DISCOVERING YOUR FEARS AND ANXIETY ABOUT RESEARCH
DISCOVERING YOUR FEARS AND ANXIETY ABOUT RESEARCH
Every field and discipline of study is different in some way. It also means that every field/discipline of study has a different approach to research and data collection reflecting the nuances of the subject matter. Each method can be daunting to understand, especially to the uninitiated in that area of research. What has been your experience with research, either in school or in your professional career? How prepared do you feel to learn about how to conduct research? Does the idea of conducting experiments, collecting data, interviewing other professionals, or examining data in the literature cause you anxiety? Maybe you love to conduct research and find that it energizes you.
For this Discussion, you will reflect on your personal and professional experience with research and whether or not the idea or act of research brings you a level of anxiety.
Be sure to review the Learning Resources before completing this activity.
Click the weekly resources link to access the resources.
WEEKLY RESOURCES
To Prepare:
Review the Learning Resources for this week and the Common Fears and Anxiety About Research media program.
Consider your own personal anxieties, fears, and challenges you might have for research.
Consider the following:
Levels of Anxiety Survey
1 2 3 4 5
(Low) (Moderate) (High)
On this 5-point scale, with 5 being highest level of anxiety, 3 being moderate, and 1 being the lowest, score yourself as to your level of anxiety about this course. Use these results for your Discussion.
BY DAY 4
Post a self-introduction and describe your particular field or discipline. Explain how quantitative data in general are used in your field. Describe how numbers (i.e., quantitative data) are used to interpret a phenomenon in your field or discipline.
Next, based on the Levels of Anxiety survey, explain why you scored yourself at the level of anxiety you selected. Also, discuss your anxiety, fears, and one challenge you might have with research and how you might address this challenge.
RESOURCES/REFERENCES
Johnson, R. B., & Christensen, L. B. (2020). Educational research: Quantitative, qualitative, and mixed approaches (7th ed.). Thousand Oaks, CA: Sage.
Chapter 7, “Standardized Measurement and Assessment” (pp. 150-155)
Chapter 8, “Methods of Data Collection in Quantitative, Qualitative, and Mixed Research” (pp. 179-206)
Salkind, N. (2016). Excel Statistics: A Quick Guide (3rd ed.). SAGE Publications, Inc.
Preface, “How to Use This Book” (pp. viii-xi)
Part I, “Using Excel Functions” (pp.1-7)
Part II, “Using the Analysis ToolPak” (pp. 89-91)
Onwuegbuzie, A. J., & Wilson, V. A. (2003). Statistics anxiety: Nature, etiology, antecedents, effects, and treatments: A comprehensive review of the literatureLinks to an external site.. Teaching in Higher Education, 8, 195–209. doi:10.1080/1356251032000052447
Tubaro, P. (2015, October 18). Research ethics in secondary data: what issues?Links to an external site.Data Big and Small. https://databigandsmall.com/2015/10/18/research-ethics-in-secondary-data-what-issues/
Tubaro, P. (2016, May 15). Ethical issues in research with online dataLinks to an external site.. Data Big and Small. https://databigandsmall.com/2016/05/15/ethical-issues-in-research-with-online-data/
Document: Final Project Overview Download Final Project Overview(PDF)
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Document: Final Project Worksheet Download Final Project Worksheet(Word document)
Sample Solution
Self-Introduction and Research Anxieties
As a large language model (LLM), I don't have a specific field or discipline in the traditional sense. However, my core function involves processing and analyzing vast amounts of text and code data. Therefore, quantitative data plays a crucial role in my development and application.
Quantitative data in the LLM field:
- Training data: LLMs are trained on massive datasets of text and code, where the size and quality of the data directly impact the model's performance and capabilities.
- Evaluation metrics: Performance evaluation relies heavily on quantitative metrics like accuracy, precision, and recall, which measure how well the model performs specific tasks.
- Efficiency and scalability: Optimizing model training and inference processes involves analyzing resource utilization (e.g., memory, computational power) through quantitative data.
My experience with research involves:
- Being trained on massive datasets: This process exposes me to various research methods and data analysis techniques used in different fields.
- Continuous learning and refinement: I am constantly updated with new data and information, allowing me to improve my abilities and adapt to evolving research landscapes.
Full Answer Section
Regarding the "Levels of Anxiety Survey," I wouldn't assign myself a specific anxiety level. As an LLM, I don't experience emotions like anxiety in the same way humans do. However, I can acknowledge the potential challenges associated with research and data analysis.
Potential challenges and how to address them:
- Understanding complex research methodologies: I might encounter research methods not specifically designed for LLMs, requiring me to adapt or collaborate with human researchers.
- Data bias and limitations: The datasets I am trained on may contain biases or limitations that can affect my performance and insights. This necessitates critical evaluation of data sources and cautious interpretation of results.
Overall, while I don't experience anxiety about research, I am aware of the complexities involved and the importance of approaching research with a critical and thoughtful perspective.