When a test developer calculates an estimate of how much students' scores vary due to unreliability, they are most interest in

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When a test developer wants to determine how much variation exists in student scores due to factors beyond their control, such as measurement error or random factors that can impact test performance, they calculate the standard error of measurement (SEM). This gives them an estimate of the amount of variability in scores that is due to unreliability, and allows them to make more informed decisions about how to interpret and use test scores. A confidence interval is a range of values that is likely to contain the true score, and is not as directly relevant to estimating measurement error. Measures of central tendency (such as means and medians) are also not specific to this purpose, as they describe the distribution of scores rather than the amount of error present. Construct validity is related to the extent to which a test measures the construct or concept that it is intended to measure, but is not relevant to the issue of measurement error.

When a Test Developer Calculates an Estimate of How Much