How to Make Sense of Statistics By Stephen Gorard
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How to Make Sense of Statistics By Stephen Gorard

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Pages
309
File size
6.51 MB
Format
Digital PDF
Category
eBook[PDF]
About this ebook
How to Make Sense of Statistics" by Stephen Gorard is an introductory textbook designed to build basic statistical literacy by focusing primarily on non-inferential statistics. It assumes no previous mathematical background and emphasizes the logical link between numbers and real-life social data. [1]
The book is structured into 21 chapters that span foundational concepts, descriptive analytics, data management, and predictive modeling: [1, 2, 3]
Part I: Introduction & Core Concepts
  • Why We Use Numbers in Research (Chapter 1): The foundational role of quantitative measurements and data in empirical studies.
  • What is a Number? (Chapter 2): Classifying numbers into real (continuous) and categorical (nominal/ordinal) groupings. [1, 2, 3]
Part II: Basic Analyses & Describing Data
  • Working with One Variable (Chapter 3): Techniques for summarizing a single dataset cleanly using graphs, frequencies, percentages, and modes. [1, 2]
  • Measures of Spread: Computing central tendencies and variances, explicitly covering the mean, standard deviation, and absolute mean deviation. [1, 2]
  • Significance Testing Demystified (Chapter 6): Teaching readers how to interpret two common significance tests when reading outside research, while critiqueing why researchers should avoid relying on this "archaic approach" for their own work. [1]
Part III: Research Design & Data Management
  • Researcher Judgement (Chapter 8): Emphasizing that statistical calculations are just the beginning of an analysis and require human interpretation.
  • Research Design & Planning (Chapter 9): Why robust structural design dictates the overall value of data collections.
  • Populations and Sampling (Chapter 10): The mechanics and terminology of extracting target measurements from standard cases.
  • Handling Missing Data (Chapter 12): Frameworks to actively detect, document, and ethically manage missing values. [1]
Part IV: Advanced & Predictive Modelling
  • Multiple Linear Regression (Chapters 17 & 18): Deploying multiple predictors simultaneously to forecast real number outcomes, alongside their deeper mathematical assumptions.
  • Logistic Regression Modelling (Chapter 19): Combining diverse predictor variables to determine categorical outcomes.
  • Factor and Reliability Analysis (Chapter 20): Conducting and interpreting basic data-reduction and measurement-consistency techniques.
  • Simplification and Reporting (Chapter 21): The crucial role of clear communication so a broad audience can evaluate a study's trustworthiness. [1, 2]

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Topics

Classifying numbers into real (continuous) and categorical (nominal/ordinal) groupings. Factor and Reliability Analysis Handling Missing Data Logistic Regression Modelling Measures of Spread: Computing central tendencies and variances Multiple Linear Regression role of quantitative measurements and data in empirical studies. Significance Testing Demystified Simplification and Reporting summarizing a single dataset cleanly using graphs frequencies percentages and modes The mechanics and terminology of extracting target measurements from standard cases. Why robust structural design dictates the overall value of data collections
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