Purpose
Statistical Data Analysis in SPSS (Statistical Package for the Social Sciences) is a software package used for data analysis in the social sciences, health sciences, education, marketing, and other fields. It provides a wide range of statistical analysis tools and techniques, including descriptive statistics, inferential statistics, and data visualization. SPSS can be used to analyze data from various sources, such as surveys, experiments, and observational studies, and it can handle large and complex datasets.
Overall, SPSS is a powerful and widely-used software package for statistical data analysis that provides a wide range of tools and techniques for data preparation, descriptive statistics, inferential statistics, and data visualization. It is widely used in various fields such as social sciences, health sciences, education, and marketing, and it can handle large and complex datasets.
Course Objectives
- Data preparation: Organizing and structuring data using tools such as recoding, variable creation, and data cleaning to make it suitable for analysis.
- Descriptive statistics: Generating summary statistics and measures of central tendency and dispersion to describe the characteristics of the data.
- Inferential statistics: Using statistical techniques such as t-tests, ANOVA, and chi-square tests to draw inferences and make predictions about the population from the sample data.
- Predictive modeling: Building predictive models using techniques such as linear regression and logistic regression to make predictions about future outcomes based on the data.
- Data visualization: Creating visual representations of the data, such as charts, plots, and maps, to help communicate the results of the analysis.
- Exploration of data: Identifying patterns, outliers, and anomalies in the data using techniques such as factor analysis and cluster analysis.
- Hypothesis testing: Testing hypotheses about the relationships between variables and the population parameters using techniques such as t-tests, ANOVA, and chi-square tests.
- Automation: Automating repetitive tasks, such as data preparation and statistical analysis, to improve efficiency and reduce errors using scripting and syntax editor.
- Data Governance: Enforcing data governance rules, such as data lineage and data quality, to ensure data integrity and compliance.
Overall, the main objective of using SPSS for statistical data analysis is to efficiently collect, organize, and analyze large amounts of data, and gain insights to support research and decision-making. SPSS provides a wide range of tools and techniques that can be used to accomplish these objectives and it is widely used in various fields such as social sciences, health sciences, education, and marketing.
Target Audience
- Social scientists: Researchers in fields such as sociology, anthropology, psychology, and political science who use SPSS to analyze data from surveys and experiments.
- Health scientists: Researchers in fields such as medicine, nursing, and public health who use SPSS to analyze data from clinical trials and observational studies.
- Educators: Researchers in fields such as education and psychology who use SPSS to analyze data on student achievement and teacher effectiveness.
- Business analysts: Professionals who use SPSS to analyze data on sales, marketing, and customer behavior to inform business decisions.
- Market researchers: Professionals who use SPSS to analyze data from surveys and experiments to understand consumer behavior and market trends.
- Government agencies: Professionals in government agencies who use SPSS to analyze data on population, crime, and other social issues to inform policy decisions.
- Data analysts: Professionals who use SPSS to clean, transform, and analyze data for different organizations.
- IT professionals: Professionals in IT departments who use SPSS to automate data management tasks, enforce data governance rules, and provide data access to different stakeholders.
Program Outline
Topic 1. Introduction to SPSS
Overview of the SPSS software and its features, including data preparation tools, descriptive statistics, inferential statistics, and data visualization tools.
Topic 2. Data preparation
Techniques for cleaning and transforming data, including recoding, variable creation, and data validation.
Topic 3. Descriptive statistics
Generating summary statistics and measures of central tendency and dispersion, including frequency distributions, measures of central tendency, and measures of dispersion.
Topic 4. Inferential statistics
Using statistical techniques such as t-tests, ANOVA, and chi-square tests to draw inferences and make predictions about the population from the sample data.
Topic 5. Predictive modelling
Building predictive models using techniques such as linear regression and logistic regression to make predictions about future outcomes based on the data.
Topic 6. Data visualization
Creating visual representations of the data, such as charts, plots, and maps, to help communicate the results of the analysis.
Topic 7. Exploration of data
Identifying patterns, outliers, and anomalies in the data using techniques such as factor analysis and cluster analysis.
Topic 8. Hypothesis testing
Testing hypotheses about the relationships between variables and the population parameters using techniques such as t-tests, ANOVA, and chi-square tests.
Topic 9. Automation
Automating repetitive tasks, such as data preparation and statistical analysis, to improve efficiency and reduce errors using scripting and syntax editor.
Topic 10. Data Governance
Enforcing data governance rules, such as data lineage and data quality, to ensure data integrity and compliance.
Topic 11. Applications
Hands-on projects and case studies to apply the learned techniques to real-world scenarios.