The purpose of the course is to teach students technical material and the book is well-designed for achieving that goal. The quantitative]qualitative incompatibility thesis 175. It should be pointed out that logistic regression is using a logistic function to model a binary dependent variable.
Grounded theory studies 161. Chapter 19 Reporting, disseminating and moving forward 487. In particular, I like that the probability chapter (which comes early in the text) is not necessary for the chapters on inference. Establishing trustworthiness in flexible design research 168. The content stays unbiased by constantly reminding the reader to consider data, context and what one's conclusions might mean rather than being partial to an outcome or conclusions based on one's personal beliefs in that the conclusions sense that statistics texts give special. Glossary Visit the website to view the Glossary, References and author index Visit the website to view the References and Author Index, Subject Index 517. All of the notation and terms are standard for statistics and consistent throughout the book. The chapters are well organized and many real data sets are analyzed. Calculations by hand are not realistic. There are separate chapters on bi-variate and multiple regression and they work well together.
Dealing with discrepancies in findings 184. On occasion, all of us in academia have experienced a text where the progression from one chapter to another was not very seamless. Some topics in descriptive statistics are presented without much explanation, such as dotplots and boxplots. To help you get off to a smooth start this term, these documents will cover essential information about using and accessing Oxford's digital resources within your school's LMS via Learning Link Direct. The way the chapters are broken up into sections and the sections are broken up into subsections makes it easy to select the topics that need to be covered in a course based on the number of weeks of the course.
Also, the convenient sample is covered. The final chapter (8) gives superficial treatments of two huge topics, multiple linear regression and logistic regression, with insufficient detail to guide serious users of these methods. I was impressed by the scope of fields represented in the example problems - everything from estimating the length of possums' heads, to smoke inhalation in one's line of work, to child development, and so on. There are also pictures in the book and they appear clear and in the proper place in the chapters. There are some things that should probably be included in subsequent revisions.
Chapter 3 Developing your ideas 45. It covers all the standard topics fully. The topics all proceed in an orderly fashion. There are lots of graphs in the book and they are very readable. As an example, I suggest the text provides data analysis by using Binomial option pricing model and Black-Scholes option pricing model. There is one section that is under-developed (general concepts about continuous probability distributions), but aside from this, I think the book provides a good coverage of topics appropriate for an introductory statistics course. However, it would not suffice for our two-quarter statistics sequence that includes nonparametrics. Some of these will continue to be useful over time, but others may be may have a shorter shelf life.
The structure and organization of this text corresponds to a very classic treatment of the topic. The text provides enough examples, exercises and tips for the readers to understand the materials. Researchers and practitioners 204. Each chapter starts with a very interesting paragraph or introduction that explains the idea of the chapter and what will be covered and why. Preparing for analysis 406. For example, a goodness of fit test begins by having readers consider a situation of whether or not the ethnic representation of a jury is consistent with the ethnic representation of the area. I do not think that the exercises focus in on any discipline, nor do they exclude any discipline. The audience for this book 7. Chapter4 (foundations of inference), chapter 5 (inference of numerical data) and chapter 6 (inference of categorical data) provide clear and fresh logic for understanding statistics. The text is easily and readily divisible into subsections. The real data sets examples cover different topics, such as politics, medicine, … etc. There are a variety of exercises that do not represent insensitivity or offensive to the reader.
The authors do a terrific job in chapter 1 introducing key ideas about data collection, sampling, and rudimentary data analysis. In addition, it is easy to follow. Then, the basics of both hypothesis tests and confidence intervals are covered in one chapter. The writing is clear, and numerous graphs and examples make concepts accessible to students. The main steps when carrying out a project xxiii.
Designing surveys 245. No issues with consistency in that text are found. The book has a great logical order, with concise thoughts and sections. Make sure you are in the right Discipline. Each section ends with a problem set. More modern approaches to statistical methods, however, will need to include concepts of important to the current replicability crisis in research: measures of effect, extensive applications of power analyses, and Bayesian alternatives. I did not encounter any issues. Research questions 59. Analysing and interpreting data 404. In addition, some topics are marked as "special topics". Doing realist reviews 97. Chapter 18 The analysis and interpretation of qualitative data 459. I have seen other texts begin with correlation and regression prior to tests of means, etc., and wonder which approach is best.
The content is accurate in terms of calculations and conclusions and draws on information from many sources, including the U. S. Census Bureau to introduce topics and for homework sets. There are no proofs that might appeal to the more mathematically inclined. There are several ways that the digital resources for your text can be accessed or assigned, so be sure to ask your instructor whether you need to access your Oxford material through your school's local learning management system or through Oxford Learning Cloud. However, there are some sections that are quite dense and difficult to follow.
The supplementary material for this book is excellent, particularly if instructors are familiar with R and Latex.
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