Data from the agricultural surveys include various types ranging from numeric to non-numeric data. These mainly include crop yield data, livestock production data, farmer demographics, and farming practices. Statistical Package for the Social Sciences (SPSS) is well-suited for analyzing both numeric and non-numeric data from such surveys. Despite the availability of many resources on data analysis using SPSS, the complexity and multifaceted nature of agricultural survey data require precise statistical methods to achieve meaningful conclusions. The chapter aims to bridge this gap by providing a step-by-step guide on analyzing survey data, supported by practical-based field data. It starts by providing an overview of SPSS, showing its structure and key functionalities. We start by demonstrating how to compute the various descriptive statistics, including frequencies, central tendency measures, and variability. Following the descriptives, the chapter covers inferential statistics, demonstrating how to calculate chi-square statistics (test of association) between two categorical variables. It also covers the Pearson correlation to assess the relationship between two numeric variables. The chapter also demonstrates how to compute simple linear and binary logistic regression. By the end of this chapter, students are expected to have a solid understanding of how to use SPSS to analyze survey data in agricultural research effectively.