Make effective evidence-based arguments based on quantitative data and communicate relevant implications.
Develop increased confidence in understanding, evaluating, and doing social science research.
Cultivate self-efficacy strategies to sustain one’s intrinsic motivation for learning by developing a tolerance for and resiliency from learning challenges.
This module was developed for Aging & Society, a 200-level sociology course offered at an open admissions institution. It is also a required course for students in the Human and Social Services pathway.
In this learning module, students engage with original data sets through SSDAN related to aging and the life course. This module includes a lecture and lab session where foundational concepts like percents, rates, and raw counts are introduced, as well as the skills to properly read univariate and bivariate tables with opportunities to see examples and practice independently. Student learning is assessed through a two- to three-page report that effectively communicates their results and the implications of such to a lay audience.
What will students do?
In this learning module, students will learn to distinguish between raw counts and rates to identify situations where each is most appropriate to use. Students will learn how to interpret bivariate tables, and then generate tables to answer their own research questions. Students will generate a suitable data visualization that effectively communicates their results and the implications of such to a lay audience in a two- to three-page report.
What will students learn?
Student Learning Objectives (SLOs)
1. Discuss how the older population and the diversity of aging both affect and are affected by the social structure.
2. Make effective evidence-based arguments based on quantitative data and communicate relevant implications.
3. Develop increased confidence in understanding, evaluating, and doing social science research.
4. Cultivate self-efficacy strategies to sustain one’s intrinsic motivation for learning by developing a tolerance for and resiliency from learning challenges.
Why this module?
This module provides the opportunity for students to see sociology as it is practiced to understand how we know what we know as opposed to only what is known. The main goal of this module is for students to learn about how knowledge is produced in the social sciences. Students will learn discipline-specific skills, methods, and techniques while gaining research experience. These skills are largely transferable and marketable for students in the labor market. Course-based undergraduate research experiences, like this one, have been shown to increase student engagement and foster more meaningful educational experiences.
Instructions
About a week prior to the due date of the final report, as a class we will meet in Computer Lab 115 during our regularly scheduled class meeting time to work with the Social Science Data Analysis Network (SSDAN) WebCHIP online data tool. During this lab, students will become familiar with how to interpret bivariate tables and how to generate tables to answer their own research questions using the available data collections. Students will write a two- to three-page report that describes their research methods (including their research question, the data collection used, the dataset, their row and column variables, and control variables (if applicable)), their findings (including a data visualization and a written interpretation), and their recommendations based on theoretical and empirical evidence. The write-up should be two- to three- pages long. If any outside sources are used, they must be properly cited using any recognized citation style and include a bibliography/references page with full citations. Papers should be in Tahoma, Calibri, Helvetica, Arial, Verdana, or Times New Roman and 11- or 12- point font with 1″ margins on all sides and double-line spacing. Write-ups should be submitted as a Word document or a PDF file. Microsoft Office is available to all students for download at no extra cost through our website here.
Instructor Manual
To prepare:
Reserve a computer lab. You will want to ensure there are enough computers for each student and that the space allows for a projector system. You will want to remind students ahead of this day to come prepared with their login information for your institution. Some students may want to bring in and use their own laptops; however, I assume all students will need a computer when reserving my computer lab space.
Prepare your lecture. I structure my instruction in the “I do, we do, you do” format and do not use any prepared visual aids other than the Student Worksheet, which we go through together with lecture interspersed between each section. I display the Student Worksheet through the projector, have a PowerPoint slide with PollEverywhere questions already embedded, and have a web browser open.
Create a student engagement platform. I use PollEverywhere embedded within a prepared PowerPoint that includes the True/False statements from the Student Worksheet. Alternatively, you might use Clickers or some other form of anonymous, synchronous student feedback system.
Assessment
In this module, students are introduced to foundational quantitative data literacy, including raw counts versus percentages, univariate table interpretation, and bivariate table interpretation. As a class, we walk through examples using WebCHIP with interpretation then generating our own tables. After the in-class workshop, students identify a research question and appropriate variables available in WebCHIP. The module culminates in written student reports, though alternative deliverables could be developed to assess proficiency in the SLOs.
PollEverywhere (polleverywhere.com): PollEverywhere is an engagement tool like Kahoot or Clickers for real-time survey, short-response, or Q&A feedback from students that they can access through the web or text messaging. It has a free version that meets the needs of this module for my small class size, though there are additional services available at cost and for larger numbers of respondents.