title: Missing data within a quantitative research study: How to assess it, treat it, and why you should care.
abstract: Missing data typically refer to the absence of one or more values within a study variable(s) contained in a dataset. The development is often the result of a study participant choosing not to provide a response to a survey item. In general, a greater number of missing values within a dataset reflects a greater challenge to the data analyst. However, if researchers are armed with just a few basic tools, they can quite effectively diagnose how serious the issue of missing data is within a dataset, as well as prescribe the most appropriate solution. Specifically, the keys to effectively assessing and treating missing data values within a dataset involve specifying how missing data will be defined in a study, assessing the amount of missing data, identifying the pattern of the missing data, and selecting the best way to treat the missing data values. I will touch on each of these processes and provide a brief illustration of how the validity of study findings are at great risk if missing data values are not treated effectively.
authors: William Bannon Associates, Inc, Brooklyn, New York.
authors: William
authors: W
authors: Bannon
mesh_terms: D000068598:Data Accuracy; D003627:Data Interpretation, Statistical; D006801:Humans; D012107:Research Design / Q000592:standards; D058028:Research Report / Q000592:standards; D011795:Surveys and Questionnaires / Q000592:standards* / Q000706:statistics & numerical data