sentences.
  • Avoid common errors of punctuation and grammar.
  • Use the first person (I, we) rather than the passive voice.
  • Link your ideas into a sensible sequence without repetitions or discontinuities.
  • Get feedback on your article from colleagues.

The subheadings in this article (Background, Aim, etc.) should make it easier to write your article for Sportscience than for other journals. You will also be less likely to omit important data or important points for discussion. Please follow the instructions for each subheading. Omit or change the subheadings as necessary for a paper in another journal.

Use the first paragraph or two of the Background to explain what is known generally in the area of your study. Cite key references, but do not write an extensive review of literature; instead, direct the reader to a recent review. Then focus in on the problem that your study addresses.


State the aim in the first sentence of this section. Write a few more sentences if necessary to justify the aim and/or explain your unique approach to realizing the aim. Make it clear to the reader that your aim or your approach is interesting and that your study is therefore valuable.


State the design, then justify your choice of this design. Explain the optimum sample size, then justify the size of the sample you studied. Describe any relevant time frame. Use a figure to explain a complex design or a design involving several assays at different times.

Devote a separate paragraph to a description of the experimental and control treatments (e.g., drug, diet, training) in an experimental study.


Explain how the subjects were recruited, then list means and standard deviations of the relevant demographics (age, weight, height) plus any other relevant characteristics (recent best performances, recent training). Show ranges of characteristics only if there are unusually distant outliers in the sample. If possible, report recent best competitive performances of athletes as a percent of the world record, to make it clear what caliber of athlete the outcome of your study can be generalized to.

Show all the above characteristics for any major subgroups of subjects (e.g., males and females, non-athletes and athletes). Include the number of subjects in each subgroup. Use a table like this (tables for other journals have similar formats):

Table 1: Characteristics of the subjectsa.




sample size



age (y)

22 ± 3

23 ± 4

weight (kg)






Data are means ± standard deviations.
aPut any footnotes here. Note that the caption and footnotes are in cells of the table.


List the measures (variables) you used and explain why you chose them, as shown below. Then describe the assay for each under its own sub-subheading. Give an outline of established procedures and refer the reader to previous published accounts for details; for new procedures show enough detail to allow the reader to reproduce the procedures successfully.

Dependent (outcome) variables: list them. Explain why you chose them.

Independent (predictor) variables: list them. Explain why you chose them. For repeated-measures designs omit the obvious treatment variable, but include numeric and nominal variables you have analyzed as covariates.

Mechanism variables: list them. These are variables in repeated-measures designs that you have assayed to try to explain the effect of the treatment. Explain why you chose them.


Describe the assay for the first measure under a sub-subheading, as shown here. You may wish to group some measures under one sub-subheading, such as Training, Anthropometric, or Environmental Measures.


Describe the assay for the second measure under a sub-subheading, as shown here, and so on. When mentioning a piece of equipment, you must state the model, the manufacturer, and the city and country of origin. Include relevant information on sampling or digitizing rates and data processing that led to the measure.


Name the statistical package or program you used. Describe the statistical procedures. Finish this section with this paragraph, or something similar:

We have used means and standard deviations to represent the average and typical spread of values of variables. We have shown the precision of our estimates of outcome statistics as 95% confidence limits (which define the likely range of the true value in the population from which we drew our sample). The p values shown represent the probability of a more extreme absolute value than the observed value of the effect if the true value of the effect was zero or null. Statistically significant effects are those for which the zero or null value of the effect lies outside the 95% confidence interval (i.e., p < 0.05).


Most journals have separate Results and Discussion sections. I believe this separation makes research articles more difficult to write and read.


How close to reality were your measurements? In a repeated-measures study, how reproducible were the dependent variables? How do the answers to these questions impact your findings? Address such questions about the validity and reliability of your measures here. You can also report any ancillary methodological findings. Use sub-subheadings if you wish.


Summarize the spread of values between subjects with the standard deviation, never with the standard error of the mean. Show the precision of your estimates of outcomes with confidence limits. Try not to mention p values, statistical significance, null hypotheses, type I errors, and type II errors.

State each result and discuss it immediately. Interpret the magnitude of each outcome in a qualitative way, using both your experience of the magnitudes that matter in this area of human endeavor and also any published scales of magnitudes (e.g., Cohen, 1988; Hopkins, 1998). You must interpret the observed effects and the 95% confidence limits. For example, you might have to say that you observed a moderate effect, but that the true value of the effect could be anything between trivial and very strong.

If it is more convenient to show p values than confidence limits, show the exact p value to one significant digit (for p < 0.1) or two decimal places (for p > 0.10). Do not use p < 0.05 or p > 0.05. Examples: p = 0.03; p = 0.007; p = 0.09; p = 0.74. Do not give values of test statistics (F, t, etc.).

Show data in figures rather than in tables or in text. See below for examples (Figures 1-4). Avoid repetition of data in figures, tables and text. For Sportscience articles, follow the instructions in the template on how to create figures. Paste figures and tables into the document after the paragraph where you first refer to them (other journals: tables and figures go at the end of the manuscript).

Figure 1: Informative title for a time seriesa.

Data are means. Bars are standard deviations (shown only for Groups B and C).
aUse letters to label footnotes, if necessary.

Figure 2: Informative title for a scattergram.

Least-squares lines are shown for each variable.

Figure 3: Informative title for a bar graph.

Data are means. Bars are standard deviations.

Figure 4: Informative title for an outcomes figure.

Data are means. Bars are 95% confidence intervals.


Do your findings apply to people in the real world if they have characteristics and behaviors different from the people in your sample? Bring together the outcomes and any technicalities in a statement that addresses this question about the generalizability of your findings to the population of subjects from which you drew your sample. Then speculate about the applicability to other populations, such as athletes of a different caliber, athletes from other sports, and non-athletes. Finish with specific justified suggestions for future research projects rather than a non-specific call for more research.


List the people who have helped you and what they did. List substantial sources of funding for the project.


There is a wide variety of styles for citing and listing references. Make sure you follow the instructions for the journal you are submitting your paper to. These references are in Sportscience style:

Cohen J (1988). Statistical power analysis for the behavioral sciences (second edition). Hillsdale, New Jersey: Lawrence Erlbaum

Hopkins WG (1998). A scale of magnitude for effect statistics. sportsci.org/resource/stats/effectmag.html: Internet Society for Sportscience

Hopkins WG (1999). Guidelines on style for scientific writing. Sportscience 3(1), sportsci.org/jour/9901/wghstyle.html (4397 words)


Check these before you submit your article.

  • You have read the article on style.
  • The Summary does not exceed the word limit for the journal.
  • The Summary includes real data and magnitudes of effects.
  • The content of the Summary is an accurate summary of the content of your article.
  • The content of each section is appropriate to the section.
  • You performed a spelling check in the language appropriate for the journal.
  • References are in the style required by the journal.

Webmastered by Will Hopkins
Published March 1999

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