Difference between qualitative quantitative research ppt powerpoint presentation inspiration diagrams cpb

Basically, quantitative research is all about numbers and stats - surveys, experiments, stuff you can measure. Qualitative is more like diving into the messy human side through interviews and observations. I honestly thought you had to choose one approach, but you don't! Mix them if it makes sense for your project. Use quantitative when you need hard data or want to test theories. Go qualitative when you're trying to figure out the "why" behind what people do. Both have their place, and combining them actually gives you a fuller picture than just picking one.

So mixed methods are great because you get triangulation - when your survey data and interviews align, you know you're onto something real. Numbers show the "what" but people tell you the "why" (honestly the qualitative stuff is usually more interesting anyway). Each method covers the other's weaknesses too. Surveys can't capture complexity, interviews miss the big picture scale. You can also use findings from one phase to design the next. Just don't treat it like frankenstein - plan how they'll work together from day one or you'll end up with a mess.

So hypothesis testing is like having a game plan before you dive into research. Make a specific, testable prediction first - then design your study around proving or disproving it. This stops you from randomly digging through data hoping something cool shows up (which honestly we've all done lol). The trick is being super clear about what you're testing before collecting anything. That way your conclusions actually mean something and other people can repeat your experiment. Oh, and it keeps your whole methodology on track too. Start with one solid, measurable statement and build everything else around that.

Definitely get your IRB approval first - that's non-negotiable for any human subjects research. Informed consent is huge too; people need to know what they're getting into and that they can bail anytime. Privacy protection is critical because honestly, nobody wants to deal with a data breach nightmare. Try to minimize harm and make sure the benefits actually justify the risks. Working with kids or prisoners? There's a whole extra layer of requirements for vulnerable groups (which makes sense). Oh, and don't leave the ethics protocol for last - I learned that the hard way. Build it into your design from the start.

Honestly, surveys can be awesome for hitting a lot of people fast without breaking the bank. The anonymous thing helps too - people actually tell you what they think instead of what sounds nice. Plus the data's clean and easy to crunch numbers on. But man, getting people to actually respond is rough these days. Everyone's so over surveys. And once you send it out, you're stuck with whatever questions you wrote - no do-overs if something's confusing. Sometimes the answers feel pretty surface-level compared to actually talking to someone. I'd say go for it when you need the big picture, but maybe throw in some interviews too if you want the real story.

So basically, bigger sample size = way more reliable results. When you only survey like 10 people, you're gonna get all sorts of random weird answers that don't mean anything. But grab 1,000 people? Now you're talking - the data actually tells you something useful about everyone, not just those specific weirdos you happened to ask. Your margin of error shrinks down too, which is obviously what you want. I learned this the hard way on my last project tbh. Just go for the biggest sample you can afford and have time for. Short answer: more people = better science.

Standardized protocols are your best friend - same procedures for everyone. Randomize your sampling and when you collect data too. Train your whole team so they're doing things consistently. Blinding helps massively if you can swing it. Keep participants in the dark about group assignments, yourself too if possible. Oh, and definitely pre-register your methods before starting. Trust me, it stops you from accidentally fudging things later when the data looks weird. I've watched good studies completely tank because people got careless with this stuff partway through.

So basically, longitudinal studies follow the same people over time - like months or years. Cross-sectional just takes a snapshot of different groups at once. Say you're looking at how age affects attitudes. Longitudinal would track one group as they get older, while cross-sectional compares 20-year-olds to 40-year-olds to 60-year-olds all at the same time. Way faster but you miss the actual changes happening to individuals. Longitudinal shows real development patterns but honestly? It's expensive and takes forever. Cross-sectional is quick and cheap but only gives you group differences. Go longitudinal if you actually need to see how things change over time.

Okay so here's the thing - you absolutely need to do your lit review before finalizing research questions. It shows you what's been done already and where the gaps are. Don't make my mistake of jumping straight into questions without checking what exists first. You'll find some topics are totally oversaturated while others have interesting angles nobody's touched. The research will actually help you narrow down what's both doable and worth studying. Plus honestly? It's way easier to defend your work later when you know exactly how it fits into the bigger picture. Start reading early and let it shape your direction.

Code your data systematically first - look for patterns and themes in your interviews. NVivo's good but honestly? Spreadsheets work fine for smaller stuff. You'll need to read everything multiple times because you always miss things the first pass. Pay attention to what people say AND what they avoid saying. Document your coding choices as you go (trust me on this one - future you will be grateful when you're writing up results). Oh, and be transparent about your whole process. Double-check that your interpretations actually match what's in the data instead of what you think should be there.

So first things first - figure out if your data is categorical or continuous. Trust me on this one, it'll save you from going in circles later. For the actual analysis, you'll probably lean heavy on descriptive stats (means, standard deviations) and correlation analysis to spot relationships. Then there's inferential tests - t-tests, ANOVA, chi-square depending what you're working with. Regression is honestly way more useful than I thought it'd be when I started. Oh, and don't just chase p-values. Effect sizes matter too because statistical significance doesn't always mean it actually matters in real life.

Honestly, case studies are perfect when you need the full story behind what's happening. Surveys just give you surface-level stuff, but case studies let you dig into the messy, real-world details. They're great for "how" and "why" questions - like not just seeing that something failed, but understanding all the weird factors that led to it. I've found some of my most surprising insights come from case studies actually. You get multiple angles on the same situation, which is clutch for understanding complex relationships. Use them when you need context and depth over breadth.

Definitely pilot test first with like 10-15 people - catches the weird confusing questions that'll mess up your data later. Multiple items for each thing you're measuring works way better than single questions. Get some experts to look over your stuff for content validity. Oh and test-retest reliability is clutch for showing consistency over time. Honestly? Just use established scales when you can instead of making everything from scratch - why reinvent the wheel, right? I learned this the hard way on my thesis lol. Put in the work upfront and your results will actually mean something.

Honestly, plain language is everything here - ditch the jargon completely. Break it into smaller chunks instead of one giant overwhelming form. Visual aids work great too, or even videos for the really complicated stuff. Vulnerable populations are tricky though, that's where you really need to slow down. I always pilot the process first with a few people to catch confusing parts. Document everything obviously. Oh and don't forget consent isn't a one-time thing - people can bail whenever they want. Multiple conversations help way more than shoving everything into one session.

So peer review is basically quality control for research. Other experts in the field look over your work before it gets published - they'll catch mistakes, check if your methods make sense, and see if your conclusions actually match what the data shows. Obviously it's not foolproof since reviewers are human too, but it filters out the really bad stuff. The whole process makes researchers think harder about their methodology (which honestly is probably needed). When you're looking at studies for your own research, always check if they went through peer review first. It's the best way to know the findings have been properly checked.