DPLS 722: Quantitative Data Analysis

Expected Competencies:

  1. Statistical Reasoning and Literacy:
    Grasp foundational concepts such as variability, sampling, central tendency, and hypothesis testing. Apply statistical logic to understand and analyze data in a leadership context.

  2. Application of Inferential Statistics:
    Conduct hypothesis testing using z, t, and F distributions, including single-sample and two-sample t-tests, ANOVA, and regression analysis.

  3. Integration of Quantitative and Qualitative Paradigms:
    Explore the interplay between statistical reasoning and qualitative methodologies. Critically analyze mixed-methods research in leadership studies.

  4. Interpretation of Global Data:
    Understand descriptive and inferential statistical methods in a global context, including awareness of bias and ethical implications of data interpretation.

  5. Engagement with Quantitative Research Tools:
    Develop familiarity with Microsoft Excel for data analysis and explore foundational statistical software tools for hypothesis testing.

Achieved Competencies:

  1. Deepened Understanding of Quantitative Methodology:
    Through consistent engagement with the required texts and Blackboard problem sets, I developed greater fluency in statistical logic, particularly in relation to hypothesis testing and interpretation of significance.

  2. Increased Confidence with Statistical Software:
    By applying statistical methods using Microsoft Excel and practicing with provided datasets, I improved my comfort level navigating data structures and performing practical analysis.

  3. Recognition of Leadership Relevance:
    I identified how statistical reasoning complements and enriches leadership studies, particularly in its role in legitimizing research outcomes and informing data-driven decisions.

  4. Expanded Conceptual Frameworks:
    The course challenged me to wrestle with my prior assumptions about data, inviting an expanded epistemological perspective that integrates empirical rigor with interpretive sensitivity.

Applied Competencies:

  1. Critical Data Interpretation:
    Demonstrated the ability to interpret z-scores, correlation coefficients, and probability values in the context of leadership-related scenarios.

  2. Ethical Considerations in Statistical Use:
    Reflected on issues of bias, power, and legitimacy in the use of statistics, particularly as applied to global datasets and systemic inequities.

  3. Mixed-Methods Comprehension:
    Connected statistical findings with qualitative narratives in leadership contexts, especially when evaluating peer-reviewed research for dissertation planning.

  4. Self-Directed Problem Solving:
    Met the demands of this highly self-guided course by working through complex statistical problems and engaging in dialogue with peers during optional synchronous meetings.

Artifact Inclusion:

Due to the applied and problem-set nature of this course, my primary artifacts include annotated statistical exercises, discussion reflections from Zoom meetings, and analysis notes from “Learning from Data” and “Statistics with Microsoft Excel.” These materials demonstrate conceptual understanding and my journey toward statistical fluency within a leadership framework.

References:

  • Glenberg, A. M., & Andrzejewski, M. E. (2008). Learning from data: An introduction to statistical reasoning (3rd ed.). Psychology Press.

  • Dretzke, B. (2011). Statistics with Microsoft Excel (5th ed.). Prentice Hall.

  • Gardner, H. (2011). Frames of mind: The theory of multiple intelligences (3rd ed.). Basic Books.

Keywords:


Statistical reasoning, hypothesis testing, inferential statistics, ANOVA, regression, quantitative leadership research, epistemology, statistical literacy, Microsoft Excel, mixed-methods integration, global data ethics, variance analysis