Statistical Data Analysis Using SPSS Base

Date/Time: Date(s) - 24/05/2021 - 26/05/2021( 8:00 am - 5:00 pm )
Location: Malaysia, Kuala Lumpur


Whatever data you have collected, if you want to make sense out of it, you have to use excellent analytical skills. Quantitative data involves techniques of converting data to numerical forms and subject them to statistical analysis. The complete findings and results & discussion of your research depend on the analysis of your data and how you interpret the same.
The SPSS® software platform offers advanced statistical analysis, a vast library of machine learning algorithms, text analysis, open source extensibility, integration with big data and seamless deployment into applications.

Its ease of use, flexibility and scalability make SPSS accessible to users of all skill levels. What’s more, its suitable for projects of all sizes and levels of complexity, and can help you and your organization find new opportunities, improve efficiency and minimize risk.

Within the SPSS software family of products, SPSS Statistics supports a top-down, hypothesis testing approach to your data while SPSS Modeler exposes patterns and models hidden in data through a bottom-up, hypothesis generation approach.


  • Basic concepts of statistics & research
  • Introduction to SPSS
  • Define variables & labels
  • Data entry and data transformation
  • Quantitative data analysis
  • Screen Data
  • Coding & computing new variables
  • Recording
  • Missing data
  • Outlier Data
  • Testing Data Normality
  • Multicollinearity Test
  • Parametric and Non-parametric analysis
  • Correlation
  • Exploratory Factor Analysis (EFA)
  • Discriminant Analysis
  • Reliability Analysis
  • Descriptive analysis (Mean and S.D)
  • Cross tabulation & Chi-Square test
  • T-test
  • One Sample T- test
  • Independent Samples T-test
  • One Way Anova
  • Two Way Anova
  • Manova
  • Simple linear regression Analysis
  • Multiple Regression Analysis
  • Testing of Mediating effect
  • Testing of Moderating effect
  • Cluster Analysis
  • Discriminant Analysis
  • Bootsrapping

Training Plan

  • 35% Lectures & Theories
  • 40% Workshops, Assessments, Group Work & Exercises
  • 20% Role Play & Case Studies
  • 05% Videos
  • Pre-Test and Post Test
  • Test Results Sheet
  • Trainee Performance Analysis Reports

Training Methodology

  • Case Studies
  • Group Discussions
  • Group & Individual exercise
  • Intensive Workshop by using templates, Diagrams & Charts
  • Planning Activities
  • Presentations
  • Self Assessments
  • Assignments
  • Combine case studies, and analysis of real world examples
  • Action plan

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