# Essay on Quantitative Data Anaylsis Using the IBM SPSS Statistics Software

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This paper is an illustration of quantitative data analysis using the IBM SPSS Statistics software. It does not provide the details of technical skill to operate SPSS but focuses on developing a set of decisions and actions in order to set up, describe, manipulate and analyse data in the specific context of the study of Jackson and Mullarkey (2000). In order to fulfil the task, this paper illustrates a step-by-step of actions that were made on the data. It also gives the insight into the determination of each step that helps interpret the findings from the data.

1. DATA SET UP IN SPSS
It is important to set up the data before conducting further activities on data by using SPSS. The establishment of data needs a preliminary handling of the raw data in Excel and then defines the data characteristics and deals with missing variables in SPSS.
(1a) Prepare Excel file
 Review the raw data file in Excel
 Additional coding: Replace the text into numbers
o Column Site of Location: Replace A with 1, B with 2, C with 3, D with 4
o Column Gender: Replace Female with 1, Male with 2
o Column Type of Work Design:
 Replace PBS Work Design with 1,
 QRM Work Design with 2
 and then replace Work Design with a blank space (considered Work Design as a missing value because it did not reflect the choice between PBS Design and QRM Work Design)

(1b) Import the Excel file into SPSS
 Save recent changes
 Close Excel before open data from SPSS
(1c) Define the variables: Make changes in the Variable View
 Name: Change the labels adapted in the first row of Excel file into new variable names (regard the variables background of the conceptual framework, must be short, no space), as indicated in the below table.
 Type of variables: Numeric
 ...

... middle of paper ...

...o the Interval and Ratio variables of the Numerical data. Furthermore, the Categorical data are often accompanied by non-parametric statistics; the Numerical data are often used with parametric statistics. In short, the measurement of data (Numerical vs. Categorical) and the kind of statistics (parametric vs. non-parametric) will distinguish one statistic from another.
 Be able to interpret the statistical results: A significant step in analysing data is the explanation of the statistics. SPSS dedicates to create the statistical results very quickly but it is the responsibility of the analyst to understand and to express the finding from the software logically. It is the critical point to determine and demonstrate the exploration of the research. Such a misunderstanding or a wrong interpretation of the statistical results can destroy the whole work of the research.

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