Sampling and data collection are intertwined methods. Sampling is essential for meaningful data collection. It entails selecting a sample from a large population that is representative of the characteristics of the entire population. Researchers have to use various sampling strategies for data collection, and they must select a large sample to generate rich findings that provide deep insights into the issue or examined population or group. Sampling and data collection are primarily used in primary research, which relies heavily on first-hand data. Researchers can collect both quantitate and qualitative data using sampling techniques. This article will guide you about the sampling and data collection methods you can use to complete your dissertation.
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What Are Sampling And Data Collection Methods?
Statistics help you transform the data into meaningful information. In our everyday discussions and newspapers, we come across statistics. Statistics state that global warming is increasing, inequality is rising, the gender gap is closing, and so on. But have you ever wondered where does the data come from? And how the researchers gather it? What measures are taken to ensure data accuracy? What makes the data reliable?
And most importantly, Is the data representative of the traits of the population from which it was taken? The answer to these questions lies in the sampling and data collection methods employed by the researcher. Researchers can use various sampling and data collection methods for the research, and to understand both of them better, let us analyse each one of the methods separately.
Sampling
Sampling and data collection methods are interdependent, i.e., one cannot sustain or operate without the other. Sampling is the planning stage, and data collection is the implementation phase. A representative sample can yield meaningful data, whereas a partial or incomplete sample leads to vague results. Therefore, it is imperative to use effective sampling strategies to select the sample size.
Why Do We Use A Sample?
You must be wondering what the need for a sample is, and rather than entangling in the specifics of sampling, why not just go out there and collect information from every passerby? The simple answer is that collecting data from every individual worldwide is impossible. Therefore, sampling is important because a sample that is proportionate in traits to the larger category of people helps you decipher the traits of the large population. Sampling makes your job easier and confines your focus to a specific group of people that are representative of the traits of the entire population. There are various sampling strategies that the researchers, and the most popular strategies are the following:
Random Sampling
Random sampling means selecting the people at random. Random sampling gives everyone an equal chance of being selected. In random sampling, the researcher plugs in the number in the computer software and generates a random sample that considers the diverse characteristics of a population. It entails specifying the sample size. It is usually preferred to select a large sample size to make your research credible; however, the choice of sample size depends upon the available resources and the nature of your research. Random sampling entails the following:
Stratified Random Sampling
Stratified random sampling makes your job much easier by selecting a pool of people who are highly representative of the population traits. For example, if you want to evaluate drug consumption among university students, you must select a sample that is representative of the entire student body. The student body is a diverse amalgamation of students belonging to different areas, ethnicities, races and religions. For example, if you categorise the students into Latinos, African Americans, Asians and White Students.
After categorisation, you must review the university database and identify the percentage each category occupies in the student body. Based on the identified percentage, you will have to ensure that for each sample, the identified percentages of students are reflected in the sample size. In this way, you make your sample diverse and representative of the entire population.
Data Collection Methods
The researcher can use multiple data collection methods for their research. However, the choice of a data collection method depends upon the nature of the research, research design and aims of the research. The most popular data collection methods are the following:
- Observations
- Experimentation
- Surveys
Observation
The observation method is the most useful data collection method in research. We observe things in our daily lives; however, those observations are momentary and do not entail rigorous planning or purpose. A good researcher is always a good observer. Observation requires being trained in the observation methods, i.e., knowing what should be observed and how it should be observed. Observation encompasses two techniques: direct and indirect observation. Direct observation pertains to observing people in their natural settings with their consent.
However, researchers argue that direct observation does not yield credible data because when people are conscious that they are being observed, they change their behaviour. Alternatively, researchers can use indirect observations, which entails observing the people without letting them know that they are being observed. But it raises ethical questions and requires researcher’s commitment to not breach the privacy of the observed people. Observation requires systematic planning and employing sampling and data collection methods for collecting credible data.
Surveys
Survey data collection method collects opinions of the people on various issues. Surveys require designing a questionnaire that can collect information from people. Surveys are designed to gather information on people’s opinions, views, experiences and preferences on various issues. For example, market researchers frequently employ surveys to assess the market mood. They circulate surveys before the launch of a particular product to understand the preferences of people. Sometimes surveys are circulated after launching the product to assess the receptivity of a particular product in the market. It helps businesses determine whether the product should be continued or discontinued.
In a similar vein, social scientists use surveys to assess the perceptions of people on various issues. Survey design must be strong, i.e., the questions must be clear, straightforward and diverse. It is imperative to avoid using leading questions or unambiguous lines of questioning because such surveys fail to gather reliable information. Survey design requires devising meaningful questions that are aligned with your research objectives. If you need any help designing a solid survey when working on your graduate dissertation, you can take help from experts working at the best dissertation writing services like The Academic Papers UK.
Conclusion
Sampling and data collection methods help you incorporate rich data into your research. Employing these strategies helps you convert raw data into meaningful information and conduct statistical analysis to generate deep insights into an issue.
