Once you read the data to IBM SPSS Statistics, you should at least do a cursory data check of the inputted data. Do you see numeric data? String data? Is the data in the expected scale and range? Is the data complete?

Of course, even if your data is not really very large in either the number of rows or columns, it can be difficult to assess via a simple visual inspection. For this reason, you might use SPSS Statistics to produce a tabular summary of variables showing counts and percentages. Doing so produces tables showing all the data codes in the designated variables. Once you have defined the SPSS Variable Properties such as value labels, you can control the tabular display to show data values (the data codes), value labels, or both.

A further consideration is how the data values are represented for categorical variables. Let's...

Here is a snapshot of a portion of an Excel spreadsheet:

Note that row 1 of the Excel spreadsheet is a header row containing variable names for the columns.

IBM SPSS Statistics can directly read an Excel sheet. There are different implementations in different recent releases of IBM SPSS Statistics but, in general, the capability exists on the File menu. In IBM SPSS Statistics 24, use the following path:

File | Import Data

Here is the Read Excel File dialog box:

By default, IBM SPSS Statistics shows the entire range of data that it encounters in the Excel sheet. You can use the Range portion of the dialog box to specify a subset range. Also by default, IBM SPSS Statistics expects to find variable names in...

Here is a snapshot of a portion of a comma-delimited text file:

A delimiter is a character such as a comma that serves as a separator between elements across a line of data. A text file is a kind of computer file that is structured as a sequence of lines of electronic text, often using the ASCII character set. Such a file is easily viewed in Windows accessories such as Notepad. Note that row 1 of the comma-delimited text file is a header row containing variable names for the columns.

IBM SPSS Statistics can directly read a delimited text file. There are different implementations in different recent releases of IBM SPSS Statistics, but the capability exists on the File menu. In IBM SPSS Statistics 24, use the following path:

File | Import Data

Here is the Read CSV File dialog box:

The Read CSV File dialog box shows the name of the file and a...

Use the SAVE command to save a data file in the IBM SPSS Statistics format, which contains data plus a dictionary. The dictionary contains a name for each variable, plus variable metadata (variable properties) such as assigned variable labels, value labels, missing values, and formats. As an aside, the dictionary also contains text that was specified on the DOCUMENTS command, if this command is used.

To save the data file along with variable properties from the menus, specify as follows:

File | Save

This brings up the Save Data As dialog box:

At a minimum, specify the folder in which you wish to save the file, along with a filename. Then press either Paste to paste the syntax or Save to write the file to disk. In addition, you can use the Variables… button to select variables to keep or drop.

Here is an example of the pasted syntax:

SAVE...

The IBM SPSS Statistics file is a data file created and saved in IBM SPSS Statistics. This file contains data in binary form, variable names, and other variable properties, and is directly readable by IBM SPSS Statistics.

In IBM SPSS Statistics 24, use File | Open | Data.

Here is the Open Data dialog box:

Click on OK to read the file, or click on Paste to paste the constructed syntax to the syntax window.

Here is the constructed syntax:

GET
 FILE='C:\Users\Tony\Documents\KSBSPSSBOOK_DATA\chapter2\gss2014\gss2014extract170113.sav'.
DATASET NAME DataSet1 WINDOW=FRONT.

Running the syntax populates an IBM SPSS Statistics Data Editor window with the data.

The GSS 2014 data extract has 2,538 rows. You can inspect the data by visually scanning and scrolling through the data window, but the sample size makes it difficult to grasp all of the data at once. For example, how many unique values are there in a given variable? Do the values in a given variable occur with about the same frequency, or do certain values predominate? Running Frequencies on the data can serve as a useful first look because it produces summary tables that show all data values on the specified variables.

To run Frequencies from the menus, specify as follows:

Analyze | Descriptive Statistics | Frequencies

This opens the Frequencies dialog box.

Move all variables except ID from the left-hand side to the right-hand side variable list. Why leave out ID? This is because the resulting frequency table is quite lengthy.