through practical exercises and coursework.
This book takes you only to the foothills of statistical analysis. A reasonable competence with arithmetic and a little algebra are required. For the application of more demanding and complex statistical techniques, the help of a statistician will be needed. Once you have mastered the material in this book, you may want to attempt a more advanced course on statistics.
AIMS AND SCOPE
The aim of this book is to provide clear, uncomplicated explanations and examples of statistical concepts and techniques for data analysis relevant to learning and research in nutrition and dietetics. There are lots of short, practical exercises to work through. These support insight into why various tests work. There are also examples of SPSS1 output for each test. This makes it is possible to marry up the outcomes computed manually with those produced by the computer. Examples are taken from around the globe relating to all aspects of nutrition, from biochemical experiments to public health nutrition, and from clinical and community practice in dietetics. All of this is complemented by material online, including data sets ready for analysis, so that students can begin to understand how to generate and interpret SPSS output more clearly.
The book focuses on quantitative analysis. Qualitative analysis is highly valuable, but uses different approaches to data collection, analysis, and interpretation. There is an element of overlap, for example when quantitative statistical approaches are used to assess opinion data collected using questionnaires. But the two approaches have different underlying principles regarding data collection and analysis. They complement one another, but cannot replace one another.
Two things this book is not. First, it is not a ‘cookbook’ with formulas. Learning to plug numbers in to formulas by rote does not provide insight into why and how statistical tests work. Such books are good for reminding readers of the formulas which underlie the tests, but useless at conveying the necessary understanding to analyze data properly or read the scientific literature intelligently. Second, it is not a course in SPSS or Excel. While SPSS and Excel are used to provide examples of output (with some supporting syntax for clarity), it is no substitute for a proper course in computer‐based statistical analysis.
Scope
The book provides:
a basic introduction to the scientific method
an understanding of populations and samples, principles of measurement, and confidence intervals
an understanding of the basic theory underlying basic statistical tests, including ‘parametric’ tests (those intended for use with data that follow mathematically defined distributions such as the so‐called ‘normal’ distribution); and ‘non‐parametric’ tests, for use with data distributions that are not parametric
lots of worked examples and exercises that show how to compute the relevant outcome measures for each test, both by hand and using SPSS
real examples from the nutrition and dietetics literature, including biochemical, clinical, and population‐based examples
principles of research design, transformations, the relevance of sample size, and the concept and calculation of Power
All of the exercises have worked solutions.
Some students say, ‘Why do we have to do the exercises by hand when the computer can do the same computations in a fraction of a second?’ The answer is: computers are stupid. The old adage ‘garbage in, garbage out’ means that if you don’t have insight into why certain tests work the way they do, a computer will generate output that might be meaningless, but it won’t tell you that you’ve made a mistake, or ask ‘Is this really what you wanted to do?’ So, the purpose of the textbook and supporting learning materials is to help ensure that when you do use a computer, what goes in isn’t garbage, and what comes out is correct and provides meaningful answers to your research questions that you can interpret intelligently.
Finally, it is worth saying that some students will find this textbook providing welcome explanations about why things work the way they do. Others will find it annoyingly slow and detailed, with too much explanation for concepts and applications that seem readily apparent. If you are in the first group, I hope you enjoy the care with which explanations and examples are presented and that it helps to demystify what may at first seem a difficult topic. If you are in the second group, read quickly to get to the heart of the matter, and look for other references and resources for material that you feel is better suited to what you want to achieve. However hard or easy the text seems, students in both groups should seek to make friends with a local statistician or tutor experienced in statistical analysis and not try and do it alone.
Unique features
There are many unique features in this textbook and supporting material:
Examples specific to nutrition and dietetics
Clear simple language for students unfamiliar with statistical terms and approaches. For many students, the study of statistics is seen as either a burden or irrelevant to their decision to study nutrition and/or dietetics. But they will be required to pass a statistics module as part of their course. The aim is to make this as engaging and painless as possible.
Lots of worked examples, with examples of SPSS output to help students with the interpretation of their analyses in the future.
Putting statistics into context so that it is relevant to many undergraduate and postgraduate research projects.
A website that provides complementary exercises, data sets, and learning and teaching tools and resources for both students and tutors.
CONTENTS
This textbook is based on over 20 years of teaching experience. There are four parts:
Part 1: Setting the statistical scene
This introduces concepts related to the scientific method and approaches to research; populations and samples; principles of measurement; probability and types of distribution of observations; and the notion of statistical testing.
Part 2: Statistical tests
This covers the basic statistical tests for data analysis. For each test, the underlying theory is explained, and practical examples are worked through, complemented by interpretation of SPSS output.
Part 3: Doing research
Most undergraduate and postgraduate courses require students to collect data and/or interpret existing data sets. This section places the concepts in Part 1 and the learning in Part 2 into a framework to help you design studies, and determine sample size and the strength of a study to test your hypothesis (‘Power’). A Flow Chart helps you select the appropriate statistical test for a given study design.
The last chapter explores briefly how to present findings to different audiences – what you say to a group of parents in a school should differ in language and visual aids from a presentation to a conference
