Unit 1: Data Set Analysis

Students will:

• Define the difference between qualitative and quantitative data.
• Evaluate data types used to communicate an environmental issue.
• Evaluate the implications of different data types used in environmental problem communication.

This unit is appropriate for classes in the social sciences, natural and physical sciences, or humanities as a way to introduce students to differing types and uses of data to understand the world. We tend to privilege quantitative data sets; yet, many research fields, including the geosciences, social sciences, and rhetorical analysis, rely heavily on other data forms. This unit sets the stage for subsequent learning by providing a basic foundational understanding of data and a set of shared definitions for data types. It also introduces students to the notion that different types of data have different practical utility and emotive responses in understanding and characterizing environmental problems. Students will also become aware that while different types of data may be privileged by professionals who work in different disciplines, a variety of data is necessary to accurately characterize an environmental concern.

This unit assumes no prior exploration of these ideas.

Intro to data and the module (10 min)

All-class brainstorm activity:

Begin by asking students: "What do we mean by data?" and "What are some examples of data?" Write student responses on the board. The discussion should make apparent that data are pieces of information used for analysis and can be used to help us understand the world. The instructor should explain that "data" is plural and "datum" is singular (e.g. "The data suggest . . ." instead of "The data suggests . . .")

Ask students to explain the difference between qualitative and quantitative data. Quantitative data refers to information that is measured and expressed using numbers. Qualitative data refers to information that is characterized in a non-numerical manner. Ask students if qualitative or quantitative is better. Through a class discussion and by using examples of data from the brainstorming activity, show that one type of data is not inherently better than another; different data types are important to helping us answer different types of questions about the world. Discuss how people from different academic backgrounds/disciplines tend to use similar types of data. For example, an atmospheric scientist interested in climate change might be interested in CO₂ levels in the atmosphere — quantitative data. A sociologist interested in climate change might be interested in interview transcripts exploring the climate change-related experiences of people living on small island nations — qualitative data. A writer interested in long-term, systemic climate change might be interested in changing contemporary perceptions through fiction based in factual interpretation — qualitative data. A geologist interested in climate change might be interested in gas trapped in bubbles in glacial ice — quantitative data. Increasingly, researchers are seeing the benefit of drawing from multiple data sets and types — across academic backgrounds/disciplines — to better understand real world problems.

Qualitative vs Quantitative activity (20 min)

Place students into groups of two to five and provide two or three different pieces of each data set to each member of the small group. (Each group should have qualitative and quantitative data). The instructor will explain that each component of the data set tells us something about water in Phoenix, Arizona. Managing water supply and quality is one of the Phoenix Metropolitan Region's greatest technical and political challenges. All the data are real and come from the research of Phoenix-area geoscientists, sociologists, artists and other researchers. Student groups should address the following questions:

• What is your initial response to each data set?
• Are they quantitative or qualitative?

Data Sets Handout (Microsoft Word 2007 (.docx) 323kB Jul14 24)

Report out to the class and agree upon qualitative/quantitative responses as a class. Hold a brief discussion about students' initial responses to the data sets.

Discussion: How might each data set be used in environmental decision-making? (15 min)

Discuss the following examples with students, allowing students to add ideas about how each data set could be used.

Example 1: Social Survey Data: These data suggest that Phoenix residents are concerned about declining water supply but also that residents generally believe people should be able to access all the water they need. (This is useful information for local and regional policy-makers and suggests that implementing regulations on water use might be difficult.)

Example 2: Historic annual rainfall: The figure suggests that rainfall in Phoenix does not vary haphazardly, but seems to follow a pattern of ebbs and flows. (This might be useful for reservoir management.)

Example 3: Photos from Phoenix transect project: (These could serve as useful data in demonstrating to the public the extent to which the region has managed its surface water and to facilitate public dialogue around related decisions.)

Discussion prompts:

• How do these data sets relate to one another?
• What do they tell us about the complexity of the water system (including the social and natural aspects of the system) in Phoenix?
• Is the water system open or closed? What are the inputs and outputs? How do the inputs (rainfall and infiltration from rivers and water users) relate to the outputs (withdrawal for human use, evaporation and transpiration)?
• In what ways can the system be impacted (positively or negatively) by humans?
• How can data be used by professionals from different disciplines? Which data sets would be most useful in developing concern among the residents of Phoenix about their water supply? Which data sets would be most useful in developing concern among political decision-makers about the water supply in Phoenix?
• What value is there in considering the data sets together as opposed to separately?

Students should note that in a warm, arid place like Phoenix, evaporation plays an important role in water management: surface water evaporates readily into the atmosphere, where it is not usually readily usable for irrigation and residential use. In other words, the water system in Phoenix is largely an open system — water moves readily from one form to another. In some places, human water needs are so strong that municipalities are engaging in cloud seeding — injecting chemicals into the clouds to alter "natural" condensation processes and encourage precipitation.

Unit reflection (5 min)

Ask students to take a minute or two to jot down new insights they have gained and any remaining questions they have about qualitative/quantitative data and the use of data in environmental decision-making. Ask a few students to share their responses and collect all the responses.

Wrap-Up and Preview (5 min)

Instructors should then preview the rest of the module and introduce the sensory log assignment. Explain to students that they will need to maintain an individual log of their immediate sensory experiences, which they are to record once each hour for any ten-hour period before the next class session meets. Sensory Log Assignment and Rubric (Microsoft Word 45kB May16 16)

Society tends to privilege quantitative data, but this unit emphasizes the important role of many different types of data in environmental decision-making. Instructors may want to think about this as students discuss their reactions to the data sets and seek to draw out the importance of qualitative data.

During the discussion, students should be thinking about the value of data sets within specific disciplines and to specific audiences. They should also consider the value of synthesizing a more comprehensive view by combining data sets and considering the interrelationships between them. Integration of data from the geosciences, chemistry, social sciences, and humanities can lead to a greater appreciation of the causes and impacts of environmental issues and can result in a greater imperative to address the underlying issues. One overarching goal of this module is to help students develop their ability to think more broadly.

The data set categorization activity (conducted in class) serves as a class-wide summative assessment. Data Sets Handout (Microsoft Word 2007 (.docx) 372kB May16 16) If the students are unable to categorize the data sets as quantitative or qualitative, the instructor may want to review these concepts.

• Teaching with Data module, by Robert MacKay, Clark College.
• Background Information on Phoenix Water Resources, from the Morrison Institute for Public Policy at Arizona State University.