Whether you’re collecting data on your own or to help another person or organization, one thing must not be tampered with: the data accuracy. Data is important because it acts as a decision making platform.

Importance of Data Accuracy

Irrespective of the school of thought, preference, or discipline from which you’re defining data, data accuracy is an essential ingredient in ensuring that the integrity of your research remains intact. You need to use the most appropriate techniques or instruments for data collection, whether new, old, or modified. Improperly collected data may result in the following:

  • Inability to address research questions correctly
  • The study in question cannot be validated
  • Distorted findings

Good Data Sources

Generally, accurate data (both qualitative and quantitative) are collected from multiple sources. When two or more sources are used, the gathered information becomes more reliable.

1. Pre-existing Data

Pre-existing data refers to already documented information and may include such things as photographs, case laws, and newspaper clippings. It can also be occurrence reports, annual reports, or any other information an organization creates on a routine basis. Although using pre-existing data as a source is efficient, the extent of reliability depends on the accuracy of the people collecting the data.

2. Focus Groups

A focus group is a group of selected people who are brought together for purposes of generating particular information. Typically, a focus group session is facilitated by an interviewer. The interviewer asks questions, while the group provides answers. Depending on the preference or goal of an organization that is collecting the data, the facilitator can either be a professional or a nonprofessional. No matter how the session is formatted, it’s always advisable to manage focus groups in a fashion that encourages the participants to freely share their experiences.

3. Governmental Agencies

These are institutions or departments that form the government, and they may include meteorological departments, immigration departments, census bureau, tax departments, among others other agencies.

Qualities of a Good Data Source

No matter the person you’re communicating to, whether your client, employer, or an expert such as a payroll specialist, good data must start with a good source, period! But what does a good source of data look like? A good source must have the following components.

1. Originality

Your data, whether internal or external, should be original in nature. You can ensure that data is original by checking its primary source. If you encounter any data in a magazine or Wikipedia, it is always important to trace its origin. This way, you will know if the methods used to collect such data were flawed (the questionnaire was biased; the sample used was too small etc).

2. Current

The world is so dynamic- it changes by day, and even the data that was collected a year ago could be obsolete. This is why your data source must be as current as possible. When you’re collecting data, look for sources that update their data on a regular basis. For instance, the Department of Labor has its data revised every month, which is a good idea. Ideally, your data should be two years old (at most).

3. Comprehensiveness

The purpose of collecting data is to answer certain preset questions. Your data source should try to answer as many questions as possible to avoid leaving a wide knowledge lacuna. In a nutshell, your data has to tell a full story.

4. Reliability

A reliable source must be relevant, unbiased, and legitimate if your data has to be good. Statistics compilations done by government agencies, such as the Department of Labor and the U.S. Census Bureau, are reliable data sources. Scholarly papers are also a reliable source of data.

Data Collection Techniques

Any data is as good as the method used to collect it. In other words, choosing the most appropriate data collection method can go a long way to make the difference between good and bad data. The commonly used methods of data collection include observation, interviews, and questionnaires. Let’s have a peek at each of them.

1. Observation

The wise have said it time without number that seeing is actually believing, and this is a fact when it comes to data collection. Collecting data through direct observations can be both quick and effective. As a researcher, all you need to do in order to effectively apply this method is put in place a proper mechanism to facilitate your observation. However, the method can only be applicable when gathering simple information. Also, the interpretation of the collected data solely rests on the expertise of the researcher since the method is not interactive.

2. Interviews

If correctly done, this technique can be helpful in overcoming the shortfalls of observation. Here, you will have to gain a better understanding of the respondents because the technique allows you to interact with them. Your presence will give those interviewed confidence to answer questions, and this may enhance the response rate significantly. However, it takes a lot of time and resources to reach a wide audience for interviews.

3. Questionnaires

Questionnaires refer to stand-alone data collection instruments. They can be administered through various ways, including online, phone, or mail, and they are very popular. You can reach a wide range of respondents (virtually every part of the globe) as long as the medium you’re using allows. The respondents can fill the questionnaires when they feel it‘s convenient for them. Though reliable, the rate of response can be very low since encouraging the respondents may be impossible- you’re not talking to them directly.