SPSS From Scratch to Advanced

• The course is built from scratch so no prior knowledge of SPSS or Statistics is required. We cover all the required details in the course both theory and practical part.
• The learners must have a copy of SPSS software to practise the steps taught in this course.

Overview

This course aims to equip learners with ability of independently carrying out in-depth data analysis with professional confidence and accuracy. It will specifically help those looking to derive business insights, understand consumer behavior, develop objective plans for new ventures, brand study, or write a scholarly articles in high impact journals and develop high quality thesis/project work.

A good knowledge of quantitative data analysis is a sine qua none for progress in academic and corporate world. Keeping this in mind this course has been designed in such way that students, researchers, teachers and corporate professionals who want to equip themselves with sound skills of data analysis and wish to progress with this skill can learn it in in-depth and interesting manner using IBM SPSS Statistics.

What you'll learn

• Analyse any type of numerical data using SPSS with confidence
• Independently plan your research study and Data Analysis from scratch.
• Understand the research design and results presented in high quality journal articles
• Do data analysis accurately and present the results in APA format.
• Data Entry and Data Cleaning in SPSS
• Data Organization Using SPSS
• Data Transformation Using SPSS
• Sample as well as Population Level Descriptive Analysis Using SPSS
• Analysis of Group Differences Using t-Test and ANOVA
• Linear and Multiple Regression Analysis in SPSS
• Hierarchical and Advanced Regression Analysis in SPSS
• Logistic Regression
• Exploratory Factor Analysis (EFA)
• Chi-Square and Measures of Association
• Reliability Analysis and Scale Validation Using SPSS
• Graphical Representation and Advanced Data Visualization Using SPSS
• Moderation and Mediation Analysis Using PROCESS Macro in SPSS
• General Linear Modelling Vs. Generalized Linear Modelling in SPSS
• Repeated Measure ANOVA
• Correlational Analysis in SPSS
• Analysis of Associations in SPSS
• Analysis of Covariance (ANCOVA)
• Multivariate Analysis of Variance (MANOVA)
• SPSS Programming Using Python
• Ratio Statistics in SPSS
• TURF Analysis in SPSS
• Survival Analysis
• Meta Analysis

Who this course is for:

• PhD students and researchers looking to master SPSS skills and publish in high impact journals
• Professionals looking for a career in analytics in corporate sector
• Faculty members looking to master SPSS and advance their data analysis skills

• Introduction
• Dataset & 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 & Data Visualization in SPSS
• Logistic Regression
• Moderation and Mediation Analysis using PROCESS Macro
• General Linear Modelling (GLM) & 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