In many fields, possessing a bachelor’s degree is sufficient to land an entry-level job. However, if you aspire to achieve greater career success, you may need to improve your academic qualifications. Many professionals decide to return to school to earn a doctoral degree and qualify for high-level, supervisory or executive positions.
If this sounds like you, you may be researching your options and pondering questions such as how to write a dissertation and whether you should opt for quantitative or qualitative research methods. These are important questions you’ll need to answer with the help of your dissertation advisor and other dissertation committee members. If you do choose quantitative research methods for your dissertation, you can explore the following quantitative data collection methods as you begin your research.
Writing Your Dissertation
Writing a dissertation can be an ambitious undertaking, especially for new doctoral students. The prospect of producing a document spanning several hundred pages can feel overwhelming. However, breaking down this task into manageable steps can significantly alleviate the stress. Before delving into quantitative data collection methods, it’s imperative to complete several preliminary stages. Firstly, selecting a compelling and research-worthy topic in collaboration with your advisor is crucial. Next, developing a comprehensive outline that provides a solid background on your chosen topic will provide structure to your dissertation. Additionally, conducting a thorough literature review to assess existing research related to your subject matter is essential groundwork.
Only after completing a rigorous literature review should you shift your focus towards your data collection methods. At this juncture, you’ll outline your chosen methodologies and the specific steps you’ll take during your research. This groundwork will pave the way for the subsequent analysis and interpretation of your findings within your dissertation.
What Is Quantitative Data?
Quantitative data is characterized by its numeric representation and is used to address questions related to quantity, frequency, or magnitude. This type of data is divided into two primary categories: discrete and continuous. Discrete data refers to values that cannot be further subdivided, such as whole numbers (e.g., number of siblings). In contrast, continuous data can be infinitely refined and measured at varying levels of precision (e.g., height measurements).
Quantitative Data Collection Methods
Several primary methods are available for collecting quantitative data to inform your dissertation research. These methods include:
Surveys and Questionnaires
- Surveys and questionnaires are among the most straightforward and widely utilized methods for data collection. They are applicable to both quantitative and qualitative research. When planning a survey or questionnaire, it’s essential to consider factors such as the target demographic, the formulation of closed-ended questions to facilitate quantitative analysis, strategies for mitigating biases, distribution channels, and potential incentives for participation.
Interviews
- While interviews are more commonly associated with qualitative research, they can also be structured for quantitative data collection. Structured interviews should employ closed-ended questions, such as yes or no queries or scaled responses. Consistency in question delivery is crucial across all interview sessions to ensure data integrity and minimize research biases.
Observations
- Observations involve visiting specific environments to record and analyze behaviors or phenomena of interest. For quantitative purposes, structured observations are typically employed. Researchers identify specific behaviors to observe and record relevant data points to support their analyses and conclusions.
Document or Archival Reviews
- Reviewing existing documents or archival data can yield valuable quantitative insights. Researchers can extract relevant quantitative data from public records (e.g., census data) or personal documents (e.g., academic transcripts) to support their research objectives.
By employing these quantitative data collection methods effectively, doctoral students can generate robust data sets that contribute to the rigor and credibility of their dissertations.
Enhancing your academic qualifications through doctoral studies can significantly augment your career prospects and position you as an expert in your field. Consider exploring degree programs such as the Doctor of Business Administration: Data Analytics or the Doctor of Education in Organizational Leadership: Special Education to pursue your academic and professional aspirations. Click the “Request Info” button to initiate your journey towards a doctoral education at Grand Canyon University’s College of Doctoral Studies.
Conclusion
The journey towards earning a doctoral degree and completing a dissertation is instrumental for professionals aiming to excel in their careers and attain higher-level positions within their industries. This article underscores the significance of advancing beyond a bachelor’s degree and explores various quantitative data collection methods, emphasizing the importance of strategic planning and meticulous execution in research. By breaking down the dissertation writing process into manageable steps, such as topic selection, literature review, and methodological outline, doctoral candidates can navigate this complex endeavor with clarity and purpose. Collaborating closely with advisors and committee members ensures the development of robust research that contributes substantively to their fields of study. Ultimately, achieving a doctoral degree signifies a significant milestone and offers expanded opportunities for leadership and expertise in one’s chosen field. Grand Canyon University’s specialized doctoral programs, such as the Doctor of Business Administration: Data Analytics and the Doctor of Education in Organizational Leadership: Special Education, provide tailored pathways for professionals seeking to elevate their academic qualifications and career trajectories.
