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Tuesday, October 10, 2023

Interpreting Test Scores by ordering and ranking|Educational Assessment and Evaluation

QUESTION

Write how to interpret test scores by ordering and ranking?

CourseEducational Assessment and Evaluation

Course code 8602

Level: B.Ed Solved Assignment 

ANSWER  

Interpreting Test Scores by ordering and ranking

Organizing and reporting students’ scores starts with placing the scores in ascending or descending order. Teachers can find the smallest, largest, range, and some other facts like the variability of scores associated with scores from ranked scores. Teacher may use ranked scores to see the relative position of each student within the class but ranked scores does not yield any significant numerical value for result interpretation or reporting.

Measurement Scales

 Measurement is the assignment of numbers to objects or events in a systematic fashion. Measurement scales are critical because they relate to the types of statistics you can use to analyze your data. An easy way to have a paper rejected is to have used either an incorrect scale/statistic combination or to have used a low-powered statistic on a high-powered set of data. The following four levels of measurement scales are commonly distinguished so that the proper analysis can be used on the data a number can be used merely to label or categorize a response.

Nominal Scale.

Nominal scales are the lowest scales of measurement. A nominal scale, as the name implies, is simply some placing of data into categories, without any order or structure. You are only allowed to examine if a nominal scale datum is equal to some particular value or to count the number of occurrences of each value. For example, the categorization of blood groups of classmates into A, B AB, O, etc. The only mathematical operation we can perform with nominal data is to count. Variables assessed on a nominal scale are called categorical variables; Categorical data are measured on nominal scales which merely assign labels to distinguish categories. For example, gender is a nominal scale variable. Classifying people according to gender is a common application of a nominal scale.

Nominal Data

 • classification or categorization of data, e.g. male or female

• no ordering, e.g. it makes no sense to state that male is greater than female (M > F), etc

• arbitrary labels, e.g., pass=1 and fail=2, etc

Ordinal Scale.

 Something measured on an "ordinal" scale does have an evaluative connotation. You are also allowed to examine if an ordinal scale datum is less than or greater than another value. For example rating of job satisfaction on a scale from 1 to 10, with 10 representing complete satisfaction. With ordinal scales, we only know that 2 is better than 1 or 10 is better than 9; we do not know by how much. It may vary. Hence, you can 'rank' ordinal data, but you cannot 'quantify' differences between two ordinal values. Nominal scale properties are included in the ordinal scale.

Ordinal Data

• ordered but differences between values are not important. Differences between values may or may not same or equal.

 • e.g., political parties on left to right spectrum given labels 0, 1, 2

• e.g., Likert scales, rank on a scale of 1..5 your degree of satisfaction

 • e.g., restaurant ratings

 

Interval Scale

An ordinal scale has quantifiable differences between values and becomes an interval scale. You are allowed to quantify the difference between two interval scale values but there is no natural zero. A variable measured on an interval scale gives information about more or better than ordinal scales do, but interval variables have an equal distance between each value. The distance between 1 and 2 is equal to the distance between 9 and 10. For example, temperature scales are interval data with 25C warmer than 20C and a 5C difference has some physical meaning. Note that 0C is arbitrary so it does not make sense to say that 20C is twice as hot as 10C but there is the exact same difference between 100C and 90C as there is between 42C and 32C. Students’ achievement scores are measured on an interval scale

Interval Data

• ordered, constant scale, but no natural zero

• differences make sense, but ratios do not (e.g., 30°-20°=20°-10°, but 20°/10° is not twice as hot!

 • e.g., temperature (C, F), dates

Ratio Scale

Something measured on a ratio scale has the same properties that an interval scale has except, with a ratio scaling, there is an absolute zero point. Temperature measured in Kelvin is an example. There is no value possible below 0 degrees Kelvin, it is absolute zero. Physical measurements of height, weight, and length are typically ratio variables. Weight is another example, 0 lbs. is a meaningful absence of weight. This ratio holds true regardless of which scale the object is being measured in (e.g. meters or yards). This is because there is a natural zero.

Ratio Data

• ordered, constant scale, natural zero

 • e.g., height, weight, age, length One can think of nominal, ordinal, interval, and ratio as being ranked in their relation to one another. Ratio is more sophisticated than interval, interval is more sophisticated than ordinal, and ordinal is more sophisticated than nominal.


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