Level of Measurement Problems. Sources of Information Sample Problems. Level of measurement - 1. Level of measurement is one of the key determinants for selecting the correct statistic to use.
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Sources of Information
Data is represented by numbers. Sometimes we may be able to look at a column of data and guess its type, e.g. hrs1 and prestg80 are probably interval level. Most of the time, the numbers will not be an adequate indicator of level of measurement.
SPSS includes codebook information in a data set on the tabbed page titled "Variable View."
Some of the items in this help us determine level of measurement.
SPSS includes a "Measure" column in the "Variable View" as a spot for recording level of measurement, but there is no guarantee that is has been used accurately. In fact, nothing in SPSS data analysis uses the information in this column.
In our dataset, the "Measure" column has been deliberately left to the default setting of "Scale,“ though we clearly have variables at other levels of measurement.
The variable "Labels" column gives us a better idea of the characteristic that a variable is measuring than does the variable "Name" column, but it is little help in determining level of measurement.
The "Values" column usually provides us with the best information about level of measurement.
Click on the button with the ellipsis on it to open the Value Labels dialog box.
The Value Labels Dialog shows the numeric code values that have been entered for a variable and the label that is associated with each code value.
SPSS also has a column for "Missing" values. Missing values are data values or codes that stand for reasons why a question was not answered with one of the valid choices. Cases with these values will not be included in SPSS calculations. 0 and 9 represent missing data for LABOR FRCE STATUS.
The missing value codes are often included in the list of value labels, but we should ignore them when we are determining level of measurement. 0 = NAP is a value label that we should ignore.
The data values show a narrow range of possible values, which might lead us to suspect that the variable is not interval level.
The first two values are ordered by amount of work, but that ordering breaks down when the third category changes to reasons for not working.
The value label “0=NAP” stands for missing data: NAP means “not applicable.”
The variable is nominal level.
The data values show a wide range of possible values, which might lead us to suspect that the variable is interval level.
The variable is interval level.
“Income” might lead us to jump to the conclusion that the variable is interval level.
The data values show a wide range of possible values, which might lead us to suspect that the variable might be interval level.
After the missing data category (NAP which means “not applicable,” the categories appear to be ordinal. If the categories were the same width (e.g. $1000 each), the variable might even be considered interval. However, we note that category 2 is $2000 wide, while categories 3 and 4 are $1000 each.
The variable is ordinal level.