Welcome to the Deep Dive. We're here to help
you quickly grasp complex topics. Making sense
of the complex fast. Exactly. And today, we're
tackling something fundamental in research. Data
quality. It's a big one. We're drawing on an
article from HackScience .Education by Gary Ackerman.
Yeah, Ackerman's got experience in both quantitative
and qualitative research, which is, well... really
useful here. It gives him a good perspective,
right? Definitely. His main point is that data
quality isn't, you know, one single thing. It
really shifts depending on the research approach.
OK, so that's our mission for this deep dive,
to unpack those differences. We want you to feel
more confident when you come across research
findings. So let's start with the two main approaches
Ackerman talks about, quantitative and qualitative.
Right, quantitative first. Yeah, quantitative
research, basically. tries to reduce things down.
Control variables, isolate them. See how one
thing affects another. Think controlled experiments,
numbers, statistics. Measuring specific things.
Precisely. And qualitative. Ackerman describes
it differently. Yeah, it's more about looking
at the whole picture. Studying things in their
natural setting, letting all the variables interact.
The goal is often deep understanding, context,
perspectives. Like, observing a group to understand
how they work together, maybe. Less about counting,
more about understanding why. You got it. And
that's the key takeaway Ackerman highlights.
Yeah. Because the goals and methods are so different.
What counts as good data must also be different.
Makes sense. It really does. You can't use the
same checklist for, say, survey results and interview
transcripts. The very nature of the data is different.
But Ackerman is clear that all researchers care
about quality, right? whatever their approach.
Absolutely. It's crucial. It's about the trustworthiness,
the reliability and validity of the findings.
OK, let's pause there. Reliability, that's about
consistency. Like, if someone repeated the study,
would they get similar results? Generally, yes.
And validity is more, are you actually measuring
what you think you're measuring or describing
what you intend to describe? Got it. So bad data
means shaky conclusions, regardless of method.
Exactly. And Ackerman mentions using Hopeful's
framework from back in 1997, specifically for
understanding quality in qualitative research.
Hopeful. OK, so that suggests there are established
ways to think about quality beyond just numbers.
Right. It introduces different ideas, different
criteria suited for, say, interview data or observational
notes, things like credibility, transferability,
concepts that fit qualitative work. Which underlines
Ackerman's point. We need different terms different
concepts even to talk about quality in these
two worlds We really do for quantitative you
might focus on you know sample size statistical
significance measurement error standard stuff
for stats yeah, but for qualitative you might
look at the richness of the description or Do
the researcher check their findings from multiple
angles triangulation they call it? Ackerman also
touches on things like subjectivity and objectivity,
referencing Philip's work. Seems like there's
a whole philosophical layer, too, especially
in qualitative research. There is. How does the
researcher's perspective play into it? It's a
key consideration. So bringing this back to our
listeners, understanding this basic difference...
quantitative versus qualitative quality criteria
is actually pretty powerful, isn't it? I think
so. It gives you a lens. When you see information,
you can ask, OK, what kind of information is
this? Is it based on measurement or interpretation?
And then what are the right questions to ask
about its quality? Not just is it good, but good
in what way? Precisely. So the core takeaway
is Data quality isn't universal. It's judged
differently depending on whether the approach
is quantitative, focused on numbers and control,
or qualitative, focused on context and understanding.
Right. Which leads to a final thought for everyone
listening. Think about how you judge information
in your daily life. When you see, say, a news
report full of statistics versus hearing a personal
story from a friend, do you maybe subconsciously
use different standards to decide if you trust
it, if it feels quality? That's a really interesting
question to ponder. How the type of data shapes
our own sense of its quality. Something to think
about. Definitely. Well, thanks for exploring
this with us today. Thank you.