This book facilitates researchers in checking the assumptions of statistical tests used in their research. This book has six chapters. Chapter 1 discusses the importance of assumptions in analyzing research data. The concepts of hypothesis testing and statistical errors are discussed in detail, as well as the concepts of power, sample size and effect size. In Chapter 2, the authors introduce SPSS functionality to the readers. They show how to segregate data, draw random samples, file split and create variables automatically. These operations are extremely useful for researchers in analyzing the survey data. Additionally, acquaintance with SPSS in this chapter facilitates the readers to understand the procedure involved in testing assumptions in different chapters.  In Chapter 3, the authors discuss different assumptions required in survey studies. The importance of designing surveys in reporting the efficient findings is covered. Chapter 4 provides various parametric tests and the related assumptions. The procedures for testing these assumptions by using SPSS software is shown. To motivate the readers to use assumptions, the authors have shown many situations where violation of assumptions affects the findings. In this chapter, the authors discuss assumptions in statistical tests that are commonly used by researchers such as z, t and F tests, ANOVA, correlation and regression analysis. In chapter 5, assumptions required for different non-parametric tests such as Chi-square, Mann-Whitney, Kruskal Wallis and Wilcoxon signed-rank test are discussed. The authors show the procedure of testing these assumptions, as well. Finally, in Chapter 6, assumptions in non-parametric correlations such as bi-serial correlation, tetrachoric correlation and phi coefficient are covered. These types of analyses are often used by researchers in survey studies.

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