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Data Collection

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What is Data Collection?

Data collection is the primary/first step in most kinds of research. It’s the process of gathering data and measuring it on targeted variables using established systems. There are many different methods of collecting data depending on the information required, and the field of study the research falls under.

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Data Collection Methods

The following four are the main methods of data collection

1. Web/Online Surveys

In this method of data collection, surveys and/or questionnaires are administered via the internet to the survey’s target audience. 

The main advantage of conducting online surveys, over others, is that it is cost effective enabling researchers to utilize larger sample groups which can give more reliable research results. Additionally, the data collected will be more generalizable to the target population.

However, a disadvantage of this method is that not all customers may feel comfortable disclosing their information on online platforms, which may lead them to input unreliable results or not attempt the survey at all.

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2. Phone Surveys

In this method of data collection, trained interviewers call respondents in the target population on their telephones to gather information from them.

The main advantage of phone/CATI surveys is that the data acquired from phone surveys tend to be more insightful and accurate as interviewers can clarify and expand on a respondent’s answers.

A disadvantage of phone surveys is that they tend to be more expensive than a lot of other data collection methods, for example, web/online surveys.

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3. In-Person Interviews

In-person interviews is a form of data collection that refers to the meeting of interviewer and interviewee face-to-face.

An advantage of this method of data collection is that, like phone interviews, answers can be clarified. Additionally, interviewers have the scope to read body language and facial expressions.

A disadvantage of in-person interviews is they are one of the most expensive methods of data collection and also one of the most time-consuming. A lot of research studies may not have the financial backing or the time to conduct this method of data collection.

4. Mail Surveys

In this method, surveys or questionnaires are sent to the sample group via their email address.

An advantage of using mail surveys is that they don’t require much man-power, hence, are a relatively low-cost method of data collection.

A disadvantage of this method, however, is that it has really low response rates. The response rates for mail surveys tend to range between 3-15%. Even if the upper limit is considered, it still means that out of every 100 surveys sent, only 15 people respond. Therefore, in order to effectively conduct mail surveys, mails need to be sent to a really large number of elements in the target population in order to get the required amount of responses.

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Example of Data Collection

Let’s consider an FMCG company called company XYZ that is trying to release a new anti-hairfall shampoo. Before they do so, company XYZ wants to conduct market research gauge the market for shampoos, anti-hair fall shampoos in specific perhaps, so they decide to conduct an online survey. They conduct this survey on their website and ask questions relating to what consumers may want from their shampoo.

The following are a few examples of the questions that they may ask:

  1. What are your hair goals?
  • Increased volume
  • Getting rid of split-ends
  • Silkier hair
  • Oil-control
  • Frizz-control

      2. Which of the following factors affect your choice of shampoo?

  • Price
  • Brand
  • Effect
  • Packaging
  • Other

Survey Design for Data Collection

In order to make a good survey, these are a few things a researcher should keep in mind:

  1. Avoid using long questions in the survey
  • Survey dropout rate is the percentage of respondents who start giving the survey but do not complete it, likely due to facing certain hurdles while doing the survey. Long survey questions increase the survey dropout rate as it takes up a lot of the respondent’s time and they may get bored.

     2. Avoid the use of jargon 

  • It is important for survey questions to be easily understood by all respondents. Using complicated or unfamiliar words may just confuse or frustrate the respondent. If the respondent doesn’t understand a question on the survey, they may pick a random result, giving rise to unreliable responses.

     3. Ensure that the questions are relevant and effective

  • To get the most out of survey results, it is critical to ask the right questions in regard to what the research is about. They must be relevant and effective in order to acquire useful information.

     4. Avoid asking questions that may breach a respondent’s privacy

  • It is important that none of the questions on the survey make a respondent feel like their privacy is being invaded. If a respondent feels that way, they may leave the survey, increasing the survey dropout rate, or they may input wrong information which will lead to unreliable survey results.

     5. Use mutually exclusive response categories

  • Avoid any sort of overlap in the response options as this may make it more difficult for the respondent to choose an answer, and may also give rise to unreliable responses. 

     6. Avoid questions that ask about two different points

  • Every question should be precise and should focus on one single point. If one question focuses on more than one point, the respondent may have multiple different responses but will end up choosing just one of them. This gives rise to unreliable responses. Instead, each question should specifically only address one point.

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