Factors To Consider when Choosing Data Collection Tool

Data Collection Process

Before embarking on data analysis, either quantitative or qualitative, a researcher is required to collect data. Unfortunately, this is a process that is often underestimated and given little emphasis despite its immense importance. The quality of your data determines levels of reliability, validity and accuracy of the findings. Most researchers fail to involve consultants at this stage ending up with half baked results. For academic research, hiring a dissertation data analysis services will go a long way in having your methodology refined.

The title 21st century will go in history as the century that was characterized by a spike in technology as compared to its predecessors and one that has almost completely shifted all its operations to online platforms. The concept of making the world a global village has relatively been achieved where we achieve miles from the comfort of our seats. However, the digital age has been characterized by data mining and collection for practically every aspect of living. Governments conduct national census and statistics to run the country while companies require your details for you to sign up and with this, they are able to get more information on your lifestyle to strategically place their products and use the same data to learn what the market wants. Learning institutions are no strangers to these systems as they have thrived on them from the early days and this is all that encompasses most of humanity’s learning.

Today we shall be getting to understand this on a deeper and friendly way. NO jargons, but only plain facts in the simplest and most understandable phrasing possible. So, what is data collection? And why is it essential? What are data collection tools and what actors should you consider when choosing data collection tool.

Data collection is the process of gathering information as guided by a laid systematic order in an attempt to test theories, hypothesis or even to answer research questions. Data collection can be as vast as stipulated and could also be restricted to certain scope by given guidelines.

Research goal

I do hold the fact that the best way to work is when you know what your target is and you work towards it. The end game can be stipulated by facts surrounding your research or a targeted result. When looking to introduce a new product into the market, a firm may need a set of data to shine the light on the kind of market they will be meeting. This calls for research on the products in the market by competitors, the age of the target market and their reaction to variables such as pricing and product delivery.

The goal here is aimed at marketing but the same company could hold another research before developing a product and try to understand the gap in the market., This will use a different spectrum and the goal will be to help develop a new product that will be the solution that people need.

The research goal is the driving force behind most research programs as it shows that everything else will have to come to a halt as we wait for this data to give the go-ahead on the direction and possibilities open that can be pursued. In the end, think of data as the bridge or a means to an end.

Statistical significance

Statistical significance is as a result of data representation which is directly determined by the means or data collection tool that was chosen. So, first things first, what is statistics? According to Wikipedia Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. From the basic definition, it suggests that it is inclined towards quantitative data. This kind of data is most suitably gotten using specific methods. While all methods are capable of delivering this to you, it will be more effortless on others and will not disappoint.

Statistical significance of the data is also reflected on the purpose for which the data is being collected, that sets the tone for its significance. Accuracy of the data must be on the higher end of the spectrum and data representation must be done in a form easiest for interpretation such as in a tabular form, chart or even graphically.

Sample size

dissertation statistical consultant — *Respondent filling questionnaire*

Data collection is not as easy as it sounds as it is the heart of any research. You might have all the technology you need to conduct experiments or theories to test but without data, there is no progress. With this in mind, you need to understand what you are working with. When working with a large group, it might take a long time to get data from each and every unit of the population which is why people opt to go for smaller portions of the whole population which is called the sample. When choosing a sample, there are a couple of criteria that can be used among them simple random sampling, stratified sampling, cluster sampling, systematic and quota sampling among many others. All these have different procedures on how the samples are to be chosen. While one method suggests that the sample be taken randomly in no given order, one will advocate for equality I term of sex, age or even gender.

If you are to work with a large sample then it Is only logical that you use a data collection tool that is both efficient and effective. In this, you would rule out interviewing a large population and instead use questionnaires or another method.

Time

Some research projects are time-sensitive and having the clock ticking on your project is definitely a reason for you to consider all your options. With time being a major factor then you will need to consider what scope you need to cover and what resources you have at hand. With this, you will be in a position to make an estimate of how much time you need to hit your deadline. You will have this as a leading point when deciding on which data collection tool will work for you.

Cost

Money is a strong agent and the world revolves around it. The possibility or impossibility of a task will mostly lie with the availability of resources and money is the first resource to be put into consideration. Give that different data collection tools have different approaches and use different methods to get the required data then this means that the budget allocation of each data collection tool will be different.

This runs right from equipping manpower with all the required resources and facilitating the data collection itself. While some methods are relatively expensive some are very cheap and even considered to be the easiest for anyone on a tight budget. However, at the end of the day, you will have to inject some money in the process.

Accuracy

Different sources of information give a different feel or experience altogether. In this light, they also give different information. Data collection tools may vary in terms of mode of interpretation right to the basic core of the audience involved. In the case of a questionnaire, it is fully dependent on how you phrase the questions and how the target sample interprets the questions. This will make the whole difference. Open-ended and close-ended question receive different results and the limitation to them are quite consequential. ON the other hand, if you opt for a means that will engage you in terms of observation and give you the first hand feeling them this will increase the accuracy of your data.

Depending on why you want this data and what you intend to do with this data then you have to explore different options. In the case of accuracy, it is usually mostly a question of quality which overruns other factors such as cost as it aims at getting the best that is available, think of the national census as the perfect example for this. IT takes time and vast resources to engage in door-to-door interviews for accurate data. This is because the data required is essential to the economic planning of the country and resource allocation. However, this is not a process that is done annually, but the info given will have to suffice for periods of up to a decade.

Validity and reliability

Data validity is a thin line between quality and quantity. Data validity means the presentation of data that serves the exact purpose that it has been extracted for and checks all boxes for a job well done. On the other hand, data reliability means that you can trust the data and use it for the intended purpose without second-guessing it at any point. This is as a result of getting valid data. These two are actually intertwined. Reliable data has to be valid.

The validity and data may take many forms and shapes depending on stipulated conditions. While some sets of data demand to be collected in a certain way, if acquired by any other means it is in violation of basic guiding principles and this invalidates it. Again, I will use the example of a countrywide scenario. During elections, the electoral bodies rely on data collected from all voters to determine who has won in an election. This requires that all voting station be subjected to similar conditions and that all candidates receive similar treatment. Secondly, you cannot take a sample and ask them to vote so as to determine who has won in such an election. When there is a violation of these facts, this leads to a situation of electoral irregularity which can easily render the results null and void and calls for a rerun.

Practicability

So, what does practicability mean in this context? Practicability is basically the unique combination of all the above factors. When considering time, cost, sample size, research goal and validity you end up with the question of how practical are they in relation to the data we need. Time factor will have to be considered but then again how does working under a tight deadline affect the quality of data you need.

The means that how you choose to collect your data should also be tailor-made to suit your target audience. Giving a questionnaire to a semi-literate or illiterate group will render your whole research useless. ON the other hand, if you need to learn of behavioral patterns then you need to have a better feel which calls for observation, videography, surveys or direct participation.

Finally, data security and discretion are also factors to be considered. Some means of data collections lead to incorrect data as they influence the research specimen to act in a different way or shy away from giving accurate inform on or even give no information at all. Silent data collection tools are preferred in such instances as they allow you to get the data needed without the person under review even knowing that you are. This has been made easier in the present day as you can use micro-cameras and recording devise to get that data that you synthesize later. It allows for the most truthful version of the data.

At this point do believe that you are enlightened on what you should consider when choosing data collection tools for your research. Data collection can be done by anyone and it only takes an enlightened mind to decide on the best approach. In case you require assistance, feel free to talk to our dissertation statistics consultant for assistance in choosing and design of data collection tool

How To Write Qualitative Data Analysis

So you want to grasp how to write qualitative data analysis. You’ll pretty soon since you’re already on the right page. Here, you’ll learn what qualitative data analysis is. Most importantly, you’ll learn how to carry out a qualitative data analysis. Additionally, you’ll understand how qualitative data analysis differs from quantitative data analysis. Ready? Let’s roll.

The other name for qualitative data is descriptive data. Qualitative data is essentially non-numerical data that concerns itself with capturing concepts and opinions. When you interview someone, you’re collecting qualitative data. Similarly, when you make audio or video recordings, you’re gathering qualitative data. Also, notes made while observing a certain phenomenon count as qualitative data. Now that we know what qualitative data is, let’s learn what qualitative data analysis is.

What’s Qualitative Data Analysis and Why Does it Matter?

Qualitative data analysis refers to the process of working through qualitative data to glean useful information. The information you obtain from this exercise helps you develop a plausible explanation for a particular phenomenon. The process is hugely important as it reveals themes and patterns in the data you’ve gathered.

Additionally, data analysis enables you to link your data to the objectives and research questions of your study. Also, the process helps you organize your data and interpret it. Most importantly, successful qualitative data analysis leads you to informed conclusions. What’s more, the conclusions you end up with are verifiable.

How Does Qualitative Data Analysis Differ from Quantitative Data Analysis?

As a researcher, you MUST understand how qualitative data analysis differs from quantitative data analysis. Gaining a clear understanding of the difference between the two helps you choose the right research method for your study. In addition, grasping the difference prevents you from getting sidetracked while executing the research methodology you’ve chosen.

Here’s the main difference. Qualitative data analysis helps researchers get useful information from non-numerical or subjective data. By contrast, quantitative data analysis is about mining knowledge from your data using statistical or numerical techniques.

Some disciplines, especially those in the humanities and social sciences, tend to favor qualitative data analysis. Quantitative data analysis, on the other hand, tends to find greater relevance within the sciences including chemistry, physics, and biology.

How Researchers Approach Qualitative Data Analysis

There are 2 main approaches when it comes to qualitative data analysis. These approaches include the Deductive Approach and the Inductive Approach. As a student and future academic researcher, it’s critical that you understand each approach.

What the Deductive Approach is and When to Use it

The deductive approach is about analyzing data on the basis of a structure that you as the researcher have predetermined. The approach relies heavily on the research questions that inform your study. Your research questions should direct and guide you as you group and analyze the data you’ve collected.

When should I use the deductive approach to qualitative data analysis? A good question right there. Employ this approach when you can fairly predict the responses you might get from your sample population. The beauty of this approach is that it’s easy to use. There’s more. The deductive approach works fast.

What the Inductive Approach is and When to Use it

Here’s the main difference between the deductive approach and the inductive approach. The inductive approach, unlike the deductive approach, doesn’t rely on a predetermined structure or framework. The good thing about the inductive approach is that it’s a bit more thorough than the deductive approach. However, the approach is more time-consuming than the deductive approach.

So, when should I use the inductive approach to qualitative data analysis? Favor this approach when your knowledge of your research phenomenon is very little.

You now clearly (hopefully) understand the nature of qualitative analysis. Now, it’s time to learn how to write qualitative data analysis.

How to Write Qualitative Data Analysis

There are several steps involved while writing qualitative data analysis. Understanding the steps we’re about to discuss will have you writing the data analysis section of your qualitative study pretty fast. Let’s now dive right in and learn how to write qualitative data analysis.

Convert data into text

Before you do anything else, you’ll want to turn all of the data you have into textual form. The other name given for this conversion process is transcription. Now, converting your data to textual form should be pretty easy. Just find the right tool and get it done. EvaSys,NVivo are two tools we consider particularly powerful. With either of these two tools, you should handle the task real fast. Also, you can perform the transcription job manually. However, doing it manually tends to be tiring and time-consuming.

Organize qualitative data

Now that you’ve your data in textual form, organize it. It’s normal to have vast amounts of data you don’t know what to do with especially after transcription. If you’re not careful, all of the unorganized data lying around can confuse or even stress you out. That’s why you should organize your data.

So how do I organize my data? It’s simple. Revisit your research questions and objectives and start organizing the data from there. The objectives of your study can greatly help your turn that messy data into organized data. Generate tables and graphs as they’re a great way to organize your data. Also, consider using appropriate tools to make the process more efficient.

Code qualitative data

What’s coding your qualitative data? Coding qualitative data means compressing it into easy-to-understand concepts and patterns. Essentially, coding enables you to attach meaning to the field data you’ve collected.

How do I code my data? Let your study’s objectives guide you. You can code your qualitative data on the basis of the theories they related to. Also, collected qualitative data gives you hints as to how best to code it.

Most data analysts prefer the following 3 coding approaches. First, a data analyst may use descriptive coding. Here, they code data on the basis of the central theme emerging from the dataset. Second, a data analysis expert might prefer In-vivo coding. In this approach, the data analyst lets respondents’ language guide them. Finally, an analyst might identify patterns in the data and let those patterns direct them during data coding. You may want to research further for other coding techniques you might use for your qualitative research.

Ensure qualitative data isn’t flawed

Well, this isn’t exactly a distinct step for how to write qualitative analysis. However, we chose to include it given the importance of data validation. Data validation is a method that helps researchers todetermine the accuracy of their methods or research design. Data validation also reveals the extent to which a researcher’s procedures and methods led to consistent and reliable results.Shall we say you’vejust learned how to write qualitative data analysis?

Conclude qualitative data analysis

The conclusion of your data analysis is arguably its most important part. So you better make sure this section packs a hefty punch. At this point of the writing process, you must state your findings, linking them to your research’s objectives and questions. After you’ve concluded the data analysis process, you need to craft a final report.

Qualitative Analysis Report Writing

Describe the methods your research relied on and the procedures you performed. But don’t stop there. State the strengths and weaknesses of your study. Next, outline your research’s limitations. Finally, state the implications of your findings while pinpointing a few areas that future research might explore. Here’s a structure your final report might follow:

Introduction

Methods and Procedures

Strengths and Weaknesses of Your Study

Limitations of the Study

Findings: (State the findings and link them to the research questions and objectives

Conclusion

Final Thoughts: How to Write Qualitative Data Analysis Report

Finally, you’ve understood how to write qualitative data analysis. Hopefully, writing the data analysis section of your academic papers should be easier in the future. The process starts with converting the data into textual form. Then, the data gets organized. Data coding swiftly follows. Then, data validation happens. Finally, the final report gets prepared.

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