mobile data solutions

MOBILE DATA

SOLUTIONS

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Table of Contents

0. Orientation ........................................................................................................ 8

0.1 Home ........................................................................................................... 8

a. Course Orientation ..................................................................................... 8

b. Module 1: Introduction to Mobile Data Solutions ........................................ 9

c. Module 2: Project Design ........................................................................... 9

d. Module 3: Implementation........................................................................ 10

e. Module 4: Data Analysis, Visualization and Sharing ................................ 10

f. Course Conclusion ................................................................................... 11

0.2 Welcome ................................................................................................... 11

0.3 Course Introduction ................................................................................... 12

0.4 Navigation Tutorial .................................................................................... 13

a. Home ....................................................................................................... 13

b. Progress .................................................................................................. 14

c. Module Menu ........................................................................................... 14

d. Resources & Transcript ........................................................................... 15

e. Volume..................................................................................................... 15

f. Seekbar .................................................................................................... 16

g. Navigation buttons ................................................................................... 16

1. Module 1: Introduction to Mobile Data ............................................................ 17

1.1 Overview ................................................................................................... 17

a. Laura Walker Hudson on the Power of SMS ........................................... 18

1.2 Benefits ..................................................................................................... 18

a. Affordability .............................................................................................. 19

b. Impact ...................................................................................................... 19

c. Transparency ........................................................................................... 20

d. Quality...................................................................................................... 20

e. Aggregation & Scale ................................................................................ 21

f. Dynamism ................................................................................................. 21

g. User Friendly ........................................................................................... 22

h. Timeliness ................................................................................................ 22

1.3 Challenges ................................................................................................ 23

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a. Challenges - Cont. ................................................................................... 23

1.4 Limitations ................................................................................................. 24

a. Feedback: Beneficiaries will not like it/Enumerators cannot learn it ......... 24

b. Feedback: It’s hard to type using basic mobile devices ........................... 25

1.5 Frequently Raised Concerns ..................................................................... 25

a. Beneficiaries won't like it .......................................................................... 26

b. Enumerators cannot learn it ..................................................................... 26

c. It is too expensive .................................................................................... 27

d. It will be too difficult .................................................................................. 27

e. Electronic devices are unreliable ............................................................. 28

f. Paper and pen has worked in the past ..................................................... 28

g. Our project does not need it ..................................................................... 29

1.6 Components .............................................................................................. 29

a. Mobile Data Collection Client ................................................................... 30

b. Mobile Devices ........................................................................................ 32

c. Data Transfer Method .............................................................................. 34

d. Server-side Components ......................................................................... 37

1.7 Example .................................................................................................... 38

a. TextIt Demo Video ................................................................................... 38

1.8 Case Studies ............................................................................................. 41

a. uReport .................................................................................................... 42

b. mTrac ...................................................................................................... 43

1.8 Principles for Digital Development ............................................................. 44

a. Design With the User ............................................................................... 45

b. Build for Sustainability ............................................................................. 45

c. Reuse and Improve .................................................................................. 46

d. Understand the Existing Ecosystem ........................................................ 46

e. Design for Scale ...................................................................................... 47

f. Be Data Driven ......................................................................................... 47

g. Address Privacy & Security...................................................................... 48

h. Use Open Standards, Open Data, Open Source, and Open Innovation .. 48

i. Be Collaborative ........................................................................................ 49

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1.9 Conclusion................................................................................................. 49

2. Module 2: Project Design ................................................................................ 50

2.1 Introduction ................................................................................................ 50

a. Interview with Kidus Asfaw ...................................................................... 51

2.2 Needs Analysis .......................................................................................... 51

a. A .............................................................................................................. 52

b. B .............................................................................................................. 53

c. C .............................................................................................................. 54

d. D .............................................................................................................. 55

e. E .............................................................................................................. 57

f. F ............................................................................................................... 59

2.3 Scenario 1 ................................................................................................. 60

a. Question .................................................................................................. 61

2.4 Readiness Assessment ............................................................................. 62

a. Infrastructure ............................................................................................ 63

b. Data Acquisition ....................................................................................... 63

c. Data Acquisition - Cont. ........................................................................... 64

d. Respondents ............................................................................................ 64

e. Respondents - Cont. ................................................................................ 65

f. Quality Control .......................................................................................... 65

g. Quality Control - Cont. ............................................................................. 66

2.5 Scenario 1 (cont.) ...................................................................................... 66

a. Question .................................................................................................. 67

2.6 Additional Requirements ........................................................................... 68

a. Language ................................................................................................. 69

b. Initiative.................................................................................................... 69

c. Initiative - Cont. ........................................................................................ 70

d. Hardware ................................................................................................. 70

e. Hardware - Cont. ..................................................................................... 71

f. Privacy ...................................................................................................... 71

g. Interoperability ......................................................................................... 72

2.9 Scenario 2 ................................................................................................. 72

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a. Question .................................................................................................. 73

2.10 Conclusion ............................................................................................... 75

a. CRS ......................................................................................................... 76

3. Module 3: Implementation ............................................................................... 77

3.1 Introduction to Project Implementation ...................................................... 77

3.2 Form Design .............................................................................................. 78

a. Skip Logic ................................................................................................ 79

b. Data Type Validation ............................................................................... 79

c. Range Checks ......................................................................................... 80

d. Question Type ......................................................................................... 80

3.3 Program the Survey .................................................................................. 81

3.4 Setup Data Transfer to/from the Server .................................................... 82

a. Testing Data Transfer and Communication ............................................. 83

b. Configuring the Server Application .......................................................... 83

c. Acquiring a Fixed IP Address (Local or Public IP) .................................... 84

3.5 Preparing and Testing Equipment ............................................................. 84

a. 1 ............................................................................................................... 85

b. 2 ............................................................................................................... 85

c. 3 ............................................................................................................... 86

d. 4 ............................................................................................................... 86

e. 5 ............................................................................................................... 87

f. 6 ................................................................................................................ 87

g. 7 ............................................................................................................... 88

h. 8 ............................................................................................................... 88

3.6 Develop Protocols ..................................................................................... 89

a. Charging .................................................................................................. 89

b. Data Backup Plan .................................................................................... 90

c. Paper Documents .................................................................................... 91

3.7 Training ..................................................................................................... 91

a. Additional Trainees .................................................................................. 92

b. Project Staff ............................................................................................. 93

c. End Users ................................................................................................ 93

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3.8 Fielding the Survey .................................................................................... 94

3.9 Wrap Up .................................................................................................... 95

3.10 Conclusion ............................................................................................... 95

a. Mike Frost JSI Presentation ..................................................................... 96

4. Module 4: Data Analysis, Visualization, & Sharing .......................................... 97

4.1 Introduction to Data ................................................................................... 97

4.2 Data Analysis ............................................................................................ 98

a. Qualitative ................................................................................................ 99

b. Quantitative .............................................................................................. 99

c. Quantitative - Cont. ................................................................................ 100

d. Inferential Statistics ................................................................................ 100

e. Inferential Statistics - Cont. .................................................................... 101

f. Descriptive Statistics ............................................................................... 101

g. Descriptive Statistics - Cont. .................................................................. 102

h. Cross-tab ............................................................................................... 102

i. Cross-tab - Cont. ..................................................................................... 103

4.3 Data Visualization .................................................................................... 103

4.4 Good vs. Poor Data Visualization ............................................................ 104

a. 1 ............................................................................................................. 104

b. 2 ............................................................................................................. 107

c. 3 ............................................................................................................. 109

4.5 Other Types of Data Visualization ........................................................... 112

a. Sparklines .............................................................................................. 112

b. Software Applications ............................................................................ 113

c. Online Interactive Graphs ...................................................................... 113

4.6 Data Sharing ........................................................................................... 114

a. Recipients .............................................................................................. 115

b. Policies .................................................................................................. 115

c. Policies - Cont. ....................................................................................... 116

4.7 Methods of Sharing Data ......................................................................... 116

a. Methods of Sharing Data (cont.) ............................................................ 117

4.8 Conclusion............................................................................................... 117

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5. Conclusion .................................................................................................... 118

5.1 Review .................................................................................................... 118

a. Question 1 ............................................................................................. 118

b. Question 2 ............................................................................................. 119

c. Question 3 .............................................................................................. 120

d. Question 4 ............................................................................................. 121

e. Question 5 ............................................................................................. 122

f. Question 6 .............................................................................................. 123

g. Question 7 ............................................................................................. 124

h. Question 8 ............................................................................................. 125

5.2 Conclusion............................................................................................... 126

6. Resources ..................................................................................................... 127

6.1 Resource Library ..................................................................................... 127

a. Module 1 ................................................................................................ 127

b. Module 2 ................................................................................................ 128

c. Module 2 - Cont. .................................................................................... 128

d. Module 3 ................................................................................................ 129

e. Module 4 ................................................................................................ 129

6.2 Acknowledgements ................................................................................. 130

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0. Orientation

0.1 Home

a. Course Orientation

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b. Module 1: Introduction to Mobile Data Solutions

c. Module 2: Project Design

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d. Module 3: Implementation

e. Module 4: Data Analysis, Visualization and Sharing

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f. Course Conclusion

0.2 Welcome

Transcript:

To view closed captioning for this video, please go to: https://www.youtube.com/watch?v=uL0ozP8Ywhs.

https://www.youtube.com/watch?v=uL0ozP8Ywhs

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0.3 Course Introduction

Transcript:

Hi, I'm Merrick.

And I’m Samita.

Welcome to our mData self-paced course. As you saw in the previous video, using mobile technologies to collect and disseminate data offers an opportunity to improve efficiency and the quality of the information used to make decisions.

By the end of this course, you will be able to:

Describe examples of mobile data solutions from collection through visualization

Articulate the benefits of using these solutions

Analyze challenges and limitations associated with mobile data solutions

Assess whether or not particular mobile data solutions are appropriate for a project, program, or problem

Outline how to design a project or activity to include mobile data solutions

Explain the steps involved in implementing mobile data solutions

Summarize how to analyze, visualize, and share mobile data

This course should take you about 2 hours to complete. Feel free to stop the course at any point and come back to it. Your progress will be saved and you will be able to start at the point at which you stopped.

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0.4 Navigation Tutorial

Transcript:

Here is where you would find a transcript of any audio that is included in that slide.

a. Home

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b. Progress

c. Module Menu

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d. About, Download, Resources, and Transcript

e. Volume

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f. Seekbar

g. Navigation buttons

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1. Module 1: Introduction to Mobile Data

1.1 Overview

Transcript:

Innovative uses of mobile technologies are transforming the ways in which data is collected, aggregated, analyzed, reported, and shared. For the purposes of this course, we will define technologies and systems that perform these functions as mobile data solutions.

Mobile data collection, which is one aspect of a mobile data solution, has the potential to improve efficiency, accuracy, and timeliness of data gathering and reporting, especially for monitoring and evaluation of programs and projects. While mobile devices have been used to great effect to disseminate information, we will emphasize data collection throughout the course, including in this overview, and the use of this data for decision-making.

SMS (Short Messaging Service) is a mobile service using standardized communications protocols that allow the exchange of short text messages between mobile phone devices. Commonly available, even on the most basic phones, SMS can be used for effective data collection in the right circumstances and to share information with a widespread audience. In this module we will be exploring SMS and introducing you to a variety of introductory materials for mobile data solutions.

In the following animation, Laura Walker Hudson, CEO of Frontline SMS, describes why SMS is a good option to communicate with people in the developing world, going beyond the basics of the increasing reach of cellular networks and adoption of mobile phones. She also offers examples of how to integrate this technology into development work

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a. Laura Walker Hudson on the Power of SMS

Transcript:

To view closed captioning for this video, please go to: https://www.youtube.com/watch?v=6nToj3dud9M.

1.2 Benefits

Transcript:

There are many benefits to using mobile data solutions. For an overview of the most prominent benefits, click each of the following icons.

https://www.youtube.com/watch?v=6nToj3dud9M

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a. Affordability

b. Impact

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c. Transparency

d. Quality

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e. Aggregation & Scale

f. Dynamism

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g. User Friendly

h. Timeliness

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1.3 Challenges

Transcript:

As illustrated in the previous sections, the benefits of utilizing mobile technologies are numerous and the tools flexible. Although things such as bad electricity grids or internet connections can pose challenges, most can be overcome with appropriate planning.

Click the arrow for possible solutions for these challenges, which can be implemented with proper planning.

a. Challenges - Cont.

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1.4 Limitations

a. Feedback: Beneficiaries will not like it/Enumerators cannot learn it

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b. Feedback: It’s hard to type using basic mobile devices

1.5 Frequently Raised Concerns

Transcript:

Organizations often raise concerns about using MDC. Click on each concern to reveal effective answers to address them.

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a. Beneficiaries won't like it

b. Enumerators cannot learn it

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c. It is too expensive

d. It will be too difficult

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e. Electronic devices are unreliable

f. Paper and pen has worked in the past

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g. Our project does not need it

1.6 Components

Transcript:

There are four major components that are common to the mobile solutions we are discussing in this course: mobile devices, mobile data collection clients, data transfer methods, and server-side administrative components. Click each component for more information.

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a. Mobile Data Collection Client

Transcript:

There are various ways in which data can be captured. It is important to note that not all data collection methods will work with all types of mobile devices. Click on each type of data collection client to learn more.

i. Short Messaging Service (SMS)

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ii. Electronic Forms

iii. Sensors and Instruments

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iv. Voice or Interactive Voice Response (IVR)

b. Mobile Devices

Transcript:

Mobile phones, tablets, and netbooks are used to input and transfer data. Mobile phones are available in a wide range of functionalities and capabilities; they are generally categorized as basic, feature, or smart phones, although the differences are not always clearly defined. Click on the different types of phones to learn more about them.

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i. Smartphones

ii. Feature

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iii. Basic

c. Data Transfer Method

Transcript:

Data collected on a mobile device must be transferred from the device to a server. The most widely used data transfer mechanisms include the following two mechanisms.

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i. Direct Transfer

ii. Over the cellular network

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iii. LEARN MORE

Drag Item Drop Target

Nearly all phones support the protocol SMS

Each message is typically limited to 160 characters

SMS

Message charges are based on a fixed unit of cost vs. quantity of data transmitted

SMS

Nearly all phones support the protocol SMS

Possible to encrypt data GPRS/3G/4G

Guaranteed timely delivery GPRS/3G/4G

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d. Server-side Components

Transcript:

For a mobile data solution to work, a project will need one or more server-side components, which are software applications that run on a web-server. This administrative software may be located on cloud-based server or a server owned and managed by the project. How tasks are handled and what components are required differs between solutions. Server-side components include:

A software application for the creation of electronic forms or SMS messages, where these are part of the solution.

A component that receives the submitted data, checks if for errors and then either rejects the data and sends an appropriate error message to the data collector, or inserts the data into a database.

A database system to store the data

In some cases, solutions may include one or more software applications that facilitate data analysis and visualization, and the generation of reports by various system users.

In some cases, solutions may include one or more interfaces that facilitate linking the server to other, external software systems.

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1.7 Example

Transcript:

Now that we know the components of mobile data solutions, let’s take a look at an example of a mobile data tool that you could use for a mobile data solution. In the following video, you will learn how to use TextIt, which represents a server-side component. The data transfer method with TextIt is over the cellular network, and the mobile data collection client is SMS. Any mobile device with SMS capabilities can respond and send data to the TextIt application.

a. TextIt Demo Video

Transcript:

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Hi this is Nick Pottier and I’m here to give you a brief overview of TextIt. At its core, TextIt lets you build interactive workflows using SMS. The easiest way to explain that is just to show it to you. Here we’re going to create a flow to register customers.

So in this case, we’re registering customers who have bought water filters. We’re going to start things off by asking them where they get their water. Let’s get them to respond with either well, tap, or stream.

So that was our outgoing message. Now lets deal with the incoming response. So let’s say that their response is a variable named water source. So we know they’re going to respond with either well, tap, or stream, so we’re going to create categories for each one of those. So here we’re saying if the response contains the word stream, we want it categorized as stream. So now we’ve done that for all of our possible responses.

Now in the flow we can see each of the categories we just created. We can create our next message by just dragging one of these boxes. Now let’s find out if they’re boiling their water. This time, we expect them just to answer with Yes or No.

So let’s connect this new question into the rest of our flow. We’re going to drag tap, stream, and any other response into this new question. We want to save their response to this question as well. This time we’re going to save it as a variable named boil and we’re going to say that they can either respond with yes or no. And you know what, in this case we’re also going to let them reply with Y for yes and N for no. That way it’s just a bit easier for them.

All right. Ok great, so now our flow is coming together. So for the people that said yes, let’s let them know that boiling is the right thing to do. And then, let’s ask them the next question: how many members do they have in their household? In this case, we’re just expected a number, one to twenty. Just as before, we’re going to drag the box, and then save this response as a new variable. In this case, it’s numeric, and we’re going to call it the “household members” and we’re going to say it’s from one to twenty.

Ok, so we’re almost done now. Let’s go back up here and deal with this response for no. In this case we’re actually going to tell them that they should consider boiling their water before they use the filters. But we’re still going to ask them the same question. This is one of the things that TextIt does that is a bit unique.

Let’s hook it up so that we save their response. And then you know what, let’s deal with this other case. So this box is when they reply with something that isn’t one through twenty. Let’s give them a quick reply asking them to try again and let’s remind them what the original question was too. All right.

Back on the flow page, we’ll just connect it back up so that any response they have is handled the same way. Let’s do the same thing for our yes/no question about boiling. Again, we’re just going to tell them we didn’t understand what they replied with and remind them to reply with yes or no. We connect that up the

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same way and now we’re done with that part of the flow.

Ok so now we’re just going to finish things off by sending them a quick message thanking them for their time. One of the neat things here is we can actually greet them by their name. Those sort of little touches make a big difference towards making the system feel personalized.

Cool so now we know a lot about our customers. Let’s give this a quick run in the simulator and see how it looks. So, we get our first question here and we’re going to go ahead and respond with stream.

One of the neatest parts of TextIt is just how interactive we make it. So over here we can see that there is one response for stream already and then it’s currently waiting for us to respond about boiling. We’re going to respond with sometimes. That doesn’t really match anything at all, but we can see again, one response went through our other rule. Let’s go ahead and answer with yes this time. Again, the activity moves forward and you can see down here now we’re waiting for the household members. Let’s go ahead and answer with four.

Being able to see the activity in your flow, live as it happens, is one of the most powerful features of TextIt, and it helps you refine your flows over time.

So we need to let people join this flow and we do that by creating a trigger. In this case we’re going to say that anybody who sends the word “join” via SMS is going to be added to the “customers” group and is going to start the flow we just built. So from the triggers page we can see all the keywords that we have, including the one we just created.

After we have gotten some registrations, we can go look at the data. So here we can see a breakdown from all of the different water sources that people have and the three different variables that we’re collecting information for. Let’s try looking at this data for only those who don’t boil their water. So we’re going to uncheck this, and we can see the breakdown by water source.

There’s a lot more to show on the analytics, including how you can compare variables from different flows, but we’ll leave that for later.

One of our big goals with TextIt was to make SMS as easy to use as email. So in your inbox you can see every message that comes in and you can label them, you can search, everything you’d expect. When you click on a contact, you can see their full message history, including any missed calls from them. If we’d like, we can even send them a message right from this page. Here we’re just going to check how other filters work.

So that’s a quick overview of TextIt. We have a lot more features you can discover. So come try it out for free, at textit.in.

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1.8 Case Studies

Transcript:

By now you should be familiar with the benefits and limitations of mobile data solutions as well as their components. To explore how organizations have applied mobile data tools to address development issues, consider the case studies below. These case studies exemplify the potential for mobile-based data collection and analysis to impact social change on a national scale. View each case study while considering the following questions: What elements have made these programs successful? What can we learn from them in designing our own projects?

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a. uReport

i. uReport Video

Transcript:

To view closed captioning for this video, please go to: https://www.youtube.com/watch?v=LkzE1PHg6EU.

https://www.youtube.com/watch?v=LkzE1PHg6EU

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b. mTrac

i. mTrac Video

Transcript:

To view closed captioning for this video, please go to: https://www.youtube.com/watch?v=LkzE1PHg6EU.

https://www.youtube.com/watch?v=LkzE1PHg6EU

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1.8 Principles for Digital Development

Transcript:

When developing mobile data solutions, keep the following principles in mind. These Principles for Digital Development capture critical lessons learned by the development community in the implementation of information and communications technology for development - also known as ICT4D - projects. They evolved from a previous set of standards endorsed by over 300 organizations, and seek to serve as guidelines that inform, but do not dictate, the design of technology-enabled development programs, such as mobile data solutions.

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a. Design With the User

b. Build for Sustainability

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c. Reuse and Improve

d. Understand the Existing Ecosystem

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e. Design for Scale

f. Be Data Driven

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g. Address Privacy & Security

h. Use Open Standards, Open Data, Open Source, and Open Innovation

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i. Be Collaborative

1.9 Conclusion

Transcript:

The body of knowledge and experience amassed over the last decade on implementing mobile technologies to solve data challenges is considerable. As a rapidly evolving and dynamic field with an ever increasing variety of technology platforms, tools, and applications, mobile data solutions are presenting organizations with an opportunity to actively address key development issues related to human resources, sustainability, going to scale from pilots, conducting more rigorous evaluations of projects, and increasing the scope of data

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dissemination and sharing. By facilitating accurate, reliable, timely, and cost-effective end-to-end responses to the need for relevant, useful information, mobile data solutions can help us promote evidence-based decision-making and improve development outcomes.

However, not all projects, programs, or problems are appropriate for mobile data solutions. Click next to move on to Module 2, which covers how to assess whether or not mobile data solutions are appropriate for an activity.

2. Module 2: Project Design

2.1 Introduction

Transcript:

This module is designed to help determine the appropriateness of a mobile data solution and how to select appropriate devices and tools. The material in this section is not intended to provide a detailed project design process or a framework to guide implementation, but rather a general overview of elements of project design for mobile data solutions.

This overview is divided into three main parts:

● Needs Analysis

● Readiness Assessment

● Additional Requirements

Watch the following video to hear from Kidus Asfwah from the World Bank on the importance of project design thinking proceeding the implementation of a mobile data solution.

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a. Interview with Kidus Asfaw

Transcript:

To view closed captioning for this video, please go to: https://www.youtube.com/watch?v=a06K5v4eVL4.

2.2 Needs Analysis

Transcript:

The first step in determining if a mobile data solution is appropriate is defining what data is needed. The first question to consider is whether the research question calls for qualitative data, such as those that intend to explore the

https://www.youtube.com/watch?v=a06K5v4eVL4

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motivations, desires, and understanding of the respondents.

Although mobile data can supplement qualitative data, as previously discussed in Module 1, mobile data collection is not an optimal tool for gathering purely qualitative data. Open-ended responses with lengthy transcripts are difficult to capture on mobile devices unless built-in digital voice recording is used. Even then, sufficient resources need to be allocated for the transcription and analysis of this data.

However, if research questions intend to gather quantitative data, mobile data collection tools may be appropriate. In particular, mobile technologies can be particularly appropriate if the quantitative data desired reflects any of the following conditions.

Click through the endpoints on the flowchart to learn more.

a. A

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b. B

i. B - Cont.

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c. C

i. C - Cont.

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ii. C - Cont.2

d. D

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i. D - Cont.

ii. D - Cont.2

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e. E

i. E - Cont.

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ii. E - Cont.2

iii. E - Cont.3

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f. F

i. F - Cont.

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2.3 Scenario 1

Transcript:

Given what you just learned about conducting a needs analysis, consider the following scenario and decide whether or not mobile data collection is appropriate for the data needs of the project.

A project in Uganda seeks to generate and analyze rigorous empirical data on the effectiveness of integrated agriculture, health, and nutrition programming on maternal and child health outcomes. In order to gather this data, the project aims to survey 640 households randomly selected from seven districts. After collecting baseline data, the project aims to conduct mid-line and end-line surveys. Finally, the project plans to analyze data using STATA and share de-identified data (i.e., data that is stripped of information).

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a. Question

i. Feedback: No

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ii. Feedback: Yes

2.4 Readiness Assessment

Transcript:

If after assessing the data needs of a project program you find that mobile technologies are indeed appropriate, the next step is to assess the readiness of the site you will be working in. There are four main parameters to consider when evaluating if a site is ready for a mobile data solution. These parameters are:

1. Infrastructure

2. Enumerators

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3. Respondents

4. Quality Control

Click on each part of the scene to learn more about each.

a. Infrastructure

b. Data Acquisition

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c. Data Acquisition - Cont.

d. Respondents

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e. Respondents - Cont.

f. Quality Control

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g. Quality Control - Cont.

2.5 Scenario 1 (cont.)

Transcript:

Continuing with the scenario we began earlier, consider the following additional information about the project’s operating environment and decide if mobile data collection is still appropriate for this project.

The districts the project is targeting all have very good cellular coverage for voice and text messaging. Although there is also fairly good GPRS coverage, 3G coverage only exists in the district capital towns. Additionally, access to electricity is limited in the rural areas and the district capitals only have access for 3-5

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hours a day.

The project plans for about 30 enumerators to interview respondents and record the data. Although the enumerators are somewhat tech-savvy, the project has the resources to train them.

Researchers would be able to perform daily data quality control checks.

a. Question

i. Feedback: Yes

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ii. Feedback: No

2.6 Additional Requirements

Transcript:

It is very important that you select the equipment, software application, and transmission option(s) that meet your needs and are optimally suited to the intervention environment and existing infrastructure. This process is primarily driven by answering the questions posed in the “Needs Analysis” and “Readiness Assessment” sections of the project design. In addition to the parameters provided in those stages, the following parameters will help you further define your requirements and guide you in selecting the appropriate set of tools.

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Click on each parameter to learn more.

a. Language

b. Initiative

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c. Initiative - Cont.

d. Hardware

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e. Hardware - Cont.

f. Privacy

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g. Interoperability

2.9 Scenario 2

Transcript:

Applying what you just learned about additional requirements, let’s look at another scenario. Consider the following information and decide what type of mobile data collection solution would be most appropriate.

A project in Jamaica aims to generate and analyze data on the use of social media by youth and to determine which social media websites have the greatest potential as sources of health information for this population. Specifically, the project is interested in youth who are part of a local civil society organization

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(CSO). The project has many of their mobile numbers and all of them live within Kingston, which has strong mobile network services throughout the city.

The project aims to interview 200 randomly selected youth over a one week timeline. No photographs, video, audio data, or GPS coordinates will be necessary. English is the only language required.

The project does not plan to link the data to any prior research and any identifying information will be disassociated from survey responses.

a. Question

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i. Feedback: Purchasing tablets with multimedia and multilingual capabilities and hiring enumerators to carry out the interview in person

ii. Feedback: Purchasing 200 smartphones and distributing them among youth to deliver an electronic form

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iii. Feedback: Sending the youth an SMS survey to their personal mobile phones

2.10 Conclusion

Transcript:

In this Module we outlined 3 general elements of project design and addressed how to answer the following key questions:

1.Needs Analysis: What data do you need? Are mobile data solutions appropriate for this need?

2.Readiness Assessment: What is the setting you are working in? Are mobile

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data solutions appropriate for this setting?

3.Additional Requirements: In addition to data and setting needs, what additional factors need to be taken into account before deciding on which software and hardware to use to implement a mobile data solution?

We also applied these elements of project design to hypothetical examples of mobile data projects in Uganda and Jamaica. Now let’s now hear from Carol Bothwell, Chief Knowledge Officer and CRS. In the following animation, Carol discusses how mobile data projects have been implemented appropriately and successfully, a topic we will discuss further in the next module.

a. CRS

Transcript:

To view closed captioning for this video, please go to: https://www.youtube.com/watch?v=Onxiq4ZfsLw.

https://www.youtube.com/watch?v=Onxiq4ZfsLw

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3. Module 3: Implementation

3.1 Introduction to Project Implementation

Transcript:

As covered in Module 1: Introduction to Mobile Data Solutions, there are various ways in which data can be captured. This Module will focus on electronic forms, through which data are collected using an application installed on a mobile device or in some cases via an online web form. When the survey is completed, the data are uploaded to a database where each survey can be aggregated with others for data analysis, visualization, report generation, and dissemination.

This Module describes the steps to take to go from survey concept into the field through wrap-up. The steps covered include:

1. Form Design

2. Programming the Survey for the Mobile Data Collection Client

3. Setting Up Data Transfer to/from the Server

4. Preparing and Testing Equipment

5. Developing Protocols

6. Training

7. Fielding the Survey

8. Wrap-Up

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3.2 Form Design

Transcript:

In order to deploy a survey using an electronic form on a mobile device, the survey must be designed, programmed for the mobile data collection client you will be using, and uploaded to the device or accessed online from the device’s web browser. Begin by designing your data collection instrument in a document, not the survey design software, including composing the wording for the questions. You should consider the order in which questions should be posed and decide if related questions should be grouped together.

Also, think about how to use the following features offered by most electronic forms. Click on each feature to continue.

Be sure to ascertain whether the survey design software available to you offers the functionality you need. Not all products offer all features, nor are they equally easy to use in each tool. Confirm that you have human resources with the capacity needed to employ them.

After the survey instrument is designed in a document, you should conduct rigorous testing among representatives of both data collectors and respondents to be surveyed to ensure usability, reliability, and validity prior to conversion to mobile format. It is much more cost-effective to test and revise your form before you incur the expense of developing an electronic version. In addition, although it represents another step in the process, completion of the survey instrument before commencing programming will speed up the development and deployment process. If the survey instrument is intended to be administered in multiple languages, completing the translations and testing each version prior to commencing programming forms will ensure a smoother process.

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a. Skip Logic

b. Data Type Validation

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c. Range Checks

d. Question Type

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3.3 Program the Survey

Transcript:

Once you have designed and tested your survey, you are ready to develop the electronic data collection form using the application of your choice. See the “Resources” for links to demonstrations of some of the tools available. The process varies significantly depending upon the software selected so it is not discussed in detail in this Module.

Regardless of which tool you select, it is important to consider who will program the survey and who will be expected to make changes to the survey once it is programmed. Some tools have easy-to-use graphical user interfaces that do not require programming skills or experience with these types of tools. Others require programming in an Excel document or using XML. The tool should match the skills of the programmers and the level of training the project will provide.

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3.4 Setup Data Transfer to/from the Server

Transcript:

As discussed in Module 1, mobile data solutions require a server. The functions of the server may include designing the form, distributing programmed forms to mobile devices, receiving and storing the gathered data, supporting the aggregation of data received from multiple transmissions and multiple devices, and making the data available to users for analysis, visualization, report generation, and sharing.

The server you use may be owned by the project or one of its stakeholders, or it may be used on a fee-for-service basis through a cloud-based vendor, in which case the server may be physically located in-country or elsewhere. You will have determined through your needs analysis and readiness assessment whether the project itself has the infrastructure and human resources to support the server (installation, customization, maintenance, etc.) or whether you need to outsource these activities to a vendor. If considering a non-local vendor or cloud solution, you will have determined if there are any legal and/or policy issues that should influence your decision.

Whether you use your project’s own server or a cloud-based or hosted solution, setting up data transfer mechanisms includes the following activities.

Click on each program to learn more.

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a. Testing Data Transfer and Communication

b. Configuring the Server Application

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c. Acquiring a Fixed IP Address (Local or Public IP)

3.5 Preparing and Testing Equipment

Transcript:

Based on your needs analysis and readiness assessment, you will have determined which mobile device best provides the functionalities you require. Once purchased and available in the country or countries where you will be working, the devices need to be fully and properly prepared in order to deployed to the end users (respondents and/or enumerators.) Depending on the number of devices being deployed, this may require a significant investment of human resources and time. Additionally, the advice and services of your vendor or a

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local mobile network operator may be required to make certain that the device is properly configured for the country.

Click on the numbers on the phone to reveal specific steps involved in preparing and testing equipment.

a. 1

b. 2

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c. 3

d. 4

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e. 5

f. 6

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g. 7

h. 8

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3.6 Develop Protocols

Transcript:

Procedures for how each step in the data collection process is carried out should be developed, tested, documented, and included in the training curriculum and provided to participants in multiple formats (e.g., paper, on mobile device) as needed.

Click on the three highlighted items to learn how to develop protocols for 3 activities.

a. Charging

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i. Charging - Cont.

b. Data Backup Plan

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c. Paper Documents

3.7 Training

Transcript:

Developing and delivering well planned, high quality training to project staff and end users is essential to the efficient deployment of the mobile data collection solution you have built. Your resource investment in curriculum design, instructional materials development, and training delivery will be reflected in the future costs associated with data collection and in the quality of the data gathered. Training for a mobile data solution differs from training for a paper survey. Data collectors must become fluent not only in using the data collection

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instrument, but also in the use of the mobile device and the processes and procedures for entering data to the electronic form and transmitting data to the server.

In most cases, you will need to train two separate cadres: the project staff that will provide technical support to end users and the end users themselves. However, there are additional staff and stakeholders to consider for training as well.

Click on each type of trainee to learn more.

a. Additional Trainees

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b. Project Staff

c. End Users

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3.8 Fielding the Survey

Transcript:

Once the training is complete, the project is ready to deploy the data collection team to the field. Depending on the nature and size of the survey, enumerators can be split into smaller groups each with a supervisor.

Supporting project enumerators who have been deployed to the field is very important. Project staff should maintain regular communication with enumerators to monitor performance, provide technical support, and understand issues that may affect data quality. They should track technical support issues and share their solutions with all team members. This may help prevent problems and/or shorten the amount of time lost trying to resolve the same issue multiple times. The quality of data collected is directly linked to the ability of enumerators to do their work with a minimum of stress.

Project staff should consistently monitor data collection progress and check data quality by accessing the server through the Internet. Management will be able to identify which enumerators are producing the highest quality data and conducting the greatest number of interviews. This information can be used to identify potential problems, avoid pitfalls, and provide encouragement and recognition to staff.

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3.9 Wrap Up

Transcript:

After a mobile data solution has been implemented - or in the case of an on-going data collection activity, at scheduled milestones - it is important to evaluate the project. Even if this information is gathered in informal ways, document the lessons learned and develop a catalog of best practices to apply in future mobile data collection projects.

Click through each section in this evaluation form to continue.

3.10 Conclusion

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Transcript:

At this point you should be familiar not only with parameters to consider when designing a mobile data solution, but also the steps involved with implementing a mobile data solution. Specifically, we have covered:

1. Designing a mobile survey using skip logic, data type validation, range checks and a variety of question types

2. Anticipating skills and training needed to program mobile surveys

3. Setting up and testing data transfer to/from the server

4. Preparing and testing equipment in the field

5. Developing protocols for charging, data back-up, and paper documents

6. Training project staff, end users, and others

7. Supporting, communicating, and monitoring while fielding a survey

8. Wrapping up an implemented mobile data solution via evaluation

Now let’s take a look at a case study of a mobile survey. Click “PLAY” to hear Mike Frost, Director of JSI Center for Mobile Health, discuss JSI’s implementation of a mobile survey.

a. Mike Frost JSI Presentation

Transcript:

To view closed captioning for this video, please go to: https://www.youtube.com/watch?v=LkPUqOsIsX4.

https://www.youtube.com/watch?v=LkPUqOsIsX4

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4. Module 4: Data Analysis, Visualization, & Sharing

4.1 Introduction to Data

Transcript:

Welcome to the final module of the Mobile Data Solutions online course. In the previous modules, you learned about the key elements needed for assessing whether a mobile data solution is appropriate and how to select devices and tools fitting the specific task; implementing surveys using mobile devices; and innovative uses of sensors connected to mobile devices. In this Module we will focus on data analysis, visualization and sharing.

Before we move forward, let’s define a few key terms:

Data analysis refers to the process of transforming raw data into information that is easier to understand using descriptive and inferential statistics, and other data analysis techniques.

Data visualization is the process of representing data in a schematic form with the goal of communicating information clearly and effectively through graphical means.

Data sharing is the process and practice of making data available to other users.

There are many data analysis and visualization software applications and numerous approaches to data sharing. This overview highlights some of these and raises issues to consider when evaluating options; however, it does not provide recommendations for which application or approach to select in a given situation.

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4.2 Data Analysis

Transcript:

Data sets do not answer questions or “speak for themselves.” Until they are analyzed, data are “raw” and, for most people, indigestible. Information useful for decision making is extracted from the raw data through the process of data analysis. Depending on the type of data collection technique used, you may have produced data sets consisting entirely of numerical values (quantitative data), entirely words and narratives (qualitative data), or a mix. The nature of your data, whether it is quantitative or qualitative, is an important consideration in choosing the right data analysis application.

Click through the different elements of this application to learn more.

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a. Qualitative

b. Quantitative

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c. Quantitative - Cont.

d. Inferential Statistics

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e. Inferential Statistics - Cont.

f. Descriptive Statistics

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g. Descriptive Statistics - Cont.

h. Cross-tab

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i. Cross-tab - Cont.

4.3 Data Visualization

Transcript:

Using mobile devices for data collection can generate large volumes of data that deal with complex issues. How can we help decision makers better understand such complex data?

Since visual stimuli are processed much faster by the brain than cognitive content, we can use visualize data to make sense of complex information and communicate it more efficiently. Stephen Few <http://www.interaction-design.org/encyclopedia/data_visualization_for_human_perception.html>, a

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highly-regarded visualization thought leader, writes, “Important stories live in our data and data visualization is a powerful means to discover and understand these stories, and then to present them to others.” At first, this may seem trivial, but well-designed data visualization can tell the story in a clear, meaningful, and compelling way, lending credence to the saying, “A picture is worth a thousand words.”

Most data analysis and visualization applications are hybrid in nature, serving multiple purposes including analysis, visualization and sharing. However, the visualization capability of most data analysis packages is rudimentary.

Data collected on mobile devices are usually exported into spreadsheets or databases that support graphical presentations such as pie charts and bar charts. Some packages offer less common data presentation choices such as heat maps (two-dimensional maps where the color intensity represents a variable of the underlying data). Choosing software that offers the options most suited for your needs requires both planning and research. For programmatic monitoring and evaluation purposes, data is usually captured at specific intervals to reflect some change in activity or behavior over time and can be visualized in graphical form.

4.4 Good vs. Poor Data Visualization

a. 1

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i. Graph 1

ii. Graph 2

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iii. Feedback: Graph 1

iv. Feedback: Graph 2

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b. 2

i. Graph 1

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ii. Graph 2


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c. 3

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i. Graph 1

ii. Graph 2

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4.5 Other Types of Data Visualization

a. Sparklines

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b. Software Applications

c. Online Interactive Graphs

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4.6 Data Sharing

Transcript:

As discussed in the previous modules, data collected using mobile devices is transmitted to a server for storage and to facilitate making it available to users for analysis, visualization, report generation, and sharing. The data sets in the server can be shared with other stakeholders by granting them permission to use and/or download all or part of the data set. This includes partners like community health workers and even beneficiaries who may benefit from access to the data collected.

Click on the following two icons to learn more about what to keep in mind when sharing data.

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a. Recipients

b. Policies

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c. Policies - Cont.

4.7 Methods of Sharing Data

Transcript:

Keeping in mind the policies and recipients respective to your unique circumstances, consider the following ways you may share data.

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a. Methods of Sharing Data (cont.)

4.8 Conclusion

Transcript:

Congratulations on finishing Module 4! In this module we have:

Defined data analysis, data visualization, and data sharing, as well as descriptive statistics, cross-tabulation, and inferential statistics

Listed software application options for qualitative and quantitative data analysis as well as data visualization

Distinguished good data visualization from poor data visualization

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Described sparklines and online interactive maps

Explained several ways data can be shared and factors to consider when sharing data

5. Conclusion

5.1 Review

a. Question 1

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i. Review

b. Question 2

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i. Review

c. Question 3

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i. Review

d. Question 4

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i. Review

e. Question 5

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i. Review

f. Question 6

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i. Review

g. Question 7

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i. Review

h. Question 8

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i. Review

5.2 Conclusion

Transcript:

Congratulations on finishing this course! You should now be able to:

Describe what mobile data solutions are and examples of successful implementation of mobile data solutions

Articulate benefits, limitations, and challenges of mobile data solutions

Assess if mobile data solutions will be useful and feasible for a project, program, or problem

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Select the appropriate mobile technology tool for a project, program, or problem

Implement a mobile data solution

Analyze, visualize, and share mobile data

We hope you have enjoyed this course. Don’t forget to take advantage of the additional resources. Thank you for your participation!

6. Resources

6.1 Resource Library

a. Module 1

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b. Module 2

c. Module 2 - Cont.

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d. Module 3

e. Module 4

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6.2 Acknowledgements

mobile data solutions

Documents

module menu

course introduction

data analysis

mobile data solutions

course orientation

course conclusion

navigation tutorial

navigation buttons