The Importance of Scales

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Del curso dictado por University of California, Davis

Quantitative Research

29 calificaciones

University of California, Davis

Quantitative Research

29 calificaciones

Curso 3 de 4 en SpecializationMarket Research

In this course, you will obtain some insights about marketing to help determine whether there is an opportunity that actually exists in the marketplace and whether it is valuable and actionable for your organization or client. Week 1: Assess methods available for creating quantitative surveys, along with their advantages and disadvantages. Identify the type of questions that should be asked and avoid unambiguous survey questions. Week 2: Design, test, and implement a survey by identifying the target audience and maximizing response rates. You will have an opportunity to use Qualtrics, a survey software tool, to launch your own survey. Week 3: Analyze statistical models that can be applied to your marketing data, so that you can make data-driven decisions about your marketing mix. Week 4: Predict most likely outcomes from the marketing decisions and match the type of analysis needed for your business problem. Take Quantitative Research as a standalone course or as part of the Market Research Specialization. You should have equivalent experience to completing the second course in this specialization, Qualitative Research, before taking this course. By completing the third class in the Specialization, you will gain the skills needed to succeed in the full program.

De la lección

Analyzing a Survey

In this module, you will be able to review your survey data and look for any errors. You will be able to analyze your survey conducting descriptive and inferential analysis techniques to test your hypotheses. You will be able to conduct association analysis and causal analysis with your data. Please note that this module features a fair amount of advanced statistics related math. Throughout the module, optional activities setup as practice quizzes have been provided to help reinforce what you're learning.

Analyzing a Survey Introduction1:09

Reviewing Your Data and Looking for Errors4:47

The Importance of Scales5:50

Descriptive Analysis: Nominal Data6:14

Descriptive Analysis: Ordinal and Interval Data5:21

Inferential Analysis: Confidence Interval6:27

Testing Hypotheses, Part 1: Overview3:27

Testing Hypotheses, Part 2: An Example Application6:40

Association Analysis: Comparing Groups of People4:37

Association Analysis: Chi-Square Test6:02

Association Analysis: Correlation Analysis2:01

Causal Analysis6:15

Conoce a los instructores

Susan Berman
President, Impact Research
UC Davis Division of Continuing and Professional Education
Olivier Rubel, PhD
Associate Professor, Graduate School of Management
UC Davis

Different types of data can give you different insights into your customers or

what they think about your products and services.

Some types of data help you categorize your respondents.

Some types of data help you understand the feelings of your customers for

your products.

Other data gives us an idea of the gap between what you think your customers

think about your products and what they actually think about your product.

By the end of this lesson you'll be able to describe the importance of scales,

describe the insights scales can provide, as well as the limitations,

and list three important types of scales: nominal, ordinal, and interval.

Now before we go into the [INAUDIBLE] of the data, it's important to recognize that

the variables are measured on different scales.

So the way we are going to analyze the question should account for

the different types of scales.

The type of analysis being descriptive, being inferential,

being causal, should be based on the scale that you use in the questionnaire.

Scales can be used to describe something.

For example, whether or not you are likely to use something, or what is your gender,

what is your age, what is your highest level of education and so on.

The questions asked to get this answer are important

in that they describe a particular entity.

A question can also be measured to get a sense of rank ordering.

So for example, what are the brands that you like the most?

Can you rank various brands from most favorite to least favorite?

What are the most important factors that influence your decision to buy a product?

The last characteristic that is important is the idea of distance.

For example, is your intention to buy a product higher or

lower than someone in a different region.

In other words, what is the distance between those two intentions to buy.

And so those characteristics are useful to classify the data in one category or

another and therefore it's important to understand which scale has been used.

There are three types of scales that we are going to cover here, nominal scale,

ordinal scale, and interval scale.

Based on what we've just discussed, you can already see that these

three characteristics emerge that will be used to describe scales.

Can this scale be descriptive in some way, yes or no?

Does a scale provide a sense of ordering between different options?

And the last characteristic,

does the scale provide a sense of distance between different answers?

All right, let's look at some examples.

The nominal scale is used to categorize or provide labels on different entities.

One example could be, what is highest level of education, high school level,

college, post-graduate, or which brand of toothpaste did you last purchase?

So as you can see such scales are very good in describing and

categorize something.

However, there is really no sense of ordering, right?

Which toothpaste brand you last purchased doesn't give you a sense of

the preferences for brand A over brand B over brand C for example.

It doesn't give you either a sense of distance between brand A and

brand B, right?

So it's the same thing with education level or gender.

They're just used to describe customer and

not used to provide a sense of rank ordering of distance between customers.

That's where ordinal scales come into play.

These scales are used to inform about the rank ordering of some entity.

One example might be, rank the following four automotive

brands in order of preference, Toyota, Honda, Ford, and Chevrolet.

So these types of scales have two characteristics.

First, they provide a description of an entity and/or they help

to categorize different answers.

The second characteristic of such scale is a sense of preferences

through rank ordering.

If I prefer Toyota over Honda,

that's going to appear where the question has been asked.

And this information would be useful for marketing decisions.

However, ordinal scales do not provide a sense of distance.

So when a respondent says,

for example, I prefer Toyota over Honda, it does not really tell you by how much.

Does this person prefer Toyota tens times more that Honda,

five times more than Honda, two times more than Honda?

You can't discern that from an ordinal scales,

because a sense of distance doesn't exist.

To get a sense of distance, you should use and analyze interval scales.

Interval scales use numbers to rank objects or entities, or categorize

those entities and provide a sense of distance or intensity between these items.

One example might be measuring customer satisfaction.

Could you rate United Alliance in terms of customers service from poor to very good?

As you can see the distance between poor and very poor can take any values.

Usually people use something called a Likert scale,

usually from one to five where one is quite poor and five is very good.

If someone says three,

that means that this person is basically a yes slightly, but not so great.

Someone else can decide that customer service United Airlines is going to be

very good in which case this person is going to give a five.

So contrary to nominal scales and

ordinal scales, interval scales possess three characteristics.

They allow someone to describe or categorize different entities.

They allow someone to provide a sense of ordering between objects.

And on top of that, they provide a sense of distance between those objects.

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