Research the process of sampling, probability and non -probability sampling

By the end of this week, you should be able to describe the process of data preparation which is the first step in transforming data into useful knowledge. You should also be able to discuss validation, editing and coding of survey data; as well as explain data entry procedures and how to detect errors. Finally, you should be able to describe data tabulation and analysis approaches. This week, we will also look at basic data analysis for quantitative research. By the end of this week, you should be able to explain measures of central tendency and dispersion and describe how to test hypotheses using univariate and bivariate statistics. You should also have an understanding of how to apply and interpret analysis of variance (ANOVA), as well as utilize perceptual mapping to present research findings. This week, you will read Chapter 10.

Please read the following section contained in the textbook: Chapter 10

Research the process of sampling, probability and non -probability sampling. Begin with these concepts and continue your research into other areas that interest you.

Please respond to the following:

1)    Define data validation.

2)    Define data editing.

3)    Define data coding.

4)    Describe and discuss the difference between these three concepts.

SAMPLE SOLUTION

Data validation is a process that checks the quality and accuracy of the data source before using, importing, or processing the data. It is a process that cleanses data after its collection. It is performed to ensure that data from various sources conform to the intended objectives and is not corrupted as a result of inconsistencies in context or type. Data validation ensures that a certain level of quality of the final data is achieved. Data validation gets rid of any falsification and covers areas of fraud, screening, procedure, completeness, and courtesy (Belouafa et al., 2017). Common errors that may arise during the data collection process include difficulties in defining and classifying statistical units, variations in interpreting questions, data recording errors, and other types of errors.
Data editing refers to the activity that detects and corrects errors (logical inconsistencies) in any given data. It is the process of reviewing and adjusting collected data with the aim of controlling its quality based on desired criteria (Chalmer, 2020). Errors made by the researcher and the respondents are eliminated using the data editing…