The Basics

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Data View (adapted from SPSS 12.0 Help Menu)

Many of the features of the Data view are similar to those found in spreadsheet applications (Excel). There are, however, several important distinctions:

• Rows are cases, subjects, or participants. Each row represents a singe case, person or an observation. For example, each individual respondent to a questionnaire is a case.  In the case of a repaeted measures design all the data is entered into the same row.
• Columns are the different variables. Each column represents a variable or characteristic being measured. For example, each item on a questionnaire is a variable.
• The individual cells contain the values, or hard data. Each cell contains a single value for a specific variable for an individual case. The cell is the intersection of the case (row) and the variable (column). Cells contain only data values. Unlike spreadsheet programs, cells in the Data Editor cannot contain formulas instead you need to create a new variable.
• The data file is rectangular. The dimensions of the data file are determined by the number of cases and variables. You can enter data in any cell. If you enter data in a cell outside the boundaries of the defined data file, the data rectangle is extended to include any rows and/or columns between that cell and the file boundaries. There are no "empty" cells within the boundaries of the data file. For numeric variables, blank cells are converted to the system-missing value. For string variables, a blank is considered a valid value.

Variable View (adapted from SPSS 12.0 Help Menu)

The Variable view contains descriptions of the attributes of each variable in the data file. In the Variable view:

• Rows are now variables (THIS IS NOT THE CASE IN DATA VIEW).
• Columns are variable attributes.

You can add or delete variables and modify attributes of variables, including:

• Variable name, enter the name you want for your variable- most of the time this includes shortened forms and abbreviations.
• Data type is the type of variable you are entering in the data set; the various forms include: 
• Numeric. A variable whose values are numbers. Values are displayed in standard numeric format. The Data Editor accepts numeric values in standard format or in scientific notation.
• Comma. A numeric variable whose values are displayed with commas delimiting every three places, and with the period as a decimal delimiter. The Data Editor accepts numeric values for comma variables with or without commas; or in scientific notation.
• Dot. A numeric variable whose values are displayed with periods delimiting every three places, and with the comma as a decimal delimiter. The Data Editor accepts numeric values for dot variables with or without dots; or in scientific notation.
• Scientific notation. A numeric variable whose values are displayed with an imbedded E and a signed power-of-ten exponent. The Data Editor accepts numeric values for such variables with or without an exponent. The exponent can be preceded either by E or D with an optional sign, or by the sign alone--for example, 123, 1.23E2, 1.23D2, 1.23E+2, and even 1.23+2.
• Date. A numeric variable whose values are displayed in one of several calendar-date or clock-time formats. Select a format from the list. You can enter dates with slashes, hyphens, periods, commas, or blank spaces as delimiters. The century range for 2-digit year values is determined by your Options settings (from the Edit menu, choose Options and click the Data tab).
• Custom currency. A numeric variable whose values are displayed in one of the custom currency formats that you have defined in the Currency tab of the Options dialog box. Defined custom currency characters cannot be used in data entry but are displayed in the Data Editor.
• String. Values of a string variable are not numeric, and hence not used in calculations. They can contain any characters up to the defined length. Uppercase and lowercase letters are considered distinct. Also known as an alphanumeric variable
• Number of digits or characters that you place in the cell
• Number of decimal places that each value will have
• Descriptive variable and value labels, this is where you lable exactly what the data or variable you are inputting means
• User-defined missing values:
  

  Missing Values defines specified data values as user-missing. It is often useful to know why information is missing. For example, you might want to distinguish between data missing because a respondent refused to answer and data missing because the question didn't apply to that respondent. Data values specified as user-missing are flagged for special treatment and are excluded from most calculations.
  

  • You can enter up to three discrete (individual) missing values, a range of missing values, or a range plus one discrete value.
  • Ranges can be specified only for numeric variables.
• You cannot define missing values for long string variables (string variables longer than eight characters).

            Missing values for string variables. All string values, including null or blank values, are considered valid values unless you explicitly define them as missing. To             define null or blank values as missing for a string variable, enter a single space in one of the fields for Discrete missing values.

• Column width, this defines how long a cell is
• Measurement level, there are three types:
  
  • Scale. Data values are numeric values on an interval or ratio scale--for example, age or income. Scale variables must be numeric.
  • Ordinal. Data values represent categories with some intrinsic order (for example, low, medium, high; strongly agree, agree, disagree, strongly disagree). Ordinal variables can be either string (alphanumeric) or numeric values that represent distinct categories (for example, 1 = low, 2 = medium, 3 = high). Note: For ordinal string variables, the alphabetic order of string values is assumed to reflect the true order of the categories. 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. In general, it is more reliable to use numeric codes to represent ordinal data.
  • Nominal. Data values represent categories with no intrinsic order--for example, job category or company division. Nominal variables can be either string (alphanumeric) or numeric values that represent distinct categories--for example, 1 = Male, 2 = Female.

In addition to defining variable properties in the Variable view, there are two other methods for defining variable properties:

• The Copy Data Properties wizard provides the ability to use an external SPSS data file as a template for defining file and variable properties in the working data file. You can also use variables in the working data file as templates for other variables in the working data file. Copy Data Properties is available on the Data menu in the Data Editor window. 
• Define Variable Properties (also available on the Data menu in the Data Editor window) scans your data and lists all unique data values for any selected variables, identifies unlabeled values, and provides an auto-label feature. This is particularly useful for categorical variables that use numeric codes to represent categories--for example, 1 = Male, 2 = Female.
  

Sample Files:
SPSS comes with sample files that can be found on every machine.  These files can be located by going to the c: - program files - spss.