Questions with ‘yes’ or ‘no’ answer choices are called nominal questions. When you analyze data from a single yes/no question, you need to use a type of statistics called binomial statistics.
For research surveys and studies, a yes/no scale is considered nominal data. This means the answers don’t have any ordering or scale between ‘yes’ and ‘no.’ They are just two distinct categories without any ranking or distance.
Sometimes, words like “yes” & “no” are used as codes in a system, with “yes” meaning “go ahead” and “no” meaning “stop”. We say that the objective of the system is to control a process where “go” is the desired result. In this particular context, you could argue that “yes/no” gives a clear direction.
But such situations are rare exceptions. Most of the time, “yes” and “no” answers aren’t strict commands. Their meaning depends a lot on the context & how you interpret them. You can’t just take them as absolute, normative data that must be strictly adhered to.
Understanding the Basics of Nominal & Ordinal Data
Nominal Data:
Nominal data is all about labeling or naming things into separate categories. It comes from the Latin word “nomen” which just means “name.” With this type of data, there’s no particular order or ranking to the categories. They’re just distinct labels used to identify different variables, without any numerical values attached.
For example, let’s say you were describing the color of different cars. The colors like red, blue, green etc. would be nominal data categories, since you’re just naming or labeling the color, not ranking them.
Same goes for if you were listing the different types of pets people own – dog, cat, bird etc. are just category names, with no implied order or value scale between them.
Ordinal Data:
Ordinal data is different because the categories have a natural order or ranking to them. It’s like putting things in a sequence from first to last.
For instance, when you take those customer surveys and they ask how satisfied you are, the answer choices like “unsatisfied,” “neutral,” and “satisfied” are ordinal data.
You can clearly see those responses are ranked from negative to positive satisfaction. However, you can’t really know the precise degree of difference between each level.
While “satisfied” is way more better than “neutral,” we don’t know if the gap between “neutral” & “satisfied” is the same as the gap between “neutral” and “unsatisfied.”
The key thing is ordinal data allows ordering, but not precise measurement between the categories.
Are yes No Questions Qualitative or Quantitative?
Deciding if yes/no questions are qualitative or quantitative data can be a bit tricky. Let me explain it in a straightforward way.
Qualitative data is about descriptions and observations that can’t be measured with numbers. Things like colors, feelings, and personal opinions fall under this category. Quantitative data, on the other hand, deals with numbers and things you can measure or count.
Now, even though yes/no questions only have two possible answers, they’re actually considered qualitative data. Why? Because the answers “yes” or “no” describe an attribute or characteristic, rather than providing a measurable number or quantity.
It’s kind of like asking someone’s hair color – the answer “blonde” or “brunette” is qualitative data, describing an observation without using numbers. Similarly, a “yes” or “no” response describes a quality or characteristic, making it qualitative data.
Setting the Stage: Qualitative and Quantitative Data
Qualitative Data:
Descriptions and details are key components of qualitative data. It delves deep to fully comprehend circumstance and incident. This kind of data is usually just words, gathered by talking to people in interviews or groups, or by observing them.
Qualitative data attempts to explain why people behave in certain ways, as well as how they feel and react to various situations. It provides information about people’s motivations, emotions, and behavior that numbers alone cannot provide.
Quantitative Data:
Quantitative data is all about the numbers. It measures and quantifies things using actual numerical values rather than simply describing them in words. A common way to get this numerical data is through surveys and other structured methods where you’re collecting hard numbers and amounts.
Once you’ve got all those numbers, you can then analyze and make sense of them using statistics. Crunching the numerical data lets you answer questions like “How much of something is there?” or “How many things are we talking about here?” Rather than quantitative data allows you to quantify and measure it through the numbers.
What Is the Difference Between Quantitative and Qualitative Data?
Numbers are the name of the game when it comes to quantitative data. This kind of data gives you hard facts and clear-cut conclusions – there’s no room for guesswork. Qualitative data, on the other hand, is more about descriptions and personal experiences.
You can’t just slap a number on it. Qualitative data provides insights and multiple perspectives on problems, not definitive answers. It is more subjective and prone to interpretation. But don’t write it off just yet! Qualitative analysis frequently paves the way for subsequent quantitative number crunching. When solving difficult problems, the two approaches complement each other well.
Qualitative Vs Quantitative With Examples?
Quantitative data is fixed and “universal,” while qualitative data is subjective and dynamic. For example, if an object weighs 20 kg, it can be considered an objective fact. However, two people may have very different qualitative accounts of how they perceive a given event.
Analyzing Data from Yes/No Questions
When you ask people yes or no questions, you end up with a bunch of simple answers. But what do you do with all those “yes” & “no” responses?
First off, counting is your best friend. You’ll want to tally up how many people said “yes” & how many said “no”. This gives you a quick snapshot of what most of the people think.
But don’t stop there! Look at the percentages too. If 75 out of 100 people said “yes”, that’s 75% – a pretty strong majority. This helps you to understand that how widespread an opinion is.
Sometimes, it’s helpful to make a simple chart or graph. A pie chart can show the split between “yes” and “no” answers at a glance. This makes your findings easy to grasp even for those people who don’t like the numbers.
Remember, yes/no questions only give you the basics. They can’t tell you why people answered the way they did. So, if you want to dig deeper you might need to ask more detailed questions later.
Tools and Techniques for Yes or No Data Analysis
Now, let’s talk about some handy tools.
1. Spreadsheets: Programs like Excel or Google Sheets are great for organizing your data. You can easily count responses and calculate percentages.
2. Online survey tools: Websites like SurveyMonkey or Google Forms can do a lot of the number-crunching for you. They often create charts automatically!
3. Statistical software: If you’re dealing with lots of data or need more complex analysis, tools like SPSS or R can help. But these are mainly for folks who are comfortable with statistics.
4. Visualization tools: Sometimes, a picture is worth a thousand words. Tools like Tableau or even simple online chart makers can turn your yes/no data into eye-catching visuals.
5. Cross-tabulation: This fancy term just means comparing yes/no answers across different groups. For example, do women say “yes” more often than men? This can uncover interesting patterns.
Keep in Mind that the main goal is to understand what your data is telling you. Don’t get too caught up in fancy techniques. Sometimes a simple count and percentage is all you need to get valuable insights from your yes/no questions.
By using these tools and techniques you can turn a bunch of yes and no answers into useful information. This can help you to make better decisions or understand people opinions more clearly.
Frequently Asked Questions (FAQs)
Can we use yes or no questions in qualitative research?
When you are asking questions during your research, make sure they are open ended. Avoid questions that can be answered with a simple “yes” or “no.”
The whole point of this kind of qualitative research is to gain deeper insights & also a better understanding. Open ended questions allow people to explain their thoughts and experiences more fully. That richer detail is exactly what you’re after.
What type of research question is yes or no?
Close ended questions usually require yes or no answers and are commonly used in quantitative research to collect numerical data from research participants.
What is a yes or no question called in a survey?
Dichotomous questions
When a question has two possible answers, we consider it Dichotomous. Surveys often use a variety of questions that ask for yes or no, true or false or agree/disagree responses.
How do you measure yes or no questions?
You simply need to calculate the yes & no answers for each question for all participants and divide it by the total number of participants to get the percentage of yes and no for each question.
Of course, if this is a mixed-methods study, you will have to score (assess) more qualitatively.
Yes or No Generator
You Can Also Check our Yes or No Generator at TheYesNoButton.com. Simply type your question, click ‘Press to Decide,’ and it gives you a straight yes or no answer – simple but super useful for getting the clarity you need.
References
Reference: 1 https://troubletaker.com/pages/are-yes-no-questions-nominal-or-ordinal
Reference: 2 https://www.ncesc.com/geographic-pedia/what-scale-is-yes-or-no/
Reference: 3 https://www.calendar-uk.co.uk/frequently-asked-questions/are-yes-no-questions-qualitative-or-quantitative
