Module 1Starting SPSS Statistics

1. To run SPSS, log in and click Start, Programs, SPSS Inc, and then SPSS 16.02. When SPSS is first started you are presented with a dialog box asking you to open a file:

3. Typically you start your SPSS session by opening the data file that you need to work with.4. The SPSS has three main windows: Data Editor (.sav files) Output Viewer (.spv files)

Syntax Editor (.sps files)

Module 2Working with Data Editor

1. The data editor shows the data values you are working with.2. It can also be used to redefine the characteristics of variables (change the type, add labels, define missing values, etc.), create new variables, and enter data by hand.3. The Data Editor helps you view your data in: i) Variable View and ii) Data View.4. Go to Start Window > All Programs > SPSS Inc > SPSS 16.05. Choose Type in Data option and Click OK.

SPSS 16.0 Data Editor6. Data View: Columns represent variables, and Rows represent cases or respondents.

SPSS 16.0 Data View7. Variable View: each row is a variable, and each column is an attribute that is associated with that variable

SPSS 16.0 Variable View

Entering Numeric Data1. Click the Variable View tab at the bottom of the Data Editor window.2. You need to define the variables that will be used. For example, three variables are needed: age, marital status, and income. Key-in in the name column.

3. Next, click Data View to continue to insert the data.

4. Now the variable that you key-in is now on the heading of the top three columns.

5. Now try to enter the data beginning on the first row.

6. But, the age and marital is in decimal point instead of integer.

7. To hide the decimal value. Click Variable View at the bottom of the window and change the decimal value from 2 to 0.

8. Go back to Data View, you can see that there are no more decimal value for age.

Entering String Data1. Non-numeric data, such as strings of text, can also be entered into the Data Editor.2. Click the Variable View.

3. Key in gender for the variable name.

4. Select String and then click ok.

Defining Data

1. Defining descriptive label the description of the label. Type the following statements in the Label column.

2. You can also change the Type for income. Click the button on the right side of the cell and select Dollar. Then click OK to save.

3. Now, it is time to mapping your variable value into a string label. Go to Variable View, then go to Values column on marital row. Click on the right button of the cell.

4. Add to Value = 0 and Label = single, then click Add.

5. Now add Value = 1, Label = married, then click Add and OK button.

6. Now go back to Data View and then you can see the changes that have been made earlier by clicking on View menu and select Value Label.

7. For gender, to add value for string as of F = Female and M = Male, go to Variable View.8. Go to Values column on gender row and insert as followings. Then click Add and OK button.

9. Then go to Data View, go to View on the menu bar. Uncheck Value Labels. Then key in F for the first row and key in M for the second row.

10. 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.

11. Go to Variable View. Then go to Missing column. When you click on the right button of the cell, the missing Value box will appear.

12. Select Discrete Value Missing and type 999. Then click OK.

13. Click the Values cell in the age row, and then click the button on the right side of the cell to open the Value Labels dialog box.14. Type 999 in the Value field and type No Response in the Label field. Click Add, then OK.

15. Now we have handle missing value in numeric. How about string? Go to Missing cell in the gender row, and then click the button on the right side of the cell to open the Missing Values dialog box.16. Type NR. Then click OK.

17. Missing values for string variables are case sensitive. So, a value of nr is not treated as a missing value. Click the Values cell in the gender row, and then click the button on the right side of the cell to open the Value Labels dialog box. Click Add and OK.

Skala Pengukuran1. Selang (Scale): Contoh skala seperti umur, status perkahwinan, income ( dalam bentuk integer)2. Ordinal: Mengkategori sesuatu mengikut urutan (1: Strongly Agree, 2: Agree, 3: Not Sure, 4: Disagree, 5: Strongly Disagree..)

3. Nominal: skala seperti jantina, bangsa, agama yang boleh dikategorikan. Skala umur yang bersela juga adalah di bawah skala nominal contohnya: 20-24

25-29

30-34