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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.
Gene Name1 (statistics), Jan Name2 (research assistance), Granting
Adrien Referee1, Jo Referee2.
eAuthorc=AT=server.host.edu (Erin Authorc)
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.
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
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
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
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.
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
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
Please read the accompanying editorial for detailed advice on the
various aspects of style for articles at the Sportscience site
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
Table 1: The effect of
whatever on the performance of athletes in whatever
male international distance runners
2% decrease in 10-km time
Bloggs et al. (1997)
female club runners
use this table in your article, remove the frame so that you
can position the table.
2Number footnotes as shown.
Table 2. Events in the
development of whatever in whatever sports1.
US National Association formed
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
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
Cohen, J. (1988). Statistical power analysis
for the behavioral sciences. (2nd ed.). Hillsdale, NJ: Lawrence
Hopkins, W. G. (1997). Advice on style for
contributors to the Sportscience website. Sportscience, 0, 00-00,
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).
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
References are in
Part numbers are
included for journals and magazines like Physician and