This book presents the basic statistical concepts required for researchers and students ineducation, descriptive statistics, and advanced inferential statistics in a simple manner.Itmay be helpful to the learners in master’s level as well as doctoral level. It offers anintegral approach to the study of statistics placing due emphasis on theory and applicationof computational procedures in Statistics related to Educational research.
Prof (Dr) C. Naseema with more than twenty-one years of teaching experience is the Dean, Faculty of Education and the Director, School of Education of University of Calicut. She has completed four Major research projects aided by UGC, IIPA, and SSA. She has visited many foreign countries in association with her project work. She has authored 8 books and contributed papers to reputed books and journals.
Foreword Preface 1. Statistics in Education 1.1. Introduction 1.2 Stages of the Research Process 1.3 Why You Study Statistics in Educational Research? 1.4 Descriptive Statistics 1.5 Inferential Statistics 1.6 Measurement In Education/Psychology 1.7 Characteristics of Scales of Measurement 1.8 Frequency Distribution 1.9 Graphical Representation of Frequency Distribution 2. Measures of Central Tendency 2.1 Arithmetic Mean 2.2 Median 2.3 Mode 2.4 The Range 2.5 Variance (s2) and the Standard Deviation (s) 2.6 Quartile Deviation 2.7 Average or Mean Deviation 2.8 Percentile Ranks 2.9 Derived Scores 3. Normal Distribution 3.1 What are Normal Distributions? 3.2 Why are Normal Distributions Important? 3.3 The Standard Normal Distribution 3.4 Applications of the Normal Probability Curve 3.5 Skewness 3.6 Kurtosis 3.7 Introduction to the t-Distribution 3.8 Comparison of Normal Distribution and Students’ t-distribution 3.9 Degrees of Freedom 4. Correlation 4.1 Correlation–An indication of Direction 4.2 Nature of the Relationship 4.3 Direction of the Relationship 4.4 Strength of the Relationship 4.5 Pearson’s Product-Moment Coefficient of Correlation 4.6 Bivariant Frequency Distribution 4.7 Interpretation of Pearson’s r 4.8 Spearman’s Coefficient of Correlation-r 5. Regression 5.1 Regression Equations and Regression Coefficients 5.2 Relation of Regression to Correlation 5.3 Standard Error of Estimate 6. Multiple Regression Theory and Analysis 6.1 Basic Ideas 6.2 Example with Two Independent Variables 6.3 Regression Graph 6.4 Assumptions behind Multiple Regression 6.5 Multiple Regression, Discriminant Analysis and Canonical Correlation 6.6 Canonical Correlation 7. Inferential Statistics 7.1 Sampling 7.2 Method of Sampling 7.3 Sampling Distribution 7.4 Application of the Concept of Standard Error 8. Tests of Significance of Difference Between Statistics 8.1 Levels of Significance 8.2 Errors in Hypothesis Testing 9. Analysis of Variance (ANOVA) 9.1 Conditions/Basic Assumptions of ANOVA 9.2 Computing ANOVA 9.3 One-Way ANOVA 9.4 Summary of ANOVA 10. Non-Parametric Tests 10.1 Why Nonparametric Statistical Tests? 10.2 Examples of Nonparametric Statistical Tests 10.3 Advantages of Non-parametric Statistics 10.4 Disadvantages of Non-parametric Tests 10.5 Chi-square (c2) Test 10.6 Mann–Whitney U-test 10.7 Sign-Rank Test 11. Introduction to Statistical Package for Social Sciences 11.1 SPSS Menu and Toolbars 11.2 Data View 11.3 Working with the Data Editor 11.4 SPSS Viewer 11.5 Importing and Exporting Data 11.6 Guidelines for Creating Data Sets 11.7 Guidelines for Reporting Results References Appendices