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This course provides the full range of knowledge and skills needed to conduct experimental and correlational research in psychology and other social sciences and education. Two analytical frameworks and procedures are covered - analysis of variance (ANOVA) as the analytical tool for experimental research and multiple regression used in correlational research. Complex ANOVA designs and analyses and advanced correlational data analysis such as structural equation modeling will be covered in this course. This course requires that students have (1) equivalent undergraduate training in these areas (to be approved by the ProgrammeDirector); or (2) taken PSY5010.
Multivariate statistics are techniques used for analyzing complicated data sets. Chapter 1 introduces basic conceptions. Chapter 2 gives a guide to the multivariate techniques and places them in context with the univariate and bivariate statistics. Chapter 3 briefly reviews the univariate and bivariate statistical techniques. Chapter 4 deals with the assumptions and limitations of multivariate statistical methods. Chapter 5 through 16 and Chapter 18 cover specific multivariate techniques, and each chapter includes a comparison of computer programs. Chapter 17 attempts to integrate univariate, bivariate, and multivariate statistics through the multivariate general linear model.
This book is good for students in the social and behavioral sciences who have mastered basic knowledge of statistical methods and research design. Part 1 introduces the concepts in moderation and mediation analysis and provides an example of their integration in the form of a conditional process model. Part 2 focuses exclusively on mediation analysis and how linear regression can be used to conduct a simple path analysis of a three variable X→M→Y casual system. Part 3 shifts discussion to moderation analysis. Part 4 integrates the concepts and lessons described in the prior two parts by introducing conditional process analysis.
This book is to familiarize behavioral and social science students with statistics. The difficulty upgrades as readers go through the book: Chapters 1-10 are first-year degree level; Chapters 9-16 move into second-year degree level; and Chapters 17-21 discuss more technical topics. Part 1 focuses on doing research and introducing linear models. Part 2 focuses on exploring data. Part 3 deals with lineal models with continuous predictors. Part 4 deals with lineal models with continuous or categorical predictors. Part 5 deals with lineal models with multiple outcomes. Part 6 deals with lineal models with categorical outcomes. Part 7 deals with lineal models with hierarchical data structures.
This book provides a comprehensive introduction to statistics and SPSS. It is suitable for students taking a course in psychology, applied psychology, social science and other related fields. The five beginning chapters provide core concepts for comprehending the main statistical techniques covered later in this book. The chapters that follow the opening chapters generally explain the concepts underlying specific types of tests, such as analyses of differences between two conditions, analysis of differences between three or more conditions, analysis of variance with more than one IV, and regression analysis, etc. as well as how to conduct and interpret the findings from these.
