After you have received approval from the institutional review board, the next step is to start collecting data. It is important to maintain full team involvement throughout the entire data collection phase, from training data collectors to reviewing data periodically. Otherwise, you may end up with a massive dataset that has too many errors. Listed below are 7 steps to avoid making mistakes during the data collection phase. Follow them to ensure your study’s integrity.
7 steps to reduce the risk of errors in the data collection phase:
The first step to reducing the risk of data collection errors is to understand the types of errors and their effects on the results. The type of errors will depend on the research topic and the study methodology. The next step is to identify the data sets and the business problem. Once these are established, set up the data collection methods. If there are too many variables, the results will be less reliable. In other words, a poorly designed questionnaire will result in inconsistent data.
Identifying possible sources:
Next, the data collection phase focuses on identifying possible sources of data for analysis. The data is collected through primary and secondary sources such as surveys, customer relationship management software, online quizzes, financial reports, and marketing automation tools. It should be collected from a sample of the population. After collecting the data, it is necessary to organize and clean the data. Cleansing the data will help in locating it later.
Developing a sampling plan:
Samples are usually selected and analyzed to obtain an accurate value of the population. In some cases, they are also used in the nutritional labeling phase. Scientists conduct analyses of samples for research and product development. A sampling plan is a detailed document that specifies the size of the samples, how and where they will be collected, and the documentation required for the results. A well-drafted sample plan should be detailed, concise, and easily understood.
Obtaining accurate observations or measurements:
Obtaining accurate observations or measurements during the data collection phase is essential for a research study. It involves planning implementation procedures such as the type of questions to ask and the experimental design. Some variables can be directly observed and measured while others cannot. For example, if the goal is to understand the relationship between environmental factors and the health of a population, the research team should collect information on the health of the people living in that region.
Once the research team has a good idea of what type of data they need, they can then determine how to collect it. The best way to collect data is to ask as many questions as possible, as this will help them gather enough information to make an informed decision. The purpose of data collection is to provide a complete picture of the phenomenon. This information can be used to make the necessary adjustments. The process of data collection can be very complicated or very simple.
Developing a sampling frame:
Developing a sampling frame involves defining the units of the sample and ensuring that they are representative of the population under study. Some populations are well known while others are smaller but can be identified. In either case, the sampling frame should be defined in such a way that all the elements that comprise the sample are included in it. This method is known as stratified random sampling and has numerous advantages and disadvantages.
Reason of mistakes:
One of the biggest reasons why researchers make mistakes in the data collection phase is improper sampling. Inaccurate sampling causes the sample to be biased in one direction and not in another. In addition, the population under study might not actually represent the target population. Poor sampling rules or improper sampling frames can lead to these errors. For instance, a one-pound error in measurement can affect a BMI reading or a diet plan.
Using a Python package:
When working with non-Python data files, you can incorporate those files in your Python packages. The most common mistake that a newbie can make is omitting a subpackage. Even seasoned Python developers have made mistakes in their package data configurations. The key is to test installed code to see if it is missing any necessary components. However, if you install too many Python packages, you risk creating a tangled mess.
Avoid using LEGB:
First of all, don’t use the LEGB (local, enclosing, global built-in) rule incorrectly. When you use the LEGB rule, you ignore any instances of a termed variable that are identical to those in another scope. This rule is important for Python because it is the key to making sure your data collection is as accurate as possible. However, the LEGB rule does not work in all situations.
Developing a data dictionary:
A data dictionary is an essential tool for reproducible research. It helps researchers create a consistent vocabulary for the variables used in their studies. In the first column of the data dictionary, list the variable names as they appear on the spreadsheet. The name should be readable and contain spaces, capital letters, and characters. Make sure to include measurement units for each variable. Once the names are established, developers can focus on data validation and data quality.
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