Chapter 1: The Selection of a Research Approach

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In 1999,

NASA lost a $125 million Mars climate orbiter.

Oh yeah, that was a massive disaster.

Right.

And it didn't get hit by an asteroid or suffer some catastrophic engine failure.

It literally just burned up in the Martian atmosphere.

All because of a unit conversion error.

Exactly.

One engineering team used metric units to calculate thrust,

and another team used imperial units.

Wow.

Yeah, so you had some of the smartest people on the entire planet work together, but their underlying foundational frameworks just didn't match up.

Which is a remarkably expensive way to learn that before you build something, everyone needs to be operating from the exact same set of assumptions.

Because if the foundation is mismatched, the whole structure inevitably comes crashing down.

And if you were a college student gearing up for a major research proposal, or maybe you're currently staring down a massive methodology exam.

It can feel pretty high stakes.

Making a fundamental mismatch in your research design won't cost you a spaceship,

but it will absolutely cost you your grade.

So welcome to this deep dive.

Today, our mission is to act as your personal tutor.

We are decoding chapter one of the textbook research design.

Qualitative, quantitative, and mixed methods approaches.

And look, we understand the stress of opening a methodology textbook for the first time.

Oh, it reads like an entirely foreign language.

It really does.

Yeah.

But we want to reassure you that research is not just, you know, a bunch of random arbitrary rules designed to torture college students.

Though it definitely feels that way sometimes.

Sure, it can.

But it's actually a highly logical sequence.

Your foundational beliefs shape your research questions, right?

Okay.

And then those questions shake your blueprint and that blueprint dictates exactly what tools you use to collect your data.

So consider this your shortcut to truly understanding that sequence.

We're stripping away all the dense jargon to show you how these pieces interlock.

I like that.

I mean, I like to think of this chapter as the ultimate recipe book for research.

Before you start cooking, you really need to know what kind of meal you're making, who you're cooking for, and what tools you actually have in your kitchen.

That's a great analogy.

So let's establish the vocabulary first.

Yes, let's do that.

Because before we jump into how to actually gather data, we have to understand the four levels of research design outlined in Table 1 .1.

Right.

And it works kind of like a funnel.

You're pouring really broad ideas down into highly specific actions.

Okay.

So what's at the widest part of that funnel?

The widest part is what we call philosophical assumptions.

Philosophical assumptions?

Yeah.

Long before a study even begins, a researcher brings a specific point of view to the project.

Like their personal biases?

Well, more like deeply held beliefs about how the world works, which are developed from the researcher's training, their culture, and prior experiences.

Gotcha.

So if my assumptions are the wide top of the funnel, where does that pour into next?

It narrows down into the research approach.

You can think of this as the broad methodology.

Okay.

This is where we categorize research into qualitative,

quantitative, or mixed methods.

Okay.

That makes sense.

And then narrower still.

Next is the research design.

This is the specific procedure or the blueprint within that broad approach.

I think an active analogy might help anchor this before we go any further.

Sure.

Let's hear it.

If research is like building a house,

my philosophical assumptions are the underground concrete foundation.

I like where this is going.

You don't necessarily look at the foundation once the house is built, but it absolutely holds everything up.

That tracks perfectly.

Which would make the research approach the broad architectural style.

Like you decide, I'm going to build a modern farmhouse.

Right.

Then the research design is the actual blueprint I hand to the construction crew.

It tells them where the load -bearing walls go.

Exactly.

And that leaves the narrowest part of the funnel, which is the research methods.

These have to be the actual hammers, nails, and saws, right?

Yes.

The concrete steps, where I gather the data, analyze it, and figure out what it all means.

That is a highly effective way to visualize it.

Just keep in mind that a researcher doesn't always lock these four steps in with, you know, rigid chronological perfection.

So it's not always a straight line.

Not always.

But those four elements, assumptions, approach, design, and methods, they must collectively inform each other to create a structurally sound study.

Let's focus on that architectural style for a minute.

The broad research approach.

If I'm building a study,

what are my main options?

I know there's quantitative and qualitative, which honestly seem like polar opposites to me.

A lot of people think that.

Right.

Like, do I just have to decide if I'm a math person or a feelings person?

Let's dispel that misconception right away.

The textbook explicitly warns against thinking of these as two totally separate, isolated boxes.

Oh, really?

Yeah.

They are not rigid dichotomies.

They actually represent different ends of a continuum.

So a study isn't purely one or the other.

It just kind of leans more heavily in one direction.

Usually yes.

Let's look at quantitative research first.

Historically, this dominated the social sciences from the late 19th century well into the mid 20th century.

And this is the numbers one, right?

Exactly.

This approach is built for testing objective theories by examining the relationships among variables.

And those variables are measured with numbered data.

So we're talking about closed ended questions, statistical procedures,

and deductive reasoning.

Spot on.

You start with a grand theory and you test it to see if it holds up.

And you do everything in your power to protect against bias and control for alternative explanations.

You are seeking to generalize your findings to a larger population.

Makes sense.

Now what about qualitative research?

Well, qualitative research saw a massive rise in the latter half of the 20th century.

Instead of testing an objective theory, it explores the meaning that individuals or groups ascribe to a social problem.

To make this real, let's run a scenario.

Let's say I want to study the alarming rate of college burnout at my university.

OK, good example.

If I'm taking a quantitative approach, I might send out a survey to a thousand students asking them to rate their stress on a scale of one to ten and then correlate that with their GPA.

I'm looking for a numerical relationship.

Yes, exactly.

But if you take a qualitative approach to that exact same burnout problem, you aren't using numbers.

What do you use instead?

You use words.

You ask open -ended questions.

You go to the participant's actual setting.

Maybe you observe students in the library at 2 a .m.

Oh, wow.

Yeah, that's very different.

And crucially, you use inductive reasoning.

You don't start with a preconceived theory about burnout.

You start with specific individual stories and build up to general themes.

That makes total sense.

And what about mixed methods?

I assume that's just mashing the two together.

It's a bit more deliberate than just mashing them together.

Mixed methods sits right in the middle of our continuum.

It involves collecting and integrating both quantitative and qualitative data within a single study.

For what purpose, though?

The goal is to draw what the text calls metanforances.

These are insights that you could only get by looking at the numbers and the narratives combined.

Okay, so if quantitative and qualitative are just opposite ends of a spectrum,

what actually pulls a researcher toward one end or the other?

Well...

I mean, if I'm a stressed student, my inner monologue is probably saying, I just want to send out a survey link to pass this class.

Why do I have to learn four different philosophies to do that?

Because of the foundation we talked about earlier, your philosophical worldview, those hidden beliefs about reality, dictates what you consider to be valid knowledge in the first place.

If you don't declare your worldview, your professor or whoever is reading your research won't know how to interpret your findings.

It's kind of like putting on a pair of tinted glasses.

Yes, exactly.

If I put on blue glasses, the world is blue.

If I put on red, it's red.

I have to explicitly tell the reader which glasses I'm wearing.

That's a perfect way to look at it.

So let's break down the four pairs of glasses the textbook outlines in table 1 .2, starting with post -positivism.

Post -positivism is essentially the traditional scientific method.

It aligns heavily with quantitative research.

It's deterministic, meaning it assumes that causes probably determine effects or outcomes.

It's also reductionist.

It's meaning it reduces things.

Exactly.

The intent is to reduce complex, messy reality into a small, discreet set of testable variables.

And I imagine this is the default setting for most students coming from STEM fields.

Oh, absolutely.

Like, if you grew up doing chemistry labs, you were raised as a post -positivist.

You rely on empirical observation and measurement.

But wait, why the post part?

Why not just positivism?

That's a great question.

It's because modern post -positivists acknowledge a crucial limitation.

We can never be absolutely, 100 % certain about our claims of knowledge.

Especially when studying human behavior, I imagine.

Right.

That is why, in rigorous quantitative research, you never say you proved a hypothesis.

You say you failed to reject it.

The evidence is always considered imperfect.

Okay, so what happens when a student raised on post -positivism takes a methodology class and runs into worldview number two, constructivism?

Oh, it often feels like learning an alien language.

I bet.

Because constructivism completely flips the script.

It assumes that individuals develop subjective meanings of their experiences.

So there isn't one single reality.

Right.

Reality isn't a single objective truth waiting to be measured by a survey.

Meanings are varied and multiple, and they're forged through social -historical interactions.

So if I'm studying our college burnout scenario wearing constructivist glasses, I can't start with a predetermined theory about, say, lack of sleep causing bad grades.

No, you can't.

I have to inductively generate a theory based on what the students actually tell me in open -ended interviews.

Precisely.

You are looking for the complexity of use, not trying to isolate a single reduced variable.

Which brings us to the third pair of glasses.

The transformative worldview.

The text mentions this gain traction in the 1980s and 90s, right?

Yes.

Because researchers felt the traditional scientific method was kind of ignoring marginalized groups.

Exactly.

The transformative worldview holds a very specific belief.

Research must be intertwined with politics and a political change agenda.

So it's very action -oriented.

Very.

Its entire purpose is to confront social oppression.

The research itself contains an action agenda for reform that might literally change the lives of the participants or the institutions being studied.

So the researcher isn't just like an objective observer taking notes behind two -way glass.

Not at all.

They are working collaboratively with the participants to ensure the study doesn't further marginalize them.

Exactly.

And finally, we have the pragmatic worldview.

Which honestly sounds like the most practical one.

Let me guess, this is the foundation for mixed methods.

You nailed it.

Pragmatism isn't committed to any one overarching system of philosophy.

It arises out of actions, situations, and consequences.

It is fiercely problem -centered.

So they just do whatever gets the job done?

Pretty much.

Pragmatists believe that truth is simply whatever works at the time to provide the best understanding of the research problem.

They will use all available approaches, numbers, words, whatever is necessary.

Okay, so I have my worldview foundation poured.

I know what reality looks like to me.

Now I need the blueprint.

The research design?

Right.

I need to select a research design from table 1 .3.

Let's take our college burnout example and apply it to the specific designs the book outlines, starting with quantitative designs.

Okay.

The text highlights experimental and non -experimental designs.

What's the difference?

In a true experiment, you want to test if a specific treatment influences an outcome.

So for college burnout, I might take 50 stress students, give half of them a mandatory daily meditation app, and give the other half nothing.

Right.

And then you measure their stress levels at the end of the semester to see if the treatment caused a change.

Got it.

And non -experimental.

The most common non -experimental design is a survey.

You aren't giving anyone a treatment.

You're just measuring the current reality.

Like calculating the exact percentage of the student body experiencing burnout symptoms.

Exactly.

Survey the population to describe trends.

Simple enough.

But the qualitative designs?

Man, the book lists a lot of them and the syllables get pretty dense.

They can be a bit overwhelming.

Yeah,

like descriptive, narrative, phenomenology, grounded theory, ethnography, case studies.

How do I know which blueprint to hand to my construction crew?

Well, it depends entirely on what you want to build.

Let's use your burnout example again to differentiate them.

Let's do it.

If you use narrative research, you are studying individual lives.

You might ask a few students to share the story of their college experience, and you retell it chronologically.

Okay, what about phenomenology?

That's a mouthful.

It sounds intimidating, but it simply means studying the lived experience of a phenomenon.

So not just a timeline.

Right.

You aren't just telling a chronological story.

You are trying to distill the very essence of what burnout physically and emotionally feels like for several individuals who have all experienced it.

Ah, okay.

And grounded theory.

Here you use participant interviews to build a general abstract theory about a process.

Give me an example.

You might interview students to develop a brand new theoretical model of how burnout develops step -by -step over a four -year degree.

You're grounding a new theory in their rata.

Got it.

Now, ethnography comes from anthropology, right?

So instead of just interviewing them, I would actually have to go live in their environment.

You would.

To study the shared patterns of a cultural group, you might spend an entire semester observing interactions in a freshman dorm.

Just taking field notes on their shared behaviors around studying and stress.

Precisely.

And finally, a case study.

What's the boundary there?

A case study is an in -depth analysis of a specific bounded system.

You wouldn't study burnout generally.

You would study burnout specifically within, say, the university's highly competitive pre -med program.

Treating that specific program as the case.

Exactly.

That clears up the qualitative side beautifully.

But what if I want to use mixed methods?

The Exploratory sequential and exploratory sequential.

I have to admit, explanatory and exploratory sequential sound identical to me.

I used to mix them up constantly.

It is arguably the most common sticking point for students.

The key is in the sequencing.

Right.

Well, I actually developed a little memory trick for this that might help you listening at home.

Oh, please share.

Think of the words themselves.

Let's take explanatory.

Imagine you start with a massive spreadsheet of hard data, your quantitative numbers.

You know that 80 % of students are burned out, but you have no idea why.

So you have to sequentially follow up with qualitative interviews to explain the numbers.

Quant first, then qual to explain it.

Exactly.

And on the flip side, exploratory.

Imagine you are dropped into an unknown wilderness.

There are no surveys to hand out because no one has ever studied this specific type of burnout before.

So you have nothing to measure yet.

Right.

You have to use qualitative interviews to explore the wilderness first.

Once you map it out through conversations, you can build a quantitative survey to measure it across the whole campus.

Qual first to explore, then quant to measure.

Your trick captures the precise mechanical logic of the text.

Exploratory uses words to explain numbers.

Exploratory uses exploration to build numeric instruments.

Okay, so the foundation is poured.

The blueprint is drawn.

We finally reach the bottom of the research methods from table 1 .4.

These are the actual hammers and nails used to gather data.

And just like tools in a toolbox, they serve very specific functions.

Quantitative methods are predetermined.

You use instrument -based questions, like a multiple -choice scale.

And you analyze statistical data.

Yes.

Whereas qualitative methods are emerging, you use open -ended questions, you collect text or image data, like transcripts from an interview, and you analyze it for themes.

And obviously, mixed methods utilizes both open and closed -ended questions across databases.

Let's test this entire framework.

Try to pull a specific method out of alignment and see what happens.

Okay, let me actively apply our house analogy.

Let's say my foundational worldview is constructivist.

I believe that reality is complex, subjective, and emerges purely from social interactions.

So a strong, qualitative foundation.

But then, for my actual method,

I hand the participants a strict, predetermined multiple -choice survey with only yes or no options.

What happens to your structure?

It completely collapses.

It would be like trying to build a modern glass skyscraper on a foundation made of mud.

Why exactly?

Because if I believe meaning emerges from the participant, but I give them a multiple -choice survey, I have predetermined their reality for them.

My hammer doesn't match my foundation.

Which is exactly why the chapter forces you to look at the entire funnel.

You cannot mix and match these elements randomly.

You'll have to connect.

They have a distinct, necessary causal relationship.

The methods must serve the design, which must serve the worldview.

Let's prove that causal relationship using the actual scenarios provided in the textbook in table 1 .5.

Let's see how they snap together.

Okay.

I'll play the student again.

Give me a scenario and I'll try to predict the interlocking pieces.

Let's take the book's first example, a classic quantitative approach.

Okay.

If I am forced to use a classic quantitative approach, my hidden worldview is almost certainly post -positivist.

I'm looking for cause and effect.

My design blueprint is probably an experiment.

And my methods, my specific tools would be taking pre -test and post -test measures of an attitude using a numbered scale.

Spotless alignment.

The researcher specifies a narrow hypothesis, collects numeric data, and analyzes it statistically.

Now let's try example three.

Bring it on.

A qualitative approach focusing on marginalized groups.

Ooh, okay.

If the focus is on marginalized groups, the worldview has to be transformative.

It's about social justice in action.

Yes.

The blueprint could be narrative research, and the method would have to be open -ended interviewing.

I need to collect their actual stories of oppression, not like ask them to rate it on the scale of one to five.

You've got it perfectly.

The worldview dictates the need for an action agenda, the narrative design dictates the collection of leaf stories, and the open -ended method provides the necessary space for the participant's unfiltered voice.

Awesome.

What's next?

Finally, example four.

A mixed methods approach.

Okay.

If I'm mixing methods,

my worldview is pragmatic.

I just want what works to solve the problem.

Right.

The design could be a sequential one.

Let's use my explanatory trick.

Right.

And the methods would be sending out a broad quantitative survey to the whole campus first, and then using qualitative open -ended interviews with 10 students to explain those survey results.

You have successfully synthesized the entire chapter.

You can see how the menu of options isn't just a list to memorize, it's a system of interlocking gears.

Which brings us to the final and probably most practical question of the entire chapter.

How to choose.

Exactly.

You, the student listening to this, you understand the vocabulary, the stretcherm, the philosophies, and the tools.

But when you sit down to write your own proposal tonight, how do you actually choose which approach to use?

Well, the text provides three distinct criteria for making that final selection.

First, and arguably most important, is the research problem itself.

Okay.

So what does the problem demand?

Right.

If your problem requires identifying factors that influence an outcome, or you need to test a theory,

quantitative is the logical choice.

And if the concept is totally new, or involves an understudied group where we don't even know what variables to measure yet.

Then qualitative is best.

And if neither of those alone will provide a complete picture of the problem, you use mixed methods.

Makes sense.

What's the second criterion?

The second is personal experiences.

You have to ask yourself,

what are you actually trained to do?

Right.

If I'm a computer science major who loves statistical modeling, I'm going to naturally lean quantitative.

Exactly.

But if I'm an English literature major who thrives on creative writing and deep interpersonal conversations,

I'll naturally gravitate toward qualitative.

Yes.

And if I have massive amounts of time, funding, and expertise in both, I might attempt mixed methods.

Which brings us to the third and final criterion, the audience.

The audience.

Who is actually going to read this proposal?

Is it journal editors, a faculty committee, or your specific academic advisor?

Okay, wait a minute.

I have to push back hard on that last one.

That feels profoundly unscientific.

How so?

If I am trying to discover truth or find a cure for something,

why on earth should I change my methodology just because my academic advisor prefers numbers over interviews?

Shouldn't the data just speak for itself?

It is a very common frustration for students who want science to be perfectly pure.

But the reality is that research does not exist in a vacuum.

No, it is a profoundly social endeavor.

A social endeavor.

But we're talking about science.

I know, but think about it.

If your ultimate goal is to add your findings to humanity's collective body of knowledge, your work has to be received, accepted, and published by a community of scholars.

Okay, I see where you're going.

If you present a beautiful qualitative ethnography to a committee composed entirely of strict quantitative post -positivists, they won't know how to evaluate your work.

Because it's an alien language to them.

Exactly.

Understanding the paradigms your audience values isn't selling out.

It is a pragmatic, necessary step in the research process itself.

Wow.

That is a harsh but incredibly useful reality check.

So what does this all mean for you listening at home?

We have traveled from the broadest, hidden philosophical beliefs about the nature of reality, all the way down to the specific, open or closed -ended questions you put on a piece of paper.

It's a journey.

It really is.

You now understand the logic behind the ultimate recipe book for research design.

And as you move forward to write your own proposal, I want to leave you with a final thought to chew on.

Let's hear it.

We spent a lot of time discussing how all research begins with a hidden philosophical worldview, right?

We also just established that this research is what ultimately shapes the textbooks we read and the collective knowledge we hold.

So it raises a rather provocative question.

Are any of the so -called objective facts you read in your other university classes truly separate from the deeply held hidden beliefs of the researchers who originally discovered them?

Oh, wow.

That completely shifts how you view every piece of information you've ever been taught.

It really does.

An objective fact might just be a reflection of the glasses the researcher was wearing.

Exactly.

Keep that in mind the next time you are evaluating someone else's data or designing your own.

On behalf of the Last Minute Lecture team, thank you for listening and good luck with your research.