Whenever you take a psychometric test either as part of the
selection process or as a practice exercise you will usually see
your results presented in terms of numerical scores. These may be;
raw scores, standard scores, percentile scores, Z-scores, T-scores
or Stens.
Raw Scores
These refer to your unadjusted score. For example, the number of
items answered correctly in an aptitude or ability test. Some
types of assessment tools, such as personality questionnaires,
have no right or wrong answers and in this case, the raw score may
represent the number of positive responses for a particular
personality trait. Obviously, raw scores by themselves are not
very useful. If you are told that you scored 40 out of 50 in a
verbal aptitude test, this is largely meaningless unless you know
where your particular score lies within the context of the scores
of other people. Raw scores need to be converted into standard
scores or percentiles will provide you with this kind of
information.
How Scores are Distributed
Many human characteristics are distributed throughout the
population in a pattern known as the normal curve or bell curve.
This curve describes a distribution where most individuals cluster
near the average and progressively fewer individuals are found the
further from the average you go in each direction.
The illustration above shows the relative heights of a large
group of people. As you can see, a large number of individual
cases cluster in the middle of the curve and as the extremes are
approached, fewer and fewer cases exist, indicating that
progressively fewer individuals are very short or very tall. The
results of aptitude and ability tests also show this normal
distribution if a large and representative sample of the
population is used.
Mean and Standard Deviation
There are two characteristics of a normal distribution that you
need to understand. The first is the mean or average and the
second is standard deviation, which is a measure of the
variability of the distribution. Test publishers usually assign an
arbitrary number to represent the mean standard score when they
convert from raw scores to standard scores. Test X and Test Y are
two tests with different standard score means.
In this illustration Test X has a mean of 200 and Test Y has a
mean of 100. If an individual got a score of 100 on Test X, that
person did very poorly. However, a score of 100 on Test Y would be
an average score.
Standard Deviation.
The standard deviation is the most commonly used measure of
variability. It is used to describe the distribution of scores
around the mean.
The value of the standard deviation varies directly with the
spread of the test scores. If the spread is large, the standard
deviation is large. One standard deviation of the mean (both the
plus and minus) will include 66% of the students' scores. Two
standard deviations will include 95% of the scores.
Percentiles
and Norming >
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