Statistical Techniques to explore relationship among variables using SPSS
SPSS provides several techniques of analysis to explore relationships, namely Correlation, Partial Correlation, Multiple Regression and Factor Analysis.
Correlation Analysis: Correlation analysis is used to describe the strength and direction of the relationship between two variables that is usually continuous in nature. It also can be used when one of the variables is dichotomous (it has only two values), such as sex: males/females. The statistic obtained is Pearson's product-moment correlation (r) with its statistical significance.
Partial Correlation: Partial correlation is used to explore relationship between two variables while statistically controlling for a third variable. This is useful when it is suspected that the relationship between the two variables of interest might be influenced or confounded by the effect of a third variable. Partial correlation analysis statistically removes the influence of this variable that can provide a clear picture of the actual relationship between the two variables intended to be examined.
Multiple Regression: Multiple regression can be used to predict a single dependent variable from a group of independent variables. It is used to test the predictive power of a set of variables and to assess the relative contribution of each variable.
Factor Analysis: Factor analysis is used to deal with a large number of related variables (for example, a number of items representing a scale) when a researcher is interested to explore the underlying ideas of these variables. It is useful to reduce a large number of items to be a smaller and manageable number of dimensions or components. (Source)
Correlation Analysis: Correlation analysis is used to describe the strength and direction of the relationship between two variables that is usually continuous in nature. It also can be used when one of the variables is dichotomous (it has only two values), such as sex: males/females. The statistic obtained is Pearson's product-moment correlation (r) with its statistical significance.
Partial Correlation: Partial correlation is used to explore relationship between two variables while statistically controlling for a third variable. This is useful when it is suspected that the relationship between the two variables of interest might be influenced or confounded by the effect of a third variable. Partial correlation analysis statistically removes the influence of this variable that can provide a clear picture of the actual relationship between the two variables intended to be examined.
Multiple Regression: Multiple regression can be used to predict a single dependent variable from a group of independent variables. It is used to test the predictive power of a set of variables and to assess the relative contribution of each variable.
Factor Analysis: Factor analysis is used to deal with a large number of related variables (for example, a number of items representing a scale) when a researcher is interested to explore the underlying ideas of these variables. It is useful to reduce a large number of items to be a smaller and manageable number of dimensions or components. (Source)
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