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A nominal scale classifies data into distinct categories in which no order or rank can be assigned. It's used in scaling to categorize qualitative data, such as by gender or eye color.

An ordinal scale sorts data into categories that are ordered but not necessarily evenly spaced. It's used in scaling to indicate relative position, like class ranks or survey satisfaction levels.

An interval scale is a scale of measurement where each position is equidistant from one another; however, it lacks a true zero (e.g., temperature). In scaling, it's used to measure differences between quantities.

A ratio scale has both equidistant measures and a true zero, which allows for a calculation of ratios. In scaling, it's used to measure absolute quantities like weight or height.

Reliability refers to the consistency or stability of a measure over time. It's used in scaling to ensure that measurements can be repeated reliably under the same conditions.

Validity is the extent to which a test measures what it is supposed to measure. In scaling, it's important for ensuring that instruments accurately capture the intended variable.

A Likert scale measures attitudes by asking respondents to indicate their level of agreement with a statement. It's used in scaling for quantifying subjective data like opinions.

A Guttman scale is a cumulative scale where items are arranged so that agreeing with a certain item implies agreement with items of lower rank. It’s used in scaling for determining the intensity of a particular trait or belief.

A Thurstone scale is designed to measure attitudes by offering statements with assigned numerical values based on the level of agreement. Used in scaling to produce interval level data about attitudes.

Standard deviation measures the amount of variation or dispersion of a set of values. In scaling, it's used to determine the variability of scores around the mean score.

Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables, called factors. Used in scaling to identify underlying relationships between measured variables.

IRT is a model for designing, analyzing, and scoring tests. In scaling, it assesses the probability of a given response to a specific item, based on the trait measured by the item.

CTT assumes that each person has a true score that could be measured perfectly if there were no measurement errors. It's used in scaling to estimate reliability and accuracy of test scores.

Scale anchoring refers to the practice of providing a clear description of what each score on a particular measure represents. It's used in scaling to give meaning to numerical scores by describing qualitative benchmarks.

Test equating involves statistically adjusting scores on different forms of a test so that scores can be comparable. It's used in scaling to ensure that measures of ability are consistent over different test versions or administrations.

A ceiling effect occurs when test scores cluster toward the high end, making it difficult to distinguish between high-performing individuals. In scaling, it's important to avoid this to effectively measure high levels of an ability or trait.

A floor effect happens when a large number of individuals score at the lower end of a scale, leading to a clustering that makes it hard to discern differences in low performance. In scaling, it's important to mitigate this to measure lower levels of an ability or trait effectively.

Content scaling involves creating a measurement instrument based on the domain of content to be covered, aiming for a representative sample of that content. It's used in scaling to ensure that the scope and domain of a test are appropriately reflected.

Convergent validity refers to the extent to which two different measures of the same concept agree with each other. It's used in scaling to demonstrate that the scale correlates well with other measures of the same construct.