How missing data can affect data analysis

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Reference no: EM132435334

Assignment: 400 words not including title & ref min 3 APA

As you progress through this course, you may continue to understand some of the limitations to using secondary data. Missing data is a common and important issue for researchers trying to use secondary data. The amount of missing data could alter the study design and render a study useless. You may recall from Week 4 that researchers may find that some variables are incomplete or missing. In addition, the variables may be altered by skip patterns in the secondary data due to respondents not answering all of the primary study questions.

Most experienced researchers anticipate missing data. For example, researchers know that it may be a challenging task to get all respondents to answer all of the study questions. Although there may be a variety of reasons for missing data, each one should be explored (Fink, 2013). In addition, you must know the settings of your statistical software programs. Many of the programs will automatically remove records with missing data (Langkamp, Lehman, & Lemeshow, 2010). Therefore, a judgment has to be made about whether or not the researcher can ignore the missing data.

Post an explanation of the importance of handling missing data and skip patterns, and how missing data can affect data analysis. Then, provide a technique on how you may handle missing data appropriately and explain why.

Reference no: EM132435334

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