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Course Outline

  • Introduction
  • Dataset and Resources
  • Conceptual Foundation of Statistics
  • Data Entry: Learning to Enter Data in SPSS
  • Working with Various File Types in SPSS
  • Data Transformation in SPSS: RECODE and Other Transformation Functions
  • Descriptive Statistics using SPSS
  • Advanced Descriptive Statistics in SPSS
  • Independent Sample t-test: Comparing Two Independent Group Means
  • Paired Sample t-test: Comparing Differences between Two Correlated Group Means
  • One-Way ANOVA: Comparing Differences between More than Two Groups
  • Linear Regression: Cause and Effect Analysis of One IV on One DV
  • Multiple Regression: Causal Effect of Many IVs on One DV
  • Hierarchical Regression Analysis
  • Exploratory Factor Analysis
  • Chi-Square Test
  • Reliability Analysis
  • Graphical Presentation and Data Visualization in SPSS
  • Logistic Regression
  • Moderation and Mediation Analysis using PROCESS Macro
  • General Linear Modelling (GLM) and Generalized Linear Modelling (GLIM)
  • One-Way Repeated Measure ANOVA
  • Correlations
  • Measures of Association
  • Bug Fixing in SPSS
  • ANCOVA: One-Way Analysis of Covariance
  • MANOVA (Multivariate Analysis of Variance)
  • Python for SPSS Users
  • Ratio Statistics in SPSS
  • TURF Analysis in SPSS
  • Advanced Data Visualization in SPSS
  • Survival Analysis
  • Meta Analysis
  • Assignments

Requirements

  • As the course is built from the ground up, no prior knowledge of SPSS or statistics is required. All necessary theoretical and practical details are covered throughout the course.
  • Learners must have access to a copy of SPSS software to practice the techniques taught.
 35 Hours

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