Data Crash Course: Types of Data and Scales of Measurement

Before you begin to collect data for your thesis or dissertation, it may be helpful for you to review the different types of data and scales of measurement available to you. You can use the following cheat sheet as a reference guide as you prepare to collect your dissertation data.

Scales of Measurement

Nominal: (a.k.a., categorical) refers to characteristic data that have no numeric value (i.e., ethnicity)

Dichotomous: refers to types of nominal data that only have two categories (i.e., alive or dead)

Ordinal: refers to numeric values of rank order (i.e., first, second, third, etc.) that have indeterminate intervals between adjacent values

Interval: refers to values that are continuous in nature and that are in the same metric

Ratio: refers to values that are continuous in nature and that are in the same metric and includes a nonarbitrary zero

Measures of Central Tendency

Mean: arithmetic average of a variable

Median: midpoint of a distribution (i.e., the same number of scores are above and below the median)

Mode: most frequently occurring value

Variability

Variability: quantitative measure of the degree to which data in a distribution are spread out or clustered together, which is used to describe distribution

Range: difference between the upper limit and lower limit of a variable

Deviation: distance from the mean

Variance: mean of the squared deviation scores

Standard deviation: standardized measure of the variability of a variable and the square root of variance

Kurtosis: refers to the sharpness of the peak of a distribution

Mesokurtic: normal frequency

Leptokurtic: sharp peak (i.e., little variability)

Platykuritc:flat peak (i.e., much variability)

Distribution

Distribution: arrangement of values that variables take in a sample (i.e., the shape of the data)

Types of Distribution

Normal distribution: (a.k.a., the normal curve) occurs when the mean, median, and mode are the same value; normally distributed data will have a mean of 100 and a standard deviation of 15

Negatively skewed: occurs when the mode is greater than the median and the mean and when the median is greater than the mean

Positively skewed: occurs when the mean is greater than the median and the mode and when the median is greater than the mode