Theories, Models, and Hypotheses
Materials and methods
Results: presenting data
Results:
interpreting data
Conclusions
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Results: Interpretation of Data
It is unlikely that you will you title a separate section of your report
“Interpretation of Data.” Usually, this section, combined with
your presentation of data, will be called
“Results” or “Discussion.” Sometimes, presentation
of data and interpretation of data may be split into separate categories,
with presentation called “Results,” and interpretation called
“Discussion.” Regardless of its title, interpretation of data
is also crucial to a successful research report.
This section of the report is important because it demonstrates the meaning
of your research. Without this section, readers will not necessarily understand
what your research proves, or they might not see how it differs from or
improves on other research. In this section you will interpret your results
and your research as a whole and discuss the relationship of your findings
to earlier research. This section of the paper draws upon writing skills
that other sections do not because you need to write persuasively in this
section as you convince readers that your interpretation of data is logical
and correct. As you develop your argument in this section, consider arranging
your evidence in the order that best highlights your main point, cite
authorities that have come to similar interpretations under similar circumstances,
and consider the superiority of your conclusions to opposing viewpoints.
Your interpretation will be most convincing if it proceeds logically.
There may be many ways to organize your interpretation of data logically;
consider your readers’ needs to help you decide how to organize your
information:
- What does your reader need to know before anything else in order to
understand and be persuaded to believe your argument?
- What does your reader need to know next, or what naturally follows
from this first idea?
- What is the most important thing for your reader to understand from
your interpretation? Consider placing this first.
One basic way to organize your information logically is to move from
what you are most certain about to what you are least certain about. For
most research reports, the most certain part of your case will be your
data, and many research reports will develop along this outline:
- begin with a discussion of the data
- move on to generalize about or analyze the data
- consider how the data addresses the research problem or hypothesis
outlined in the Introduction
- discuss what can be inferred from the data as they relate to other
research and scientific concepts
It is also very important for you to identify the nature and extent of
any limitations of your research in this section of your report, especially
if your results are inadequate, negative, or not consistent with earlier
studies or with your own hypothesis. Do not try to defend your research
or minimize the seriousness of the limitation in your interpretation;
instead, focus on the limitation only as it affects the research and try
to account for it.
The authors of “Birth Weight And Cognitive Function In The British
1946 Birth Cohort: Longitudinal Population Based Study,” published
in the British Medical Journal, provide a particularly clear example
of a section in which they interpret the results of their study and consider
the limitations of their research.
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Interpretation
Caution is needed in the interpretation of repeated tests of birth
weight with different outcomes, particularly when different numbers
are included in each analysis. The problem of assessing cognitive
change over time is compounded because there is no single cognitive
test that can be used throughout life, as cognitive measures must
change with cognitive development. Repeated measures models, unlike
the conditional regression models used here, are dependent on the
outcome scale used, and standardised scores may not be a realistic
scaling in this respect as they assume no cognitive growth with
age, and no increased variation in scores with age occurs. [16]
Further investigation of these data using such models is in progress.
We took a more simple approach here, considering the association
between birth weight at the earliest time point then assessing the
influence of birth weight on subsequent relative changes in cognitive
function. Regression to the mean [17] [18] occurs when fitting such
models, as they assume that the score at the earliest age is fixed
(that is, measured without error). However, for the measurement
error to have a substantial impact on the association between birth
weight and change in cognitive score presented here, the cognitive
test scores would have to be notably unreliable.
If birth weight is associated with cognitive function in the general
population, explanatory factors must be similarly distributed in
the normal population. From this perspective, birth weight is strongly
related to head circumference at birth [1] which in turn is closely
correlated with brain size [19] and so is associated with childhood
cognitive function. [20] The most parsimonious explanation for the
current results, therefore, is that the relations between these
variables, established for comparisons between low and normal birthweight
children, also hold across the normal range in the general population.
At the neurochemical level, birth weight is associated with insulin-like
growth factors, [21] and interest has been growing in the role of
glucose metabolism, insulin, and insulin-like growth factors in
the development of the central nervous system and cognitive function.
[22] [23] How these processes are distributed in the population
is not known. However, three key risk factors for low birth weight,
nutrition, smoking, and alcohol misuse [15] all influence brain
glucose concentrations or the function of insulin-like growth factors,
[1] [24] [25] although the pathways are likely to be complex. A
reduction in birth weight after maternal starvation in the Dutch
famine cohort of the 1940s, for example, was not associated with
subsequent cognitive performance. [26]
What is already known on this topic
- Low birth weight is associated with poor cognitive development
- Few studies have examined this association across the full birthweight
range in the normal population
What this study adds
- Birth weight is significantly associated with cognitive ability
at age 8 years, through adolescence, and into early adulthood,
independent of social background
- The associations between birth weight and cognitive function
at ages 8, 11, and 15 are evident across the normal birthweight
range (>2.5 kg) and so are not accounted for exclusively by
low birth weight
- Birth weight is also associated with educational attainment,
suggesting that the association between birth weight and cognition
may have functional implications
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