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Download as PDF; Printable version; In other projects ... administration or execution of a survey. Such errors can be caused by carelessness, confusion, neglect ...
Nonsampling error, which occurs in surveys and censuses alike, is the sum of all other errors, including errors in frame construction, sample selection, data collection, data processing and estimation methods.
Data editing is defined as the process involving the review and adjustment of collected survey data. [1] Data editing helps define guidelines that will reduce potential bias and ensure consistent estimates leading to a clear analysis of the data set by correct inconsistent data using the methods later in this article. [ 2 ]
Often, the methods employed are unique to specific agencies and organizations. For example, the United States Census Bureau has developed models using the U.S. Postal Service's Delivery Sequence File, IRS 1040 address data, commercially available foreclosure counts, and other data to develop models capable of predicting undercount by census block.
He is the lead author of a book titled Linear Mixed Models: A Practical Guide using Statistical Software, Third Edition, which compares different statistical software packages in terms of their mixed-effects modeling procedures, and is also the co-author of Applied Survey Data Analysis, among other books.
In survey-type situations, these errors can be mistakes in the collection of data, including both the incorrect recording of a response and the correct recording of a respondent's inaccurate response.
Non-sampling errors in survey estimates can arise from: [3] Coverage errors, such as failure to accurately represent all population units in the sample, or the inability to obtain information about all sample cases; Response errors by respondents due for example to definitional differences, misunderstandings, or deliberate misreporting;
The main reason for maintaining data integrity is to support the observation of errors in the data collection process. Those errors may be made intentionally (deliberate falsification) or non-intentionally (random or systematic errors). [5] There are two approaches that may protect data integrity and secure scientific validity of study results: [6]