• Summary
  • Contents

This book is designed to help the managers and researchers in solving statistical problems using SPSS and to help them understand how they can use various statistical tools for their own research problems. SPSS is a very powerful and user friendly computer package for data analyses. It can take data from most other file-types and generate tables, charts, plots, and descriptive statistics, and conduct complex statistical analyses. This book will help students, business managers, academics as well as practicing researchers to solve statistical problems using the latest version of SPSS (16.0). After providing a brief overview of SPSS and basic statistical concepts, the book covers: Descriptive statistics t-tests, chi-square tests, and ANOVACorrelation analysisMultiple and logistics regressionFactor analysis and testing scale reliabilityAdvanced data handling

Data Reduction and Scale Reliability: Factor Analysis
Data reduction and scale reliability: Factor analysis

Factor analysis (FA) and Principal Components Analysis (PCA) are techniques used when the researcher is interested in identifying a smaller number of factors underlying a large number of observed variables. Variables that have a high correlation between them and are largely independent of other subsets of variables, are combined into factors. A common usage of PCA and FA is in developing objective instruments for measuring constructs which are not directly observable in real life. The examples given below demonstrate the usage of factor analysis:

  • A researcher is interested in identifying celebrity characteristics which are perceived by the general public to be important. The researcher may choose a variety of variables such as intelligence, ...
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