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Exploratory and Descriptive Statistics

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Overview

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Learn the fundamentals of descriptive and exploratory statistics with practical examples. This guide covers data measurement, visualization, and real-world applications for research. This course provides a clear and practical introduction to descriptive and exploratory statistics, focusing on real-world data analysis and research applications. It guides learners through essential statistical concepts and tools like SPSS and Excel, emphasizing data management and visualization. Designed for those new to statistics, it builds foundational skills through hands-on learning and real-life examples. This course is ideal for beginners or researchers with a basic understanding of statistics who want to improve their data analysis skills. No prior statistical expertise is required, but familiarity with basic research methods is recommended. It is particularly useful for students, researchers, and professionals working with quantitative data. This course takes a hands-on, practical approach to learning descriptive and exploratory statistics. It focuses on real-world applications of statistical tools, providing step-by-step guidance for measuring and analyzing data, visualizing results, and making informed decisions in research. This course is based on Exploratory and Descriptive Statistics, by Julie Scott Jones and John Goldring. Copyright ©2021 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

  • Introducing Descriptive and Exploratory Statistics
    • This module introduces the fundamental concepts of descriptive and exploratory statistics, focusing on how to summarize and interpret data using tools like SPSS and Excel. Learners will explore measures of central tendency, dispersion, and data distributions, gaining practical skills for analyzing real-world datasets. The module also covers strategies for sampling and examining relationships between variables.
  • Finding Data to Describe
    • This module teaches learners how to locate, manage, and clean data for analysis, covering the use of secondary data, administrative data, and open data sources. It emphasizes strategies for accessing and evaluating data quality, and introduces practical steps for working with real-world datasets.
  • Measure Everything – Learn Something – Answer Nothing
    • This module examines the fundamentals of measurement in social science research, including variable classification, cultural influences, and the challenges of interpreting data. It explores how variables are categorized, measured, and used to draw meaningful insights from social phenomena. Learners will gain the ability to critically assess measurement practices and their implications.
  • I Am Not a Number, I Am a Categorical Variable
    • This module covers the analysis of categorical data using SPSS and Excel, focusing on univariate and bivariate techniques. Learners will gain skills in handling missing data, interpreting valid percentages, and presenting categorical variables effectively for accurate statistical reporting.
  • I Like Being Average, I Am an Interval Variable
    • This module explores the concept of averages and their limitations in data analysis, focusing on how different measures of central tendency can be used to interpret social data. It covers the practical application of these measures, including how to calculate them using tools like SPSS and Excel. Learners will also analyze how outliers and income inequality affect the interpretation of average values.
  • Visualising Our Data
    • This module introduces learners to various data visualization techniques, helping them understand how to effectively communicate data through charts, graphs, and maps. It covers different types of visual representations and their appropriate use cases, empowering learners to make informed choices in data storytelling.
  • The Story Waiting to Be Told
    • This module explores how narratives influence the interpretation of social data, examines gender and sex differences through statistical analysis, and evaluates the accuracy of data measurement methods. Learners will gain insights into the stories behind numbers and how they shape societal understanding.

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