The book employs an easy and organized method in presenting statistical tests and methods for data processing and analysis by computer. For each statistical test, it begins with a brief introduction explaining the test and its use cases, followed by its application conditions (assumptions) and requirements, whether related to the data used before analysis or the design specific to each statistical test. This is followed by one or more practical examples from a carefully selected data file attached to the book (on a CD). In each example, all necessary SPSS steps are applied step-by-step in detail, with screenshots of SPSS screens and windows to illustrate the steps in both text and image. This enables the user to apply each example themselves and practice any statistical test without needing assistance. After completing the application steps, the process moves to the SPSS results screen, where the analysis results are displayed and interpreted one by one, table by table, not all at once as is common in other books. This allows the user to understand every part of the analysis results and its relationship to the statistical test as a whole and to other sub-results.

An appendix on a CD is included with this book, containing 21 SPSS files with the data used in the practical application of the statistical tests covered in the book. These files were chosen with great care to suit each statistical test and to align the example results with the technical aspects of the book, such as content presentation, printing, layout requirements, page size, and the overall book format.

The book consists of nine chapters on statistical data analysis. The first chapter, "Introduction to Statistical Data Analysis," aims to set the stage for the other chapters by studying the most important concepts and issues related to statistical analysis such as variables, their types, levels of measurement, their relationship to research design, choosing the appropriate statistical test, and testing research hypotheses. This chapter also covers important concepts related to research design and reporting its final results, such as defining the target population for the study and selecting the application sample. Additionally, this chapter discusses the topic of statistical test power and its influencing factors, and the famous types of statistical designs with examples for each, summarizing all these designs at the end of the chapter in a single figure called the "Family Tree of Statistics."

Chapter Two, "Reviewing Data Before Statistical Analysis," focuses on how to review data entered into the computer to ensure its correctness, accuracy, completeness, consistency, and freedom from problems. This chapter presents two famous data problems: Outliers and Missing Data, in addition to studying the most important assumptions or conditions required by statistical tests and how to verify them.

Chapter Three, "T-Tests," covers the three t-tests: One-Sample t-test, Dependent Sample t-test, and Independent t-test. In addition to comparing these tests, their assumptions, and use cases, a comprehensive example is applied to each, and its results are studied and explained in detail.

Chapter Four, "Analysis of Variance (ANOVA)," as its name suggests, deals with the topic of analysis of variance and its tests. The chapter begins with a simplified analytical presentation of the logic of the ANOVA test and a study of its assumptions. It then covers and applies the ANOVA tests: One-Way ANOVA, Two-Way ANOVA, Repeated Measures ANOVA, and finally, One-Way ANCOVA. For each test, a comprehensive example was applied using SPSS, and its results were discussed in detail.

Chapter Five, "Multivariate Analysis of Variance (MANOVA)," continues the study of variance analysis. This chapter explains cases that require more than one dependent variable and also studies the number of dependent variables in MANOVA, its assumptions, in addition to presenting a comprehensive practical example and analyzing and interpreting its results.

After studying comparison tests, Chapter Six, "Correlation," begins by examining the relationships between variables. This chapter first covers Bivariate Correlation, then Pearson Correlation, and then the Scatter Plot. The chapter then presents a comprehensive example of how to calculate the correlation coefficient in SPSS, with a discussion and interpretation of the example's results. The chapter also covers the Coefficient of Determination and Partial Correlation, presenting examples of how to calculate each using the computer and then interpreting the results in detail.

After studying the relationship between variables, we begin to explore the possibility of predicting the values of one variable through the values of another or other variables, which is the topic of Chapter Seven, "Regression." This chapter first discusses variables in regression in general, then Simple Regression, presenting detailed SPSS steps for conducting a simple regression, and discussing and interpreting every detail and table of its results. In the same way, Multiple Regression, its assumptions, application steps, and results are studied. The chapter also addresses a very important topic in multiple regression: methods for selecting independent variables to form the best regression model. The chapter concludes by studying a famous problem in multiple regression, which is Multicollinearity, and discusses how to diagnose this problem first and then how to mitigate its negative impact on the regression results and the accuracy of the prediction equation.

Chapter Eight covers "Factor Analysis," one of the important tests in statistical analysis with enormous applications and uses in social, humanitarian, educational, administrative, and economic fields. The chapter begins by presenting the two types of factor analysis: Exploratory and Confirmatory, then explains why we need factor analysis by studying the problem of numerous correlations in phenomena and data. The chapter then studies the mechanism of factor analysis and its assumptions, followed by a comprehensive example of how to apply factor analysis steps using SPSS. This is followed by a detailed presentation of all results and their interpretation one by one. The chapter concludes with a study of rotation, its types, and its importance in choosing the best factor analysis model among several models to represent the phenomenon under study.

The ninth and final chapter, "Reliability and Validity," studies two of the most important psychometric properties of tests and scales: reliability and validity. On the topic of Reliability, the chapter studies types of reliability: test-retest reliability (stability), parallel-forms reliability (equivalence), Inter-Rater Reliability, and internal consistency. This chapter also discusses factors affecting the amount of reliability, the acceptable reliability standard, reliability across different measurement tools, and how to calculate the reliability coefficient using SPSS through a comprehensive example and a detailed discussion of its results. On the topic of Validity, this chapter studies common types of validity: Content Validity, Criterion Validity, and Face Validity. The factors affecting the validity of a measurement tool, the relationship between reliability and validity, and other matters related to the quality of measurement tools are also studied.

• Number of Pages: 228
• Year of Publication: 2018
• Binding Type: Paperback
• Edition Number: 3
• Printing Color: Black
• Dimensions (cm): 17x24
• Weight (kg): 0.600
• Barcode: 9789957065997