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So, rightly secondary research is also termed “ desk research ”, as data can be retrieved from sitting behind a desk. The following are popularly used secondary research methods and examples: 1. Data Available on The Internet. One of the most popular ways to collect secondary data is the internet.
Jan 3, 2024 · Secondary Research Examples. 1. Literature Review. A literature review summarizes, reviews, and critiques the existing published literature on a topic. Literature reviews are considered secondary research because it is a collection and analysis of the existing literature rather than generating new data for the study.
Common examples of secondary research include textbooks, encyclopedias, news articles, review articles, and meta analyses. [2] [3] When conducting secondary research, authors may draw data from published academic papers, government documents, statistical databases , and historical records.
Sep 20, 2023 · Secondary Research Examples. To better understand how secondary research is applied in various contexts, let's explore a few real-world examples that showcase its versatility and value. Market Analysis and Trend Forecasting. Imagine you're a marketing strategist tasked with launching a new product in the smartphone industry.
Oct 16, 2023 · 16 October 2023. 6 min read. Secondary research involves the analysis and interpretation of existing data and information collected by others. It provides valuable insights for informed decision-making without conducting new surveys or experiments.
Feb 7, 2023 · In this guide, we explain in detail what secondary research is, including the difference between this research method and primary research, the different sources for secondary research, and how you can benefit from this research method.
Mar 13, 2023 · Some examples of secondary research sources are books, academic journals, reports, online databases, and news articles. When conducting secondary research, it is crucial to critically evaluate the quality and relevance of the sources used and to consider any potential biases or irrelevancies of the data.
