5 Data collection challenges and how to overcome them in Academic Projects in Nigeria
Introduction
Data collection must be done accurately or as close to accurate as possible. Inadequate data collection has several negative effects, including being unable to address research questions, failing to confirm the results, distorted findings, waste of resources, inaccurate recommendations, and injury to participants. When gathering data, many different things might go wrong, ultimately jeopardizing the reliability of the conclusions.
The process of obtaining information for use in commercial and academic decision-making, strategic planning, research, and other applications is known as data collection. Applications and research initiatives involving data analytics must include The information required to deliver answers, analyze business performance or other outcomes, and forecast future trends, actions, and scenarios provided by effective data collecting.
Although data analysis tasks can be fruitful and significant, they also frequently present difficulties that impede growth and output. You might run into problems with data quality, complexity, communication, and ethics, regardless of whether you are working on personal assets, a professional assignment, or a group project.
Data collecting is frequently an extra specialized process for research in science, health, higher education, and other professions. Researchers devise and put into place measures to collect particular sets of data. However, for analytics conclusions and research findings to be reliable, the data must be correct in both the business and research contexts. Data analysis is a critical stage in any research effort, but it may also be difficult and stressful.
If you’re utilizing either quantitative or qualitative methods, you should be aware of certain common mistakes and how to avoid them. In this article, we’ll talk about some of the most typical problems data analysis researchers encounter and some useful advice and tactics to help you overcome these obstacles and advance your data analysis abilities.
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Methods of data collection
Data can be gathered from one or many sources depending on the required information. For instance, to determine a soil-bearing capacity, the researcher must collect a soil sample, which will be tested in a lab to get the necessary data. Different data collection techniques are available depending on the subject, area of specialization, and type of application. While some are manual processes, others make use of technology. However, the following are some typical techniques for gathering data:
- Measurements: The act of measuring involves allocating values or labels to variables or analytical units.
- Experiments: One important primary data-gathering method is the experiment, in which the researcher has complete control over the participating variable.
- Observation: As may be specified by the research question, observation entails actual witnessing and documenting of the characteristics of an event, occurrence, or variable.
- Questionnaires: is a list of questions that are sent to a sample of the population from which data is sought.
- Interview: this two-way process allows for an exchange of information and idea.
- Schedule: The term “schedule” is typically used to refer to a collection of questions that are asked and answered face-to-face by an interviewer.
- Official data archives: data gathering from external data sources and information service providers
Common challenges in data collection and ways to overcome them
- Data quality problems
Raw data frequently contains mistakes, discrepancies, and other problems. The best data-gathering practices aim to prevent or reduce such issues. But that is not always a guarantee. Making sure your data is precise, dependable, and error-free is one of the most fundamental issues in data analysis. Inaccurate data can result in incorrect conclusions, false ideas, and the waste of funds. To avert this, one must complete data cleansing and validation before beginning your analysis.
Data cleaning is known as finding and correcting errors, anomalies, omitting numbers, duplicates, and discrepancies in your data. Data validation entails examining the origin, structure, and applicability of your data to your research objectives. Therefore, to detect problems and correct them, acquired data is typically subjected to data profiling and cleansing. To overcome this problem of data cleansing and validation problem, one can use various tools and methods, including spreadsheets, databases, scripting languages, and data quality frameworks.
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- Obtaining appropriate data
For project students, data scientists and other data users in an organization, acquiring data to analyze can be challenging due to the variety of systems one must navigate. For example, companies such as Shell, Agip, institutions and many others acquire data in large volume, and researchers seek to obtain such data for analysis.
Acquiring such data can be a problem if such data cannot be properly navigated. To solve such a problem, data access and discovery should be made easier using data curation strategies. For instance, building a data catalogue and searchable indexes might fall under this category.
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- Managing Data complexity
For a postgraduate student who is expected to acquire a high but wide range of data for analysis, managing such variety and range of data sources, types, and forms can presents difficulties in data analysis. A variety of sources, including surveys, online scraping, application programming interfaces, sensors, and social media, can provide data such a data.
Other categories of such data include numerical, category, textual, and spatial data. Additionally, such data can be in various forms, including CSV, JSON, XML, and HTML. To overcome such difficulty, when the student obtains such data in any of the above forms, he must have a thorough awareness of such data sources, types, and formats as well as how it connect to his research goals to manage such complexity. To control, evaluate, and display the data, he must employ the proper tools and techniques, such as data integration, transformation, modelling, and visualization.
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- Communicating data findings
Many times, the ability of a research student to communicate the results of their data interpretation and insights to their intended audience presents another hurdle in data research. Data communication entails converting the findings of your data research into appealing messaging that can educate, persuade, or inspire your audience.
To overcome this problem, the student must consider his message’s audience, setting, and expectations. This will help the student to adjust the tone, format, and content of his presentation as necessary. To engage his audience and effectively communicate his essential ideas, the researcher should employ excellent data storytelling strategies like tales, graphics, interactivity, and emotions.
- Data integrity
Once more, ensuring data analysis processes are morally responsible is one of the challenges research students encounter in data analysis. This is because some students lack moral standards due to parental upbringing and inability to acknowledge the rights, confidentiality, and integrity of data participants, stakeholders, and users.
As a result, protecting the participant against damage, bias, and improper use of their information will be lacking. To overcome this important problem, the research student must be strictly warned to abide by the applicable laws, rules, and guidelines that control the gathering, storing, processing, and sharing of such data to uphold data ethics. As the student makes decisions and takes actions based on the data analysis in his hand, he must also adhere to ethical values, including responsibility, openness, fairness, and consent.