2. Also called as z scores
Measures the difference
between the raw score and the
mean of the distribution using
standard deviation of the
distribution as a unit of
4. By itself, a raw score or X
value provides very little
information about how that
particular score compares
with other values in the
distribution.
5. A score of X = 53, for
example, may be a
relatively low score, or an
average score, or an
extremely high score
depending on the mean and
standard deviation for the
distribution from which the
score was obtained.
7. If the raw score is
transformed into a z-score,
however, the value of the z-
score tells exactly where the
score is located relative to
all the other scores in the
distribution.
8. 𝑧 =
(𝑥 − 𝑥)
𝑠
Where:
Z = standard score/z-score
X = Raw Score
𝒙 = Mean
S = Standard Deviation
9. 𝑧 =
(𝑥 − 𝜇)
𝜎
Where:
Z = standard score/z-score
X = Raw Score
𝝁 = Mean
𝝈 = (sigma) Standard Deviation
10. Z-scores can be positive
(above the mean),
negative (below the
mean), or zero (equal to
the mean)