Taking the Fear Out of Data Analysis provides readers with the necessary knowledge and skills to understand, perform, and interpret quantitative data analysis effectively. Acknowledging that people often dislike statistics and quantitative methods, this book illustrates that statistical reasoning can be a fun and intuitive part of our lives.

Key Features:

Split into three sections covering how to understand data, preparing data for analysis and carrying out the analysis

Blends theory with practical examples in a logical and straightforward manner to guide readers in making sense of statistical inference

Offers universal knowledge that can be applied to a variety of software applications with limited technical complexity to aid the learning process

Short and concise chapters focusing on the essence of the topics covered, such as analytical techniques that are typically used in behavioral and social science research

Significantly revised and updated, this textbook is an essential text for both undergraduate and postgraduate students in fields such as information systems, international business and marketing. It will also be beneficial for practitioners involved in data science, data analytics, and market research.

?Written with wry wit and incredible clarity, the authors provide the reader with a detailed understanding of seminal issues in data analysis. A masterful work that truly does ?take the fear out of data analysis? ? this book is a rare treat indeed.?

Table of Contents:

Contents: Pre-publication reviews from around the world Introduction to Taking the Fear out of Data Analysis PART I UNDERSTANDING DATA 1. What is data (and can you do it in your sleep)? 2. Does sampling have a purpose other than providing employment for statisticians? 3. Why should you be concerned about different types of measurement? PART II PREPARING DATA FOR ANALYSIS 4. Have you cleaned your data and found the mistakes you made? 5. Why do you need to know your objective before you fail to achieve it? PART III CARRYING OUT THE ANALYSIS 6. Why not take it easy initially and describe your data? 7. Can you use few numbers in place of many to summarize your data? 8. What about using estimation to see what the population looks like? 9. How about sitting back and hypothesizing? 10. Simple things first: One variable, one sample 11. Getting experienced: Making comparisons 12. Getting adventurous: Searching for relationships 13. Getting hooked: A look into multivariate analysis 14. Getting obsessed: A further look into multivariate analysis 15 It?s all over ? or is it? Index