This document discusses quantitative data analysis using IBM SPSS Statistics software. It does not provide details of the technical skills for using SPSS but focuses on developing a series of decisions and actions in order to set up, describe, manipulate and analyze data in the specific context of Jackson and Mullarkey's (2000) study. To accomplish the task, this document explains step by step the actions performed on the data. It also provides detailed information about determining each step which helps in interpreting the results from the data.1. SETTING UP DATA IN SPSSIt is important to set up the data before conducting further work on the data using SPSS. Creating the data requires preliminary handling of the raw data in Excel, then defining data characteristics and handling missing variables in SPSS. (1a) Prepare the Excel file Review the raw data file in Excel Additional coding: replace text with numbers o Location Column: replace A with 1, B with 2, C with 3, D with 4 o Column gender : replace Female with 1, Male with 2 or Work Design Type Column: Replace PBS Work Design with 1,  QRM Work Design with 2 and then replace Work Design with a blank (considered Work Design as a missing value because 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 data from SPSS(1c) Define variables: Make changes in 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, it must be short, without spaces), as indicated in the table below. Type of variables: Numeric .... .. half of the sheet ......or the Interval and Ratio variables of the Numeric data. Furthermore, categorical data are often accompanied by nonparametric statistics; Numerical data is often used with parametric statistics. In short, the measurement of the data (numerical vs. categorical) and the type of statistic (parametric vs. nonparametric) will distinguish one statistic from another. Being able to interpret statistical results: a significant step in data analysis data is the explanation of statistics. SPSS strives to create statistical results very quickly, but it is the analyst's responsibility to understand and logically express the software's results. It is the critical point for determining and demonstrating research exploration. Such a misunderstanding or misinterpretation of statistical results can destroy the entire research work.