The quality of secondary data can vary widely. Issues might include:
Inaccuracies: Errors in data entry or collection by the original source. These are outright errors introduced during the original data collection or entry phase. This could range from simple typos (e.g., "age 255" instead of "age 55"), incorrect transcription of responses from paper surveys into digital files, or even systematic errors in measurement instruments or sensor readings. Such inaccuracies, if not identified and addressed, can lead to skewed descriptive statistics, erroneous correlations, and ultimately, incorrect conclusions. A single significant outlier due to an inaccuracy can dramatically distort the mean or standard deviation of a variable.
Inconsistencies: Data recorded differently over time or across different collection points. This refers to a lack of uniformity in how data was recorded, categorized, or measured, either over time within the same data set (longitudinal inconsistency) or across different collection points or sub-samples. For example, a variable indicating "employment status" might use different coding schemes (e.g., "1=employed, 2=unemployed" in one year, but "0=employed, 1=unemployed" in another), or "income" might be recorded in different currencies or units (e.g., thousands vs. actual figures) without clear indication. Such inconsistencies necessitate careful data cleaning and recoding to ensure comparability before analysis, and if unaddressed, they can lead to misleading trend analyses or invalid comparisons between groups.
Missing Data: Gaps in observations, which can bias results or reduce statistical power. This is a pervasive issue in nearly all real-world data sets, referring to observations where values for certain variables are simply absent. Missing data can occur for various reasons: survey respondents declining to answer sensitive questions, equipment malfunctions, or data entry errors. The presence of missing data can significantly impact your analysis. If data are missing randomly, it primarily reduces your statistical power (i.e., makes it harder to detect real effects). However, if data are "Missing Not at Random" (MNAR), meaning the reason for missingness is related to the value of the missing data itself or another variable in your analysis, it can introduce significant bias into your results, leading to inaccurate parameter estimates and faulty conclusions. Researchers must assess the extent and pattern of missingness and employ appropriate imputation or analytical strategies.
Outdated Information: The data might not be current enough for your research question, especially for rapidly changing phenomena. The currency of secondary data is a critical factor, especially for research topics that are dynamic and evolve rapidly. Data collected five, ten, or even just one year ago might not accurately reflect the current state of affairs for fast-changing phenomena such as technology adoption, social media trends, public opinion on breaking news, or financial market behaviors. Using outdated information can lead to conclusions that are no longer relevant or even directly contradict present realities.