Statistical Analysis using SPSS
About This Course
This course offers participants hands-on experience in applying IBM SPSS, covering various data analysis techniques essential for informed decision-making. The training equips participants with a versatile statistical analysis toolkit, enabling the identification of data insights, patterns, and correlations. Participants gain Proficiency in effective data analysis and interpretation, making it invaluable for students, researchers, and academics involved in statistical research, experiment evaluation, and survey assessments across fields like social sciences, psychology, economics, and epidemiology.
Learning Objectives
By the end of this course, participants should be able to:
Understand the fundamentals of statistical analysis and its applications.
Calculate and interpret descriptive statistics and exploratory data analysis.
Apply a range of inferential statistical tests, both parametric and non-parametric.
Formulate hypotheses, conduct hypothesis tests, and interpret results.
Create meaningful data visualizations and charts using SPSS.
Perform basic and advanced data analysis procedures using SPSS.
Target Audience
- A diverse range of individuals and professionals across various fields can benefit from enrolling in SPSS training
- Students: Those pursuing degrees in fields like social sciences, economics, psychology, healthcare, and more can enhance their analytical skills and research capabilities through SPSS training.
- Researchers and Academics: Individuals involved in academic research, experiments, and survey analysis can effectively analyze data and draw meaningful conclusions using SPSS.
- Data Analysts: Professionals responsible for interpreting and deriving insights from data can further strengthen their data analysis skills with SPSS.
- Economists: Economists can utilize SPSS to analyze economic data, trends, and correlations for policy analysis and research.
- Business Analysts: Individuals involved in business analytics can use SPSS to analyze market trends, customer behavior, and sales data.
- Nonprofit Organizations: Professionals in nonprofit organizations can use SPSS for program evaluation, impact assessment, and fundraising analysis.
Curriculum
33 Lessons
Module 1: Introduction to Statistical Analysis and SPSS
Statistical analysis is a critical method used in research and data-driven decision-making to understand and draw conclusions from data. It involves the application of mathematical techniques to analyze and interpret data, uncover patterns, and make predictions. SPSS (Statistical Package for the Social Sciences) is a widely-used software tool that simplifies the process of statistical analysis by providing a user-friendly interface for data manipulation, visualization, and statistical testing, making it a valuable tool for researchers and analysts across various fields.
Understanding the role of statistics in data analysis30:50:00
Introduction to IBM SPSS Statistics software
Navigating the SPSS interface and workspace
Module 2: Data Preparation and Importing
Data preparation and importing in SPSS involve the initial steps of loading and organizing raw data for analysis. This process includes tasks like importing data from various sources, cleaning and transforming the dataset to ensure data quality, and preparing variables for statistical analysis, setting the foundation for meaningful insights and research outcomes.
Module 3: Descriptive Statistics
Descriptive statistics in SPSS refer to the initial analysis of data that provides a summary of key characteristics such as measures of central tendency (e.g., mean, median), variability (e.g., standard deviation, range), and distribution of variables. These statistics help researchers and analysts gain a fundamental understanding of their data's basic properties and patterns before delving into more advanced analyses.
Module 4: Exploratory Data Analysis (EDA)
Exploratory Data Analysis (EDA) in SPSS involves a comprehensive approach to visually and statistically inspecting datasets to uncover patterns, relationships, and potential outliers, helping researchers generate hypotheses and guide further statistical analyses.
Module 5: Inferential Statistics: Parametric Tests
Parametric Tests encompass a set of statistical techniques used to make inferences about population parameters based on sample data, assuming specific distributional assumptions such as normality, including tests like t-tests and ANOVA for hypothesis testing and assessing group differences.
Module 6: Inferential Statistics: Non-Parametric Tests
Non-parametric tests are useful when data does not meet the assumptions of parametric tests, and this this module participants will learn how to perform and interpret non-parametric tests for various research scenarios.
Module 7: Hypothesis Testing and Confidence Intervals
Hypothesis testing in SPSS involves statistical procedures to assess the validity of hypotheses about population parameters, while confidence intervals provide a range of values within which a population parameter is likely to fall, both essential tools for drawing conclusions from sample data.
Module 8: Data Visualization and Presentation
Data visualization and presentation in SPSS involve creating meaningful graphs, charts, and tables to effectively communicate research findings and insights, aiding in the clear and concise presentation of statistical results for both technical and non-technical audiences.
Module 9: Basic Data Analysis Procedures
In this module we will focuses on fundamental data analysis procedures in the context of statistical analysis. It covers one-way and factorial ANOVA for comparing means among multiple groups, linear and logistic regression analysis for modeling relationships between variables, and repeated measures analysis for analyzing data collected from the same subjects or items over time or under different conditions, providing essential tools for researchers to draw meaningful conclusions from their data.
Module 10: Advanced SPSS Features
Participants will explores advanced features in SPSS, including data transformation and recoding, handling missing data through various techniques, and customizing SPSS output and syntax for more sophisticated and tailored data analysis processes.
Module 11: Reporting and Interpreting Results
In this module participants will gain skills and techniques for summarizing and presenting analysis outcomes, interpreting statistical output, and effectively communicating findings to non-technical audiences, enhancing the practical application of data analysis skills.