Common Issues Using Secondary Data Analysis

What is secondary data analysis? Secondary analysis of survey data comprises of a set of research techniques that make use of existing surveys. It refers to the use of existing research data to find the answer to a question that was different from the original work. Successful secondary analysis of data requires a systematic process that acknowledges the challenges of utilizing existing data and addresses the distinct characteristics of secondary analysis.

What is WEIRD research? WEIRD research comes from people who are from Western, Educated, Industrialized, Rich, Democratic societies. Most published research often does not include non-white race and ethnicity demographic groups.

What are complex sampling approaches? Generally incorporated in surveys that aim to provide nationally representative data. Sampling features may include clustering, stratification, disproportionate sampling, and multiple stages of sample selection, each of which has its own corresponding degree of complexity.

What are weighted variables? Weighting is one of the central steps in surveys. The typical weighting process involves three major stages. At the first stage, each unit is assigned a base weight, which is defined as the inverse of its inclusion probability. The base weights are then modified to account for unit nonresponse. At the last stage, the nonresponse-adjusted weights are further modified to ensure consistency between survey estimates and known population totals. When needed, the weights undergo a last modification through weight trimming or weight smoothing methods in order to improve the efficiency of survey estimates.

What are subject terms? Subject terms are used to assist in searching for studies with specific keywords. Subject terms may change over time which can make historical comparisons difficult.

What are the limitations of secondary analysis? A major problem is data availability, despite the development of data archives. Researchers may find it difficult to locate what they need. Researchers may also find that data quality may have measurement problems. Data files from surveys employing nationally representative samples, properly designed questionnaires, and rigorous procedures or interviewing and coding do not always exist. Using multiple surveys to assemble arguments that cannot be developed with the data from one survey alone compounds potential error, and issues of comparability arise when measures of a concept are not strictly equivalent. Another disadvantage of secondary analysis is the possible inhibition of creativity because researchers use the same data sets repeatedly and are limited by the variable contained therein. Scientific progress will be thwarted to some extent.

What are the advantages of secondary analysis? The primary advantage of secondary analysis is its potential for resource savings. It requires less money, time, and fewer personnel. A researcher can complete a research project independently while circumventing data collection problems. Many available data sets provide the benefits of nationally representative samples, standard items, and standard indices. Both data availability and improvements in technology facilitate secondary analysis with a growing number of researchers having access to computer facilities and computer software packages such as SPSS and SAS to simplify analysis. Existing data can also be combined with other types of data to investigate a problem more thoroughly.