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Quantitative Analysis

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Data Analytics
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Liberty University
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Quantitative Analysis-Data Coding, Entry, and Checkinga
Stacey Greer
BUSI820-D03
10/30/2021
A1

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Quantitative Analysis-Data Coding, Entry, and Checking
A1. 2.1. Steps taken after collecting data and before running analysis
The data collected should be accurately checked before analysis is carried out. A check if the
participants clearly marked the questionnaires or score sheets properly should be done; to ensure
every question is given the right number of responses instead of more answers or less answers (if
given two instead of one) or if word answers are given instead of numerical answers. The data
should be checked if it has answered research questions correctly and if it contains errors in
specific items of data to ensure high data quality. Also, we can apply rules for consistence that
can clean up answers that are marked between two rating points. A database of the data collected
should be prepared with correctly defined and labeled values of variables and the best option is
the frequency tables which will help in having a quick glance on the data distribution for easier
interpretation. The best practice is to write the decisions in callout boxes in a different color.
A1. 2.2. Reasons for labeling values of nominal variables
Values of nominal variables are labeled for easier understanding of what each value denotes. The
variable labels will increase efficiency when doing analysis and one does not have to go back to
the raw data to check what each nominal variable name meant. It will save one a lot of time to
label values of nominal variables since in the later stages of result reviewing it will be quick to
determine the inclination of the study.
A1. 2.3. Reasons for printing a codebook or dictionary
A codebook gives crucial information regarding the data contents, its structure, and layout which
makes it important for it to be printed. The codebook is printed to ensure there is a proper guide
to responses to be provided and also provide a document containing both data file code

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Quantitative Analysis-Data Coding, Entry, and Checkinga Stacey Greer BUSI820-D03 10/30/2021 A1 Quantitative Analysis-Data Coding, Entry, and Checking A1. 2.1. Steps taken after collecting data and before running analysis The data collected should be accurately checked before analysis is carried out. A check if the participants clearly marked the questionnaires or score sheets properly should be done; to ensure every question is given the right number of responses instead of more answers or less answers (if given two instead of one) or if word answers are given instead of numerical answers. The data should be checked if it has answered research questions correctly and if it contains errors in specific items of data to ensure high data quality. Also, we can apply rules for consistence that can clean up answers that are marked between two rating points. A database of the data collected should be prepared with correctly defined and labeled values of variables and the best option is the frequency tables which will help in having a quick glance on the data distribution for easier interpretation. The best practice is to write the decisions in callout boxes in a different color. A1. 2.2. Reasons for labeling values of nominal variables Values of nominal variables are labeled for easier understanding of what each value denotes. The variable labels will increase efficiency when doing analysis and one does not have to go back to the raw data to check what each nominal variable name m ...
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