Data Management
In today’s world of information technology, enormous data is generated in every organization. These data can help in strategic decision-making process. It is therefore important to store such data in a warehouse so that effective mining can be done later
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Data Management
Learning Objectives After completing this chapter, you should be able to do the following: • Explain different types of data generated in management research. • Know the characteristics of variables. • Learn to remove the outliers from the data by understanding different data cleaning methods before using in SPSS. • Understand the difference between primary and secondary data. • Know the formats used in this book for using different commands, subcommands, and options used in SPSS. • Learn to install SPSS package for data analysis. • Understand the procedure of importing data in other formats into SPSS. • Prepare the data file for analysis in SPSS.
Introduction In today’s world of information technology, enormous data is generated in every organization. These data can help in strategic decision-making process. It is therefore important to store such data in a warehouse so that effective mining can be done later for getting answers to many of the management issues. Data warehousing and data mining are therefore two important disciplines in the present-day scenario. Research in any discipline is carried out in order to minimize inputs and effectively utilizing the human resources, production techniques, governing principles, marketing policies, and advertisement campaigns to maximize outputs in the form of productivity. To be more specific, one may be interested to identify new forms of resources, devise organizational systems and practices to motivate culturally diverse set of individuals, and evaluate the existing organizations so as to make them more productive to the new demands on them. Besides, there may be any number of other issues like
J.P. Verma, Data Analysis in Management with SPSS Software, DOI 10.1007/978-81-322-0786-3_1, # Springer India 2013
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effective leadership, skill improvement, risk management, customer relationships, and guiding the evolution of technology, etc., where the researcher can make an effective contribution. A researcher may use varieties of data analysis techniques in solving their research problems like: How to motivate people for work? How to make a television or FM channel more popular? How to enhance the productivity at work? Which strategy becomes more efficient? How organizational structure promotes innovation? How to measure training effectiveness? Due to cutthroat competition, the research issues have grown in number, scope, and complexity over the years. Due to availability of computer software for advanced data analysis, researcher has become more eager to solve many of these complex issues. The purpose of data analysis is to study the characteristics of sample data for approximating it to the population characteristics. Drawing conclusion about the population on the basis of sample would be valid only if the sample is true representative of the population. This can be ensured by using the proper sampling technique. However, large sample need not necessarily improves the efficiency in findings. It is not the quantity but the quali
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