SPSS Questions and Answers 

1. What is SPSS? 
Answer: 
SPSS (Statistical Package for the Social Sciences) is a statistical software used for data management, statistical analysis, and graphical representation. It is widely used in social sciences, healthcare, marketing, and education. 

2. What types of analyses can you perform in SPSS? 
Answer: 
You can perform descriptive statistics, correlation, regression, t-tests, ANOVA, factor analysis, cluster analysis, chi-square tests, and more. 

3. How do you handle missing values in SPSS? 
Answer: 
SPSS provides multiple methods to handle missing values, such as: 

  • Listwise or pairwise deletion 
  • Replace with mean/median 
  • Multiple imputation 
  • Custom value replacement 

4. Can SPSS connect to SQL databases? 
Answer: 
Yes, SPSS can connect to SQL databases using ODBC/JDBC connections to import data directly into SPSS for analysis. 

5. What is the difference between a variable and a value in SPSS? 
Answer: 
A variable is a column in the dataset (e.g., Age, Gender), while a value is the actual data entry for a variable (e.g., 25 for Age, Male for Gender). 

6. What is a syntax file in SPSS? 
Answer: 
An SPSS syntax file contains lines of commands that can be executed to perform tasks like data transformation, analysis, or visualization, instead of using the GUI. 

7. What are value labels in SPSS? 
Answer: 
Value labels assign descriptive text to numeric codes (e.g., 1 = Male, 2 = Female) for easier interpretation of categorical data. 

8. How do you create a new variable in SPSS? 
Answer: 
You can use the Transform > Compute Variable option or write syntax like: 

COMPUTE new_var = var1 + var2. 

9. What is the difference between a bar chart and a histogram in SPSS? 
Answer: 

  • A bar chart is used for categorical variables. 
  • A histogram is used for continuous variables to show frequency distribution. 

10. What is the use of the “Split File” function in SPSS? 
Answer: 
The Split File feature allows you to run analyses separately for groups within your data (e.g., analyzing males and females separately). 



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