Chapter 19 Processes of Quantitative Data Analysis.docx

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Chapter 19 Processes of Quantitative Data Analysis Danielle Marrugi, Carmen Salazar, Tina Waldron, Misa Wilcox, Patty Willis Researchers who collect quantitative data typically progress through a series of steps in the analysis and interpretation of their data. The steps are Preanalysis phase, Preliminary assessments, Preliminary Actions, Preliminary analyses, and the Interpretive phase. Researchers should layout a data analysis plan in advance. Quantitative data typically must be coded into numerical values; codes need to be developed for legitimate data and missing values. Key Concepts Coding - is the process of transforming raw data into a standardized form for data processing and analysis in quantitative research, the process of attaching numbers to categories. Whichever method is adopted, it must be used consistently for all participants. Examples: height in feet and inches or inches only, or code males- 1 and females- 2 however it is coded they must be consistent for all participants. Missing Values - Values missing for specific variables for some participants as a result of such factors as a refusal, withdrawals from the study, failure to complete forms, or researcher error. Data Cleaning - The preparation of data for analysis by performing checks to ensure that the data are consistent and accurate. Outliers -A value that lies outside the normal range of values in a measure, especially in relation to other cases in a data set. Wild Code -A coded value that is not legitimate within the coding scheme for that data set.
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  • Winter '18
  • MCAR, Missing values, Quantitative Data Analysis

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