In an era when information is power, how we gather that information should be one of our main concerns. In this way, we must establish which of the many data collection methods is best for our particular needs. One thing is for sure: whether you're a company, organization, agency, entrepreneur, researcher, student, or just a curious individual, data collection should be one of your top priorities.
Still, raw information doesn't always have to be particularly useful. Without the right context and structure, it's just a set of random facts and figures. However, according to Sánchez Carrión (1995), if these data are organized, structured and analyzed, we will obtain a powerful input for decision making.
Data Collection Methods
Data collection can be divided into sampling methods and data collection methods with the use of surveys.
There are two main sampling methods for quantitative research: probabilistic and non-probabilistic sampling.
In this case, a probability theory is used to filter the individuals in a population and thus create a sample. The participants in the sample are chosen in random selection processes. Each member of this population has an equal opportunity to be selected.
According to Freedman et al (1991), there are four main types of probabilistic sampling:
- Simple random sampling: As the name implies, simple random sampling is nothing more than a random selection of elements for a sample. This sampling technique is implemented when the target population is considerably large.
- Stratified random sampling: In the stratified random sampling method, a large population is divided into groups (strata) and the members of a sample are randomly chosen from these strata. The various segregated strata should ideally not overlap each other.
- Cluster sampling: Cluster sampling is a probabilistic sampling method in which the main segment is divided into clusters, usually using geographical and demographic segmentation parameters.
- Systematic sampling: Systematic sampling is a technique in which the starting point of the sample is chosen at random and all other elements are chosen by a fixed interval. This interval is calculated by dividing the population size by the target sample size.
Non-probabilistic sampling is where the researcher's knowledge and experience are used to create samples. Due to researcher intervention, not all members of a target population are equally likely to be selected to be part of a sample.
There are five non-probability sampling models:
- Convenience sampling: In convenience sampling, the elements of a sample are chosen only for one main reason: their proximity to the researcher. These samples are quick and easy to implement, as there is no other selection parameter involved.
- Consecutive sampling: Consecutive sampling is quite similar to sampling for convenience, except for the fact that researchers can choose a single element or a group of samples and conduct consecutive investigations for a significant period and then perform the same process with other samples.
- Quota sampling: Using quota sampling, researchers can select elements using their knowledge by determining the target traits and personalities to form strata. Members of various strata can be chosen to be part of the sample according to the researcher's understanding.
- Snowball sampling: Snowball sampling is done with target audiences, which are difficult to contact and obtain information. It is popular in cases where the target audience for research is rare to gather.
- Judgment sampling: Judgment sampling is a non-probabilistic sampling method in which samples are created only based on the experience and skill of the researcher.
Use of surveys
Once the sample is determined, surveys can be distributed to collect the data to conduct quantitative research. A survey is defined as a research method used to collect data from a predefined group to gain information and knowledge on various topics of interest. The ease of distribution of the survey and the large number of people that can be reached according to the time of research and the objective of the survey make it one of the most important aspects of conducting quantitative research.
There are four measurement scales that are critical to creating multiple-choice questions in a survey. They are nominal, ordinal, interval and ratio scales of measurement. A combination of multiple question types can also be included, including semantic differential scale questions, rating scale questions, etc.
Forms of Survey Distribution
There are different forms of survey distribution. Some of the most commonly used methods are:
- Email: Sending a survey by email is the most widely used and most effective method of distributing surveys. The response rate is high in this method even more so if respondents know you. The QuestionPro app can be used to send and collect survey responses.
- Embed the survey on a website: Embedding a survey on a website increases the number of responses since the respondent is interested in the information when the survey appears.
- Distribution through Social Networks: The use of social networks to distribute the survey helps to collect a greater number of responses from people who already know us.
- QR Code: QR codes store the URL of the survey. This code can be printed and published in magazines, posters, business cards, or on almost any object or medium.
- SMS Survey: A quick and effective way to conduct a survey to collect a large number of responses is the SMS survey.
- QuestionPro app: The QuestionPro app allows you to circulate surveys quickly and responses can be collected both online and offline.
- API integration: Api integration can be used if we have an application that has it, so that potential respondents answer your survey.
At Online-tesis.com,our experts are widely familiar with all of these techniques, so we can help you choose the one that is most appropriate for your research.
What we should do after Data Collection
According to Díaz de Rada (1999), after the collection of raw data, there has to be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the objective of the research and establish the statistical relevance of the results.
It is important to consider aspects of the research that were not considered for the data collection process and report the difference between what was planned and what was actually executed.
Also, It is then required to select an accurate statistical analysis method such as SWOT, Joint Analysis, Cross-Tabulation, etc. to analyze the quantitative data.
- SWOT Analysis: SWOT analysis represents the acronym for Strengths, Weaknesses, Opportunities and Threats analysis. Organizations use this statistical analysis technique to evaluate their internal and external performance to develop effective improvement strategies.
- Joint analysis: Pooling analysis is an analysis method for learning how people make complicated purchasing decisions. Compensation is involved in an individual's daily activities, and these reflect their ability to decide from a complex list of product/service options.
- Cross-Tabulation: Cross-tabulation is one of the preliminary statistical analysis methods of the market that establishes relationships, patterns, and trends within the various parameters of the research study.
- TURF Analysis: It is an acronym for Non-Duplicate Total Reach and Frequency Analysis, it runs in situations where the scope of a favorable communication source must be analyzed along with the frequency of this communication. It is used to understand the potential of a target market.
The choice of the many methods for collecting data will depend on the variables to be measured, the source and the resources available. In many cases, there is a natural way to collect particular variables. Relatively static variables, for example, are often best collected through a system of record. Highly dynamic variables, such as capture or effort, are often best obtained through daily logs, such as log sheets. For the same variable, different methods can be used.
Data collection should be carried out at intervals sufficiently frequent for the purpose of handling. For example, data for stock monitoring must be collected constantly, while household data can be at much longer time intervals. In general, frequently collected data will likely have to rely on industry personnel providing it. Less frequent data may use methods with much lower collection costs.
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Díaz de Rada, V. (1999). Data analysis techniques for social researchers: practical applications with SSPS for Windows. Madrid: Ra-Ma
Freedman, D., et al. (1991). Statistics. Barcelona: A.Bosch Ed.
Sánchez Carrión, J.J. (1995). Data AnalysisManual. Madrid: Alianza Universidad.