Statistics Without Assumptions: A Filipino Student's Guide to Nonparametric Analysis Using SPSS

Even though this book focuses on nonparametric methods, the fundamentals of statistics remain necessary. Knowing how to organize and describe your data helps you decide which test is most appropriate for the type of information you have. For example, if your data come from Likert-scale survey responses or small classroom samples—common in the Filipino research setting—then using median or mode instead of mean becomes more useful (Delacre et al., 2019). These concepts also help you explain your results in a way that others can easily understand. Understanding these key concepts gives you a strong starting point for learning more advanced statistical methods. Whether you're working with parametric or nonparametric tests, knowing how to describe your data, recognize its type, and understand how it’s distributed is essential. Many Filipino students collect data without realizing how the level of measurement affects test selection—nominal, ordinal, interval, or ratio—and end up choosing incorrect methods. When you understand your data’s structure, you can match it with the correct nonparametric tool, whether it’s the Mann–Whitney U test or Chi-square. This is especially useful in SPSS, where selecting the right procedure depends on understanding what kind of data you are entering (Carver & Nash, 2011) These basic ideas help you choose the right tools for your analysis and avoid errors in interpretation. They also help you understand how one variable affects another and what kind of test fits the situation. As we move forward in this book, you will learn how nonparametric methods are used when your data do not meet the normality or equal variance assumptions required by parametric tests. These methods are especially relevant for student researchers working with small, uneven, or ranked data—typical in many Filipino classroom and community studies (Gibbons & Chakraborti, 2011). In the next chapters, you’ll see how these basic concepts connect to step-by-step SPSS procedures for real-world analysis.