Student groups construct simple conductivity probes and then integrate them into two different circuits to test the probe behavior in solutions of varying conductivity (salt water, sugar water, distilled water, tap water). The activity culminates with student-designed experiments that utilize the constructed probes. The focus is to introduce students to the fabrication of the probe and expose them to two different ways to integrate the probe to obtain qualitative and quantitative measurements, while considering the application and utility of a conductivity probe within an engineering context. A provided handout guides teams through the process: background reading and questions; probe fabrication including soldering; probe testing and data gathering (including circuit creation on breadboard); probe connection to Arduino (including circuit creation and code entry) and a second round of testing and data gathering; design and conduct their own lab experiments that use the probes; online electrolyte/nonelectrolyte reading, short video, comprehension check and analysis questions.
Epidemiological investigation requires a good understanding of different data types, as this will strongly influence data analysis and interpretation. Data can broadly be classified as qualitative and quantitative, and within each of these groups, data can be further categorised as shown below. Although different grouping systems are available, it is important to consider the type of data being dealt with prior to any analysis. If desired, data can often be changed into different types through manipulation (for example, the quantitative variable weight can be converted to qualitative variables such as low/medium/high or low/not low).
Students investigate the difference between qualitative and quantitative measurements and observations. By describing objects both qualitatively and quantitatively, they learn that both types of information are required for complete descriptions. Students discuss the characteristics of many objects, demonstrating how engineers use both qualitative and quantitative information in product design.
Fields closely related to empirical legal research are enhancing their methods to improve the credibility of their findings. This includes making data, analysis code, and other materials openly available, and preregistering studies. Empirical legal research appears to be lagging behind other fields. This may be due, in part, to a lack of meta-research and guidance on empirical legal studies. The authors seek to fill that gap by evaluating some indicators of credibility in empirical legal research, including a review of guidelines at legal journals. They then provide both general recommendations for researchers, and more specific recommendations aimed at three commonly used empirical legal methods: case law analysis, surveys, and qualitative studies. They end with suggestions for policies and incentive systems that may be implemented by journals and law schools.
Addressing issues with the reproducibility of results is critical for scientific progress, but conflicting ideas about the sources of and solutions to irreproducibility are a barrier to change. Prior work has attempted to address this problem by creating analytical definitions of reproducibility. We take a novel empirical, mixed methods approach to understanding variation in reproducibility conversations, which yields a map of the discursive dimensions of these conversations. This analysis demonstrates that concerns about the incentive structure of science, the transparency of methods and data, and the need to reform academic publishing form the core of reproducibility discussions. We also identify three clusters of discussion that are distinct from the main group: one focused on reagents, another on statistical methods, and a final cluster focused the heterogeneity of the natural world. Although there are discursive differences between scientific and popular articles, there are no strong differences in how scientists and journalists write about the reproducibility crisis. Our findings show that conversations about reproducibility have a clear underlying structure, despite the broad scope and scale of the crisis. Our map demonstrates the value of using qualitative methods to identify the bounds and features of reproducibility discourse, and identifies distinct vocabularies and constituencies that reformers should engage with to promote change.
Qualitative research has long suffered from a lack of free tools for analysis, leaving no options for researchers without significant funds for software licenses. This presents significant challenges for equity. This panel discussion will explore the first two free/libre open source qualitative analysis tools out there: qcoder (R package) and Taguette (desktop application). Drawing from the diverse backgrounds of the presenters (social science, library & information science, software engineering), we will discuss what openness and extensibility means for qualitative research, and how the two tools we've built facilitate equitable, open sharing.
This module introduces the concepts of qualitative data, quantitative continuous data, and quantitative discrete data as used in statistics. Sample problems are included.