editor=AT=sportsci.org · webmaster=AT=sportsci.org · Homepage · Copyright ©1997
Affiliations: Alison Authora MS (research student) and Erin Authorc PhD (lecturer), Department Name, Institution, City, State Zipcode, Country; Dale Authorb DPhil (lecturer) on study leave from Another Dept, Institution, City, State Zipcode, Country. Acknowledgments: Gene Name1 (statistics), Jan Name2 (research assistance), Granting Body (funding). Reviewers: Adrien Referee1, Jo Referee2. Correspondence: eAuthorc=AT=server.host.edu (Erin Authorc)
SUMMARY
In this Background section, make the topic interesting by explaining it in plain language and by relating it to actual or potential practical applications. Explain any scientific principles underlying the topic. Define and justify the scope of the review: why you are limiting it to certain sports, why you are including studies of non-athletes and non-human species, and so on.
Before we move onto the next section, there are a few points about length and format that need to be got out of the way.
LITERATURE
Be specific about any database search you performed. Include the key words you used, and the ways you refined your search if necessary. For example: "A search for overtrain* produced 774 references, which reduced to 559 when we limited the search to intermediate or advanced levels (not le=basic). Further restricting the search to psych* or mood produced 75 references. We read 47 of these as full papers. Of the 41 papers cited in this review, we were able to obtain the following only in abstract form: Jones et al., 1979; Smith and Brown, 1987." Describe and justify briefly any papers or areas that you decided not to include. FINDINGS
We have identified four themes for this section: assessing the quality of published work; interpreting effects; points of grammar and style; and a few remarks about tables and figures. These themes are dealt with under subheadings. We encourage you to use such subheadings, which will make it easier for you to write the review and easier for others to read it.
Quality of Published Work
Look critically at any published work. The fact that something has been published does not mean it is worthwhile.
Some research designs are better than others. The most trustworthy conclusions are those reached in double-blind randomized controlled trials with a representative sample of sufficient size to detect the smallest worthwhile effects. The weakest findings are those from case studies. In between are cross-sectional studies, which are usually plagued by the problem of interpreting cause and effect in any relationship detected.
How subjects was sampled is an important issue. You can be confident about generalizing results to a population only if the sample was selected randomly from the population and there was a low proportion of refusals and dropouts (<30%).
Be wary of generalizing results from novice athletes to elites: something that enhances performance in young or untrained individuals may not work so well in highly trained athletes, who may have less headroom for improvement.
There are big differences in the way data can be collected. At one extreme are qualitative methods, in which the researcher interviews subjects "in depth" without using formal psychometric instruments (questionnaires). At the other extreme are quantitative methods, in which biological or behavioral variables are measured with instruments or techniques of known validity and reliability. In the middle are techniques with uncertain precision and questionnaires with open-ended responses.
Qualitative assessment is time consuming, so samples are usually small in size and non-representative, which in turn limit the conclusions that can be made about effects in a population. The conclusions may also be biased by the prejudices of the researcher-interviewer. Quantitative data collection is more objective, but for some projects it could miss important issues that would surface in an interview. A combination of qualitative methods for pilot work and quantitative methods for a larger study should therefore produce valuable conclusions, depending, of course, on the design.
You will probably find that your topic has been dealt with to some extent in earlier reviews. Cite the reviews and indicate the extent to which you have based your review on them. Make sure you look at the key original papers cited in any earlier reviews, to judge for yourself whether the conclusions of the reviewers are justified.
Reviews, like original research, vary in quality. Problems with reviews include poor organization of the material and lack of critical thought. Some of the better reviews attempt to pull together the results of many papers using the statistical technique of meta-analysis. The outcomes in such reviews are usually expressed as effect sizes, correlations, relative risks, or odds ratios, terms that you will have to understand and interpret in your review if you meet them.
Interpreting Effects
You cannot assess quantitative research without a good understanding of effects and statistical significance. An effect is simply an observed relationship between variables in a sample of subjects. It's also known as an outcome. If an effect is statistically significant, there is probably an effect in the population from which the sample was drawn. In short, statistically significant effects are likely to be real effects. A p value is often used to indicate statistical significance. P values less than 0.05 indicate effects significant at the 5% level.
Problems of interpretation arise when a statistically nonsignificant effect (p>0.05) is obtained. If the sample size is too small--as in almost all studies in sport and exercise science-a statistically nonsignificant effect does not exclude the possibility of a real effect in the population. Authors of small-scale studies who do not understand this point will interpret a statistically nonsignificant effect incorrectly as evidence for no relationship. Whenever you see a result that is not statistically significant, ignore what the author says and look yourself at the size of the effect in question: if it's around zero and the sample size isn't too small, chances are there is indeed no relationship in the population; if the effect is large, there may well be a substantial relationship. But in either case, more research is required to be sure about what is going on. Sometimes the research may have been done: for example, moderate but nonsignificant effects in several studies probably add up to a moderate real effect, if the designs were trustworthy.
A more enlightened approach to the problem of statistical significance is to show outcomes with confidence intervals or limits rather than p values. A 95% confidence interval represents the range within which you can be 95% sure that the true (population) value will fall. For example, if the 95% confidence interval for the effect of a training program on jump height is 3 to 15 mm, then the real effect of the program could be (with 95% certainty) an enhancement of as little as 3 mm or as much as 15 mm. If the confidence interval overlaps zero (for example, -7 mm to +25 mm), then it should be clear that in reality the program could work well (up to 25 mm) but it could also have a negative effect (down to -7 mm). A confidence interval that overlaps zero is equivalent to a statistically nonsignificant effect at the 5% level, but the confidence interval tells you much more than the fact that the p value is greater than 0.05. As yet, few papers in sport and exercise science give confidence intervals on outcomes, and it is sometimes difficult or impossible to calculate them from the data in the papers.
How big is a moderate effect anyway? And what about large effects, small effects, and trivial effects? As a reviewer, make sure you look closely at the effects and interpret their magnitude, regardless of whether they are statistically significant. The authors often don't. There are two approaches: statistical and practical.
In the statistical approach, effects or outcomes are expressed as statistics that are independent of the units of measurement of the original variables. These statistics are the same ones referred to in the previous subsection: effect size, correlation, relative risk, and odds ratio. Statisticians have come up with rules of thumb for deciding whether the magnitude of the effect is to be considered trivial, small, moderate, or large. For example, an effect size of 0.2 and a correlation of 0.1 are often considered to be the smallest effects worth detecting (Cohen, 1988). In the practical approach, you look at the size of the effect and try to decide whether, for example, it would make any difference to an athlete's position in a competition. For many events, a difference in performance of 1% or even less would be considered worthwhile. This practical approach is easier to understand.
For a full treatment of the statistical issues raised here, see A New View of Statistics.
Style
Please read the accompanying editorial for detailed advice on the various aspects of style for articles at the Sportscience site (Hopkins, 1997).
Tables and Figures
You may find that a table is a good way to give an overview of a topic or summarize the results of a large number of publications. Examples are shown in Table 1 and Table 2. Use these tables, and add or delete columns or rows as necessary. Copy and paste the whole table if you need more than one. Do not try to create a table from scratch.
Table 1: The effect of whatever on the performance of athletes in whatever sports^{1}.
subjects ^{2}
findings
reference
Sport1
male international distance runners
2% decrease in 10-km time
Bloggs et al. (1997)
female club runners
whatever
whoever
Sport2
^{1}If you use this table in your article, remove the frame so that you can position the table. ^{2}Number footnotes as shown.
Table 2. Events in the development of whatever in whatever sports^{1}.
SportA
1947
US National Association formed
1956
Whatever
1968
SportB
^{1}Put any footnotes here. Note that the caption and footnotes are in cells of the table.
A table spanning two columns will be retained as such in the reprint of your review, but in the Web version the width will be reduced to about 70% of the original (depending on the width of the reader's browser window). You should therefore space the columns a little more widely than usual. If you have too many columns of data for one table, consider making two smaller tables.
Try to include a key graph or diagram from a paper, or draw one yourself, to liven up the appearance of the review. Make sure you get copyright clearance for any verbatim copying.
Do not use scanned images of graphs or diagrams, because the lines and symbols become too "pixelly." Redraw the figures directly in a computer, using preferably Microsoft Graph or the drawing window of Microsoft Word. Whenever possible, figures should be exactly one column wide (8.3 cm). Do not make figures any wider than 1.5 columns (12.5 cm), because they need to be viewable on the Web without the viewer having to scroll sideways. Place the title and any footnotes for the figure within the figure itself, not in the body of the text. Use Arial or Helvetica fonts, and make sure the font sizes are similar to that of the body of the text (12-pt Times New Roman) when the figure appears at its final magnification on the page.
Hierarchical diagrams summarizing the relationships between concepts or variables can be confusing. Make them as simple as possible.
Place each figure or table immediately after the paragraph that first refers to it (Figure 1). The editorial staff at Sportscience will reposition it to achieve the best appearance in the reprint document.
CONCLUSIONS
FURTHER RESEARCH
REFERENCES
Cohen, J. (1988). Statistical power analysis for the behavioral sciences. (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
Hopkins, W. G. (1997). Advice on style for contributors to the Sportscience website. Sportscience, 0, 00-00, http://www.sportsci.org/journal/jour9701/style/style.htm.
Check these before you submit your review.
The authors have read the editorial on style.
The style of the title page is identical to the template, including punctuation.
The Summary is absolutely no longer than 200 words (including the subheading words).
For relevant reviews, the Summary includes real data and magnitudes of effects.
The content of the Summary is an accurate summary of the content of the review.
The content of each section is appropriate to the section.
A US-spelling check was performed.
References are in APA-published style.
Part numbers are included for journals and magazines like Physician and Sportsmedicine.