This book explains about research data analysis and how to make interpretation. The calculation is done using the IBM SPSS version 21, 22, 24 and 25. There are some changes in ways of calculation in these versions, such as canonical correlation and mean comparison. Some new analysis procedures that cannot be found in the previous versions will be discussed in this book as well. Besides, a comprehensive research sample will be described in details in one of the parts of this book. This book will include: -Basic concepts ...
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This book explains about research data analysis and how to make interpretation. The calculation is done using the IBM SPSS version 21, 22, 24 and 25. There are some changes in ways of calculation in these versions, such as canonical correlation and mean comparison. Some new analysis procedures that cannot be found in the previous versions will be discussed in this book as well. Besides, a comprehensive research sample will be described in details in one of the parts of this book. This book will include: -Basic concepts underlying IBM SPSS: discussing terms used in calculation, such as variables, confidence interval, significance level, degree of freedom, critical values, cases, measurement scales and hypothesis testing -How to compare mean using an independent t test: how to use mean in comparing two different conditions -Finding valid predictors using principal component analysis: drawing the most valid and reliable predictor in order to get an accurate predictor. -More than one independent and dependent variable relationship in canonical correlation: a new technique to make calculation using canonical correlation that is not found in previous versions -How to make a good model simulation: this is a technique to make a good model to assist a researcher in designing a standard model. -How to analyze the data having different measurement scales: this is a solution to calculate data having different measurement scales -Automatic linear modeling: make use one of the best IBM SPSS feature to make a good model in the research, such as a standard model, an increase of model accuracy, model stability and making a good model using large data. -How to present the research result in graphs -Data transformation to increase the quality of the data that will be used in the research -How to use analysis of covariance in an experimental research -A Thesis Research Example: Using a linear regression procedure that meet the classical assumptions, such as normality, heteroscedasticity, multi-collinearity, auto-correlation; and how to solve the un-met criteria of the compulsory assumptions. . Direct Marketing
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