Doing Science: Design, Analysis, and Communication of Scientific Research

By Ivan Valiela | Go to book overview

2
Elements of Scientific Data
and Tests of Questions

Before we venture into how research might be done, we must discuss some elements of scientific studies, including what kinds of data we might find, what is meant by accuracy and precision of data, and how the nature of data might be revealed by means of frequency distributions. I also need to introduce a few descriptive statistics that will be useful for later discussions. Once we have covered these essentials of data handling, we start our examination of how we might test questions of interest.


2.1 Kinds of Data

We have repeatedly referred above to variables without stopping to define what they might be; in our context, variables refer to properties subject to change. That may be too general; perhaps we can narrow the idea to characteristics with respect to which measurements in a sample will vary.

We collect data to ask, “What is the value of the variable?” Amount of rain, number of leaves per tree, a person's height, class rank, wind velocity, ratio of carbon to nitrogen in soil, concentration of molybdate in seawater, hair color, and electrical resistance all are examples of variables. Each of these can be given a value, which is what we measure or observe.

If we think about the list of variables cited in the preceding paragraph, it may become evident that there are different types of variables. The list seems to include items with rather different properties.


Nominal Data

Nominal data are those that cannot be assigned quantitative properties. Rather, these data can be thought of as classifications, categories, or attributes. Examples might include studies in which we record the number of species of plants, hair color types, country of origin, or whether the subjects are alive or dead.

Nominal data can be examined quantitatively by combining the observations into frequencies. Data that are subject to such grouping are referred to as enumeration data. For example, in genetic studies, peas

-29-

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Doing Science: Design, Analysis, and Communication of Scientific Research
Table of contents

Table of contents

  • Title Page *
  • Preface v
  • Contents vii
  • Doing Science 1
  • 1 - Obtaining Scientific Information 3
  • Sources and Further Reading *
  • 2 - Elements of Scientific Data and Tests of Questions 29
  • Sources and Further Reading *
  • 3 - Statistical Analyses 49
  • Sources and Further Reading *
  • 4 - Principles of Research Design 79
  • Sources and Further Reading 97
  • 5 - Communication of Scientific Information: Writing 99
  • Sources and Further Reading *
  • 6 - Communicating Scientific Information: the Scientific Paper 127
  • Sources and Further Reading *
  • 7 - Other Means of Scientific Communication 147
  • Sources and Further Reading *
  • 8 - Presenting Data in Tables 171
  • Sources and Further Reading *
  • 9 - Presenting Data in Figures 183
  • Sources and Further Reading *
  • 10 - Case Studies of Graphical Data Presentation 219
  • 11 - Perceptions and Criticisms of Science 255
  • Sources and Further Reading *
  • Index 285
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