An Introduction to Online Quantitative Market Research

The market research industry is big, and is comprised of a lot of different disciplines, sub-sectors, and methodologies. From qualitative methods like interviews to quantitative methods like online surveys, it's important that you know the differences, benefits and trade-offs of different types of market research.

Polls, online questionnaires, surveys, quant, whatever you want to call it, this lecture will drill down into what makes online research unique. We'll explore some of the benefits and trade-offs of online MR so you know exactly when it's the right fit for your research needs.

Good research always starts with a clear and concise research objective. In this lecture we'll look at how you can unpack your research objectives in ways that said you in designing better surveys.

In order to study a given population, you need to know what makes them unique. Audience definition in online market research involves identifying detailed criteria that your target audience must meet in order to participate in your study. Whether you want to research a population based on their demographics, attitudes or behaviours, having clearly defined audience criteria is key to collecting good data.

Designing effective questionnaires for online research is as much an art as it is a science. Small mistakes can have big implications for your data, and it's essential that you adhere to good design principles when crafting surveys. In this video we'll explore some common design mistakes, and tips for overcoming them.

When you open up most survey building tools today, you're presented with a vast and confusing array of different question types, from rating scales to sliders to grids. But the truth is, you really only need to master 5 main question types to be market research Pro. In this video I cover the essential question types that you need to understand and use in order to design more effective surveys.

Programming a survey can get complicated. From response piping to skip logic, it can be hard to know when you should use different features in a programming tool. In this video I cover everything you need to know about 4 of the most common and important survey programming mechanics.

There's certainly no shortage of survey building tools today, but knowing which one you should use can be confusing. In this video I outline the primary features you want to consider when deciding on a tool, and I provide a few examples of popular tools today and where they sit on a spectrum of research requirements, from everyday use-cases to enterprise use-cases.

In this video I provide a live demo of how to program a simple survey in Survey Monkey.

In this video I provide a live demo of how to program a simple survey in Google Forms.

In this video I provide a live demo of how to program a simple survey in Quatrics.

There are a lot of different ways to source your audience, from tapping into your own network to purchasing samples from a panel provider. In this video I explain the most common ways to sources respondents, as well as the pros and cons of each.

When it comes to defining your audience, one of the most common terms you'll come across in online MR is Incidence Rate, or IR for short. In this video I define the term and give some hands-on examples of what it means and how to understand it.

How do I choose the right sample size? Have I collected enough samples to have statistically robust data? These are common questions every researcher will ask, and in this video I explain some of the methods you can use to determine what's the right sample size based on your research needs.

In the previous lecture we covered what Margin of Error is and why it's important. In this video, I demonstrate how you can use an online MoE calculator, as well as how you can go about determining a population size.

In this lecture we go even deeper into margin of error (MoE), using charts and visualization to help explain how adding more samples affects MoE and the overall accuracy of your data.

So you've designed your survey, programmed it in your survey tool of choice, and now you're ready to launch it, right? Wrong. Launching your survey without going through a pre-launch checklist can be risky, as you want to make sure your survey script is error-free, and that you've configured your survey and distributions correctly.

Having a solid foundation in how to prepare, process and analyze your survey data in Excel is key. In this video I explain how survey data is structured and cover some of the most common formats for working with raw dataa.

All online survey data needs some amount of cleaning to ensure you have usable results. If you're not cleaning out poor responses, such as speedsters or streamliners, then you run the risk of making decisions based on bad data. In this video I cover some of the most common types of data cleaning as well as provide tips for how you can do this effectively.

Using open-ended (OE) questions are a great way to catch respondents who may not have been paying attention. In this video I show you how you can leverage OE data for cleaning up your data.

Looking for conflicts (i.e. 2 results that can't or shouldn't be true) is one of the most effective ways to clean up your dataset, but it can also be the most tricky. In this video I explain what data conflicts are, and how you can find them in your data.

Over and under selection are common issues in online research, and are a clear sign that respondents may not have been paying attention. In this video I explain what over/under selection is, and how you can spot it within your dataset.

In this lesson we'll go deeper into weighting data, specifically what it is, when you should do it, and how you can weight data using Excel or Google Sheets.

This lesson builds on the previous one by showing you how to weight data in excel for more complex, interlocking quotas plans.

Pivot tables are an incredibly powerful tool for running analysis on your data, and in this video I demonstrate how you can use pivot tables when working with raw, respondent level data.

Multi-Select questions can be challenging to work with when it comes to raw, respondent level data. In this video I outline some of the challenges you may face in trying to aggregate and analyze multi-select data, and I offer some tips for how to go about working with these types of questions.

Although multi-select questions are not easily pivotable in a raw, respondent level data file, there are some tricks for getting multi-select data into more manageable formats. In this video I share 2 different ways that you more easily pivot and analyze multi-select questions.

Crosstabs are an incredibly powerful tool for analyzing data, and are a staple of any seasoned researchers analysis. In this video I explain what crosstabs are, how they can be used and how to build them.

Effective data visualization can aid in your analysis, and in this video I demonstrate different ways you can visualize questions based on the type of data you have.

In this video I share a few tricks for using conditional formating in Excel for fast and rapid data analysis.

Recoding and TOP box analysis are useful tools for analysts to dig deeper into their data in order to draw meaningful conclusions. In this video I explain what recoding is and how you can use it.

A good researcher / analyst will always dig deep, and go beyond surface-level readings of their data. And in this  lecture, we look at how you can unpack your dataset and make meaningful observations that can power better decision making.

Most data that you will work with when it comes to survey-based research is structured. However, when it comes to open-ended questions, you need to approach analyzing the data in a very different way. In this lecture, we'll introduce you to the concept of open-end (OE) analysis and OE coding.

Deductive coding is a process where you approach your open-end (OE) data with a predefined system of codes. In this lecture, we'll look at how to carry out deductive coding.

Inductive coding is a process where you approach your open-end (OE) data without a predefined system of codes, and you look for new ideas/concepts in the text. In this lecture, we'll look at how to carry out inductive coding.

Please watch this video before taking the practical test, as I provide some important information about how to complete the test.

Step-by-step answers for questions 1 to 5 in the practical test.

Step-by-step answers for questions 6 to 10 in the practical test.

Step-by-step answers for questions 11 to 15 in the practical test.

Note. I've made changes to my A/V configuration and the audio for videos in this section is louder than videos in previous sections. So I recommend turning your volume down a bit if you're continuing from previous lectures.

In this lecture I provide an overview of the common types of use-cases in online market research, from brand or product studies to employee engagement surveys.

Note. I've made changes to my A/V configuration and the audio for videos in this section is louder than videos in previous sections. So I recommend turning your volume down a bit if you're continuing from previous lectures.

In this lecture, I break down the Brand Position study, also known as a Brand Dipstick, which can be used to help you understand how your brand is performing compared to your competitors.

Note. I've made changes to my A/V configuration and the audio for videos in this section is louder than videos in previous sections. So I recommend turning your volume down a bit if you're continuing from previous lectures.

In this lecture, I break down the Campaign Uplift study, which can be used to help you measure the impact that your campaign has had on your brand's health.

Note. I've made changes to my A/V configuration and the audio for videos in this section is louder than videos in previous sections. So I recommend turning your volume down a bit if you're continuing from previous lectures.

In this lecture, I break down the Concept Test study, which can be used to help you test a product or service idea before going to market.