Association Analysis: Comparing Groups of People

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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

We're now going to start discussing more complex types of analysis

beginning with association analysis in comparing groups of people.

In this lesson, we'll work through an example to

determine if a company should consider changing the advertising strategy for its app.

So, let's start with association analysis,

which helps us determine whether or not differences between groups exist.

So far we've used states that involve one homogeneous groups.

In marketing however, being able to measure access,

whether there are differences between groups,

is important for segmentation and positioning purposes,

two very important strategic process in marketing.

A common type of association analysis is to

determine whether certain groups or types of customers behave differently or not.

Consider the following question: Do men drink more soft drinks than women?

If I were to work for a company selling soft drinks,

it will be interesting to know if one group has a higher consumption than another.

That could allow me, for example,

to define different marketing campaigns.

To answer questions such as this a test exist.

Consider the following example,

imagine you're working for a food delivery mobile application,

and that this company just finished an advertising campaign.

The advertising campaign was on both offline, let's say,

on TV or radio,

and online, let's say, through display advertisements.

Whenever a person downloaded the app,

he or she was asked to answer a short survey,

to answer whether or not she remembers seeing some of

the company's advertisements in the week prior to downloading the app.

Some of these people then decided to subscribe to the app,

for which they paid a monthly fee to have food delivered to the door,

and some people decided not to.

You can probably see that for the company,

understanding what drives people from simply downloading the app to pay for a service,

is something that is very important.

So, what we want to understand is whether or not

there are differences in how people join,

or do not join based on that exposures.

When we look at these data we see that when people subscribed,

45 people remember seeing the ads for the app offline,

and 89 of these people remember seeing the ad online.

For those that chose not to subscribe,15 people

remember seeing the ad offline and 20 people online.

So, the question here is,

are there any significant differences between the two groups?

We are going to assume again 95% confidence level.

If there are differences between the two groups,

this could be useful for managing ad budgets.

Recall that the null hypothesis is going to be rejected,

if the Z-value that we are going to compute is below [inaudible] threshold value.

Again, the null hypothesis in this case would be,

that the two groups are going to be equal,

that the proportion will be equal across the two groups.

And the alternative hypothesis is that those differences exist,

so the proportions should be different.

At the 95% confidence level,

the critical value of Z is equal to 1.96 and

the formula for the simple statistic is going to be equal to proportion one

minus proportion two divided by

the standard deviation of the difference of the two proportions which itself is

equal to the square root of (p1×q1÷n1) + (p2×q2÷n2).

Plugging the numbers in the formula,

we see the Z-value for online ads is 0.48 which is below 1.96.

So, in the case of offline ads,

we can say that there is not enough support to reject the null hypothesis

with respect to differences between the people who join and people who didn't join.

Therefore, we can assume that offline ads do not affect these groups differently.

On the other hand, when you compute the statistics for online ads,

we see that the Z-value is above the critical value 1.96 number.

And in this case, we can conclude that we should reject the null hypothesis,

which means that there are differences between the two groups

in how they subscribe to the service.

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