Spss code syntax
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It makes no difference to the actual analysis it just gives you the option of hiding or emphasising certain variables which might be useful when you are looking at your data. You can give these values labels in the normal way using the Values setting.Ĭolumns: This option simply dictates how wide the column for each variable is in the Data View. N/A, Do not know, left blank) which can be useful. By allowing for multiple missing values you can make distinctions between types of missing data (e.g. You can type in up to three individual values (or a range of values) which you wish to be coded as missing and treated as such during analysis. Clicking on the cell for the relevant variable will summon the pop-up menu shown below. not there? Actually there are lots of different types of missing value to consider and sometimes you will want to include missing cases within your analysis ( Extension B talks about missing data in more detail). This might sound silly at first – surely SPSS can assign a value as missing when a value is well.
Spss code syntax how to#
Missing: This setting can also be very important as it allows you to tell SPSS how to identify cases where a value is missing. When you are satisfied with the list of value labels you have created click OK to finalise them. You can also Change or Remove the value labels you have already placed in the box. Simply type the value and label you want in the relevant boxes at the top of the menu and then click Add to place them in the main window. This menu allows you to assign a value to each category (level) of your variable. Clicking on the cell for the relevant variable will summon a pop-up menu like the one shown below. For example you will need to assign numeric values for gender (0 = boys, 1 = girls) and ethnicity (0 = White British, 1 = Mixed Heritage, 2 = Indian, etc.) so that you can analyse them statistically. Values: This is another important one as it allows you to code your ordinal and nominal variables numerically. It is very easy to forget what exactly a variable represents or how it was calculated and in such situations good labelling is crucial! Label: This is just a typed description of the variable, but it is actually very important! The Name section is very restrictive but here you can give a detailed and accurate sentence about your variable. 0.84 is much easier on the eye and in most cases just as meaningful. This can make certain variables easier to interpret. you may only want values with two significant figures – a range of -99 to 99).ĭecimals: Similar to Width, this allows you to reduce the number of decimal places that are displayed. This allows you to restrict the number of digits that can be typed into a cell for that variable (e.g. Width: Another option we don’t really use. but we have never used anything but numeric! Remember that even categorical variables are coded numerically. You can specify that the data is entered as words (string) or in dates if you have a specific purpose in mind. Type: This is almost always set to numeric.
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This inevitably results in variable names that make no sense to anyone but the researcher! It needs to be short and can’t contain any spaces or special characters. Name: This is the name which SPSS identifies the variable by. Correctly setting up your variables is the key to performing good analysis – your house falls down if you do not put it on a good foundation!Įach variable in your dataset is entered on a row in the Variable View and each column represents a certain setting or property that you can adjust for each variable in the corresponding cell. We briefly introduced the Variable Viewon.
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If you would prefer a more detailed introduction you can look at the Economic and Social Data Service SPSS Guide, Chapter 5 (see Resources ). This page will talk you through the basics of altering your variables, computing new ones, transforming existing ones and will introduce you to syntax: a computer language that can make the whole process much quicker. It is important that you know how to add and edit variables into your dataset.