Open browser for the SPSS website; this will lead to the downloading software application. Start with the SPSS free trial version. The bottom line is though Excel offers a good way of data organization, SPSS is more suitable for in-depth data analysis. This tool is very useful in the analysis and visualization of data. You can also go through our other suggested articles to learn more —. Submit Next Question. By signing up, you agree to our Terms of Use and Privacy Policy.
Forgot Password? This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy. What is SPSS? Popular Course in this category. In the first example below, we create a numeric variable called nvn1. Because we did not list a format on the numeric command, this variable has the default format, which is f8.
This means that the variable has a length of 8, with 2 spaces after the decimal, 1 space for the decimal, and 5 spaces for integer portion of the number. In the second call to the numeric command, we create two new variables, nvn2 and nvn3. These two variables have different formats.
The three compute commands populate our three new variables. This brings up the topic of changing numeric formats, which can be done with the formats command.
The formats command only works with numeric variables. This command does not change how many decimal places you see in tables in your output the only output modified by this command is the output for the list command , and it does not change the actual values used by SPSS when doing computations. However, it can be very useful when making graphs with the ggraph command and GPL. Creating standardized variables in SPSS is very simple. You can use the descriptives command with the save subcommand.
If you want to name the new standardized variable instead of using the SPSS default name, you can put that name in parentheses after the variable you wish to standardize. You can also create multiple standardized variables in a single call to descriptives. Note that a variable label was automatically created for the new variables.
We also see that the command descriptives is another command name that can be shortened to desc. Some of the keywords that we will use in this presentation include to , all and thru. When creating variables, the SPSS keyword to will create variables with consecutive numbering. When using to in syntax to refer to variables that already exist in the data set, SPSS assumes that variables are sequential, or positionally consecutive all variables between the first variable listed and the last variable listed in the command will be included.
There are some commands in SPSS that will use the keyword to in both a positionally and a numerically consecutive manner, depending on whether existing variables are being modified in some way or whether new variables are being created. Some of these commands include autorecode , recode , aggregate and rename variables. The rename variables command does exactly what you think it does: it renames variables. If you rename more than one variable at a time, you may need to use parentheses.
You can also use this command to exchange variable names, as shown in the third example. The recode command recodes the values of either numeric or string variables. There are several input keywords that you can use with this command, including lo , lowest , hi , highest , thru , missing , sysmis and else.
The keyword thru includes the specified end value. The keywords lowest and highest and lo and hi include user-defined missing but not system-missing values. The keyword missing specifies both user-defined missing and system missing, while the keyword sysmis only specifies system-missing. Output keywords include copy and sysmis. There are other keywords that can be used when recoding string variables, but we will not cover those here. The delete variables command is a very handy command, but obviously, this command needs to be used with caution.
The delete variables command was introduced in SPSS version Another way to do the same thing is to use the save outfile command with either the keep or the drop subcommand. The difference is that with the delete variables command, you are not saving a new data file. The variable levels are shown in the Variable View window of the Data Editor in the second column from the right. With earlier versions of SPSS, the only procedure that used the variable level was igraph , which is now a deprecated graphing command.
However, in more recent versions of SPSS, other procedures are making use of the variable levels, so it is becoming more important that users know how to modify them. Here is a simple example.
The variable role command was introduced in SPSS version 18, and it is used with some of the commands that were introduced in that and later versions of SPSS. Some of the roles a variable can take include input independent variable , target dependent variable , both and none. The specified role does not matter when you are writing syntax, but it does matter when you are using the point-and-click interface. This command is mentioned in our seminar on SPSS syntax because we realize that many people will use the point-and-click interface to help them get a template of the syntax for a procedure, and it can be confusing when some variables appear in some dialog boxes and not others.
The sort variables command was introduced in SPSS version You can sort the variables in your dataset by name, type, format, label, values, missing, measure, role, columns, alignment, and attribute.
Sorting variables is a good way to make sure that you have done all of the data documenting that you meant to do by sorting the variables by labels or values, for example.
We will cover the topic of documenting data shortly. There are two different types of missing data in SPSS: system-missing and user-defined missing.
System-missing is displayed as a dot. System missing values are considered the lowest possible value in SPSS. You can define your own missing values, which are called user-defined missing for numeric variables. Although displayed differently, both system-missing and user-defined missing values are just missing values to SPSS, and they are treated the same way except in filter variables, see below. Both will be deleted from analyses that call for listwise deletion.
There are some keywords that can be used with numeric value lists, and they include lo , lowest , hi , highest and thru. You can declare, change or clear missing values by issuing the missing values command again. To clear all missing values, simply leave the parentheses empty. It is important to realize is that you can create the same variable in different ways, and that the missing values may be handled differently.
Note that above we defined -8 and -9 as missing values for the variable q1 in the third missing values command , and -9 as a missing value for the variable q2 in the first missing values command. There are many ways to document your data using SPSS. We have already covered some of these topics, and we will discuss some others shortly. You can view the documentation that you have created using the sysfile info and display commands.
When using the sysfile info command, you must specify the file path. Also, the maximum length of a variable label is characters and the maximum length of a value label is bytes approximately characters. The display command allows you to display certain information about the dataset and the variables within it. For example, you can display all of the variable names, documents, dictionary information, attributes, labels, scratch variables, vector names and macros.
The document command is very handy and allows you to keep notes with your data set. You can use the document drop command to remove a document from your data file. The add document command can be used to include additional notes to your document. Unlike the document command, you will need to use quotes around each line of the text when using this command.
The file label command is used to label the data file itself. This is particularly useful when you have multiple copies of a data set that are slightly different.
The variable labels command allows you to assign labels to your variables. Doing so is an important part of developing a codebook. We strongly recommend that all data sets have a codebook, even if the researcher is not planning on sharing the data with others. The codebook reminds you of all of the details of your data set, which is important when you have to come back to the data at a later time.
The value labels command allows you to assign labels to the values of a variable. You can use the add value labels command to labels values that were not labeled with the value labels command.
The variable attribute command was introduced in SPSS version 14 and allows users to assign attributes to variables in the active dataset. The attributes are saved with the data dictionary.
In the examples below, we assign attributes that tell us what type of response was required for q1 and what formula was used to create the variable nvrndmean.
This type of information is important to keep with the dataset, especially if the written code book becomes lost. The datafile attribute command is similar to the variable attribute command, but, of course, it sets attributes for the data file.
The delete keyword allows attributes to be removed. The codebook command was introduced in SPSS version 17 and updated in version The codebook command displays dictionary information and summary statistics for variables in the active dataset.
For nominal and ordinal variables, summary statistics include counts and percents. For scale continuous variables, the mean, standard deviation and quartiles are displayed. The split file status of the dataset is ignored, but the filter status is honored for computing summary statistics. In the interest of saving space, only part of the output from the first codebook command is shown. Up to now, we have been focusing mostly on data management.
However, the reason researchers do data management is to prepare the data for analysis, and we will see some descriptive statistics shortly. There are several different ways that you can do this in SPSS. Perhaps the simplest way is to have SPSS run the same analysis for each level of a categorical variable. We will use the split file command to do this. Before we can issue this command, we will need to sort the data by gender.
Sometimes you want the analysis only for one gender, not both. Of course, you could use the split file command and only look at the output for the gender of interest, but if your analysis takes a long time to run, this can be a problem. There are at least two ways to analyze only one subset of your data. You can use a filter or you can keep only the cases that you want to analyze in your dataset. Usually, researchers prefer to create and use a filter rather than eliminate cases from the dataset.
To filter cases in SPSS, you need a binary variable a variable that only has the values 0 and 1 and use that variable with the filter command. Only the cases coded as 1 will be included in the analyses; the cases coded as 0 or missing will not.
You can look in the lower right corner of the SPSS data window to see if a filter is on. You can permanently delete cases from your dataset with the select if command. Cases coded as 1 will remain in your dataset; cases coded as 0 or missing will be deleted from the dataset.
Once you run the select if command, you will not be able to recover the cases that were deleted. If you delete cases that you did not intend to delete, close the data file without saving it and reopen the data file.
We do not want to permanently delete any cases from our sample data file, so we will put the temporary command before the select if command. The temporary command is available only via syntax not through the point-and-click menu system. The temporary command stays in effect only until the next executable command is executed.
That is why the output for the first list command which is the first executable command after temporary has only seven observations the seven that met the criteria listed on the select if command , while the second list command includes all of the observations from our data set. Although for this seminar we only use the temporary command while subsetting, it has many other uses.
Another way to use only part of your data is to use the n of cases command. This command will limit the number of cases in the active dataset to N cases of course, you decide what N will be. This command is especially handy when you have a very large dataset, you are debugging your syntax and running a command takes longer than you would like. You can simply limit the number of cases such that your commands run quickly, so that you can see if you get any error messages, if you have all of the desired options specified, etc.
We will use the temporary command with the n of cases command in the examples below. Avoid ending with a period, since the period may be interpreted as a command terminator. Avoid ending with an underscore to prevent conflict with variables automatically created by some procedures. Length of name cannot exceed 64 bytes. Sixty-four bytes typically means 64 characters in single-byte languages for example, English, French, German, Spanish, Italian, Hebrew, Russian, Greek, Arabic, Thai and 32 characters in double-byte languages for example, Japanese, Chinese, Korean.
Cannot include spaces and special characters for example,! Must be unique. Can use any mixture of uppercase and lowercase characters; case is preserved for display purposes.
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