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Develop your ability to analyze and visualize both categorical and continuous data in political science using SPSS. Gain practical skills in data visualization, statistical inference, and group comparisons. This course focuses on the practical application of SPSS for analyzing and visualizing political and international relations data. Learners will master techniques for visualizing categorical and continuous data, conduct inferential tests such as chi-squared and ANOVA, and understand how to compare group means. The course emphasizes interpreting results and presenting data effectively for academic and professional audiences. Learners engage with step-by-step demonstrations and clear explanations to build proficiency in data analysis and visualization. The course emphasizes practical skills and interpretation, enabling learners to communicate statistical findings with clarity and confidence. This course is part two of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Statistics for Politics and International Relations Using IBM SPSS Statistics, by Helen M. Williams. Copyright ©2020 by Sage Publications Limited. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies. Published by Sage Publications Limited, London. Used by arrangement with Sage Publications Limited.

Syllabus

Visualizing Categorical Data
- This module covers the creation and customization of pie and bar charts for categorical data, focusing on techniques like color adjustments, labels, and exporting. Learners will gain hands-on skills in using SPSS's Chart Builder and Chart Editor to produce visually effective data visualizations.
Inference with Categorical Data
- This module covers advanced techniques for analyzing categorical data using multivariate crosstabs, chi-squared tests, and measures like Cramer's V. Learners will gain skills in interpreting statistical significance, assessing associations, and handling missing data in complex datasets. The content also explores how to use adjusted standardized residuals for deeper insights into variable relationships.
Describing Continuous Data
- This module covers the fundamental statistical measures used to describe continuous data, including central tendency, dispersion, and correlation. Learners will gain skills in interpreting and applying these concepts to analyze data effectively. The module also addresses techniques for handling outliers and improving data quality for accurate statistical analysis.
Visualizing Continuous Data
- This module covers the creation and customization of continuous data visualizations using Chart Builder. Learners will gain skills in selecting and interpreting histograms, line charts, scatterplots, and boxplots, as well as enhancing chart appearance for effective communication.
Comparing Group Means
- This module covers statistical techniques for comparing group means, including t-tests, ANOVA, and non-parametric alternatives. Learners will explore how to assess differences between groups using inferential statistics, understand key assumptions, and apply these methods in real-world data analysis scenarios.