Performing Data Analysis - Coursework Example

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Data cleaning is important because it detects and eliminates possible errors in a data set to ensure accuracy and consistency in collected data and validity and reliability of…
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Performance data analysis January 30, Performance data analysis Research aims at analyzing data on a phenomenon of interest but errors may induce external factors. Data cleaning is important because it detects and eliminates possible errors in a data set to ensure accuracy and consistency in collected data and validity and reliability of analysis results. Analyzing data that has errors or missing values would convey contrary knowledge to factors around an investigated phenomenon (Rubin and Babbie, 2013).
“Possible code cleaning” is an example of data cleaning (Rubin and Babbie, 2013, p. 550). It involves detection of an odd response, through a computer program of manual inspection, and the detected anomaly is then corrected. The cleaning eliminates errors that the mistake would induce in a data set and therefore ensures data accuracy and ultimate validity of results (Rubin and Babbie, 2013).
Descriptive statistics are important in illustrating trends in data. The statistics summarizes major features of a data set through measures of central tendencies and measures of dispersal to help in understanding distributions. Descriptive statistics also help to understand possible associations between data sets through identifying symmetry in distributions and this offers a basis for hypothesis testing. Identifying difference in means of two data sets, for example, forms ground for testing significance of the difference (Rubin and Babbie, 2013).
Hypothesis informs data collection strategy and data analysis approaches because it dictates the type of data to be collected. My hypothesis that emotional support improves employees’ job performance in the end, for example, dictates collection of data on emotional support from relevant sources and employees’ performance. The hypothesis further dictates use tests for difference in means of performance across levels of emotional support from the investigated sources.
Rubin, A. and Babbie, E. (2013). Brooks/Cole empowerment series: Research methods for social work. Belmont, CA: Cengage Learning. Read More
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