The Statistical Package for the Social Sciences (SPSS), a program extensively used for data analysis and statistical modeling, is introduced in the SPSS course. It helps students become accustomed to the fundamental elements, data entry, manipulation, and organizing. Participants acquire the skills necessary to conduct descriptive statistical analyses, which also cover measures of central tendency and variability. Introduction to inferential statistics is also covered in the course, including correlation analysis and hypothesis testing. By the end, students have the knowledge and abilities necessary to use SPSS for data analysis and interpretation in social science research.
₦45,000
1 weeks
3 days weekly
2hrs per session
In this course
Data Visualization
Course Description
The Statistical Package for the Social Sciences (SPSS), an IBM program that is often used for statistical analysis in a variety of sectors, is thoroughly trained in the SPSS course for our students. Professionals in the fields of marketing, healthcare, and educational research, among others, might benefit from the course's coverage of the fundamental ideas and methods of data analysis using SPSS. Students will gain knowledge on how to use SPSS for descriptive statistics, calculating probabilities, and classifying data. In order to help students manage and analyze data using SPSS, the course also focuses on data transformation, graphing, and direct marketing tools. students will learn how to use SPSS for intelligent data analysis and decision-making in their respective disciplines through practical exercises and real-world examples.
Why SPSS?
SPSS is the preferred choice for statistical analysis due to its wide range of functionalities and ease of use. Its long-standing reputation as a reliable and robust statistical software package, with its inception dating back to 1968, makes it a trusted tool in the research community. SPSS is widely utilized in healthcare, marketing, and educational research, offering specialized features that cater to the unique requirements of these industries. Whether you are a health researcher, market analyst, or education professional, SPSS provides powerful tools for data analysis, prediction, and visualization. Its user-friendly interface, extensive library of statistical techniques, and ability to handle large datasets make it an essential tool for professionals seeking to derive meaningful insights and make informed decisions based on data analysis.
Course Outline
Data Transformation
Manipulating and modifying data to improve its structure, quality, or format, such as recoding variables, creating new variables, or transforming variables using mathematical functions.
Chart
Visual representation of data using various graphical elements, such as bar charts, line graphs, or pie charts, to summarize and communicate patterns, trends, or relationships in the data.
Simple Analysis Techniques
Basic statistical methods used to summarize and analyze data, including measures of central tendency (mean, median), measures of dispersion (standard deviation, range), and frequency distributions.
Assumption Checking
Evaluating the underlying assumptions of statistical analysis, such as normality, independence, and homogeneity of variance, to ensure the validity of the results.
One Sample Tests
Statistical tests used to compare a sample mean to a known population mean, such as the one-sample t-test or one-sample chi-square test.
Association Tests
Statistical tests used to examine relationships between variables, such as correlation analysis or chi-square test of independence, to determine if variables are associated or related.
Test for Mean Difference
Statistical tests used to compare means between two or more groups, such as the independent samples t-test or analysis of variance (ANOVA).
Predictive Techniques
Statistical methods used to develop models and make predictions based on existing data, such as regression analysis or logistic regression, to estimate future outcomes or behavior.
Scaling Techniques
Methods to transform and standardize variables to a common scale, such as z-score transformation or min-max scaling, to facilitate comparisons and interpretations.
Principal Component Analysis
A multivariate technique used to reduce the dimensionality of a dataset by creating new variables (principal components) that capture the most significant variation in the original variables.
Highlight The Skill You Will Gain With Our Verifiable Certificate
Upon completing the course and a building successful project, you will be awarded a certificate to showcase your new skills. Your certificate is also verifiable on our website so that potential employers can easily confirm its authenticity.
