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Person Analyzing Statistics

Introduction to SPSS

Unlock the Power of Data Analysis with SPSS

12 hours of 1:1 lessons

Course Overview 

Our SPSS and statistics course provides a comprehensive overview of using SPSS for data analysis. Students will learn data entry, manipulation, and visualization techniques. They will master descriptive and inferential statistics, including hypothesis testing and regression analysis.  By the end, students will have the skills to confidently analyze data, interpret results, and make informed decisions using SPSS.

12 High Quality 



hands on learning

Customized course structure

Flexible payment plan

By the End of the course Student will be able to

Topic Covered


Data Collection

Data Analysis



  1. Confidently navigate and utilize the SPSS software for statistical analysis.

  2. They will gain proficiency in applying descriptive statistics and data visualization techniques to summarize and interpret data effectively.

  3. Students will acquire the skills to conduct various inferential statistical tests and hypothesis testing using SPSS.

  4. They will learn to analyze relationships between variables through correlation and regression analysis

What we will be covering in 12 Lesson :
  1. Lesson 1: Introduction to SPSS

    • Overview of SPSS interface and functionalities

    • Data entry and importing data into SPSS

    • Variable types and properties

  2. Lesson 2: Descriptive Statistics

    • Calculation and interpretation of measures like mean, median, mode, and standard deviation

    • Generating frequency distributions and histograms

  3. Lesson 3: Data Visualization

    • Creating charts and graphs in SPSS (bar charts, pie charts, line graphs)

    • Customizing visualizations for effective data representation

  4. Lesson 4: Inferential Statistics Part 1

    • Introduction to hypothesis testing and p-values

    • Conducting t-tests (independent samples, paired samples)

  5. Lesson 5: Inferential Statistics Part 2

    • Analysis of Variance (ANOVA) and post-hoc tests

    • Chi-square tests for categorical data

  6. Lesson 6: Correlation Analysis

    • Computing correlation coefficients (Pearson, Spearman)

    • Interpreting correlation results and assessing significance

  7. Lesson 7: Regression Analysis Part 1

    • Simple linear regression analysis

    • Assessing regression model fit and interpreting results

  8. Lesson 8: Regression Analysis Part 2

    • Multiple linear regression analysis

    • Dealing with multicollinearity and interpreting coefficients

  9. Lesson 9: Data Transformation

    • Data cleaning and handling missing values

    • Recoding variables and creating new variables

  10. Lesson 10: Factor Analysis

    • Exploratory Factor Analysis (EFA) for data reduction

    • Interpreting factor loadings and assessing model fit

  11. Lesson 11: Cluster Analysis

    • Performing hierarchical and k-means clustering

    • Interpreting cluster solutions and profiling clusters

  12. Lesson 12: Multivariate Analysis

    • Introduction to techniques like MANOVA and discriminant analysis

    • Overview of other advanced topics and further learning resource


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