Challenges PhD Students Face While Analyzing Data

Challenges PhD Students Face While Analyzing Data

Learning how to analyze academic data and to critically evaluate arguments is an important component of the educational process. It trains students in the habits of thought in complete readiness for daily activities in their study disciplines. But scholarly data analysis is a daunting task, which is the gist of this post.

In this article, I’ll closely monitor the challenges PhD students face while analyzing data. You know my style by now: we’ll kick off with academic instances when either qualitative or quantitative data analysis is called for. Right after, I’ll describe the most common frameworks and models of analyzing data.

Next, you’ll experience firsthand the challenges associated with qualitative data analysis. At the fourth heading, expect an answer to: “Is it hard to analyze quantitative data?” Before winding up, I’ll give students modern solutions to both qualitative and quantitative data analysis challenges.

Academic Scenarios When Students Have to Analyze Data

For every CIPD scholarly engagement (and for other Master’s and beyond), you’ll face never-ending academic scenarios where data analysis can’t be wished away. There are two challenging times when students need outstanding academic data analysis skills, namely:

When Planning for the Project, Especially During the Review of Literature

The main purpose is to formulate a theoretical research framework; or to inform the choice of research methodology. It also helps students to develop a sound research model to test their arguments and advance their point of view. Oh yes, you need to sieve through the mound of available data looking for relevant data to build a study-specific pattern.

During the Actual Project Paper Writing

There is a whole section of academic papers named after data analysis. After conducting project investigations and collecting scholarly data, students fill in a raw data table. They then comb out for inconsistencies before representing the information in graphical forms.

It’s this meaningfully analyzed data that answers the project question, grounding the findings and recommendations on solid evidence.

Important Note!

Arming students with clear, time-conscious directions for academic data analysis helps them to comprehensively respond to the thesis statement.

The Most Common Scholarly Data Analysis Frameworks and Models

Students use a collection of tools and affiliated libraries to help them discover, gather, arrange, clean, and interpret scientific information. Data analysis frameworks and the student’s approach to handling research findings are an important scholarly skill. Because it’s hard to pick this art in four short academic years, we offer project-specific data analysis help at student-friendly rates.

If you’re a CIPD or Master’s student in any business-related course, Contact us TODAY and claim your place at the academic leaderboard!

Major Data Analysis Frameworks used by PhD Students in Data Analysis

The 4 major data analysis frameworks utilized by PhD student include:

Descriptive Analytics

Under descriptive analytics, students first collect and aggregate raw data from various sources. Next, convert the information into a common analysis format using data intelligence tools before forming conclusions and action plans. Some common descriptive data analysis techniques include:

Frequency tables
Summary statistics
Measures of dispersion
Measures of central tendency
Charts & graphs, And
Pivot tables

Predictive Analytics

Predictive analytics uses research findings to forecast future outcomes using machine learning, data analysis, AI, and statistical models. A student can find intricate data patterns that might be useful in predicting future organizational behavior.

Diagnostic Analytics

Diagnostic data analytics students examine data to understand causes of events, outcomes, or behaviors. An analyst student uses diverse tools and techniques to identify trends, patterns, and connections to explain why certain phenomena occur.

Prescriptive Analytics

Here, a business student will suggest optional, data-based decisions. They’ll demonstrate how organizations can take advantage of future opportunities (or to mitigate future risks), showing the implications of each option with authoritative data.

Did You Know?

For 13+ years, our online dissertation data analysis help service has continuously set internet standards for timely delivery of quality data analysis reports. Check out the reviews here before proceeding to Place an Order!

Some Common Data Analysis Models and Approaches

A student in post-grad school may choose any (or a workable combination) of these three data analysis models below:

1: The Dimensional Model

Enables a fast retrieval of information from large datasets by segregating inconsequential, unrelated data from the main points. The dimensional data analysis model helps to identify interrelationships between data types, allowing students to deeply analyze patterns and trends.

2: Relational Data Analysis Models

A student will discuss column and row data characteristics in a table. You’ll draw the importances of choosing good primary keys, how to use concatenated primary keys, and foreign keys to represent data relationships. Oh, there’s also the aspect of referential integrity, views, and the deployment of data dictionary tables in storing a database design.

Am I Overwhelming You?

If you’re already lost, don’t let that confusion leech into your Data Analysis assignments. We’re here to help you. Click HERE now!

3: Entity Relational Information Analysis Model

An Entity Relationship Diagram (ERD, ER Diagram, or ER Model) is a structural diagram in database design containing different symbols and connectors that visualize two important information parts. These are major system scope entities and the inter-relationships among the system entities.

The Best Data Analysis Framework and Model Designs

For every model above, students follow one of these three approaches:

The Conceptual Design

You’ll wrestle with data analysis concepts such as problem identification, ideation, research and analysis, prototyping, data visualization, iteration, and evaluation. This is the basis of a research design process, helping students to guide & develop their design as they refine ideas.

The Logical Approach

The logical data analysis approach in research entails a systematic, reasoned approach before drawing conclusions that’ll solve business problems. Students need logical research approaches in conducting experiments, to arrive at accurate conclusions, and in academic analysis of observations.

The Physical Strategy

When students opt for the physical data analysis strategy, they want to relate information to material problems and offer palpable solutions. The physical strategy is rarely used in isolation.

Yet Another Important Note

Each of the frameworks above calls for specialized approaches / methodologies in order to address their different analytical intentions.

Challenges with Qualitative Data Analysis

Many students that I interact with recite a common liturgy of qualitative data analysis challenges. You’ll often hear of contextual variability, researcher bias, lack of generalizability, and subjective interpretation. Here, I’ll inspect 6 unique challenges associated with qualitative data analysis.

A Quick Aside…

There are 4 simple steps to follow when your professor insists on a qualitative data analysis. They’re:

Gather all the relevant feedback (collect data).
Code the comments.
Run your queries, and finally
Report your findings.

Qualitative data analysis investigates people’s perceptions / feelings about a situation, event, or business trends. It’s therefore susceptible to these challenges:

Challenge #1: Choosing a Method

Please add “…and getting started” before I forget. Thanks to the overwhelming variety of qualitative research methodologies, students sometimes find it hard to decide on a single study design.

Challenge #2: Identifying the Research Problem

Most students confuse this part with challenge #1 above, although they’re closely related. Methodology takes instruction from the research question and due to uncountable choices, it’s hard to pick the most appropriate thesis statement. The two elements greatly inform research data collection and analysis frameworks / models.

Challenge #3: Reliability and Validity

It’s challenging to maintain research data findings’ validity or reliability with qualitative data analysis. This is mainly because qualitative processes aren’t as standardized as their quantitative counterparts. Personal biases, for example, are likely to skew qualitative research data results.

Challenge #4: Time-intensive, with Tons of Data to Analyze!

By default, qualitative studies collect tons of relevant research information. Data analysis therefore becomes a resource-intensive, time-consuming, tiresome, and nerve-wracking experience.

Psssstttt…

You don’t have the time, resources, or the energy for qualitative data analysis (but we do)! Contact our 24/7/365 Client Service Desk today! We’ll do the heavy lifting at the most affordable online price while guaranteeing you an A+ Grade! Hurry!

Challenge #5: The Intricate Nature of Qualitative Research

Yes, qualitative research is difficult to conduct. It’s also less open to interpretation, and even less likely for the analyzed data to be generalized to whole populations. With qualitative data analysis, students always complain of accuracy and consistency challenges.

Challenge #6: Avoiding Bias

There are many contributory limitations that make avoiding qualitative data analysis bias such a headache, including:

Potential bias in study responses.
Possible small sizes of the sampled population.
Self-selection bias, Or
Potentially faulty, non-pointed questions by the student researcher.

Is it Hard to Analyze Quantitative Data?

Since quantitative data analysis deals with things a student can reduce to numbers, it’s relatively easier than qualitative data analysis. The best way to understand the challenges of analyzing quantitative data is by studying the 3 uses of quantitative analyses.

3 General Purpose Uses of Quantitative Data Analysis in Research

There are 3 general purpose uses of quantitative data analysis in research, namely;

Use #1: Firstly, quantitative data analysis assesses the relationships between variables. For example, a student can relate temperature to a certain product’s sales turnover. Challenge: Whereas quantitative data analysis can give a predictive perspective, its assumptions lack research validity and scores poorly on reliability.

Use #2: Secondly, quantitative data analyses measure the differences between grouped data. For example, a student may choose to research the popularity of different clothing colors using quantitative data analysis.

Challenge: A glaring hurdle in this usage has always been drawing reliable conclusions that a student can generalize to entire populations.

Use #3: And third, students use quantitative data analysis to test hypotheses in a scientifically rigorous way. For example, a CIPD student working under healthcare may hypothesize the impacts of a new vaccine on a specific community, and how it’ll affect organizational profits.

Challenge: Some obvious obstacles here will be:

Can the student avoid bias (See Qualitative Data Analysis Challenges above)?
Are the findings reliable?

6 Practical Solutions to Qualitative and Quantitative Data Analysis Challenges

I’ll be brief with these 6 practical solutions to effectively overcome data analysis challenges for every student.

First, create and follow a systematic approach to handle (organize) data. This helps a student to identify all relevant data for inclusion in the analysis. It goes a long way in justifying your framework, model, and methodology choices.
Secondly, students should use tools such as questionnaires or surveys to collect data / seek feedback from large sample sizes. It’s preferable if you triangulate data using reliable data collection accessories.
Thirdly, acknowledge any data analysis limitations you encounter openly and honestly in the final submission. Students who suggest further research whenever they doubt data analyses show academic maturity.
Ground yourself in the research data by always getting back to the data analysis methodology. Account for any biases you feel may hinder data analysis clarity or the intricate details of your research.
Listen to your data. Let the information you collected drive the final analysis findings or presentation. Take advantage of modern digital technology, and always stay focused.
Finally, learn from previous experiences or the mistakes of other students by intensively and extensively reviewing relevant literature.

In Brief…

The main challenges associated with data analytics include collecting meaningful data, selecting the right analytics tool, and visualizing research data. Students also report improving data quality, finding skilled analyst help, and cultivating a data-driven culture as other major obstacles.

The practical solutions given above will mostly mitigate many challenges associated with qualitative and quantitative data analysis frameworks and models.

With all the risks and challenges enumerated above, do you still want to bet on your grade by going in solo? Please don’t! The primary reason we started this online data analysis service is to attend to students like YOU!

Five Tips for Writing Quantitative Dissertation

quantitative dissertation tips Research, statistics, qualitative, quantitative, dissertation, data, reports, analysis are all phrases that you will encounter at one point or another in your academic or professional purist. They are all intertwined and all play a very crucial role to give certain desired results. However of all those, today we shall be exploring all about quantitative dissertation, which is from the term quantity. Dissertation papers are essential as far as academic and research pursuits are concerned. The test for professionalism and competence as far as the given field is concerned.

Dissertation papers are often a combination of fact as and figures while others are either one of the two and this creates the rift between quantitative and qualitative dissertation. So what is a qualitative and quantitative dissertation? What is the difference between the quantitative and qualitative dissertation? What do I need to know to prepare for such a project and how do I represent my data? What is the recommended format to use in this case? All these are questions that will have competent answers by the end of this feature and most importantly how do we write a quantitative dissertation? Without further ado let’s jump right into it.

Quantitative dissertation is used in most fields and has been integrated into everyday operation to collect, synthesized and analyze data along the guidelines stipulated by quantitative dissertation. So, we shall tackle this wholesomely and dissect it to its core and basically understand what it takes to be a guru at quantitative dissertation or any other project that necessitates quantitative data.

In lay-man’s terms, quantitative research is research that takes a research approach that focuses more on figures as part of data collected and through the application of various formulas and calculation algorithms a conclusion is made. On the other hand, the approach that preferentially uses facts to base its research on is called qualitative research. These two define the measure of quality versus quality.

Now that we have that behind us, its time to delve into the main agenda and get the BIG five secrets behind a quality dissertation paper. And yes, they are nothing extra ordinary, just practical and proven tips of writing quantitative dissertation. Note that the use of statistical analysis is imperative in presenting results in a quantitative dissertation.

Ask for statistical analysis help today for your quantitative dissertation from expert statisticians.

Define Hypothesis

When tackling a dissertation paper, we move from the unknown to the known and later on to concrete findings that will either settle the unknown. For this to be achieved, we have to develop a guiding question or statement which we shall make reference to all through the paper. This is what we call the hypothesis. The hypothesis is developed before any research is undertaken and is the whole basis for the research. As a researcher, you need to make observations through a spectrum of theories and look for something new or relatively unknown.

From this point of view, you will develop a guiding theory that will either be proven or disputed by the end of the paper. Researchers have often referred to this method as the deductive thinking method where you create something and prove its validity from results and findings.

The designing of the hypothesis is one of the most important things in your paper. It is also an all-round essential in different kinds of literature that you may compile. This is because, from the definition, the hypothesis is the opening statement that states what you will be tackling through your paper. It gives you the opportunity to present your findings and at the end of it all, it opens up the platform for analysis that will either concur or conflict your hypothesis. After going through the title of your paper, your reader or assessor might have an idea of what you will be handling but on reading your hypothesis they will be sure of what to expect

There are different types of hypothesis depending on the approach you need to take. However, we shall not go into much detail about them. In most instances, the statistical hypothesis is preferred in quantitative dissertations.

Data Collection

factors to consider when choosing data collection tool The next step after defining the intent of your paper is to engage in conscious research. What is conscious research? This is basically doing research from an educated point of view where you know what you are looking for and have ticked all boxes in terms of what is necessary for this data to be collected.

There is a variety of data collection methods at your disposal that you can use but among the most proficient ones are surveys, observation, experimentation, videography, case study and interviews. Both of these methods have their pros and cons but the most important thing is that they give you a feel of the total population through sampling. Given that the kind of data you are collecting should involve both facts and figures then it is important that you formulate the questions with utmost care. If you ask the wrong questions then ultimately the results will frustrate your research. Scholars on a global scale have always advocated for a blend of both open-ended and close-ended questions in these instances. You can classify your data in terms of time period used in collecting. It can either be as a result of the whole population over a specific time span or an isolated group over a specific time span as well.

Formatting

Dissertation papers are a way to grade students on merit and by reason of this, it is only fair that you stipulate the boundaries and a clear outline of what is to be followed. internationally, there is one globally accepted style of writing literature from the fields of social sciences. This is called the APA style of writing. And if you have done a little bit of snooping around on literature then you have encountered this before or at the very least see it is familiar.

The APA format of writing has been recommended, used and approved time and again and has received various updates since its launch. It has proved very effective since the layout is formulated in such a way that ensures that you do not miss any details or skip any section necessary in your dissertation paper.

Let me give you a glimpse of what it contains. Our dissertation formatting service is adept at handling all formatting styles.

Title page

This is the very first page of your paper and it should only contain the title which should be no more than 12 words and placed on the upper half of the paper. The author’s name comes after the title and finally the name of the institution. The title should be in the same font, size and colour as the rest.

Abstract

The abstract comes in second and is placed on a separate page after the title page, it contains an overview of what you will be tacking. It is limited to a minimum of 150 words and a maximum of 250 words. Given that you already this is a compilation of work done then you already know what it contains. It is also imperative that you don’t quote direct phrases from the article but only give an idea around it.

Body

The body is the compilation of your research, analysis and findings. It enables you to present your findings in a better way. In the body, you will start off with the hypothesis or a thesis question and work your way from there. Here you can include all that you deem relevant to your work and avoid irrelevant jargons. There is no specific length for the body but it should not be excessively long to motivate the reader to read through it all and also not so short that you seem insufficient.

Reference

Depending on the source of your information, you will decide on what to include in your references. In this section, you basically want to give credit to where it is due and this means stating sources. Apa style of writing has a provision for references and citations as well. This runs down the chances of being branded incompetent out of plagiarism issues.

Appendices

The appendices are another very crucial part though easily forgotten aspect of writing. It acts as a key and map to your writing.

Use Visual Representation

Data interpretation in quantitative research is quite unique and this needs a little extra skill to deliver exemplary work. Figures are quite complicated to analyze at first glance and despite the fact that they are already synthesized and analyzed, for them to be used in comparison with other data they have to be represented in a more conventional way. This is achieved by using graphs, pictograms, charts and other visual representations available. The human mind has been known to achieve very many milestones and push itself to great limits but at the same time, it tends to lose concentration at a very fast rate. To keep your readers mid from wandering, induce visual representations into tour paper. After all, it has always been said that the mind works in a photographic way.

Strong Conclusion

After all, is said and done, this is the point where you deliver your conclusion from your findings, it is time to land this plane. With reference to all your findings and results deduced from the synthesized data, how does it all correlate with your hypothesis? Remember that the reader at this point still remembers your introduction and has followed through your data collection, data input, data analysis and results found and is ow here to get your verdict. You should draw your conclusion from your findings in relation to the hypothesis. You have the leeway to either concur or contradict.

Make sure that you take a stand and support it. You are also allowed to state your observation and give recommendations on what you were researching on. You need to go big or go home.

Quantitative dissertation is quite vast but also not complex to grasp. The essentials that help put it into perspective and distinctively from other forms of writing are unique to it. With these 5 tips you can be sure to boost your game substantially. However, you can always opt for expert writers to take the pressure off your hands. Expert writers are highly qualified individuals who have invested highly into research and are competent in their different fields. They are up to date with changes in their fields of specialization as well as in literature. Among the reasons that most people love engaging them in their projects is that they assure quality wok delivery. Dissertation papers have never been this easy before. These guidelines will shed light on how you should handle a quantitative paper on your own and be assured of quality results.

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.

Might you need a bit of support with your qualitative data analysis?
helpwithdissertation.com might be a great data analysis help provider to consult. If you like, you can contact helpwithdissertation.com now and get start writing your qualitative data analysis.