There are four data types or scales of measurement namely nominal ordinal

There are four data types or scales of measurement

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There are four data types (or scales of measurement), namely nominal, ordinal, interval and ratio. (1) Norminal-scaled data - the nominal level of measurement classifies data into mutually exclusive (non-overlapping), exhausting categories in which no order or ranking can be imposed on the data. F Examples are the labels sex, programme of study, species name, marital status, religious affiliation, employment status, education-level . F These labels are only used to identify an attribute of the variable. F Under nominal measurements, ’numbers’ are simply to identify, classify, categorize or distinguish. F For instance, scores 0 for male and 1 for female are simply used to identify, distinguish, categorize or classify the subject by sex. Lecture Number 1 Introduction to Statistics and Biometry September 19, 2016 14 / 20
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Types of Data The next level of measurement is called the ordinal level. (2) Ordinal-scaled data - ordinal level of measurement classifies data into categories that can be ranked; however, precise differences between the ranks do not exist. F Ordinal data are generated from ranked responses (also generated from a counting process). F Examples include: grade (A,B,C,D), judging(first, second, third place), rating scale(excellent, good, poor), ranking of players etc. F The observation for each variable possesses characteristics of nominal data in that each response rating is a label for excellent, good or poor quality. F In addition, the data can be ranked with respect to quality. F An ordinal variate may also be represented by scores such as 1 = poor, 2 = fair, 3 = good, etc. F Thus, ordinal scaled-data have the properties of nominal data but the order or rank of the data is meaningful or important and data can be numeric or nonnumeric Lecture Number 1 Introduction to Statistics and Biometry September 19, 2016 15 / 20
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Types of Data The third level of measurement is called the interval level. (3) Interval-scaled data - interval level of measurement ranks data, and precise differences between units of measure do exist; however, there is no meaningful zero. These are really numeric. F This level differs from the ordinal level in that precise differences do exist between units. F Temperature is one example of interval measurement, since there is a meaningful difference of 1 C between each unit, such as 35 and 36 C . F IQ is another example of such a variable. There is a meaningful difference of 1 point between an IQ of 109 and an IQ of 110. F One property is lacking in the interval scale -there is no true zero. F For example, IQ tests do not measure people who have no intelligence. For temperature, 0 does not mean no heat at all. Lecture Number 1 Introduction to Statistics and Biometry September 19, 2016 16 / 20
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Types of Data The final and highest level of measurement is called the ratio level. (4) Ratio-scaled data - ratio level of measurement possesses all the characteristics of interval measurement, and there exists a true zero. In addition, true ratios exist when the same variable is measured on two different members of the population. F Examples of ratio scales are those used to measure height, weight, area, age, time, money etc.
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  • Fall '18
  • F. TAILOKA
  • Statistics, Level of measurement

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