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This course is designed or students in disciplines of humanities and social sciences. It provides basic training in quantitively reasoning and data analytical methods widely applicable in research in disciplines such as applied linguistics, language education, psychology, political science, etc. The major topics covered in the course include basic probability theory, sampling and statistical inferences, research variables, data treatment and data visualization, statistical relationships, testing for group differences, linear regression analysis, etc. The course adopts a “user-friendly” practical approach (lectures + tutorials via SPSS) to teach data analytical methods for quantitative research in disciplines of humanities and social sciences. Applied examples from relevant disciplines are used extensively to facilitate students’ understanding of the data analytical skills and their applications in the respective disciplines.
This book presents explanation of basic statistical concepts and instructions on statistical analysis using the conventional manual procedural steps, and offers an introduction to the SPSS software program. After two introductory chapters, the book is divided into two sections- descriptive statistics and inferential statistics. In the former section, the book goes over basic mathematical concepts and measurement, frequency distributions, graphing, measures of central tendency, measures of variability/dispersion, the normal distribution and standard scores, correlation, linear regression. In the latter section, the book goes over statistical inference and probability, hypothesis testing.
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.
