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Chapter 1: Problem 31

Determine the level of measurement of each variable. Nation of origin

Short Answer

Expert verified
'Nation of origin' is nominal.

Step by step solution

01

Understand the Question

First, identify the variable given in the exercise, which is 'Nation of origin'. Determine what type of information this variable represents.
02

Review Levels of Measurement

Recall that there are four levels of measurement: nominal, ordinal, interval, and ratio. Nominal involves categorizing data without a specific order. Ordinal involves categorizing data with a specific order. Interval involves ordered data with equal intervals, but no true zero. Ratio involves ordered data with equal intervals and a true zero point.
03

Classify 'Nation of origin'

Think about the nature of 'Nation of origin'. It refers to categories (different countries) and there is no inherent order. For instance, 'Canada' is not inherently higher or lower than 'France'.
04

Determine the Level of Measurement

Based on the information, 'Nation of origin' is a category without a specific order, which makes it nominal data.

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Nominal Data
Nominal data is used for labeling variables without any quantitative value. It is about categorizing data without any order or rank.
These variables are mutually exclusive, meaning an individual cannot belong to more than one category at a time.
  • Example: Gender, with categories like 'male' and 'female'.
  • Example: Nation of origin, like 'Canada', 'France', and 'Japan'.
It's important to note that in nominal data, one category is not better or higher than another. This type of data allows for counting of frequencies but does not support mathematical operations.
Ordinal Data
Ordinal data represents categories with a meaningful order, but the intervals between the categories are not necessarily equal.
This type of measurement allows for ranking but does not provide meaningful differences between ranks.
  • Example: Education level, with categories like 'high school', 'bachelor's degree', and 'master's degree'.
  • Example: Customer satisfaction ratings, such as 'satisfied', 'neutral', 'dissatisfied'.
While you can classify and rank the data, arithmetic operations like addition or subtraction are not appropriate for ordinal data.
Interval Data
Interval data is numeric data with equal intervals between measurements, but with no true zero point.
This means that ratios are not meaningful, but differences are.
  • Example: Temperature in Celsius or Fahrenheit, where the difference between 20掳C and 30掳C is the same as between 30掳C and 40掳C.
  • Example: Dates on a calendar, such as the difference in days or years is consistent.
Since there is no true zero, you cannot compare the data in terms of proportions, but you can perform addition and subtraction.
Ratio Data
Ratio data has all the properties of interval data, but it also includes a true zero point.
This means that the values can be compared in terms of ratios and proportions.
  • Example: Height and weight, where a person weighing 60 kg is twice as heavy as a person weighing 30 kg.
  • Example: Age, where 20 years old is twice as old as 10 years old, and a true zero age exists (birth).
Ratio data supports all arithmetic operations, including multiplication and division, because of its true zero point. This makes it the highest level of measurement in terms of the amount of detail it provides.

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