Data Analysis Research Experience (DARE) Vision Statement

The DARE initiative seeks to promote the use of best practices by infusing hands-on data analysis into a wide range of courses with the goal of improving students’ quantitative reasoning skills and strengthening their comfort and interest in working with data. Participating faculty develop data analysis projects whereby students pose meaningful research questions, connected to their experiences and concerns, that they subsequently address using quantitative data and analysis.  Our program focuses on students at minority-serving institutions and our initiative seeks to redress inequalities in quantitative literacy and promote social justice.  In today’s data-drenched world, quantitative reasoning (QR) fluencies are indispensable for informed decision-making, civic engagement, as well as professional and personal success.

QR includes the:

  • Contextualized use of numbers and data in ways that involve critical thinking, and
  • Ability to gather and interpret data, to draw conclusions based upon numerical evidence, and to communicate such information to others effectively.

The DARE project seeks to:

  • Enhance the teaching abilities of participating faculty;
  • Improve students' QR skills and attitudes;
  • Build a community of practice within CUNY and elsewhere;
  • Generate a set of methods, guidelines, and instructional materials useful to minority-serving institutions nationwide;
  • Support faculty to become agents of change at their institutions, ensuring the continuity and expansion of the initiative; and
  • Systematically evaluate the effectiveness of our efforts using both qualitative and quantitative methods.

Why Data Analysis

DARE builds on research showing that data analysis projects are ideal for teaching fundamental quantitative skills, such as facility with working with percentages, ratios, frequencies, tables and charts. It seeks to develop the kinds of skills that employers demand, such as the ability to describe, compile, analyze, and present numerical information. This is supported by studies that demonstrate that QR is most effectively learned through real-world data analysis, active learning, and the use of technology. In addition, studies indicate that students learn more rapidly, retain knowledge longer, and develop more sophisticated critical thinking skills when they are actively engaged in the learning process. In particular, students are likely to be more fully engaged when they analyze data on meaningful topics of personal interest.

DARE Pedagogy

DARE seeks to strengthen students’ QR and data analysis skills, interest, and confidence through projects that create a compelling context for the exploration of socially relevant problems.  Our approach draws on active and collaborative learning wherein students work through setbacks and develop their learning identities and cultivate a growth mindset.

Compelling Context

DARE projects are built around students posing research questions of interest. This creates compelling learning contexts to the extent that students (a) care about answers to research questions, (b) want to know results of data analysis, (c) work through barriers, (d) see how quantitative data analysis can reveal interesting answers to questions raised, and (e) come to see how quantitative data analysis can help them to better understand the physical and social world around them.

Active and Collaborative Learning

Through active engagement in all aspects of the research cycle, students become more closely connected to their research and have a vested interest in findings. DARE projects draw on best practices, such as collaborative learning, which is key to building learning communities and is especially important to developing a multicultural pedagogy tailored to the needs of under-represented minorities and women.

Engaged Data Analysis

DARE projects are built around students identifying a quantitative research question of compelling interest. Typically, this question involves a relationship between two variables. For example, “Is support for reparations related to beliefs about extent of racial discrimination over the last fifty years?” or “Do rates of vaccination vary by gender or race/ethnicity?”  In DARE projects, students work with data that they collect and/or compile (e.g., a survey, an observational study, an assembled dataset from multiple secondary sources). In addition, they engage in each of the following activities:

  • Compile data in a format compatible with spreadsheets;
  • Analyze data using spreadsheet;
  • Analyze variables one at a time (univariate analyses)
    • Frequencies, percentages, bar charts;
  • Analyze relationships among variables (bivariate analyses)
    • Cross-tabulations, stacked or adjacent bar charts;
  • Interpret research results within the context of research question or problem; and
  • Present results of data analysis; and
  • Describe limitations of data analysis and/or research.

QR Skills Developed in DARE:

  • Working with spreadsheets (Google Sheets, Excel);
  • Describing data;
    • Frequencies, distributions, measures of central tendency, etc.;
  • Calculating and interpreting percentages;
  • Preparing and interpreting tables and graphs
    • Including contingency tables to explore relationships; and
  • Writing about data.

DARE assessment is based on the following measured cognitive (QR) outcomes*:

  • Calculating & interpreting percentages;
  • Distributions and measures of central tendency;
  • Recognizing denominator group in rates in pie charts, cross-tabs, tables;
  • Interpreting relationships in tables and charts, and
  • Counts/frequencies vs. rates/percentage.

*Note: Pre-course and Post-course QR instruments have different numbers & contexts, but parallel in skills & levels of difficulty.

DARE assessment also focuses on attitudinal and motivational outcomes*

Regarding Data Analysis (both interpreting results of data analysis and doing data analysis):

  • Plans to study/pursue further data analysis;
  • Value of data analysis;
  • Perceived data analysis skills;
  • Attitudes towards data analysis;
  • Interest in data analysis;
  • Mindset;
  • Value of struggle;

*Note: through multiple items for each construct, administered pre- & post- course

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