Data collection process involves the following four main steps. The first one is the construction of data form used to enter the information collected. Initially, one is supposed to locate the place of interest where he/she would get the relevant information depending on the specifications required. The next one involves establishing organizational schemes for collecting the information so as to enhance the application of techniques to analyse and make findings (Salkind, 79).
This form is used for recording scores. Known variables are included in this form for enhancing recording efficiency. To test the relevance of the information, it is given to another person who is not familiar with the project. The person, in turn, draws primary data, which is then entered into a form. Planning is necessary to effect the process as per the standards required. In this questionnaire, there should be spaces for recording relevant information to reduce possible errors while transferring data from the primary data to the statistical program used in analysing the information.
The second step is designing the coding techniques for data representation. This helps in analysing data. This involves the use of digits instead of using words, which saves time and space. This puts across a lot of information, and in a precise manner. The researcher should avoid the use of codes, which are not difficult to understand, so as to avoid losing the meaning of the data. Researchers should aim at recording data in a form that is clear and distinct during the analysis phase (Salkind, 182).
The third step involves collecting the actual data. It involves seeking authority from the institution that permits the researcher to carry out the process. In addition, the researcher should be aware of the kind of information they need to collect. Also, one should find a relevant place for collecting the information. It is also important to ensure that one uses a decent data collection form. Most importantly, one should remember to make a duplicate of the form for reference in case the data is lost.
Another factor to consider during this step is to ensure competence by recording the data physically. At the same time, it is important to ensure that one has a good working schedule. If possible, one should get agents or people to aid with the relevant information.
Biasness should be avoided at all costs, so it is up to the researcher to ensure that there are enough controls to avoid collecting biased information. During this step, it is also crucial to keep the relevant materials, which are used to collect information for future reference (Salkind, 184)
The fourth step involves computing the collected data in the collecting form. This is achieved through the use of descriptive statistics to provide explanations for the distribution of scores that have been collected. This may be tabulated. The mathematical expression of mean standard deviation and median range should also be calculated. Finally, it is crucial to apply inferential statistics, which helps to relate the original hypotheses and how to generalize to a larger number of subjects than those tested.
The importance of systematic data collection is that it ensures that that the information collected maintains the integrity of both the instruments used and emphasises on the depiction of the correct instructions so as to ensure possible errors are reduced. It also ensures that the data collected is accurate to facilitate effective decision-making while drawing arguments from the findings. It also ensures that the information collected is not biased as it involves different sources.
Work Cited
Salkind, Neil J. Exploring Research: Pearson New International Edition. Pearson Higher Ed,
2013. Print.
