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Discrete data. A variable measured on a nominal scale is a variable that does not really have any evaluative distinction. What age group should read The Chronicles of Narnia. nominal variables; ordinal variables; interval variables; ratio variables. Let's set up a variable for age by typing in five . Unlike those of nominal variables, however, the categories that comprise an ordinal variable can be put in a logical order. Quantitative or qualitative data can be obtained at the ordinal level of measurement. e.g. Rating scales can be scaled in such a way that they have equal intervals. A good example of a nominal variable is sex (or gender). For example, for a string variable with the values of low, medium, high, the order of the categories is interpreted as high, low,medium which is not the correct order. One question that students often have is: Is age considered a qualitative or quantitative variable? Introduction to Statistics is our premier online video course that teaches you all of the topics covered in introductory statistics. The data is nominal and defined by a persons identity, can be classified in order, has intervals, and can be broken down into exact values. Each of these has been explained below in detail. *Inspect if result has plausible distribution. Nominal and ordinal data can be either string alphanumeric or numeric. Interval. Even though these are numbers, they do not imply an order, and the distance between them is not meaningful. You can specify the level of measurement as scale (numeric data on an interval or ratio scale), ordinal, or nominal. The dialog boxes for automatic linear modeling, . I would never use 5w20 oils with low zinc content on top of that. A variable can be treated as scale when its values represent ordered categories with a meaningful metric, so that distance comparisons between values are appropriate. If you are analysing your data using multiple regression and any of your independent variables were measured on a nominal or ordinal scale, you need to know how to create dummy variables and interpret their results. Each of these has been explained below in detail. The ordering of an ordinal variable is clear. At the same time, it needs to code the variables according to the categories those variables are divided into. He has written numerous SPSS courses and trained thousands of users. I.e How old are you is used to collect nominal data while Are you the firstborn or What position are you in your family is used to collect ordinal data. When surveys ask, What age group do you fall into? youd have no data on your respondents individual ages; instead, youd only know how many of them were between the ages of 18-24, 25-34, and so on. The following table provides definitions, examples, appropriate summary statistics, and graphs for variables based on their level of measurement.\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

	Nominal	Ordinal	Scale
Definition	Unordered categories	Ordered categories	Both interval and ratio
Examples	Gender, geographic location, job category	Satisfaction ratings, income groups, ranking of\r\npreferences	Number of purchases, cholesterol level, age
Measures of Central Tendency	Mode	Median	Median or mean
Measures of Dispersion	None	Min/max/range	Min/max/range, Standard deviation/ variance
Graph	Pie or bar	Bar	Histogram

","description":"Level of measurement defines which summary statistics and graphs should be used.