Competency L
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Demonstrate understanding of quantitative and qualitative research methods, the ability to design a research project, and the ability to evaluate and synthesize research literature.
Introduction
The ability of librarians to engage in research is critical for advancing the field and improving library services. Despite the expectation for librarians to conduct research, several obstacles hinder their ability to do so, including lack of time due to competing priorities, unfamiliarity with the research process, insufficient support from administration and peers, lack of confidence, and inadequate training in research methods, which collectively impede their capacity to engage in meaningful research (Luo et al., 2017). Overcoming these barriers is essential not only for individual professional development but also for the overall growth and effectiveness of library services in meeting the needs of their communities. By gaining a solid understanding of research design and methodology, librarians can enhance their research skills, leading to more effective studies that inform practice and contribute to evidence-based decision-making within their institutions.
Quantitative and Qualitative Methods
In quantitative research, adherence to the stages of the scientific method of inquiry (SMI)—which include identifying or developing a theory, defining the problem, formulating a hypothesis, and ensuring measurement validity and reliability—is crucial for success; rushing into study design and data collection can lead to inefficiency and less meaningful results, while careful planning ultimately enhances the research process (Connaway & Radford, 2017). Moreover, it is essential to consider your analysis during the early planning stages, as this influences the design of your data collection instrument and questions, helps identify variables, and shapes your analytical approach, ensuring that the data collected is relevant to your research questions. While unexpected developments may require adjustments to your plan, having a clear rationale for your questions provides a solid foundation for your analysis (Pickard, 2013). Statistical methods in qualitative research serve both descriptive and inferential purposes: descriptive statistics focus on data tabulation and presentation, while inferential statistics enable generalizations about populations and hypothesis testing, allowing for objective evaluation of quantitative data reliability through probability statements (Connaway & Powell, 2010). Ultimately, a well-structured quantitative research design, grounded in the SMI and thorough planning, is essential for producing valid and reliable findings that meaningfully contribute to research.
Qualitative research is distinguished by its emphasis on in-depth investigation to comprehend the experiences and behaviors of its subjects. For this reason, qualitative research often accepts smaller sample sizes than quantitative research while also adopting a holistic and natural approach to research, diving into the more experiential patterns of the topic studied (Connaway & Powell, 2010). This focus on depth rather than breadth necessitates a different approach to data analysis, as qualitative analysis is iterative and requires researchers to begin their analysis with the first data collected and continue throughout the data collection process, ensuring depth and detail are maintained to effectively convey the phenomenon under investigation (Pickard, A. J., 2013). As qualitative research provides rich insights into human experiences, it complements quantitative methods that offer broader generalizations. Consequently, a mixed methods approach in Library and Information Science (LIS) research effectively integrates qualitative and quantitative methodologies, recognizing that the complexity of human information interactions requires a comprehensive understanding that no single paradigm can fully address.
Research Design
The four most commonly employed research designs in academic library research include cross-sectional design, longitudinal design, experimental or quasi-experimental design, and case studies (Luo et al., 2017). Both cross-sectional and longitudinal designs focus specifically on the frame of time the study is being conducted. Cross-sectional studies take place at a specific slice of time, while longitudinal studies occur over an extended period, allowing for the observation of changes and trends over time. Experimental and quasi-experimental designs look at the relationship between independent and dependent variables, while case studies look in-depth into specific instances or phenomena, providing rich qualitative insights. A successful research study design also requires careful attention to the foundational stages of the scientific method—developing a theory, identifying the problem, formulating a hypothesis, and ensuring measurement validity and reliability—rather than rushing into data collection and analysis, as thorough planning ultimately leads to more efficient and meaningful research outcomes (Connaway & Powell, 2010).
Synthesize relevant literature
After formulation of your research question, it is essential to explore existing research from previous studies in the literature. A synthesis of relevant literature can serve as a valuable resource for gaining insights into research design and methods, allowing you to utilize existing scales in their entirety, in part, or customized to fit your needs (Luo et al., 2017). Additionally, reviewing the literature can introduce new methodological ideas and insights to previous findings that may not have previously been considered. A thorough literature review lays the groundwork for a well-informed research project, identifying gaps in the current knowledge base and also aiding in refining the research question. Although LIS researchers can use either qualitative methods or their own statistical generation to analyze existing materials, such as quantitative data like library issue statistics or textual sources like reports, the validity of their underlying assumptions greatly influences the credibility of their findings (Shenton, 2013). A critical analysis of previous studies, then, can be employed in order to enhance the rigor and validity of the methodology at hand. Engaging with the literature also allows you to contextualize the work within the broader academic discourse, facilitating a more robust discussion of your findings.
Reproducibility
In scientific research as a whole, reproducibility is in crisis. Researchers have identified a "hypercompetitive culture" in science that promotes novel and exciting discoveries and provides little incentive for other researchers to replicate their work or for journals to publish studies that don't have a significant impact (Cary, 2015). Research and data librarians spend a great deal of time and resources promoting standards that support research integrity and transparency; one such standard is the Transparency and Openness Promotion (TOP) guidelines, which are crucial for research reproducibility, but the composite standard of transparency in data, analytics, and research materials is the most pressing (Center for Open Science, 2014). This ensures that well-documented data, analytics methods, and materials act as a recipe for reproducibility, allowing other researchers to analyze and interpret the same data and use the same tools for the same outcome. When conducting quantitative research, information professionals too should follow these guidelines to the best of their ability.
Competency Development
In my professional work, one project I was assigned in my role as an information services assistant was to research the census data of the branch’s service area to gain a better understanding of our patrons in order to improve program offerings and services. I was able to locate and filter web maps on various topics, including income, transportation, languages spoken, and internet access. From these results we were able to both justify and expand the current offerings of programs and services.
Since I am following the data science pathway in my MLIS coursework, much of my focus has been on research data. INFO 200, Information Communities, established the foundations of conducting graduate-level research in terms of synthesizing sources, discovering findings, and creating new meaning. INFO 220, Data Services in Libraries, built my understanding of the research data lifecycle and involved conducting original research from data collected in an open data repository. INFO 246, Big Data Analysis and Management, strengthened my qualitative skills by teaching me how to collect large swaths of data, clean it, and analyze it statistically. In addition, the required course, INFO 284 Unobtrusive Research, strengthened not only my general research skills but also my ability to identify and utilize reliable research data sources while minimizing ethical concerns and avoiding the complexities associated with human subjects research.
Evidence
In this data visualization report, I conducted a comprehensive scientometric and bibliographic analysis of grey literature trends across eight European countries, focusing on the most prevalent subject areas and publication proportions by country. I employed descriptive analysis techniques, including data cleaning with OpenRefine and visualization through Tableau, to present findings such as time-based trends and geographical distribution of grey literature outputs. The results indicated that bio-medical sciences and humanities/social sciences were the dominant fields, although both showed a general decline in publication rates, particularly in the humanities and social sciences.
This report qualifies as evidence for Competency L as it showcases my understanding of both quantitative and qualitative research methods through the analysis of grey literature trends. By employing descriptive statistical techniques to evaluate bibliographic data, I demonstrated my ability to design a research project that effectively addresses a specific research question. My critical evaluation of publication trends and the identification of gaps in the literature reflect my ability to assess the quality and relevance of research outputs and also illustrate an ability to synthesize research literature.
In this content analysis, I investigated how academic libraries designed visual literacy LibGuides to support teachers and students in finding relevant materials. I aimed to identify trends and patterns in the organization and selection of content within these guides, contributing to best practices in visual literacy instruction. By examining eight LibGuides, I focused on aspects such as the types of resources included, the structure of information, and user experience elements like navigation bar placement. Using an inductive coding approach, I categorized content based on the presence of specific subtopics related to visual literacy and the extent of definitions provided. I also created visualizations using Matplotlib to illustrate key findings. The results revealed significant trends, including the predominance of left-side navigation bars in more recently updated guides and varying levels of detail in definitions. This analysis provided valuable insights for libraries looking to enhance their visual literacy offerings and better serve their users.
This content analysis qualifies as evidence for Competency L by demonstrating my ability to evaluate and synthesize research literature through the systematic examination of visual literacy LibGuides. By employing an inductive coding approach and creating visualizations with Matplotlib, I effectively analyzed the content and design elements of the guides, identifying trends and patterns that inform best practices in visual literacy instruction. This process reflects my understanding of both qualitative and quantitative research methods, showcasing my capability to assess and synthesize relevant information to enhance library resources.
Evidence 3
INFO 246 - Big Data Analysis Group Project - Analyzing Banned Books in Prisons Report and Presentation Video
This group project analyzed the Banned Prison Books Dataset, which includes nearly 50,000 books banned across 18 states, alongside additional datasets on U.S. prison populations and crime statistics. During the exploratory analysis phase, we utilized Rawgraphs.io to create visualizations that revealed trends in book bans over time and highlighted gaps in the data, such as missing values and reporting inconsistencies. One of my specific contributions to the project employed orange data mining for in-depth analysis, where I cleaned and merged datasets to explore correlations between book bans and the demographic makeup of incarcerated populations. Through unsupervised learning techniques like principal component analysis and K-means clustering, I discovered that each state exhibited a unique profile regarding book banning and prison population dynamics. The findings underscored the need for further research into the biases present in book banning practices, particularly concerning race and sexuality, and suggested potential directions for future studies to better understand these complex relationships
This project serves as evidence for Competency L by demonstrating my proficiency in quantitative research methods through the analysis of large datasets related to banned books and prison populations. I employed statistical techniques, such as correlation analysis using Pearson and Spearman coefficients, to identify relationships between book bans and demographic variables. Additionally, I utilized unsupervised learning methods, including principal component analysis and K-means clustering, to uncover patterns within the data. This approach not only showcased my ability to design a research project but also highlighted my skills in evaluating and synthesizing quantitative data to draw meaningful insights.
This literature review matrix is designed to systematically organize and analyze research related to open source intelligence (OSINT) hobbyists—individuals who engage in the analysis and investigation of publicly available information during their leisure time. The focus of this matrix is to explore the information-seeking behaviors and needs of OSINT hobbyists, highlighting the significance of understanding how they gather and utilize information. By synthesizing findings from different sources, this matrix aims to identify patterns and insights that can inform how information professionals can better support this community.
This literature review matrix serves as evidence of my competency in evaluating and synthesizing research literature by systematically organizing findings from various studies on OSINT hobbyists. By critically analyzing both quantitative and qualitative research methods presented in the matrix, I demonstrate my ability to assess the relevance and rigor of different sources. This synthesis not only highlights key themes and insights but also informs potential pathways for future research and practical applications, showcasing my understanding of research design and the integration of diverse literature into a cohesive analysis.
Conclusion
The ability of librarians to conduct research is vital for the continuous improvement of library services and the advancement of the profession. By embracing both quantitative and qualitative methodologies and understanding the intricacies of research design, librarians can produce valuable insights that inform practice and enhance user experiences. Fostering a culture of inquiry and supporting librarians in their research endeavors will be crucial for ensuring that librarianship remains relevant as both technology, society, and culture evolve, in sometimes diverging directions. The information age needs information professionals that conduct current and trailblazing research, and I hope to bring my career to the cusp of this endeavor.
References
Carey, B. (2015, Aug 28). Psychology's fears confirmed: Rechecked studies don't hold up. New York Times.
Center For Open Science. (2014). Guidelines for Transparency and Openness Promotion (TOP) in Journal Policies and Practices “The TOP Guidelines.” https://osf.io/ud578/
Connaway, L. S., & Powell, R. R. (2010). Basic research methods for librarians (5th ed.). ABC-CLIO.
Connaway, L. S., & Radford, M. L. (2017). Research methods in library and information science (6th edition.). Libraries Unlimited.
Luo, L., Brancolini, R., & Kennedy, M.R. (2017). Enhancing library and information research skills: A guide for academic librarians. Libraries Unlimited.
Pickard, A. J. (2013). Research methods in information (2nd ed.). Facet.
Sayre, F., & Riegelman, A. (2018). The reproducibility crisis and academic libraries. College & Research Libraries, 79(1). https://doi.org/10.5860/crl.79.1.2
Shenton, A. K. (2013). Analysis of existing, externally created material. In A. J. Pickard (Ed.), Research methods in information (2nd ed., pp. 251-261). Facet.