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Every researcher struggles to create a clear hypothesis.

You're not alone if you find it challenging.

The problem?

Most guides favour quantitative methods, leaving qualitative researchers out.

But there's a solution:

Visual approaches to formulate and 'test' hypotheses qualitatively.

Here's how to master it:
1. Map Your Research Journey

• Clearly state your research problem at the start
• Write down important questions to explore
• Identify key variables from your questions
• Map problems, questions, variables

2. Design Your Hypothesis Visually

• Use diagrams to show relationships between variables
• Create flowcharts to illustrate predicted outcomes
• Develop concept maps to connect your ideas

3. 'Test' Your Hypothesis Through Visualization

• Use content analysis diagrams to organize qualitative data
• Create narrative charts to track themes and patterns
• Develop cross-sectional analysis visuals to compare findings

💡 Expert Tip:
Use software like Figma, Draw IO, or Lucidchart to create professional-looking diagrams. These tools simplify updating visuals with your research.

Visual approaches clarify your thinking.
They make your research more engaging.

Write this down:

A clear hypothesis is the foundation of strong research.
Watch your insights come to life.
Make yours visual.

What's your go-to method for hypothesis formulation in qualitative research?

Thanks for reading my tips.

If you enjoyed this guide:

1. Follow IndexWise Research to get more resourceful in writing and research.
2. Share this guide with your network to support fellow researchers.

Have a great day.

Characteristics of quantitative and qualitative Research (based on Creswell)

Basic research is where scientists are free to pursue their curiosity and interrogate Nature, not with any short-term practical end in view but to seek knowledge for its own sake.

Maxwell wasn't thinking of radio, radar or television when he first scratched out the fundamental equations of electromagnetism; Newton wasn't dreaming of space flight or communications satellites when he first understood the motion of the Moon; Roentgen wasn't contemplating medical diagnosis when he investigated a penetrating radiation so mysterious he called it X-rays; Curie wasn't thinking of cancer therapy when she painstakingly extracted minute amounts of radium from tons of the mineral pitchblende; Fleming wasn't planning on saving the lives of millions with antibiotics when he noticed a circle free of bacteria around a growth of mold; Watson and Crick weren't imagining the cure of genetic diseases when they puzzled over the X-ray diffractometry of DNA; Rowland and Molina weren't planning to implicate CFCs in ozone when they began studying stratospheric photochemistry.

These discoveries-and a multitude of others that grace and characterize our time, to some of which our very lives are beholden-were made ultimately by scientists given the opportunity to explore what, in their opinion and under the scrutiny of their peers, were basic questions in Nature.

Of course there are many pressing problems facing our nation and our species. But reducing basic scientific research is not the way to solve them. Scientists do not constitute a voting bloc. They have no effective lobby. However, much of their work is in everybody's interest. Backing off from fundamental research constitutes a failure of nerve, of imagination, and of that vision thing that we still don't seem to have a handle on.

Carl Sagan, "Where Did TV Come From" Parade Magazine, 1995

You've spent months on your research, only to realize you used the wrong method. Ouch. Research methods confuse most people.

I see more boring, unappealing, and standard explanations by the day.

Because it's challenging to understand them all.

It requires many skills. Like:

→ Data analysis
→ Critical thinking
→ Experimental design
→ Statistical interpretation

All part of conducting great research.

Yet, many researchers fail at choosing the right method.

Here are 5 key branches of research methods:

→ Quantitative (numbers-based analysis)
→ Qualitative (exploratory, non-numerical)
→ Mixed Methods (combining both)
→ Experimental (testing cause-effect)
→ Observational (watching without interference)

A good researcher must be able to navigate these methods.

Qualitative methods are crucial for in-depth understanding.

Here's how I would structure a qualitative study:

1. Research question (what you want to explore)
2. Data collection (interviews, observations)
3. Thematic analysis (finding patterns)
4. Interpretation (making sense of findings)
5. Conclusion (addressing the research question)

I'd aim for rich, detailed descriptions in my methodology.

And for as much depth and context as my data allows.

Here are 7 qualitative methods I love:

1. Ethnography
2. Case Study
3. Phenomenology
4. Grounded Theory
5. Content Analysis
6. Action Research
7. Historical Research

Good luck with choosing a research method that doesn't fail.

It's a challenge. I think my methods could improve as well.

Please share your favourite research method in the comments if it helped you publish a paper.

It's helpful to many new researchers to get inspiration.

What do you think makes or breaks a good research methodology?

Types of variables

90% of data scientists struggle with causal inference. Let’s fix that.

Causal inference typically involves three conceptual layers:

1. Association (Statistical or Correlational Layer):

This is the most basic level, focusing on identifying relationships or correlations between variables.

It involves using statistical techniques to establish whether a change in one variable is associated with a change in another.

However, this level does not imply causation; it merely identifies patterns or associations in the data.

2. Intervention (Causal Layer):

At this level, the focus shifts from mere associations to understanding causal effects.

It involves asking what would happen to one variable if you intervene to change another.

This is where methods like Randomized Controlled Trials (RCTs), Instrumental Variables (IV), or Propensity Score Matching come into play.

These methods help establish a cause-and-effect relationship by controlling for confounding factors and isolating the impact of specific interventions.

3. Counterfactuals (Mechanistic Layer):

This is the deepest level of causal inference, involving the understanding of mechanisms and pathways.

It deals with counterfactual reasoning, which means understanding what would have happened in an alternate scenario where a different decision or intervention was made.

This layer often involves the use of causal models, like structural equation models or Bayesian networks, to understand the underlying mechanisms that drive the observed relationships.