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SPSS

SPSS. Office Management Tools By: Saima Gul. Introduction. SPSS (Statistical Package for the Social Sciences) is a computer program used for statistical analysis. In 2009, it has been re-branded as PASW (Predictive Analytics SoftWare).

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SPSS

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  1. SPSS Office Management Tools By: Saima Gul

  2. Introduction • SPSS (Statistical Package for the Social Sciences) is a computer program used for statistical analysis. • In 2009, it has been re-branded as PASW (Predictive Analytics SoftWare). • SPSS is among the most widely used programs for statistical analysis in social science. It is used by market researchers, health researchers, survey companies, government, education researchers, marketing organizations and others.

  3. Data Editor • The Data Editor displays the contents of the active data file. The information in the Data Editor consists of variables and cases. • In Data View, columns represent variables, and rows represent cases (observations). • In Variable View, each row is a variable, and each column is an attribute that is associated with that variable. • Variables are used to represent the different types of data that you have compiled. Variables come in many different types, including numbers, strings, currency, and dates.

  4. On start up, click “Type in data”, then click “OK”.

  5. Data Editor

  6. Entering Data • Click the Variable View tab at the bottom of the Data Editor window. • You need to define the variables that will be used. In this case, the needed variables are: staffNo, Fname, LName, Position, Gender, DoB, Salary and BranchNo. • In the first row of the first column, type staffNo, in the second row, type FName, in the third row, type LName and so on. • New variables are automatically given a Numeric data type. • For string data type, click the button on the right side of the Type cell to open the Variable Type dialog box. Select String to specify the variable type. Click OK to save your selection and return to the Data Editor. • If you don't enter variable names, unique names are automatically created. However, these names are not descriptive and are not recommended for large data files. • Click the Data View tab to continue entering the data.

  7. Entering Data (Contd.)

  8. Entering Data

  9. Entering Data (Contd.) • Click the Data View tab to continue entering the data. • The names that you entered in Variable View are now the headings for the first three columns in Data View. • Begin entering data in the first row, starting at the first column. • In the StaffNo column, type s21. In the Fname column, type John. In the LName column, type White and so on. • Move the cursor to the second row of the first column to add the next subject's data, and repeat the above steps. • Currently, the salary column displays decimal points, even though its value is intended to be integer. To hide the decimal points in variables: • Click the Variable View tab at the bottom of the Data Editor window. • In the Decimals column of the Salary row, type 0 to hide the decimal.

  10. Entering Data (Contd.)

  11. Defining Data • In addition to defining data types, you can also define descriptive variable labels and value labels for variable names and data values. These descriptive labels are used in statistical reports and charts. • Labels are meant to provide descriptions of variables. These descriptions are often longer versions of variable names. • Labels can be up to 255 bytes. These labels are used in your output to identify the different variables. • Click the Variable View tab at the bottom of the Data Editor window. • In the Label column of the FName row, type First Name. In the Label column of the LName row, type Last Name. In the Label column of the DoB row, type Date of Birth.

  12. Defining Data (Contd.)

  13. Defining Data (Contd.)

  14. Adding Value Labels for Variables • Value labels provide a method for mapping your variable values to a string label. In this example, there are two acceptable values for the Gender variable. • A value of F means that the subject is female, and a value of M means that the subject is male. • Click the Values cell for the Gender row, and then click the button on the right side of the cell to open the Value Labels dialog box. • The value is the actual numeric/string value. • The value label is the string label that is applied to the specified numeric (or string) value. • Type M in the Value field. Type Male in the Label field. Click Add to add this label to the list.

  15. Adding Value Labels for Variables

  16. Adding Value Labels for Variables • Now repeat the same for F. Then click ok. • Because string values are case sensitive, you should be consistent. A lowercase m is not the same as an uppercase M.

  17. Adding Value Labels for Variables • These labels can also be displayed in Data View, which can make your data more readable. • Click the Data View tab at the bottom of the Data Editor window. • From the menus choose: View -> Value Labels • The labels are now displayed in a list when you enter values in the Data Editor. This setup has the benefit of suggesting a valid response and providing a more descriptive answer. • If the Value Labels menu item is already active (with a check mark next to it), choosing Value Labels again will turn off the display of value labels.

  18. When Value Label is Active

  19. Using Value Labels for Data Entry • You can use value labels for data entry. • Click the Data View tab at the bottom of the Data Editor window. • In the first row, select the cell for gender. Click the button on the right side of the cell, and then choose Male from the drop-down list. In the second row, select the cell for gender. Click the button on the right side of the cell, and then choose Female from the drop-down list. • Only defined values are listed, which ensures that the entered data are in a format that you expect.

  20. Using Value Labels for Data Entry

  21. Handling Missing Data • Missing or invalid data are generally too common to ignore. Survey respondents may refuse to answer certain questions, may not know the answer, or may answer in an unexpected format. • If you don't filter or identify these data, your analysis may not provide accurate results. • For numeric data, empty data fields or fields containing invalid entries are converted to system-missing, which is identifiable by a single period. • The reason a value is missing may be important to your analysis. • For example, you may find it useful to distinguish between those respondents who refused to answer a question and those respondents who didn't answer a question because it was not applicable.

  22. Handling Missing Data • Click the Variable View tab at the bottom of the Data Editor window. • Click the Missing cell in the salary row, and then click the button on the right side of the cell to open the Missing Values dialog box. • In this dialog box, you can specify up to three distinct missing values, or you can specify a range of values plus one additional discrete value. • Select Discrete missing values. Type 000 in the first text box and leave the other two text boxes empty. • Click OK to save your changes and return to the Data Editor.

  23. Handling missing data

  24. Missing Values for a Numeric Variable • Now that the missing data value has been added, a label can be applied to that value. • Click the Values cell in the Salary row, and then click the button on the right side of the cell to open the Value Labels dialog box. • Type 000 in the Value field. Type No Response in the Label field. • Click Add to add this label to your data file. Click OK to save your changes and return to the Data Editor.

  25. Missing Values for a Numeric Variable

  26. Missing Values for a String Variable • Missing values for string variables are handled similarly as the missing values for numeric variables. • However, unlike numeric variables, empty fields in string variables are not designated as system-missing. Rather, they are interpreted as an empty string. • Missing values for string variables are case sensitive.

  27. File -> Display Data File Info • This option provides useful information regarding a data file, the variables it contains, their labels and their format.

  28. Edit -> Insert Variable • If you want to insert a new variable (column) between two variables, click once on the label of one of the two columns in order to highlight it, and then choose the Insert Variable option. A new column will appear in the data file. Edit -> Insert Cases • If you want to insert a new row (e.g., one questionnaire you forgot to enter) between two rows, highlight one of the two rows by clicking once on its number, and then choose the Insert Case option.

  29. Edit -> Go to Case/Variable • This option is useful if you have a large data file and you want to go directly to a particular case (participant). Type the case number and click OK.

  30. Data -> Transpose • With this option you can create a new data file in which the rows and the columns of the old file are transposed in the new file, so that the rows become columns and vice versa. • Move all the variables of the old file into the Variable(s) box; otherwise they will not appear in the new data file. • If the old file contains a variable whose values could be used as variable names in the new data file, move this variable into the Name Variable box.

  31. Data Before Applying Transpose

  32. Data -> Transpose

  33. Data After Applying Transpose

  34. Data -> Merge File (Add Cases) • This option is useful when you want to combine two different data files.

  35. Data -> Merge File (Add Cases) (Contd.)

  36. Result of Merge Cases

  37. Data -> Merge File (Add Variables)

  38. Result of Merge Variables

  39. Data -> Select Cases • You use this option to separate some cases from the rest of the sample, e.g. separating cases where BranchNo is B003.

  40. Data -> Select Cases (Contd.)

  41. Data -> Select Cases (Contd.)

  42. Data -> Split File • With this option, the results of any analysis will be presented separately for the number of groups selected. • If you change your mind and you do not want to compare groups, select the Analyze all cases, do not create groups option.

  43. Descriptives before splitting file • Go to ANALYZE/STATISTICS/DESCRIPTIVES

  44. Now apply Data -> Split File

  45. Again Analyze/Statistics/Descriptives The statistics are shown separately for males and females. From this table you can compare males and females easily.

  46. Transform -> Count values within cases • With this option you can count the number of occurrences of a particular value across the different variables of the same case(individual). • Let's assume a wholesaler has conducted a simple survey to determine the ratings given by five retailers (“firm 1,” “firm 2,” … , “firm 5”) to product quality on products supplied by this wholesaler to these retailers. • The retailers were asked to rate the products on a scale from 1-10, with a higher rating implying a higher quality rating. The data was entered by product, with one variable for each retailer. • The wholesaler wants to determine the distribution of products that got an “Excellent” rating, defined by the wholesaler to be ratings in the range 7-10. • To do this, a new variable must be created. This variable should “count” the number of firms that gave a “positive” rating (that is, a rating in the range 7-10) for a product.

  47. Data before Counting applied

  48. Transform -> Count values within cases Click “Define Values…”

  49. Transform -> Count values within cases

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