Value: The values for the selected variable. Note: The values are based on the specified number of scanned cases B. Count: The number of times a value occurs. Note: The count is based on the specified number of scanned cases B.
Missing: Defines values as missing data. Note: If a variable already has defined missing values e. H Label: Allows you to add a label for the selected variable that describes more about what the variable is.
This label is for the variable rather than for the values of the variable. I Type: Allows you to specify a particular kind of variable that helps SPSS know how to work with the variable during analyses. The types include numeric, comma, dot, scientific, date, dollar currency percent, string, and restricted numeric.
Depending on the type you select for your variable, you may be asked to supply additional information. You can also set the width and may be asked to set the decimals for your variable. J Attributes: Allows you to define custom attributes for variables.
These attributes are supplementary information not otherwise specified by the variable's label, measurement labels, and missing values. K Copy Properties: Allows you to copy properties from one variable to another variable.
You can copy the properties from another variable to the currently selected variable, or copy the properties of the currently selected variable to one or more other variables. After defining the value labels for the first item, you can use "Copy Properties" to quickly set the labels for the remaining survey item variables.
When you are finished defining your variables, click OK at the bottom of the window to apply the changes to your data. As we mentioned at the beginning of this tutorial, it is important to define the variables in your data so that you and anyone else working with your data can easily understand what was measured, and how.
In this section, we provide an example of the confusion that can result when value labels are not defined, and how to correct it. In the sample data, the variable Gender has two possible values: 0 and 1. The sample data file is not formatted with any value labels. Let's make a Frequency table of the Gender variable to see what the distribution of gender is in our sample.
Select the variable Gender , then click OK. The Frequencies command will produce a frequency table. The Output Viewer displays the following results:. But what do these values mean? Which values represent females, and which values represent males? There is no commonly accepted coding scheme for gender, so readers not familiar with the data can not be certain what is represented in this table.
In the sample data, 0 represents a Male, and 1 represents a Female. After defining the value labels using the methods described above and re-running the Frequencies command, the output is much easier for the reader to understand:.
It may also be useful to rewrite the labels so that the numeric code is included with the label. In this situation, we could alter the label for "male" to "Male 0 ", and alter the label for "female" to "Female 1 ". As you can see from this example, including value labels for each variable makes working with data and interpreting output much more straightforward. And remember: value labels are only one of many attributes that we can define for each variable.
The more information you define about each variable, the easier it will be to navigate your data and interpret the output of analyses. Suppose you have conducted a survey that has a time limit, and want to be able to distinguish respondents who refused to answer a question from respondents who ran out of time. Respondents who refused to answer a survey item are coded as Respondents who did not complete the survey item in the alotted time are coded as All other missing responses were left blank.
To have SPSS recognize these special missing value codes, you'll need to these numbersas indicators of missing values under the Variable View tab. Click on the cell corresponding to the "Missing" column for the variable of interest to open the Missing Values window. Click Discrete missing values , then enter the two missing value codes. Search this Guide Search.
SPSS Tutorials: Defining Variables Variable definitions include a variable's name, type, label, formatting, role, and other attributes. This tutorial shows how to define variable properties in SPSS, especially custom missing values and value labels for categorical variables.
Defining Variables Defining a variable includes giving it a name, specifying its type , the values the variable can take e. There are three ways of defining information about variables: The Variable View column attributes.
The Define Variable Properties window. Defining Variables in the Variable View You can define information about your variables by accessing the Variable View tab at the bottom of the Data Editor window. In the Data Editor window, in the Data View tab, double-click a variable name at the top of the column. This method has the advantage of taking you to the specific variable you clicked. The Variable View tab displays the following information, in columns, about each variable in your data: Name The name of the variable, which is used to refer to that variable in syntax.
Type The type of variable e. Width The number of digits displayed for numerical values or the length of a string variable. Decimals The number of digits to display after a decimal point for values of that variable. Label A brief but descriptive definition or display name for the variable.
Values For coded categorical variables, the value label s that should be associated with each category abbreviation. When all of the labels have been defined, the Value Labels window should look like this: Click OK at the bottom of the window. Make changes to the selected value or label as needed. Click Change. New posts. Search forums. Log in. For a better experience, please enable JavaScript in your browser before proceeding.
How many data rows can SPSS handle? FrankC New Member May 18, In a few weeks an internet survey will hopefully start that should get a response rate of somewhere between 2 and 6 thousand people. Sometimes you may need to add new variables or delete existing variables from your dataset. For example, perhaps you are in the process of creating a new dataset and you must add many new variables to your growing dataset. Alternatively, perhaps you decide that some variables are not very useful to your study and you decide to delete them from the dataset.
Or, similarly, perhaps you are creating a smaller dataset from a very large dataset in order to make the dataset more manageable for a research project that will only use a subset of the existing variables in the larger dataset. In the Data View window, click the name of the column to the right of of where you want your new variable to be inserted. New variables will be given a generic name e.
You can enter a new name for the variable on the Variable View tab. You can quick-jump to the Variable View screen by double-clicking on the generic variable name at the top of the column. You should also define the variable's other properties type, label, values, etc. You can enter values for the new variable by clicking the cells in the column and typing the values associated with each case row. Is it possible to insert a variable using syntax? Technically, there's no direct syntax command to do so.
Instead, you'll need to use two syntax commands. Suppose we want to insert a new column of blank values into the sample dataset after the first variable, ids. We can use this syntax to perform these tasks:. The ALL option at the end of the line says to retain all remaining variables in their current order.
The ALL option can only be used at the end of the line; the code will fail if you try to put it before other variable names. Now that you know how to enter data, it is important to discuss a special type of variable called an ID variable.
When data are collected, each piece of information is tied to a particular case. In this example, the survey numbers essentially represent ID numbers: numbers that help you identify which pieces of information go with which respondents in your sample.
Without these ID numbers, you would have no way of tracking which information goes with which respondent, and it would be impossible to enter the data accurately into SPSS. When you enter data into SPSS, you will need to make sure that you are entering values for each variable that correspond to the correct person or object in your sample.
However, you should never rely on these pre-numbered rows for keeping track of the specific respondents in your sample. This is because the numbers for each row are visual guides only—they are not attached to specific lines of data, and thus cannot be used to identify specific cases in your data. If your data become rearranged e. Again, the row numbers in SPSS are not attached to specific lines of data and should not be used to identify certain cases.
Instead, you should create a variable in your dataset that is used to identify each case—for example, a variable called StudentID. Here is an example that illustrates why using the row numbers in SPSS as case identifiers is flawed:. Now you have entered all of your data.
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