Assessment and Testing Flashcards
Measurement
general process of determining the dimensions of an attribute or trait
Assessment
processes and procedures for collecting info about human behavior
- assessment tools include tests/inventories, rating scales, observation, interview data, etc.
Appraisal
implies going beyond measurement to making judgments about human attributes and behaviors; used interchangeably with evaluation
Measures of Central Tendency
a distribution of scores (measures on a number of individuals) can be examined using:
mean
median
mode
!! All three of these fall in the same place (are identical) when the distribution of scores is normally distributed (not skewed) !!
Interpretation
making a statement about the meaning or usefulness of measurement data according to the professional counselor’s knowledge and judgment
Mean
the arithmetic average (M)
Median
the middle score in a distribution of scores
1, 2, (3), 4, 5
Mode
the most frequent score in a distribution of scores
1, (2, 2), 3, 4, 5
Skew
the degree to which a distribution of scores is not normally distributed
Positive Skew
The bulk of the scores falls on the left (positive skew = the tail goes out to the more positive values)
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:::::::::::::………………..
Mode, median, mean
Negative Skew
The bulk of the scores falls on the right (negative skews = the tail goes out to the left)
::::::::: ::::::::::: ::::::::::::::::: ............::::::::::::::::::: Mean, median, mode
This graph is messed up but you get the idea
Relationship between mean, median, mode in skewed distributions
- the mode is the top of the curve (most frequent scores)
- the mean is pulled in the direction of the extreme scores represented by the tail of a skewed distribution
Measures of Variability
Range
the highest score minus the lowest score
Measures of Variability
Inclusive range
the high score minus the low score, adding one (1)
Measures of Variability
Standard Deviation (SD)
describes the variability within a distribution of scores
the mean of all the deviations from the mean
Excellent measure of the dispersion of scores
(SD = standard deviation within a sample
sigma = population’s variability)
!! It is NOT equal to variance!! SD is the square root of variance!!!
Measures of Variability
Variance
the square of the standard deviation (SD^2)
does not describe the dispersion of scores as well as SD
- see analysis of variance
Normal Curve
Normal curve
essentially distributes the scores (individuals) into six equal parts - three above the mean and three below mean
Normal Curve
Normal curve distributions
2%, 13.5%, 34%, 34%, 13.5%, 2%
…………………=== 68% ===…………………….1 SD
………======== 95%========……………2 SD
============ 99% ===========….3 SD
Percentile
a value below which a specified percentage of cases falls
- for a score of 75% : this score is higher than 74% of the scores; 25% of the scores are higher than this score
Stanine
from standard nine
converts a distribution of scores into nine parts (1 to 9) with five in the middle and a SD of about 2
Standardized Scores
creates a common language of scores to compare several different test scores for the same individual
- occur by converting raw score distributions
- these derived scores provide for constant normative/relative meaning allowing for comparisons between individuals
- express the person’s distance from the means in terms of the standard deviation of that standard score distribution
- are continuous and have equality of units
- two most commonly used standardized scores: z-scores, t-scores
Standardized Scores
Z-score
mean is 0, SD is 1.0
- range for the SD is -3.0 to +3.0
Study tip: Z-score, Zero is the mean of the distribution
Standardized Scores
T-score
mean of this standardized score is 50 and SD is 10
by Transforming this standard score, negative scores are eliminated (unlike z-score)
Study tip: T-score, Ten is SD
Correlation coefficient
Pearson Product-Moment Correlation Coefficient (r) is most common