High-Range Tests

Norming

Rules and Standards



Preliminary requires at least 10 test-takers with at least 5 that submit tests that have high correlation with my test(s).
Full Norm requires at least 30 test-takers with at least 15 that submit tests that have high correlation with my test(s).
Only professional tests will be used in my norming process with various equations and percentage of solvability to make sure items are without defect.
My future tests will all be untimed and manually scored with all info sent to my email address.
Only a raw will be listed on my test certificates. This will keep the IQ accurate as per the most up-to-date norm.


Psychological Tests



Some psychological tests are also self-reports. This time, however, the distinction is that the term applies to inventories of items that been given to large numbers of people and carefully polished. They are also subjected to statistical analysis so that when they are given to an individual, the researcher or clinician has a much better idea of what the results mean. There are two general types of psychological tests: those that assess one or another type of ability and those that assess personality. Ability tests come in two varieties: intelligence tests assess your intellectual/cognitive functioning and achievement tests assess the progress you've made in acquiring academic or other skills. Clinicians often include intelligence tests in diagnosis; researchers use them the same way that they use self-reports as noted earlier. Achievement tests assess progress during grade school, and some are intended to predict success in college or other advanced training. Personality tests assess relatively stable personality characteristics, using various forms of self-reports. They also are used in diagnosing and researching mental and behavioral disorders.

Statistics



Correlation



In correlation, the resulting number can range from 0 to +1.00 or 0 to -1.00. Where it falls indicated the strength of the correlation. The sign of the correlation indicates its direction. A correlation of 0 is nonexistent; a correlation of either +1.00 or -1.00 is perfect.

For example, if the test data turns out to be noticeable, perhaps about +.63. The "63" tells us we have a relatively strong correlation, and the "+" tells us that it varies in the same direction.

A correlation between 0 and about +.20(or 0 and -.20) is weak.
A correlation between +.20 and +.60(-.20 and -.60) is moderate.
A correlation between +.60 and +.1.00(-.60 and -.1.00) is strong.

Normal curve is a special frequency polygon in which the scores are symmetrically distributed around the mean, median, and mode are well located on the same point on the curve, with scores decreasing as the curve extends from the mean.

Bell curve is an alternate name for the normal curve, which is said to be shaped like a bell.

Mean is the most commonly used measure of central tendency, the arithmetic average of a distribution of numbers. That simply indicates that you add up all the numbers in particular set and then divide them by how many numbers there are.

If you want a truer measure of central tendency in such a case, you need one that isn't affected by extreme scores. The median is just such a measure. A median is the score that falls in the middle of an ordered distribution of scores. Half of the scores will fall above the median, and the other half of the scores will fall below the median.

Standard deviation formula:

To calculate the standard deviation, we 1. Subtract each score from the mean to get a deviation score --> (X-M). 2. We square each deviation score --> (X-M)squared. 3. We add them up. Remember that's what the sigma (E) indicates --> E (X-M)squared. 4. We divide the sum of the squared deviation by N(the number of scores) --> E(X-M)squared/N 5. We take the square root of the sum for our final step.

The z score is a statistical measure that indicates how far away from the mean a particular score is in terms of the number of standard deviations that exist between and that score. It is always possible to compare different test scores or sets of data that come close to a normal curve distribution. This is done by computing a z score, which indicates how many standard deviations you are way from the mean. It is calculated by subtracting the mean from your score and dividing by the standard deviation. For example, if you had an IQ of 115, your z score would be 1.0. If you had an IQ of 70, your z score would be -2.0. So on any test, if you have a positive z score, you did relatively well. A negative z score means you didn't do as well. The formula for a z score is: Z = (X-M)/SD