Descriptive Analysis: Ordinal and Interval Data

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Del curso dictado por University of California, Davis

Quantitative Research

31 calificaciones

University of California, Davis

Quantitative Research

31 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

In this lesson, we continue in our discussion of Descriptive Analysis.

Most specifically, we look at measure of central tendency and

dispersion measures for ordinal data.

We also discuss how for ordinal data the measure of central tendency would be

the median and how dispersion can be described visually.

The median is a value separating the higher half

of the data from the lower half of the data.

A perfect example was income level because this question is relevant not only in

marketing but also in the news media.

A common question in news media surveys is to indicate your level of income.

People can choose less than 20,000, between 20,000 and 30,000 and so on.

Once you run the survey, you might obtain the following tables.

You can see that less than 10% of the respondents say that

they earn $20,000 or less.

10% of the respondents would earn $50,000 or more.

And the rest is in between.

To sum, 10 + 27 + 14 + 39 + 10, should sum up to 100%.

Now the question is, what is Median Income in this data.

We see that if we sum, 10 + 27 + 14, was we obtain, will be like 51%.

What we can infer from this table is that the median income is somewhere between

30,000 and 40,000 because more than 50% of the people would be below that 50%.

And so the median is going to give you the sense of measure of central tendency.

Similar to nominal data you can describe dispersion visually for such data.

And so here again is a bar chart representing the responses in the data.

As you can see, there seems to be some mild version disresponse.

The last type of variable that we introduced earlier is interval data.

I'm going to spend more time on this integral data

throughout this module because they're very amenable to statistical analysis.

The measure of central tendency for interval data is the Mean.

So what is the Mean?

The Mean is the arithmetic average of a set of numbers.

Mathematically the expression of the mean would be as follows, X-bar in

this formula would be the mean, Xi would be the response given by respondent i.

And n would be the total number of respondents.

And so what I do here is that I'm going to sum all the responses from respondents and

then divide these number by the sample size.

The measure of dispersion for interval data is a Standard Deviation.

Standard Deviation informs you about the average distance of

individual respondents from the mean.

Hence, Xi- X-bar.

If people are very similar from each other,

Xi's are going to be very close to the average X-bar.

If respondents are very different from each other,

Xi's are going to be very different from the mean.

And so, that's why it's a measure of dispersion because there's a notion of

distance from the mean.

If you are very close to the mean, it means that people are very homogenous and

therefore, the standard deviation should be low.

On the contrary, if respondents are very different from one another's,

their Xi's are going to be very different from the mean of the sample, and

therefore the standard deviation is going to be high.

Let's look at an example.

Imagine that you work for a company called Burger King, and you are interested

in knowing how people rate you on the value of food items that you have.

This question could be important for example,

if you're thinking about redesigning the menu of Burger King.

You could have asked the same question for competitors of Burger King's,

let's say McDonald's.

And this question could then be used to compare how respondents

would rate McDonald's versus Burger King, which we'll do later.

We have this question, on the scale of one to five,

how does Burger King rate on the variety of food items they offer?

And, you have the data for 10 respondents.

Two, three, three, four, two, five, one, four, three, three, right.

So, to compute the mean, what you need to do is sum all these numbers and

divide them by 10, which is the total number of responses.

And that will give you three.

If you apply this formula, the formula of the standard deviation for

this data, you'll get 1.5.

These formulas are very useful in order to compare similar brands or

competing brands because that will give you a sense of understanding

how different the brands are in the mind of customers.

Don't rate, more further than not, this formula the mean,

the standard deviations are already included in softwares such as Excel,

MATLAB, or HPSS, SaaS data, Qualtrics and so on.

So you don't have to remember this formulas, what is more important

is to understand what this formulas tell you about the data.

Again, the Mean is measure of central tendency.

And the Standard Deviation give you a sense of how much the responses

differ from one another.

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