This document is an illustration of analyzing quantitative data using IBM SPSS Statistics Software. It does not provide details of the technical skills needed to operate SPSS but focuses on developing a set of decisions and actions in order to configure, describe, manipulate and analyze data in the specific context of the study of Jackson and Mullarkey (2000). In order to fulfill this task, this article illustrates step by step the actions that were performed on the data. It also provides insight into determining each step which helps in interpreting the data results.1. SETTING UP DATA IN SPSSIt is important to set up the data before carrying out further activities on the data using SPSS. Data establishment requires pre-processing the raw data in Excel, then defining data characteristics and processing missing variables in SPSS. (1a) Prepare Excel file Review raw data file in Excel Additional coding: Replace text with numbers o Column Location site: Replace A with 1, B with 2, C with 3, D with 4 o Column Gender: Replace Female with 1, Male with 2 o Work Design Type column: Replace PBS Work Design with 1,  QRM Work Design with 2 then replace Work Design with an empty space (considered Work Design as a value missing because it did not reflect the choice between PBS Design and QRM Work Design)(1b) Import Excel file into SPSS Save recent changes Close Excel before opening SPSS data(1c) Define variables: Make changes in the variable view Name: Change the adapted labels in the first line of the Excel file to new variable names (consider the background of the conceptual framework variables, must be short, without spaces), as shown in the table below. Type of variables: Numeric ...... middle of paper ......o the variables Interval and Ratio of numerical data. Additionally, categorical data is often accompanied by nonparametric statistics; numerical data is often used with parametric statistics. In short, the measurement of data (numerical or categorical) and the type of statistics (parametric or non-parametric) will distinguish one statistic from another. Being able to interpret statistical results: an important step in data analysis is the explanation of the statistics. SPSS is committed to creating the statistical results very quickly, but it is the responsibility of the analyst to understand and logically express the results of the software. This is the critical point for determining and demonstrating research exploration. Such misunderstanding or misinterpretation of statistical results can destroy the entire research work..