How QtPR Differs from Mathematical Modeling, Design Research, & Qualitative Methods

QtPR vs. Mathematical Analytical Modeling

• What is Mathematical Analytical Modeling?
  • This involves using pure math—like formulas, proofs, and theoretical assumptions—to describe or predict things.
  • Example: A mathematician might propose a formula for how users behave on a website without ever collecting real data from actual users.
• How QtPR Differs
  • QtPR (Quantitative, Post-Positivist Research) always involves empirical data—meaning real-world measurements (e.g., survey responses, system logs, field experiments).
  • It also has a positivist philosophy, focusing on problem-solving and testing theories about how people and technology interact in practice.
  • Example: A QtPR study might gather data from 500 users on how often they click a “Help” button, then use statistics to see if a new interface design causes fewer help requests.

So, the key difference is that mathematical modeling can happen entirely on paper (no real data), whereas QtPR always checks actual data against a theory.

2. QtPR vs. Design Research

• What is Design Research?
  • In design research, you create and evaluate new technology artifacts (like software prototypes, models, or frameworks). Example: Designing a new “smart” recommendation engine for an e-commerce site and testing it in a lab environment.
• How They Validate Their Work
  • Design Research may rely on mathematical proofs, algorithmic analyses, or even conceptual validation (e.g., showing logically how a new algorithm should work). They don’t always need to test it on a large group of real users to call it valid.
  • QtPR generally tests (or “validates”) its hypotheses using empirical data.
    • For instance, if your hypothesis is “Introducing a chatbot increases customer satisfaction by 20%,” a QtPR researcher would collect real survey data from customers before and after the chatbot rollout.
• Overlap
  • Many design researchers are now using QtPR methods—like experiments or field studies—to show that their new artifacts truly work in the real world.
  • Example: A design researcher might build a new interface for a banking app, then run a quantitative study (a controlled experiment with real users) to measure improvements in task completion time.

Thus, QtPR focuses on testing cause-and-effect relationships with real data, while design research focuses on creating and evaluating new artifacts—though they can overlap when design researchers use data-driven experiments to validate their creations.

3. QtPR vs. Qualitative Research (Positivist and Interpretive)

• Quantitative vs. Qualitative
  • Quantitative = focuses on numbers and metrics (e.g., “How many clicks does a webpage get?”).
  • Qualitative = focuses on words, narratives, or observations (e.g., “What did users say in the interview about why the webpage was confusing?”).

3.1 Qualitative Positivist Research (QlPR)

• What is it?
  • Researchers still believe there’s a reality they can discover (similar to positivism), but they gather data in words rather than numbers (e.g., interviews, written observations).
  • They assume that if they are careful, they can measure certain aspects of reality—though the measurements are still in text form.
• Example: Conducting interviews about user satisfaction, then systematically coding responses to see if they mention “speed” or “ease of use.” They believe these codes represent something “real” about user opinions.

3.2 Qualitative Interpretive Research

• What is it?
  • Researchers believe reality is at least partly socially constructed—meaning that language, culture, and shared meanings shape our experiences.
  • They focus on how people interpret or assign meaning to their world.
• Example: Interviewing employees about a new HR system, then analyzing how they perceive the system’s fairness, rather than trying to measure “fairness” numerically. The goal is to understand why they see it that way, rather than looking for a definitive “true” score of fairness.

So, QtPR differs from both types of qualitative research because QtPR relies on numeric data and aims to find statistical or causal patterns—whereas qualitative research (positivist or interpretive) deals more heavily in text, context, and meanings.

4. Why These Differences Matter

1. Data Collection Approaches
   • QtPR: Surveys, experiments, logs (numbers, counts, scales).
   • QlPR: Interviews, observations, textual documents.
   • Design Research: Prototypes, models, artifacts.
   • Math Modeling: Theoretical formulas, assumptions, no real data.
2. Data Analysis
   • QtPR: Statistical techniques (correlations, regressions, structural equation modeling).
   • QlPR: Might do basic statistics (like averages or simple tests), but often focuses on coding text, thematic analysis, or searching for patterns in transcripts.
   • Design Research: Might rely on feasibility checks, proofs-of-concept, or algorithmic validity, though increasingly design researchers adopt QtPR-like experimentation.
   • Math Modeling: No real data analysis—pure math derivations.
3. Argument & Rhetorical Style
   • QtPR: “Here’s our theory, here’s the numeric data, here’s what the data show about cause-and-effect.”
   • Qualitative: “Here’s what people said, here are the themes we identified, here’s the deeper meaning behind those statements.”
   • Design: “Here’s the new technology solution, here’s how it was built, and here’s how we proved it works (algorithmically or with real user testing).”
   • Math: “Here’s the mathematical model, here are the assumptions, and here’s the proof that it holds under certain conditions.”
4. Mix and Match?
   • Researchers are not strictly limited; for example, a “qualitative positivist” might include some numbers (like counting how often a theme appears in interviews).
   • A design researcher might add a quantitative experiment to show that the new artifact truly improves some outcome (e.g., speed or user satisfaction).

Bottom Line in Lay Terms

1. QtPR always uses real data and a positivist approach (there’s a reality we can measure, even if imperfectly).
2. It’s not purely theoretical (like math modeling), not just building artifacts (like design research), and not text-focused (like qualitative research), though it can sometimes overlap with these areas.
3. If you’re doing QtPR, you’re typically collecting numeric information—like survey ratings or usage counts—and using statistical tests to see if your theory about how people or systems behave is supported by the data.

That’s why QtPR is unique: it’s all about numbers, testing cause-and-effect theories, and checking whether the data back up your claims—but it’s also open to the idea that we can never measure reality perfectly (the “post-positivist” part).

Summary:

• Mathematical Modeling: Theory + assumptions, no real data.
• Design Research: Building new IS artifacts, can validate with math or algorithms, but may also use real data.
• Qualitative (Positivist/Interpretive): Focus on text or interviews to understand reality or people’s interpretations.
• QtPR: Collect numeric data, test causal theories, align with a positivist/post-positivist mindset, and rely on statistical validation.