Gt Data Definitions
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DATA Types of Data I.
Quantitative Data: represented by a number and a unit of measurement based on a standard scale with equal intervals. Examples of standard scales: metric system or Arabic system of numbers. It could also be a number count, like how many freckles a person has. Examples of quantitative variables: height of a person in meters, mass of rabbits in kilograms, number of seeds germinated. 1.
continuous quantitative data: collected using standard measurement scales divisible into partial (meaning you could have less than, or part of one) units. Examples: distance in kilometers (1.2 km) and volume in liters (1.5 L)
2.
discrete quantitative data: collected using standard scales in which only whole integers are used. Examples: number of wolves born in a year, the number of people who can touch their toes
3. ratio data: when data is collected using a standard scale with equal divisible intervals and an absolute zero. Examples: temperature of a gas on the Kelvin scale, the velocity of an object in m/sec, and the distance from a point in meters. This data can be used in a ratio and proportion. 4. interval data: data that does not have an absolute zero. Example: temperature of a substance on the Celsius scale (water molecules are still moving at 0 degrees Celsius. This data CANNOT be used in a ratio and proportion.
II.
Qualitative Data: classified into categories. The categories may be discrete categories represented by a word or “number” label or measurements made with a nonstandard scale with unequal intervals. The categories are created by the experimenter. Examples include: gender of an organism and color 1. nominal data: when objects have been named or placed into discrete categories that cannot be rank ordered Examples: hair color (red, brown, black) or gender (male/female) 2. ordinal data: when objects have been placed into categories that CAN be rank ordered (best or worst). Examples: animal activity can be rated on a scale of 1 to 5, with 5 being the most active also Moh’s Hardness Scale for Minerals (diamonds are the strongest)
Quantitative Data: represented by a number and a unit of measurement based on a standard scale with equal intervals. Examples of standard scales: metric system or Arabic system of numbers. It could also be a number count, like how many freckles a person has. Examples of quantitative variables: height of a person in meters, mass of rabbits in kilograms, number of seeds germinated. 1.
continuous quantitative data: collected using standard measurement scales divisible into partial (meaning you could have less than, or part of one) units. Examples: distance in kilometers (1.2 km) and volume in liters (1.5 L)
2.
discrete quantitative data: collected using standard scales in which only whole integers are used. Examples: number of wolves born in a year, the number of people who can touch their toes
3. ratio data: when data is collected using a standard scale with equal divisible intervals and an absolute zero. Examples: temperature of a gas on the Kelvin scale, the velocity of an object in m/sec, and the distance from a point in meters. This data can be used in a ratio and proportion. 4. interval data: data that does not have an absolute zero. Example: temperature of a substance on the Celsius scale (water molecules are still moving at 0 degrees Celsius. This data CANNOT be used in a ratio and proportion.
II.
Qualitative Data: classified into categories. The categories may be discrete categories represented by a word or “number” label or measurements made with a nonstandard scale with unequal intervals. The categories are created by the experimenter. Examples include: gender of an organism and color 1. nominal data: when objects have been named or placed into discrete categories that cannot be rank ordered Examples: hair color (red, brown, black) or gender (male/female) 2. ordinal data: when objects have been placed into categories that CAN be rank ordered (best or worst). Examples: animal activity can be rated on a scale of 1 to 5, with 5 being the most active also Moh’s Hardness Scale for Minerals (diamonds are the strongest)
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