Secondary research, also known as desk research, is a method that involves using already existing data. Researchers who conduct secondary research leverage data collected by other researchers and analyze that data to derive novel insights. The secondary research definition encompasses any study that relies on pre-existing information rather than new, original data.
Unlike primary research, where researchers collect data directly from subjects through surveys, interviews, or experiments, secondary researchers source their data indirectly from published sources.
Example of Secondary research
Let’s consider an example of secondary research to illustrate how this method works in practice.
Suppose a market researcher wants to understand the factors influencing consumer behavior in the smartphone market. Instead of conducting a new survey, the researcher could gather sales data from smartphone manufacturers, customer reviews from e-commerce websites, and market reports from research firms. This secondary research example demonstrates how existing data can be used to answer new questions.
When to use secondary research
Secondary research is particularly useful when primary data collection is impractical, expensive, or time-consuming. For instance, a researcher studying rare diseases may not have access to a large enough patient population to conduct primary research. Secondary research using medical records or published case studies can provide valuable insights in such cases.
It is also helpful in the early stages of a research project, where researchers are still trying to define their research questions and hypotheses. Secondary research is a cost-effective method to identify gaps in existing knowledge and determine the need for primary research. While it can be exploratory or explanatory, it is typically used to explain the causes and effects of a well-defined problem.
Types of secondary research
There are several types of secondary research, each with its own strengths and limitations. Here are some of the most common secondary research types:
Statistical analysis
Statistical analysis involves using mathematical and statistical techniques to analyze large datasets. Researchers can use statistical software to identify data patterns, correlations, and trends. Statistical analysis is particularly useful for making predictions or testing hypotheses.
Some sources for existing data are:
- Government databases and reports: Data collected by government agencies, such as the U.S. Census Bureau, Bureau of Labor Statistics, and Centers for Disease Control and Prevention.
- Academic journals and publications: Peer-reviewed research articles published in reputable academic journals across various disciplines.
- Professional associations and organizations: Reports, surveys, and industry data published by recognized professional associations or organizations, such as trade associations or research societies.
- Established research institutes and think tanks: Data and analyses from well-regarded research institutes, such as the Pew Research Center or the Brookings Institution.
- Reputable news outlets: Data and insights from well-established news organizations known for their fact-checking and journalistic integrity.
- Commercial databases and market research firms: Data and reports from reliable commercial sources, such as market research firms or business intelligence providers.
- International organizations: Data and reports from international bodies, such as the United Nations, World Bank, or World Health Organization.
- University libraries and archives: Many university libraries provide access to a wide range of databases, archives, and special collections that can serve as valuable sources for secondary research.
Literature reviews
Literature reviews involve systematically searching for and synthesizing published research on a particular topic. By reviewing existing studies, researchers can identify key themes, debates, and gaps in the current knowledge base. Literature reviews often provide context and background for new research projects.
Case studies
Case studies are in-depth examinations of specific individuals, groups, or events. Researchers can use them to explore complex phenomena in real-world settings. They often rely on multiple data sources, such as interviews, observations, and documents.
Content analysis
Content analysis involves systematically coding and analyzing text data, such as news articles, social media posts, or interview transcripts. Researchers can use content analysis to identify themes, patterns, and trends in the data. Content analysis is particularly useful for studying media representation or public discourse on a particular topic.
Examples of secondary research
To further illustrate the different types of secondary research, let’s consider some concrete examples:
Example of Statistical analysis
A public health researcher wants to investigate the relationship between air pollution and respiratory diseases. The researcher obtains air quality data from government agencies and hospital admission records for respiratory illnesses. Using statistical techniques such as regression analysis, the researcher can examine whether higher levels of air pollution are associated with increased hospital admissions for respiratory diseases.
Example of Literature reviews
A psychology researcher wants to understand the effectiveness of cognitive-behavioral therapy (CBT) for treating depression. The researcher reviews the literature by searching databases such as PubMed and PsycINFO for published studies on CBT and depression. By synthesizing the findings from multiple studies, the researcher can conclude the overall effectiveness of CBT and identify factors that may influence its success.
Example of Case studies
A business researcher wants to understand how successful startups innovate in their early stages. The researcher selects three successful startups and conducts in-depth case studies. The researcher collects data from multiple sources, including interviews with founders and employees, company documents, and media coverage. The researcher can identify common themes and strategies successful startups use to innovate by analyzing these data.
Example of Content analysis
A sociologist wants to study how the media portrays immigrants in the United States. The researcher collects a sample of news articles from major newspapers over a one-year period and codes each article for its portrayal of immigrants (e.g., positive, negative, or neutral). By analyzing the coded data, the researcher can identify patterns in media coverage and conclude how immigrants are represented in the news.
Advantages and disadvantages of secondary research
Here is a table illustrating the advantages and disadvantages of secondary research:
| Advantages of Secondary Research | Disadvantages of Secondary Research |
| Cost-effective: Utilizing existing data is often less expensive than conducting primary research. | Lack of control: Researchers have no control over how the original data were collected, which can limit their ability to address specific research questions. |
| Time-saving: Secondary research can be conducted more quickly than primary research since data collection has already been completed. | Data quality: The quality of secondary data may be uncertain, particularly if the original researchers did not use rigorous methods or if the data are outdated. |
| Broad scope: Secondary research can draw on multiple data sources to cover a wider range of topics and populations. | Misaligned objectives: The original data may have been collected for a different purpose than the current research project, which can limit its relevance or applicability. |
| Increased sample size: Secondary research can access larger sample sizes than primary research, particularly when using government or commercial datasets. | Limited depth: Secondary data may not provide the same depth or detail as primary data, particularly for complex or nuanced research questions. |
| Identifies research gaps: Secondary research helps identify gaps in existing knowledge, guiding future primary research efforts. | Lack of specificity: Secondary data may not be specific enough to answer the researcher’s question or hypothesis. |
| Provides context: Secondary research provides background information and context for understanding a research problem or interpreting primary research findings. | Potential for bias: The original data may be biased due to the methods used or the objectives of the original researchers. |
| Enables triangulation: Secondary research allows researchers to corroborate or challenge findings from primary research, enhancing the study’s validity. | Inconsistent definitions and measures: Secondary data from different sources may use inconsistent definitions or measures, making it difficult to compare or integrate the data. |
| Supports meta-analysis: Secondary research enables researchers to conduct meta-analyses, synthesizing findings from multiple studies to generate more robust conclusions. | Incomplete or missing data: Secondary datasets may have missing or incomplete data, which can limit the analyses that can be conducted or the conclusions that can be drawn. |
What is secondary research definition?
Secondary research definition is the process of leveraging data collected by others, typically for different purposes, to conduct
What is secondary study?
A secondary study refers to research that uses existing data to explore new questions or validate previous findings.
What is secondary research examples?
Examples of secondary research include analyzing existing data from government reports, academic journals, or market research firms.