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# Quantitative Data Anaylsis Using The Ibm Spss Statistics Software

3670 words - 15 pages

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
 Decimals of variables:
o Location, Age, Gender, Type (Discrete values): Decimals = 0
o Other variables: Decimals = 2
 Label of variables: Enter the exact name of the variable, see the below table.
 Value of variables: adapted from the raw data background and raw data details.
Name Label Value
Location Site Location 1 = A, 2 = B, 3 = C, 4 = D
Age Age of Participant None
Gender Gender 1 = Female, 2 = Male
Auto1 Individual Timing Control 1 = Not at all, 2 = A little, 3 = Somewhat, 4 = A lot, 5 = A great deal
Auto2 Individual Method Control 1 = Not at all, 2 = A little, 3 = Somewhat, 4 = A lot, 5 = A great deal
Demand4 Production Pressure 1 = Not at all, 2 = A little, 3 = Somewhat, 4 = A lot, 5 = A great deal
Auto5 Role Breadth 1 = Not at all, 2 = A little, 3 = Somewhat, 4 = A lot, 5 = A great deal
Demand3 Monitoring Demands 1 = Not at all, 2 = A little, 3 = Somewhat, 4 = A lot, 5 = A great deal
Auto6 Task Variety 1 = Not at all, 2 = A little, 3 = Somewhat, 4 = A lot, 5 = A great deal
Demand1 Problem-solving Demands 1 = Not at all, 2 = A little, 3 = Somewhat, 4 = A lot, 5 = A great deal
Demand5 Production Responsibility 1 = Not at all, 2 = A little, 3 = Somewhat, 4 = A lot, 5 = A great deal...

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