SPSS IBMSPSSSTATL1P Exam Details & Actual Exam Questions

  • Exam Code/Number: IBMSPSSSTATL1P
  • Exam Name/Title: IBM SPSS Statistics Certification Level 1 (formerly PASW Statistics)
  • Certification Provider: SPSS
  • Corresponding Certification: SPSS Certification
  • Exam Questions: 70
  • Updated On: Aug,27 2026
  • Certification Level: Specialist

SPSS IBM SPSS Statistics Certification Level 1 (formerly PASW Statistics) Exam Questions

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SPSS IBMSPSSSTATL1P Exam Overview:

Certification Vendor:IBM
Exam Name:IBM SPSS Statistics Certification Level 1 (formerly PASW Statistics) / IBM SPSS Statistics Level 1 v2
Exam Number:IBMSPSSSTATL1P / C2090-011
Exam Price:USD 200
Exam Duration:90 minutes
Passing Score:67% (37 out of 55)
Certificate Validity Period:3 years
Available Languages:English
Real Exam Qty:55
Exam Format:Multiple Choice
Recommended Training:IBM SPSS Statistics Essentials
Exam Registration:Pearson VUE IBM Exam Registration
IBM Certification Portal
Sample Questions:SPSS IBMSPSSSTATL1P Sample Questions
Exam Way:Onsite at Pearson VUE test centers / Online proctored (where available)
Pre Condition:Working knowledge of IBM SPSS Statistics version 15 or higher; no mandatory prerequisites
Official Syllabus URL:https://www.ibm.com/training/certification/ibm-certified-specialist-spss-statistics-level-1-v2-47100102

SPSS IBMSPSSSTATL1P Exam Syllabus Topics:

SectionWeightObjectives
Basic Inferential Statistics22%- Chi-square tests
- Statistical error and interpretation
- Correlation analysis
- T-tests and sample analysis
- Linear regression
Output: Editing and Exploring7%- Pivot tables and output management
- Creating and editing charts
- Exporting results and OMS usage
Data Understanding / Descriptives9%- Means and summary procedures
- Frequencies and descriptive statistics
- Crosstabs and dispersion measures
Operations / Running IBM SPSS Statistics15%- General interface and navigation
- Software settings and preferences
- Syntax editor and command usage
Reading and Defining Data16%- Defining variable properties
- Handling data types and formats
- Importing and reading datasets
Data Transformations16%- Computing and recoding variables
- Conditional operations
- Creating categorical variables
- Counting values across variables/cases
Data Management15%- Adding and aggregating cases
- Selecting and splitting files
- Identifying and removing duplicates


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