entify the data set's level of measurement.
gender profile of the 111th congress
average january snowfall for 15 cities
http://drdavespsychologypage.intuitwebsites.com/Two___Two_____four.pdf
With this info, you should be able to decide.
To identify the level of measurement for each dataset, we need to understand the different types of measurement scales. There are four commonly recognized levels of measurement: nominal, ordinal, interval, and ratio. Let me explain each one to you:
1. Nominal: This level of measurement involves categorical data without any inherent order or numerical value. It simply classifies data into different categories. Examples include gender (male or female), political affiliation (Democrat or Republican), or colors (red, blue, green, etc.). In this case, the gender profile of the 111th congress falls under the nominal level of measurement as it categorizes individuals as either male or female.
2. Ordinal: This level of measurement categorizes data similar to nominal, but it also carries an order or ranking. However, the differences between the categories are not precisely defined. Examples include educational levels (high school, bachelor's, master's, etc.) or rank in a competition (first place, second place, third place, etc.). If the dataset had additional information about the ranking or importance of each gender category in the 111th congress, it could be considered ordinal.
3. Interval: This level of measurement not only classifies data into categories but also measures the differences between them. It does not have a true zero point and can be negative. Examples include temperature measured in degrees Celsius or Fahrenheit. However, in the given dataset, the average January snowfall for 15 cities, the measurements are quantitative, so it falls under the next level, ratio measurement.
4. Ratio: This is the highest level of measurement where data is categorically classified, the differences between categories are measured, and it has a true zero point. The ratio scale allows for meaningful ratios between data points. Examples include height, weight, or distance. In the given dataset, the average January snowfall for 15 cities is a ratio measurement as it quantifies the amount of snowfall, starting from zero.
In conclusion, the gender profile of the 111th congress is a nominal level of measurement, while the average January snowfall for 15 cities is a ratio level of measurement.