Seeking Patterns in Survey Results

John DeRiggi

DAI

Patterns in Data

DAI projects collect a lot of data in the field.

Patterns in the data can sometimes help us identify more targeted approaches to the delivery of our development work.

This presentation demonstrates of some of the visualization and analysis techniques we use to identify those valuable patterns.

Case Study

In the spring of 2016, Adam Fivenson collected a data sample for DAI's Consumer Insights study in Honduras.

One slice of the data we found interesting was related to the use of texting, Facebook, and WhatsApp.

This analysis seeks to identify some patterns in the data using a few simple techniques.

(minutes) 213 responses

Let's start this analysis by looking at scatter plots of the survey responses

(minutes) 213 responses

Specifically we are looking at the amount of time (minutes) people said they spent on Facebook...

(minutes) 213 responses

Time Spent on Facebook vs Whatsapp

(minutes) 213 responses

It is difficult to discern any interesting patterns looking at this scatterplot alone

Time Spent on Facebook vs Whatsapp

(minutes) 213 responses

It is difficult to discern any interesting patterns looking at this scatterplot alone

Let's see if looking at extremes in the data reveals patterns

Time Spent on Facebook vs Whatsapp

(minutes) 213 responses

This user spends a massive amount of time on Facebook and WhatsApp (probably over-reporting)

Time Spent on Facebook vs Whatsapp

(minutes) 213 responses

This user spends a massive amount of time on Facebook and comparatively little on WhatsApp

Time Spent on Facebook vs Whatsapp

(minutes) 213 responses

Let's try to identify all the super users of these systems: those who spend a lot of time with WhatsApp and Facebook

To do that we need a way of classifying what counts for a lot of time

Identifying Super Users of Facebook and WhatsApp

Super users are those who spend a lot of time compared to the rest of the survey responses

(minutes) 213 responses

We want to define a cutoff line for the users who spend a lot of time on either system

(minutes) 213 responses

We want to define a cutoff line for the users who spend a lot of time on either system

Let's use the standard deviation plus the mean to define a class of super users

(minutes) 213 responses

This line is one standard deviation plus the mean =

270.26

(minutes) 213 responses

Responses above the red line are super users of Facebook.

They spend a lot of time on FB

compared to all

the survey responses

Identifying Super Users of WhatsApp

(minutes) 213 responses

The green line is one standard deviation plus the mean =

398.94

Identifying Super Users of WhatsApp

(minutes) 213 responses

Respondents to the right of the green line use WhatsApp a lot

Identifying Super Users of WhatsApp Or Facebook

(minutes) 213 responses

In this chart we have all segments of super users for both systems

Identifying Super Users of WhatsApp Or Facebook

(minutes) 213 responses

In this chart we have all segments of super users for both systems

Identifying Super Users of WhatsApp Or Facebook

(minutes) 213 responses

In this chart we have all segments of super users for both systems

Identifying Super Users of WhatsApp And Facebook

(minutes) 213 responses

In this chart we have all segments of super users for both systems

and both

Let's Segment the Data to See if Patterns Emerge

Responses are tagged with gender for the respondent and urban or rural depending the household location

Segmenting the Survey Results by Two Categories: Gender and Urban/Rural

Survey Results by Category

The survey sample is mostly urban and has slightly more female than male respondents

Segmenting the Survey Results by Two Categories: Gender and Urban/Rural

Survey Results by Category

Let's see how the responses are distributed based on these categories

The survey sample is mostly urban and has slightly more female than male respondents

Patterns in Urban and Rural Responses for Time Spent on Facebook and WhatsApp

Scatterplots for each of the four segments in the survey

Patterns in Urban and Rural Responses for Time Spent on Facebook and WhatsApp

PATTERN: Super users are all urbanites, with one exception (male rural)

Patterns in Urban and Rural Responses for Time Spent on Facebook and WhatsApp

PATTERN: Super users are all urbanites, with one exception (male rural)

Patterns in Urban and Rural Responses for Time Spent on Facebook and WhatsApp

PATTERN: Super users are all urbanites, with one exception (male rural)

PATTERN: All super users of WhatsApp are urbanites

Patterns in Urban and Rural Responses for Time Spent on Facebook and WhatsApp

Let's see if we can identify preferences in the responses:

Which systems do users spend a greater portion of their time with?

Patterns in Urban and Rural Responses for Time Spent on Facebook and WhatsApp

PATTERN: Responses show a greater portion of time spent on WhatsApp than FB, especially among urban men

Patterns in Urban and Rural Responses for Time Spent on Facebook and WhatsApp

PATTERN: Responses show a greater portion of time is spent on WhatsApp than FB, especially urban men

Background: The preference for group messaging over social feed platforms is a global trend

Let's Compare Texting to WhatsApp

Seeking patterns in messaging platforms

Patterns in Urban and Rural Responses for Use of Text Messaging and WhatsApp

PATTERN: Super users of text messaging exist in rural areas

Patterns in Urban and Rural Responses for Use of Text Messaging and WhatsApp

(minutes) 215 responses

PATTERN: Rare is the super user of both text messaging and WhatsApp

PATTERN: Super users of texting exist in rural areas

Patterns in Rural Responses for Use of Text Messaging and WhatsApp

TEXTING vs WHATSAPP

PATTERN: Rural super users tend to be super users of SMS. Only one rural facebook super user in the responses

• Super users of FB and WhatsApp are urbanites

• Superusers exist in rural areas but only use text messaging
• WhatsApp seems to displace the use of text messaging in urban areas
• All WhatsApp super users were in urban areas
• A greater portion of time spent on WhatsApp than FB - in line with the global trend towards group messaging