This course is perfect for research scholars who are at the initial stage of their research and want to do data analysis and write papers properly. Also, good for professionals who need to learn basic statistics and SPSS for their daily work in the office. for data analytics career aspirants this course is a valuable addition to their course. This course is great even if one does not has any initial idea about SPSS. This course covers SPSS from scratch and up to master level. This covers almost all the aspects at the research level including structural equation model (SEM).

• Introduction to IBM SPSS Statistics
  • Registration and Downloading of  SPSS software.
  • About SPSS software
• Introduction to Research Methodology
• Data Loading in SPSS through Questionnaire,
• Data View, Variable View
• Width, Label, Values, Decimals, Measure,
• Data Management through Transformation,
• Normality tests
• Shapiro-Wilk, Kolmogorov Smirnov tests,
• Normality check through Histogram, Skewness and Kurtosis, box plot
• Descriptive Analysis (Frequency, descriptive and Explore options)
• Crosstab function
• Charting with SPSS
  • Explore, P P Plots and Q Q Plots and Interpretations
  • One Sample t test, critical value method, p-value method, Confidence Interval method
• Independent Sample t-test and Paired Sample t test.  
• Chi-square test for testing  association between attributes
• ANOVA (one way and 2 way), multiple comparisons
• How to write ANOVA result in APA style format for journals and reporting
• F-Statistics and p-values
• Correlation
• Simple Linear Regression, R Square, Adjusted R Square,
• Autocorrelation and Durbin Watson Statistics
• Multiple Regression Analysis
• Hierarchical Multiple Regression Analysis
• Exploratory Factor Analysis with example
• Reliability Analysis
• Non-parametric Analysis
  • Non-parametric Analysis, one sample 
  • Non-parametric Analysis, independent sample
• General Linear Models
• Logistic Regression
• Finalization of the software