#6

The precision of the average test score can be obtained from
the math model in

**two ways**: directly from the mean sum of squares (MSS) or variance, and traditionally, by way of the test reliability (KR20).
I obtained the

**precision of each individual student test score**from the math model by taking the square root of the sum of squared deviations (SS) within each score mark pattern (green, Table 25). The value is called the conditional standard error of measurement (**CSEM**) as it sums deviations for one student score (one condition), not for the total test.
I multiplied the mean sum of squares (MSS) by the number of
items averaged (21) to yield the SS (0.15 x 21 = 3.15 for a 17 right mark score)
(or I could have just added up the squared deviations). The SQRT(3.15) = 1.80 right
marks for the CSEM. Some 2/3 of the time a

**re-tested score**of 17 right marks can be expected to fall between 15.20 and 18.80 (15 and 19) right marks (Chart 70).
The test Standard Error of Measurement (

**SEM**) is then the average of the 22 individual**CSEM**values (1.75 right marks or 8.31%).
The

**traditional derivation**of the test SEM (the error in the average test score) combines the test reliability (KR20) and the**SD**(spread) of the average test score.
The SD (2.07) is from the SQRT(MSS, 4.08) between student
scores. The test reliability (0.29) is the ratio of the true variance (MSS,
1.12) to the total variance (MSS, 4,08) between student scores (see previous
post).

The

**expectation**is that the greater the reliability of a test, the smaller the error in estimating the average test score. An equation is now needed to transform variance values on the top level of the math model to apply to the lower linear level.
SEM = SQRT(1 – KR20) * SD = SQRT(1 – 0.29) * 2.07 = SQRT(0.71)
* 2.07 = 0.84 * 2.07 = 1.75 right marks.

The

**operation**of “1 – KR20” aligns the value of 0.71 to extract the portion of the SD that represents the SEM. If the test reliability goes up, the error in estimating the average test score (SEM) goes down.
Chart 70 shows the variance (MSS), the SS, and the CSEM
based on 21 items, for each student score. It also shows the distribution of
the

**CSEM values that I averaged for the test SEM**.
The i

**ndividual CSEM**is highest (largest error, poorer precision) when the student score is 50% (Charts 65 and 70). Higher student scores yield lower CSEM values (better precision). This makes sense.
The

**test SEM**(the average of the CSEM values) is related to the distribution of student test scores (purple dash, Chart 70). Adding easy items (easy in the sense that the students were well prepared) decreases error, improves precision, reduces the SEM.
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