13 Quantitative Analysis
Learning Outcomes
By the end of this chapter, learners will:
- Understand basic terminology associated with quantitative analysis,
- Describe the differences between descriptive and inferential statistics.
Introduction
Quantitative data are data represented numerically; this includes anything that can be counted, measured, or given a numerical value. Quantitative data analysis is the process of summarizing the data using statistical analysis. In this sense, quantitative data analysis allows us to describe observations that can be found in a dataset. Quantitative data analysis can:
- Succinctly summarize data
- Highlight trends in quantitative data
- Show a rate of change
- Be used to draw comparisons
- Determine the average, or mean
A lot of OER related data is quantitative. For example, counting the number of courses that have adopted, adapted, or created OER is a type of quantitative data. Quantitative data analysis can, therefore, shed light on the magnitude of the effect of OER programs or projects, the most pressing needs of an institution or a department when selecting course materials, or how different introductory courses at your institution have adopted OER at different rates. Relevant quantitative data will likely be produced via surveys, questionnaires, and web analytics. Student GPAs and student enrollment rates may also be used to tell a story about OER impact.
Terminology
There are four key terms that are commonly used to describe quantitative data:
The independent variable is the variable that is not influenced by other factors. In this sense, the outcome is independent of other circumstances. For example, if I were running a trial to see if the type of course materials used has an impact on student scores, my independent variable would be the type of course material – I might have some students use a commercial textbook and other students use an OER.
The dependent variable is influenced by other factors. The outcome is dependent on the other circumstances, especially the independent variable. To return to the above example, my dependent variable would be the test scores, since those are influenced by the course materials that are used.
There are also two different types of data. Discrete data is any data that can only be specific values. Most often, discrete data is any data represented by whole numbers. This data cannot be broken down into smaller values. Examples in OER include: the number of courses in a given faculty that use OER or test scores. Most quantitative OER data is discrete.
Continuous data can take any value. Common examples of continuous data include temperature and weight. This type of data is rarer in OER.
Quantitative Analysis
We can draw conclusions about our data and OER program through quantitative data analysis. The two types of quantitative data analysis are descriptive statistics and inferential statistics.
Descriptive Statistics
Descriptive statistics are used to describe and summ