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How To Do Secondary Research or a Literature Review

Step-by-step guide to forming keywords and searching for articles for a literature review.

Secondary research employs a range of established methods to extract new insights from existing sources, whether those sources are published literature, archival records, datasets, or media artifacts. These methods are not merely passive exercises in data collection—they are strategic approaches used to interrogate, interpret, and reframe existing information to answer new research questions, support theory development, or inform policy and practice. Depending on the research objective, you may conduct systematic reviews to map the current state of knowledge, perform statistical analyses of large datasets to uncover trends, or compare historical or cultural phenomena across time and context. Each method comes with distinct methodological requirements and offers unique applications across academic, professional, and applied settings.

Literature Reviews

  • Involve the systematic identification, evaluation, and synthesis of existing research on a particular topic or question.
  • Aim to summarize current knowledge, identify theoretical or empirical gaps, and provide a foundation for new research directions.
  • Can be narrative, offering a descriptive overview of the literature, or systematic, following predefined protocols for source selection, evaluation, and synthesis.
  • Often serve as a starting point in academic studies, policy development, and grant proposals by framing the state of knowledge in a given field.
  • Examples: Academic literature reviews in peer-reviewed journals, state-of-the-art reports in technical fields, bibliographic essays in the humanities.

Database Analysis

  • Involves the use of pre-existing datasets to conduct statistical analysis addressing new or refined research questions.
  • Enables access to large-scale, often nationally or globally representative data that would be too costly or time-consuming to collect independently.
  • Particularly useful for identifying trends over time, exploring demographic patterns, or testing theoretical models across populations.
  • Requires thorough understanding of how the data were originally collected, including any methodological limitations or biases.
  • Examples: Analyses of census or demographic data, studies using longitudinal cohort datasets, evaluations based on government administrative records.

Meta-Analyses

  • Combine statistical results from multiple independent studies on the same topic to arrive at a more robust, quantitative synthesis of findings.
  • Enhance the statistical power of smaller studies and improve the generalizability of results across diverse contexts.
  • Demand methodological rigor, particularly in selecting studies with comparable designs, populations, and outcome measures.
  • Widely used in evidence-based fields like medicine, psychology, and education to evaluate intervention effectiveness or theoretical consistency.
  • Examples: Meta-analyses of clinical trials for medical treatments, educational interventions, or behavioral therapies.

Historical Analysis

  • Focuses on interpreting historical documents, artifacts, and records to understand events, processes, or societal changes over time.
  • Seeks to reconstruct past contexts and narratives, often illuminating long-term developments or the origins of current institutions and policies.
  • Relies heavily on primary sources such as archival documents, official records, newspapers, personal letters, and organizational reports.
  • Demands critical assessment of the authenticity, credibility, and potential biases of historical sources.
  • Examples: Studies on the evolution of government policy, organizational case histories, and analyses of past social or political movements.

Content Analysis

  • Entails a systematic examination of communication content—such as text, images, video, or online media—to uncover patterns, themes, or trends.
  • Can be quantitative (e.g., counting the frequency of words, topics, or images) or qualitative (e.g., interpreting underlying meanings or discursive structures).
  • Especially effective for exploring cultural phenomena, societal values, or public discourse over time or across platforms.
  • Increasingly used in digital humanities, communication studies, media research, and marketing.
  • Examples: Analyzing news coverage of social issues, examining thematic trends in political speeches, evaluating user behavior on social media platforms.

Comparative Analysis

  • Involves the structured comparison of cases, institutions, time periods, or geographic regions using existing data or published research.
  • Aims to identify similarities, differences, and patterns that can shed light on causal mechanisms or best practices.
  • Often used to test hypotheses about social, economic, or political processes in a cross-contextual framework.
  • Requires sensitivity to cultural, historical, and institutional factors that may influence findings.
  • Examples: Cross-national policy comparisons, benchmarking studies in education or healthcare, comparative organizational research.