Collecting Your Own Data-Primary Research

Primary data can be collected in different ways and are
explained as follows:

• Data collection through Interview – In this method, there are two groups: one is the researcher asking questions to the respondents or the interviewer and the other group is the people answering to the questions or interviewee. An interview can be done either telephonic conversation or direct (face-to-face) interview. The benefits of the interview method are that the information is clear and deep, and missing data can be easily identified and we can control the size of the sample. The limitations of this method are it involves longer time to complete, manual mistakes while recording or coding the data and cost incurred is high (Pochiraju & Seshadri, 2019).

• Questionnaires and Surveys – In this case, the researcher frame a set of questions to be asked to the respondents and collect the data by circulating them. Mostly, a pilot study should be conducted when we make use of this data collection method. These data can be collected either offline mode (direct survey like a census) or on-line mode (google forum, email, etc).  The benefits of this data collection are the respondent gets enough time to answer the questions, and the cost of conducting this method is comparatively low. The limitation of this is once the question is framed, we cannot alter in the middle and it requires more time.

• Data collection through observation -This type of data collection is often used in behavioural science. The researcher uses a tool to collect the data. For example, in a teaching requirement process, the person is asked to present a demo to evaluate the capability of the teacher.  The benefits of this method are that it is not related to the past or future outcome and is objective in nature. The limitation is limited information and selected target samples.

• Data through Experiments – This type of data collection is often carried out in the medical field. Suppose a researcher wants to identify the performance of a new drug and he/she conduct an experiment by taking a sample subject. Here the subjects are pre determined because only the patients with certain disease are tested by giving the appropriate drug. The benefits of this method are there is no non-response in the data and it is also objective in nature. The limitation is that human error is possible and expensive.

The main advantages of collecting primary data over the secondary data is that (1) it provides freedom to collect data according to your research objective (2) the recorded data is more accurate than secondary data (3) you don’t need to share the data to others unless if you wish to (4) you can have proper control in the sample size and finally (5) the information is up to date (Gray, 2009).

Statistical support service Once the collected the data then proceed the next phase as statistical analysis. The statistical analysis is used to describe the data structure, classify the data, correlate or identify the relationship, validating the data, predicting the future outcomes in the study. Data analysis service offers the serveral tests that available in the literature for analysing primary data. For example, if the sample size is small, then one may use the t-distribution test and if the sample is large enough then one may use z-test, etc. In addition, there are few situations that the data may be non-numeric in nature, then one can use the classification and clustering technique for analysing the primary data (De Vaus, 1986).

To sum up, primary research involves the proper collection of data and appropriate research objective. The first step of any primary research is to identify the data type and choose the appropriate data collection method and proper statistical tool to make reliable results. The primary data collection method is often accurate and gives real time conclusions. Primary research can be either qualitative or quantitative or a combination of both types in nature. On the basis of the data type you can execute either qualitative or quantitative analysis to make conclusions.