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.
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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.
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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.
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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.
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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.
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