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Statistical Modeling in IBM SPSS Applied in the Research
Statistical Modeling in IBM SPSS Applied in the Research
Statistical modeling is useful to make a model relating to the relationship of variables in the research. Making a good model of variable relationship is not an easy matters. Nevertheless, there are many procedures which are available in IBM SPSS that can help us construct a good model for our researches. The following procedures are useful for us to make statistical modeling when we want to conduct a research that involve the more complex relationship of variables. Several procedures that will be explained in this book are:
• Automatic Linear Modeling. This procedure generates the significant and important predictors assumed to affect the dependent variable. By applying this procedure, we can select the best and convenient predictors to predict the dependent variable value.
• General Linear Model. This procedure uses fixed factors and covariates as the independent variables and one metric dependent variable
• Multivariate General Linear Model. This procedure uses fixed factors and covariates as the independent variables and more than one metric dependent variable. This procedure will be very useful when we have more than one dependent variables in which such condition cannot be solved using the linear regression procedure. As we know that in linear regression the dependent variable is only one.
• Generalized Linear Model. This procedure is an extension of the General Linear Model that allows the non-normally distributed data. This procedure is useful for the data that the value fluctuates. Mostly the time series data cannot meet the requirement of the normal distribution.
• General Linear Model Repeated Measure. This procedure is used to compare the means by means of providing the variance analysis when the repeated measure is done more than one on the similar subject
• Generalized Estimating Equation Model. This procedure is an extension of the Generalized Linear Model that allows us to do the repeated measuring and to make clusters
• Linear Mixed Model. This procedure is an extension of the General Linear Model that allows us to conduct analysis using not only the data but also the variance as well as the covariance
• Exercises
Statistical modeling is useful to make a model relating to the relationship of variables in the research. Making a good model of variable relationship is not an easy matters. Nevertheless, there are many procedures which are available in IBM SPSS that can help us construct a good model for our researches. The following procedures are useful for us to make statistical modeling when we want to conduct a research that involve the more complex relationship of variables. Several procedures that will be explained in this book are:
• Automatic Linear Modeling. This procedure generates the significant and important predictors assumed to affect the dependent variable. By applying this procedure, we can select the best and convenient predictors to predict the dependent variable value.
• General Linear Model. This procedure uses fixed factors and covariates as the independent variables and one metric dependent variable
• Multivariate General Linear Model. This procedure uses fixed factors and covariates as the independent variables and more than one metric dependent variable. This procedure will be very useful when we have more than one dependent variables in which such condition cannot be solved using the linear regression procedure. As we know that in linear regression the dependent variable is only one.
• Generalized Linear Model. This procedure is an extension of the General Linear Model that allows the non-normally distributed data. This procedure is useful for the data that the value fluctuates. Mostly the time series data cannot meet the requirement of the normal distribution.
• General Linear Model Repeated Measure. This procedure is used to compare the means by means of providing the variance analysis when the repeated measure is done more than one on the similar subject
• Generalized Estimating Equation Model. This procedure is an extension of the Generalized Linear Model that allows us to do the repeated measuring and to make clusters
• Linear Mixed Model. This procedure is an extension of the General Linear Model that allows us to conduct analysis using not only the data but also the variance as well as the covariance
• Exercises
149 pages, Kindle Edition
Published December 26, 2017
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