Data Collection Techniques And Procedures: A Case Study On Social Media Platforms

Importance of Data Collection Techniques in Experimental Design

The success of any experimental design and the quality of the result analysis depends on the data collection techniques and accuracy (Chandra et al. 2017). To accomplish this task, it is therefore vital to design the experimental procedure that will effectively lead to quality data analysis. In this experiment, I first collected the data from various sources that I use to perform the experiment. According to Levine et al. (2018), there were multiple sources of data collection in the field or research, and, therefore, I identified the existing sources that were in line with the type of data I had to collect. After the selection, I started working on it in different sections. I included data tables for the recording, and after all, I saved the files in the computer hard disk. The data collected majored explicitly on two commonly used social media platform; Facebook and WhatsApp. These platforms are the most commonly used devices in the current world of technology (Norouzizadeh et al. 2016). The data collection aimed at identifying the effectiveness of social media platforms in various areas such as the socialization among individuals by passing information and also their importance in working environments.

Before the experiment, one should first identify the problem and select the sources where to collect the data (Palinkas et al. 2015). For this assignment, I considered people of different ages and the areas where the media was commonly used. These included areas like the learning centers such as colleges or universities, the public places such as the parks and the shopping malls and finally the companies where the media was used for communication (Hudson et al. 2015).

The table below shows a recorded data in the three sources. Additional columns represent the type of data source, the charges incurred in the collection of the data and data description. All the data collected was appropriate, and it was stored in the form of a txt format.

Table 1 Data collection record

After the completion of the above table, a next step was the data collection. According to Lewis (2015), the collected data has to be stored in a table of rows and columns to make it more presentable. What is contained in the table is all what I collected from the respondents in the form of raw data. After recording the data into the table, I then saved it in my computer to ensure that the data is accessible at any time when the need for analysis arises. To ensure the data is easily visible and accessible in the storage area, it is necessary to save the file with a name and location that is easily identifiable (Reich et al. 2017). I, therefore, stored the file in the E:Research folder and saved it as “Data” in the form of a txt file format i.e.Data.txt. The data storage table contained the data from the three sources, the date the data was collected and the number of the respondents recorded in the data collection table as shown below.

Data Collection Techniques Used in the Experiment

Table 2 Data Storage

In this stage, I divided the task into sections. These sections involved the pre-processing, dimension reduction or the selection of the features, the design phase, and the implementation (Chandrasekharan et al. 2016). The sections are described below.

For any experiment involving data collection and implementation, this is one of the critical steps (Nirmal and Amalarethinam 2015). In any survey, it is not possible for all the people to participate. Moreover, it is not possible to have all the respondents giving the correct answers and also some of the questions may end up unanswered. This, therefore, calls for the data pre-processing. According to Chen (2017), reading the raw data helps in determining the void data or removing any duplicated data. This leads to the new file of newly recorded data with fewer errors. The below figure represents data pre-processing steps (Malley, Ramazzotti and Wu 2016).

According to Meng (2016), the reduction of random data numbers under consideration and the selection of the features from the results are performed after pre-processing. I created a new file for the survey and the table showing the reduction and the preprocessing of random data as shown below.

Table 3 Data reduction and selection

For the research, I based my experiment on methodology. There are various methodologies in the field of research, and therefore I selected the hybrid methodology. The methodology combines two approaches; quantitative and qualitative for the effective experiment (Lai and To 2015). I collected and analyzed the data. Moreover, I searched the social media apps from the i-tune store and Google play store, evaluate by comparing them and based on the platforms, I made various questions. There were five survey questions based on the apps. Before the survey, I designed some general features categorized by education level, gender, background and the age of the participants. These features were vital in the data collection to get a rough idea about the participants and their preferences. Moreover, it becomes easier for feature selection and reduction of the dimensions (Tang, Alelyani and Liu 2014). To record the results, I used my statistical knowledge. The below figures show the steps necessary in creation of the questionnaire (Ito and Oyamada 2015).

Table 4 Participants Demographic information

Table 5 Survey questions

I prepared the above survey questions and the demographic information to ask the respondents in the survey. With the consideration of the background, gender, age and the level of education is, therefore, easy to get information from all the users of the apps. The survey questions were vital in coming out with a clear conclusion concerning the effectiveness of the use of the apps (Özdemir 2017).

Identifying the Effectiveness of Social Media Platforms

At the implementation stage, I saved the recorded information and used in the later stages for the designing process. Statistical software such as the SPSS and the Excel are used in data analysis (Ozgur, Kleckner and Li 2015). In this section, I used the Excel software to come out with the graphs and the pie charts for the data. The personal features of the participants are shown in the table below.

Table 6 Record of demographic information of a total of 160 participants

Experiment Results, Analysis and Summary

Table 7 Questions and response of the survey from 160 participants

As per the today’s lifestyles, many people move all around with smartphones and other technological devices which can operate through the internet and they use them in multiple purposes. For the young generation, most of them are aware of the Facebook and WhatsApp social media platforms. According to Ngai et al. (2015), the operations of these platforms are based on various functions such as communication between two individuals, used in working environments and also in creating awareness concerning the government notices, adverts, tenders and many more. Most of the people who use the platforms every day are the new generation while people like the elders and the housewives have less interest on the platforms as they have less knowledge on how to use them (Chaffey 2016).

For the survey, I used various categories of people such as the students, the people from the company and also people in the public areas such as the shopping malls who have access to the use of the platforms. To ensure that the data collected was more effective, I used both the males (59.38%) and females (40.62%) of different age range between 15 to 45 years and above as my respondents. This category of the respondents also had different education backgrounds since the platforms are used by different people not only the most educated ones. According to my survey, 96.15% of the respondents were aware of the apps, 88.46% had the apps installed in their smartphones, 92.31% use the apps (not regularly), and 61.54% use the apps every day. Moreover, 82.31% feel that the apps are effective for use in various sectors while 84.62% of the participants are satisfied with the services of the platform to be used in communication and other purposes. With these results, I conclude that social media platforms are vital in the communication between individuals and also plays a vital part in the relationship between people in working premises.  

Methodology Used to Collect and Analyze the Data

1. Experiment and Result Analysis
   • Data source list
   • Data collection record
   • Data storage

• Experimentdesign and implementation
  • Data pre-processing
  • Feature selection or dimension reduction
  • Experiment design
    • Detailed steps of Experiment Design  
  • Experiment implementation records

Software and Tools used

Experiment Results, Analysis and Summary

Results Analysis

Summary of the Results Analysis

References

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Chandra, T.D., Shaked, T., Singer, Y., Ie, T.W.E. and Redstone, J., 2017. Efficient locking of large data collections, 21(5), pp. 569-581.

Chandrasekharan, S., Gomez, K., Al-Hourani, A., Kandeepan, S., Rasheed, T., Goratti, L., Reynaud, L., Grace, D., Bucaille, I., Wirth, T. and Allsopp, S., 2016. Designing and implementing future aerial communication networks. IEEE Communications Magazine, 54(5), pp.26-34.

Chen, S., Huang, T., Zhou, Y., Han, Y., Xu, M. and Gu, J., 2017. AfterQC: automatic filtering, trimming, error removing and quality control for fastq data. BMC bioinformatics, 18(3), p.80.

Hudson, S., Roth, M.S., Madden, T.J. and Hudson, R., 2015. The effects of social media on emotions, brand relationship quality, and word of mouth: An empirical study of music festival attendees. Tourism Management, 47, pp.68-76.

Ito, A. and Oyamada, M., 2015. Research for the concept of the digital signage through questionnaires and interviews. In proceedings of the annual conference of JSSD the 62st annual conference of JSSD, 16(7), pp. 77-79.

Lai, L.S. and To, W.M., 2015. Content analysis of social media: A grounded theory approach. Journal of Electronic Commerce Research, 16(2), p. 138.

Levine, S., Pastor, P., Krizhevsky, A., Ibarz, J. and Quillen, D., 2018. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection. The International Journal of Robotics Research, 37(4-5), pp.421-436.

Lewis, S., 2015. Qualitative inquiry and research design: Choosing among five approaches. Health promotion practice, 16(4), pp.473-475.

Malley, B., Ramazzotti, D. and Wu, J.T.Y., 2016. Data Pre-processing. In Secondary Analysis of Electronic Health Records, 19(4), pp. 115-141.

Meng, C., Zeleznik, O.A., Thallinger, G.G., Kuster, B., Gholami, A.M. and Culhane, A.C., 2016. Dimension reduction techniques for the integrative analysis of multi-omics data. Briefings in bioinformatics, 17(4), pp. 628-641.

Ngai, E.W., Moon, K.L.K., Lam, S.S., Chin, E.S. and Tao, S.S., 2015. Social media models, technologies, and applications: an academic review and case study. Industrial Management & Data Systems, 115(5), pp. 769-802.

Nirmal, V.J. and Amalarethinam, D.G., 2015. Parallel implementation of big data pre-processing algorithms for sentiment analysis of social networking data. International journal of fuzzy mathematical archive, 6(2), pp.149-159.

Norouzizadeh D., F., Dehghantanha, A., Eterovic, B. and Choo, K.K.R., 2016. Investigating Social Networking applications on smartphones detecting Facebook, Twitter, LinkedIn and Google+ artefacts on Android and iOS platforms. Australian journal of forensic sciences, 48(4), pp.469-488.

Özdemir, E., 2017. Promoting EFL learners’ intercultural communication effectiveness: a focus on Facebook. Computer Assisted Language Learning, 30(6), pp. 510-528.

Ozgur, C., Kleckner, M. and Li, Y., 2015. Selection of Statistical Software for Solving Big Data Problems: A Guide for Businesses, Students, and Universities. SAGE Open, 5(2), pp. 251-256.

Palinkas, L.A., Horwitz, S.M., Green, C.A., Wisdom, J.P., Duan, N. and Hoagwood, K., 2015. Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), pp.533-544.

Reich, J., Laadan, O., Misra, V., Brosh, E., Nieh, J., Rubenstein, D.S. and Sherman, A., Columbia University of New York, 2017. Methods, systems, and media for stored content distribution and access, 7(3), pp.401-415.

Tang, J., Alelyani, S. and Liu, H., 2014. Feature selection for classification: A review. Data classification: Algorithms and applications, 12(8), p. 37.

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