Chapter 5 Flashcards
z-scores
z-scores use the mean as a reference point to determine whether the score is above or below average. A z-score also uses the standard deviation as a yardstick for describing how much an individual score differs from average
- a z-score will tell you if your score is above the mean by a distance equal to two standard deviations, or below the mean by one-half of a standard deviation
raw scores
original, unchanged scores that are the direct result of measurement are called raw scores
purposes of z-scores
- Each z-score tells the exact location of the original X value within the distribution.
- The z-scores form a standardized distribution that can be directly compared to other distributions that also have been transformed into z-scores.
The z-score accomplishes describing the position of a score by transforming each X value into a signed number (+ or −) so that…
- the sign tells whether the score is located above (+) or below (−) the mean, and
- the number tells the distance between the score and the mean in terms of the number of standard deviations.
z-score
A z-score specifies the precise location of each X value within a distribution. The sign of the z-score (+ or −) signifies whether the score is above the mean (positive) or below the mean (negative). The numerical value of the z-score specifies the distance from the mean by counting the number of standard deviations between X and mu
formula for transforming scores into z-scores
z = X-mu / standard deviation (o)
The deviation score (X-mu) is then divided by o because we want the z-score to measure distance in terms of standard deviation units.
deviation score
X-mu
- it measures the distance in points between X and μ and indicates whether X is located above or below the mean
z-score transformation
A transformation that changes raw scores (X values) into z-scores.
standardized distribution
A standardized distribution is composed of scores that have been transformed to create predetermined values for μ and o (standard deviation). Standardized distributions are used to make dissimilar distributions comparable.
Standardized scores
A score that has been transformed into a standard form.
The procedure for standardizing a distribution to create new values for
μ and o is a two-step process:
The original raw scores are transformed into z-scores.
The z-scores are then transformed into new X values so that the specific
μ and o are attained.
for a sample, each X value is transformed into a z-score so that
- The sign of the z-score indicates whether the X value is above (+) or below (−) the sample mean, and
- The numerical value of the z-score identifies the distance from the sample mean by measuring the number of sample standard deviations between the score (X) and the sample mean (M).
The transformed distribution of z-scores will have the same properties that exist when a population of X values is transformed into z-scores. Specifically,
- The sample of z-scores will have the same shape as the original sample of scores.
- The sample of z-scores will have a mean of M=0.
- The sample of z-scores will have a standard deviation of s=1 .