Week 5 Analyzing and Interpreting Data

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Analyzing and Interpreting Data
Team X
QNT/351

Analyzing and Interpreting Data
The ultimate goal of descriptive statistics is to describe a set of data, identify patterns, and draw a conclusion, which enables an organization to make effective and informed decisions (McClave, Benson, & Sincich, 2011). The company, Ballard Integrated Managed Services (BIMS), a support services company will leverage statistics to gather information on the company’s employees to analyze and identify patterns. The goal of this research project is to determine the reason for the high employee turnover and low morale. The research team has developed a strategy that ensures that the management dilemma will be resolved in the most
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Second, questions one through ten qualify as ordinal data because of the relative rankings without consistent distances. Finally, the time worked for BIMS qualifies as ratio level data because it is easily ordered, consistent differences and zero is meaningful (McClave, Benson, & Sincich, 2011). Each level of data has unique characteristics, which dictate the way the information is calculated, summarized, and presented.
Coding and Cleaning the Data
Coding the data is a systematic way in which data is condensed into smaller easily analyzed units (Lockyer, 2004). By coding the surveys received by BIMs it enables the company to analyze and interpret the data to draw an informed conclusion. The coding enables the research team to identify mistakes, and outliers, which improves the data’s validity (Lockyer, 2004). To code BIMS’ data, the surveys must be numbered, and all corresponding data must be manually entered into Microsoft Excel, which increases the cost, time, and risk. The data is at risk of being entered incorrectly. Next, the raw data must be cleaned to ensure validity, relevancy, and accuracy. After the data was imported into Excel, the data must be reviewed for mistakes. One error that was identified was that a six was entered into Excel even though a five was observed. By coding and cleaning the data, the researcher will proactively identify errors or outliers while enabling a computer to complete statistical analysis and

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