Data Cleaning and Sampling

Data Cleaning

Several activities are undertaken when preparing a research paper or study, including collecting data, recording data, analyzing data, and interpreting data. When conducting research, the collected data is often "raw," meaning that it is "polluted" or disorganized. Whichever research approach is chosen, whether qualitative or quantitative, it involves collecting data from myriads of sources. For the data to be analyzed effectively, there needs to be integrity and correctness of the data to draw accurate conclusions. Data cleaning removes unwanted aspects of collected data, thereby making it more actionable or "clean." There are various ways to clean data, including removing duplicate information, removing irrelevant information, properly formatting the data, removing semantic errors (typos), and inserting the missing values.

Data cleaning does not necessarily mean that information should be removed to make room for new data; it is a way of making the data more relevant and maximizing data accuracy to elicit accurate results. In a broad perspective, there is more "fixing" than erasing when cleaning data. For instance, when preparing a report, the author has to make sure that there are no grammar mistakes, the syntax is correct, there are no missing data in fields, and that the report meets the objectives and goals. Ensuring that the data is consistent is another method of data cleaning, checking whether the datasets correlate to the arguments or logic. Data cleaning is important since it engenders accurate conclusions that may inform important decisions in different contexts. For example, if a manager conducts research about the best way to motivate employees and does not do their due diligence in cleaning the data collected before analysis, the manager's decisions based on the conclusion of the research may be misguiding and costly to the business.

Data Sampling

One of the functions of statistical methods is to find trends or patterns in a population. For instance, we might set out to investigate how many people in a country have the blood type "A." However, given the massive size of the population under study, it may be impossible to investigate the number of people of blood type A. The next best option is to use a sample of the population, say, by using the records of one city to represent the population. This is called sampling. It involves observing particular traits or properties of a given population by using a sample/representation of the population and assigning a margin of error for more accurate results. In the majority of cases, sampling involves working with ungrouped data or raw data. Ungrouped data is data that is yet to be subdivided into different categories. To extract more meaning from ungrouped data, criteria can be created to categories the data or grouping data. Grouped data is more organized and offers more information about a given sample or population. For instance, the total number of citizens in a country is ungrouped data, but if we were to investigate the number of women in that population, we would be creating a group within the data. Other categories/groups that can be investigated in the example above include the level of income, age categories, or the number of people who voted in the previous election, to mention a few.

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