CAPI methodology is best suited for complex investigations involving long and detailed questionnaires. The interviewer can help explain intricate questions, demonstrate how to fill in the questionnaire, and ensure the appropriate display of videos or other forms of stimuli.

In this article, we walk through the Computer-Assisted Personal Interviewing (CAPI) methodology, its benefits, and the steps needed to execute CAPI surveys.

Types of CAPI Surveys

Computer-Assisted Personal Interviewing surveys can be categorized into two types determined by how the sampling is handled.

• Named CAPI surveys – where the researchers know the respondents beforehand and can make appointments to conduct the study. An example would be interviewing managers of companies or medical professionals in a city. It is possible to feed the CAPI system with a database of participants. The researcher can preload known respondent information. At GeoPoll, we call this CAPI and CATI feature Sample Management.
• Anonymous CAPI surveys – conducted randomly, for example, on a street where the researchers do not know the respondents and may need to screen them by observation and using profiling questions.

Benefits of CAPI Surveys Over Traditional Paper Surveys

• Guidance – CAPI facilitates logic checks, skip patterns, and validations during the interview to make the survey more efficient and improve data quality.
• Efficiency – CAPI saves time and resources in subsequent steps of data cleaning and data entry.
• Real-time monitoring – CAPI is an excellent tool to monitor enumerators in real-time or later during validity checks. It is possible to automatically record each interview’s start time, end time, and GPS location, making it easy for supervisors to cross-check the processes and manage teams.
• Real-time results – Data collected through CAPI surveys is immediately relayed for real-time analysis and processing.
• Quality of data – Validation and quality control of CAPI surveys can be done when filling out the survey, and it eliminates human error during data entry. Sessions can also be recorded for validation.
• Rich media – During a CAPI survey, the enumerators can take photos, record audio, and take videos to complement the study.
• Personification – CAPI allows direct contact with the interviewee, which helps complete the questionnaire, both in terms of administering stimulus and offering explanations if necessary. Personal contact makes the interview fluid and warm and may result in greater collaboration from the respondent than in remote surveys.

Steps and Considerations for Planning and Executing a Successful CAPI Project

As with all types of surveys, planning and executing a CAPI survey is dependent on several factors, including the research objective, environmental influences, complexity, and the availability of other methodologies, among others. Below, we look at generic steps to consider based on our experience and standard CAPI processes:

1. Objectives and methodology choice – Determine the project objectives and factors that justify using CAPI over other methodologies. Consider the time and cost of the fieldwork, its scope, and its limitations.
2. Map the project – Design a work schedule to execute the research project, from ideation to data collection, to analysis and reporting.
3. Software selection – Evaluate and choose the best CAPI platform and apps to use. At GeoPoll, we have developed an ecosystem for CAPI surveys that includes the Interviewer App tailored to emerging markets and backed by our data processors for data validation, analysis, and reporting.
4. Hardware selection – Evaluate and select the computer equipment or mobile devices used for the data collection. It is advisable to use devices with internet connections for a real-time relay of data and portability for convenience. GeoPoll uses smartphones and tablets.
5. Personnel recruitment and training – Several factors go into selecting personnel to perform the CAPI survey in the field (interviewers or agents). There are language and cultural nuances to consider, education levels, knowledge of the subject matter, and more. The team also needs to be trained well on how to conduct effective surveys, how to use the selected apps and devices, and how to flow through the specific survey they will be administering.
6. Pilot testing – A lot can go wrong in any research project. It is therefore imperative for researchers to meticulously pilot test the survey with the interviewers and devices in the field. This process can then be used to adjust the questionnaires, provide post-exposure training, adjust the verbatim, and correct any errors detected.
7. Fieldwork – After a successful pilot, the actual data collection can commence. If applicable, arrange appointments with respondents or get the interviewers on the ground, making sure that the data collection app and devices are being used and working as planned. Monitor the survey process, detect and correct errors on the go, measure the length of the interviews (LOI), and track the productivity of the team. The GeoPoll Interviewer App includes all these functionalities.
8. Data consolidation and validation – Data is only as good as its integrity. Whether real-time or after the data collection process, it is crucial to perform quality checks and ensure that everything checks out.
9. Data analysis – The data collected and cleaned from the CAPI survey can then be tabulated and analyzed, and the open-ended questions coded according to the research question and objectives.
10. Final report and delivery of results

Conduct Mobile-Based In-person Surveys Anywhere in the World

GeoPoll administers face-to-face surveys through our own Computer-Assisted Personal Interviewing (CAPI) mobile application, which is specifically built for use in emerging markets and includes features such as offline capabilities, remote progress tracking, and interviewer metrics. We can conduct face-to-face interviews via CAPI in almost any country in the world and have experience in responding quickly to fast-moving situations. Please contact us to discuss your CAPI project needs.

The post Computer-Assisted Personal Interviewing (CAPI) Surveys: A Guide appeared first on GeoPoll.

Sample size is a research term used for defining the number of individuals included in a research study to represent a population. The sample size references the total number of respondents included in a study, and the number is often broken down into sub-groups by demographics such as age, gender, and location so that the total sample achieves represents the entire population. Determining the appropriate sample size is one of the most important factors in statistical analysis. If the sample size is too small, it will not yield valid results or adequately represent the realities of the population being studied. On the other hand, while larger sample sizes yield smaller margins of error and are more representative, a sample size that is too large may significantly increase the cost and time taken to conduct the research.

This article will discuss considerations to put in place when determining your sample size and how to calculate the sample size.

Confidence Interval and Confidence Level

As we have noted before, when selecting a sample there are multiple factors that can impact the reliability and validity of results, including sampling and non-sampling errors. When thinking about sample size, the two measures of error that are almost always synonymous with sample sizes are the confidence interval and the confidence level.

Confidence Interval (Margin of Error)

Confidence intervals measure the degree of uncertainty or certainty in a sampling method and how much uncertainty there is with any particular statistic. In simple terms, the confidence interval tells you how confident you can be that the results from a study reflect what you would expect to find if it were possible to survey the entire population being studied. The confidence interval is usually a plus or minus (±) figure. For example, if your confidence interval is 6 and 60% percent of your sample picks an answer, you can be confident that if you had asked the entire population, between 54% (60-6) and 66% (60+6) would have picked that answer.

Confidence Level

The confidence level refers to the percentage of probability, or certainty that the confidence interval would contain the true population parameter when you draw a random sample many times. It is expressed as a percentage and represents how often the percentage of the population who would pick an answer lies within the confidence interval. For example, a 99% confidence level means that should you repeat an experiment or survey over and over again, 99 percent of the time, your results will match the results you get from a population.

The larger your sample size, the more confident you can be that their answers truly reflect the population. In other words, the larger your sample for a given confidence level, the smaller your confidence interval.

Standard Deviation

Another critical measure when determining the sample size is the standard deviation, which measures a data set’s distribution from its mean. In calculating the sample size, the standard deviation is useful in estimating how much the responses you receive will vary from each other and from the mean number, and the standard deviation of a sample can be used to approximate the standard deviation of a population.

The higher the distribution or variability, the greater the standard deviation and the greater the magnitude of the deviation. For example, once you have already sent out your survey, how much variance do you expect in your responses? That variation in responses is the standard deviation.

Population Size

The other important consideration to make when determining your sample size is the size of the entire population you want to study. A population is the entire group that you want to draw conclusions about. It is from the population that a sample is selected, using probability or non-probability samples. The population size may be known (such as the total number of employees in a company), or unknown (such as the number of pet keepers in a country), but there’s a need for a close estimate, especially when dealing with a relatively small or easy to measure groups of people.

As demonstrated through the calculation below, a sample size of about 385 will give you a sufficient sample size to draw assumptions of nearly any population size at the 95% confidence level with a 5% margin of error, which is why samples of 400 and 500 are often used in research. However, if you are looking to draw comparisons between different sub-groups, for example, provinces within a country, a larger sample size is required. GeoPoll typically recommends a sample size of 400 per country as the minimum viable sample for a research project, 800 per country for conducting a study with analysis by a second-level breakdown such as females versus males, and 1200+ per country for doing third-level breakdowns such as males aged 18-24 in Nairobi.

How to Calculate Sample Size

As we have defined all the necessary terms, let us briefly learn how to determine the sample size using a sample calculation formula known as Andrew Fisher’s Formula.

1. Determine the population size (if known).
2. Determine the confidence interval.
3. Determine the confidence level.
4. Determine the standard deviation (a standard deviation of 0.5 is a safe choice where the figure is unknown)
5. Convert the confidence level into a Z-Score. This table shows the z-scores for the most common confidence levels:

6. Put these figures into the sample size formula to get your sample size.

Here is an example calculation:

Say you choose to work with a 95% confidence level, a standard deviation of 0.5, and a confidence interval (margin of error) of ± 5%, you just need to substitute the values in the formula:

((1.96)2 x .5(.5)) / (.05)2

(3.8416 x .25) / .0025

.9604 / .0025

384.16

Your sample size should be 385.

Fortunately, there are several available online tools to help you with this calculation. Here’s an online sample calculator from Easy Calculation. Just put in the confidence level, population size, the confidence interval, and the perfect sample size is calculated for you.

GeoPoll’s Sampling Techniques

With the largest mobile panel in Africa, Asia, and Latin America, and reliable mobile technologies, GeoPoll develops unique samples that accurately represent any population. See our country coverage here, or contact our team to discuss your upcoming project.

The post How to Determine Sample Size for a Research Study appeared first on GeoPoll.

For example, one might use an average to represent a fact such as the amount customers are willing to pay for ice cream. However, if 95% of respondents stated that they would spend $5 or less on a pint of ice cream, and 1% of respondents stated that they would spend $100 on ice cream, an average would be skewed by the 1% who would spend much more. In this case, a researcher may decide that a different statistic, such as the median, would more accurately represent the findings. Making these judgments is an important step in the quantitative data analysis process, as are ensuring that data is properly cleaned and coded prior to analysis.

Quantitative Analysis Methods

Quantitative data is analyzed using statistical methods, as quantitative data represents numbers from which datapoints can be calculated. Data from a quantitative dataset, such as survey results, is usually loaded into a program such as Excel or the statistics software SPSS which enables researchers to quickly create tables and charts in order to examine findings. Often the first step in analyzing a dataset is to view top-level findings using descriptive statistics such as mean, median, and mode.

Descriptive Statistics

In the below definitions, we will use the example of a survey with 400 respondents who were asked to rate their opinion of chocolate ice cream on a scale of 1 ‘strongly dislike’ to 5 ‘strongly like’. The data indicated that 100 rated an ice cream flavor a ‘5’, 200 rated it a ‘4’, and 100 rated it a ‘3’.

• Mean or average: The numerical average of a set of numbers.
  • In the above example, the average rating would be ((5×100)+(4×200)+(3×100))/400= 4
• Median: The median is the midpoint in a set of numbers.
  • In the above example, the median would be the number in the 200^th row of data. In this case it would be 4, but depending on the dataset, the median can be different from the average.
• Mode: The number that occurs the most often in a dataset.
  • In the above example, this would also be 4 as it occurred 200 times, while 5 and 3 only occur 100 times each.
• Range: A statement that represents the lowest and highest numbers in a dataset.
  • In the above example, the range would be from 3 to 5.
• Distribution or Percentage: The percent represented by each category within a group, out of the total (100%).
  • In the above example, instead of looking at the dataset as a whole this would report that ‘25% rated the ice cream a ‘5’, 50% rated it a ‘4’, and 25% rated it a ‘3’ 

Cross Tabulations

After examining descriptive statistics, researchers may use cross-tabulations to dig deeper into a dataset. A cross tabulation or crosstab is a way to show the relationship between two variables and is often used to compare results by demographic groups. For the above example, we could create crosstabs to show results by age:

Crosstabs can also be created to examine one datapoint by another, such as if those who rate chocolate ice cream highly also rate vanilla ice cream highly, or if there is a different relationship between the two variables. Crosstabs are useful to better understand the nuances of a dataset and the factors that may influence a datapoint.

Calculating Statistical Significance

When researchers are looking to prove or disprove hypotheses, they will often also use measures to calculate the statistical significance of their findings. Measures of statistical significance demonstrate if a finding is merely due to chance or if it is a significant finding that should be reported on. In the above example, without calculating statistical significance we cannot be sure if the difference in results between those aged 18-24 and 25-34 is due to the difference in age groups, or if the findings are a coincidence based on the sample that was selected and not related to age.

Statistical significance is usually represented by a statistic called a p-value. A p-value is a calculated number between 0 and 1, and the lower the p-value is, the less likely it is that the results were due only to chance. Typically, a p-value of less than 0.05 is regarded as statically significant, as it means there is a less than 5% likelihood that the results were due to chance. While having a p-value of under 0.05 doesn’t necessarily mean that the stated hypothesis is true, it decreases the chances that any differences in the dataset are occurring by chance. Researchers who are running tests to make decisions, for example to determine if populations prefer vanilla or chocolate ice cream in order to make purchasing decisions, should use a test of significance in order to have more confidence in their decision making.

Programs including Excel, R and SPSS can calculate the significance of findings through a series of steps, outlined in more detail here. If you work with a full-service research agency such as GeoPoll, we can run statistical significance tests for you and include the resulting data in our data analysis.

Conduct Quantitative Data Analysis with GeoPoll

GeoPoll is a research company that gathers data for international organizations, governments, consumer brands, and media houses which enables better decision making. Our services range from study and questionnaire design to data analysis, including the creation of data tables, crosstabs, and full research reports. To learn more about our capabilities or get a quote for your next project, please contact us.

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Difference Between Quantitative and Qualitative Data

The primary difference between quantitative and qualitative data is that quantitative data represents data that can easily be measured or quantified, such as the number of people who have bought a product. Qualitative data represent opinions or feelings and cannot be represented by a numerical statistic such as an average.

For example, if a survey asked 500 respondents the question “Did you buy ice cream today?”, and 300 responded ‘yes’ while 200 responded ‘no’, we would know that 300/500 or 60% bought milk, a quantitative fact. If the same survey asked an open-ended follow-up question: “Why did you choose the brand of ice cream you bought?” you would receive qualitative insights that are unique to each respondent. One person may say, ‘I liked the packaging and label colors’ while another may state, ‘It was the first one I saw on the shelf.’ These descriptive insights cannot easily be quantified into numbers, so they are qualitative.

Qualitative vs Quantitative Analysis

Another difference between quantitative and qualitative research is how data is analyzed. While quantitative data can be analyzed statistically and calculated into averages, means, and other numerical data points, qualitative data analysis involves a more complex system.

To glean insights from qualitative data, researchers conduct a manual analysis of datasets and often code responses into categories. For example, to analyze focus group data, researchers could review transcripts or recordings and group similar sentiments together into categories. Due to this manual process, qualitative data analysis is a longer and more labor-intensive process than quantitative data analysis, which is another factor to keep in mind when deciding what type of data to collect.

While some methods such as focus groups typically collect qualitative data, other methods such as surveys often collect quantitative and qualitative data within one survey instrument, as outlined below.

Quantitative Data Examples

Quantitative data is collected through several methods, including surveys, controlled experiments, and certain observation types. Quantitative data types include:

• Yes/no questions
  • “Did you go to work today? 1) Yes 2) No”
• Single choice questions
  • “What is your favorite flavor of ice cream? 1) Vanilla 2) Chocolate 3) Cookie Dough 4) Peppermint 5) Chocolate chip”
• Multiple choice or ‘select-all-that-apply’ questions
  • “Which of the following products did you buy last week? 1) Toothpaste 2) Soap 3) Vegetables 4) Meat 5) Grains 6) Bread”
• Ranking questions
  • “Please rank the statement ‘I enjoy ice cream’ from 1: Strongly disagree to 5: Strongly agree”
• Numerical range questions
  • “How much money did you spend at the grocery store today? Please respond with a dollar amount”
• Quantitative observations
  • Observations that can be categorized or quantified, such as the number of times a person checks their phone in a given time. These observations often take place in a controlled environment.

As all of these question types collect data that fit into set categories or can be calculated into averages and other statistics, they are quantitative.

Qualitative Data Examples

Qualitative data can also be collected through certain types of survey questions, in addition to interviews and focus groups. Examples of qualitative data include:

• Open-ended survey questions
  • “Why is cookie dough your favorite flavor of ice cream?”
• Unstructured or semi-structured interviews
  • Unstructured and semi-structured interviews allow topics and questions to flow naturally, rather than only asking questions from a set question list in a specific order.
• Focus groups
  • In focus groups, multiple people have a discussion (in-person or via an online or mobile-based chat group) facilitated by a trained moderator who gives prompts to start conversations.
• Unstructured observation
  • Researchers can gather qualitative data through unstructured observations, such as observing participants as they partake in certain activities such as shopping.
• Documents or content analysis
  • Reviewing documents to better understand a particular topic or categorize elements of documents is a type of qualitative research.

Data collected from these methods and question types do not provide numerical statistics but instead, give insights that are often longer and more detailed than their quantitative counterparts.

When Should I Use Quantitative or Qualitative Research?

Once you understand the types of data provided by qualitative and quantitative research and the methods for each, it’s essential to understand how to utilize each type of data best. Generally, quantitative data is used to answer precise questions and prove or disprove hypotheses, while qualitative data provides richer insights on a smaller scale.

Qualitative research is often conducted at the beginning of a study when researchers are looking to gather broad, unstructured information on a topic to create a hypothesis, which can then be more clearly answered by quantitative research. Qualitative data collected through unstructured interviews or focus groups can also inform the development of a more structured questionnaire administered to a larger group.

For example, a focus on different ice cream brands may uncover that participants generally consider price and packaging first. That information can then be inputted into a quantitative question: “Which is more important to you when buying ice cream? 1) Price 2) Packaging” administered to a nationally representative sample.

Qualitative data may also be used as part of a mixed-methods research study to add additional context to quantitative data. A researcher may administer both a quantitative questionnaire and conduct a qualitative analysis of interviews with subject-matter experts to form a more robust conclusion.

Surveys can also be split between qualitative and quantitative; Many surveys are mostly quantitative questions that can be quickly analyzed, plus one or two qualitative questions that provide deeper insights into the topic being studied.

Quantitative vs Qualitative Data: Definitions and Uses Cheat Sheet

GeoPoll has experience designing and administering both quantitative and qualitative research studies around the globe. Our research methods include surveys with closed-ended and open-ended question capabilities, mobile-based focus groups, concept testing, and more. To learn more about GeoPoll’s capabilities, please contact us today.

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Benard Okasi Interview

Benard Okasi is GeoPoll’s Director of Research, and oversees GeoPoll’s research team and data outputs. Below is an abbreviated version of a conversation he had with Roxana Elliott, VP Marketing, about his background in research and how the industry is shifting to mobile methods.

Roxana Elliott: Thanks for joining me! Can you start by telling me a bit about your background and where you worked before GeoPoll?

Benard Okasi: Prior to GeoPoll I was at Synovate, a research company that operates in multiple countries – I started there in 2011 and it was later acquired by Ipsos in 2012. At Ipsos I worked in different positions, moving from a research assistant up to a senior research executive. My role was mainly business development, project management and providing insights to clients through reports with actionable insights. Towards the end of 2014, my main focus was on Coca Cola account, and I was placed at Coke as an implant for a year where my main role was to support the client on the projects that were executed by Ipsos for Coke in the then CEWA business unit team.

RE: Why did you first come to GeoPoll and how long have you been here?

BO: While working at Ipsos, the CEO of GeoPoll gave a presentation to research firms in Nairobi about mobile research and where the future of data collection is heading, which is self-completion surveys through mobile, and GeoPoll is here to help bridge the gap. I was curious about the new way GeoPoll was doing data collection through mobile and not having researchers need to go out and collect data in person. I have now been at GeoPoll for 5 years, I first started as a research executive working on data and client support, then managing our partnership relationships with other research agencies, and now leading the research team.

RE: Can you tell me more about the research team and what your responsibilities are?

BO:The research team now is made up of 15 staff, within which we have a data processing team, data analysts, and media analysts. The data processing team does data cleaning, processing and data quality checks. Our data analysts deal with complex analysis of data – for example if clients need significance testing. The media data team looks at audience measurement data and generates actionable insights for our clients. Most of our team members have a research background and have studied statistics hence able to look into data from a statistical point of view and provide complex analysis when required.

RE: What excites you about working for GeoPoll?

BO: What excites me is the team energy and synergy to support clients – when you look at the speed at which we complete projects, with the combined efforts of different departments, we can deliver projects within 2-3 days or a week. We are also able to sit in a central office and collect data in over 50 countries, which shows what the future is in remote data collection. I love the combined effort of the team, and if there are issues in a project we sit down as a team and come up with solutions and the way forward without delays.

RE: How does what we offer at GeoPoll compare to traditional methods in your experience?

BO: If clients go for traditional research, you can only use past data or say old data to inform on their decisions, and what we’ve found at GeoPoll is that most clients want real time information. With mobile research, we can get a set questions from client today and be able to give clients results tomorrow. GeoPoll plays a key role in delivering quality, timely and cost-effective results to clients.

RE: Have you seen a resistance of people moving to mobile research?

There are tracker projects that have been running using traditional research for a long time targeting general population and because mobile data collection only targets a mobile owned population, some clients are resistant to changing the methodology. But markets are changing quickly as mobile penetration has grown over the years – in Kenya I think mobile penetration is over 90%, so that tells you that the people we are targeting through mobile and the information they give us won’t be different from what we would get through traditional (face to face) research. I think that mobile data collection is the future, and the future is here.

RE: Are there any new products you are working on within the research team?

As we continue to drive real-time data delivery to clients, we have made improvements in our systems, including the creation of dashboards which enable us to have more automated systems so we can deliver to clients even quickly. Clients can go directly to a dashboard and pull data and do extra analysis as soon as it’s collected. This innovation around our deliverables will help clients make decisions right away. For some of our larger clients, our team has been able to work on projects in over 30 countries at once, and we’ve created automated processes to deliver quality data regularly for them which has led us to improve our processes over time.

RE: What do you like outside of work?

I love meeting with friends and driving around, and spend most of my time with my family.

RE: Finally, what do you think it takes to be successful in a researcher?

You need to be open-minded and flexible to succeed, and must put the client first in everything you do. Teamwork plays a key role towards the success of any organization or any team so if teams work together you can achieve anything.

The post Benard Okasi on GeoPoll’s Research Processes appeared first on GeoPoll.

Shannon McCrocklin: Tell me about your background before you came to GeoPoll – what space did you work in?

John Paul Murunga: I am a statistician by training and on top of that an Accredited marketing professional from the Chartered Institute of Marketing in the UK. Before I came to GeoPoll I was working as a marketing research consultant with a focus on commercial or consumer research. Prior to GeoPoll, I worked with Synovate and then Ipsos, and earlier I was with Nielsen. Hence, I am a research industry person out-and-out.

SM: What drew you to GeoPoll and how long have you been with the team?

JPM: I have been with GeoPoll for 4 and a half years, it’s funny how time flies! What first drew me to GeoPoll was seeing how much research was evolving and wanting to be part of the next generation in research. Back then, people would not consider SMS as a method of doing surveys, and online/remote research modes were frowned upon.

Presently we live in a world that is moving so fast, we cannot afford to wait for months to receive information that should inform decision making. Can you imagine waiting for 2 months to identify what your target consumers think about your product offering? We have seen for instance from the COVID-19 pandemic how a week can alter the world as we know it. Research solutions must be timely and be specific to remain useful.

SM: Can you briefly describe your role now?

JPM: Currently, I am the Regional Director for the Eastern Africa region. I oversee a team of dynamic, highly skilled, and motivated professionals whose mission is to support clients across the region with information to inform their decision-making. We work with customers across various sectors, including large multi-nationals, local organizations, and small and medium-sized enterprises. Because our solutions are varied, we are well-positioned to support a diverse range of clients.

SM: What do you most enjoy about working for GeoPoll? 

JPM: I cherish many things about working with GeoPoll – GeoPoll epitomizes what research in the information age looks like. When research is paired with technology, I think that is where the magic happens! This axis constantly inspires me: Quality research-delivered on the back of technology, delivered with speed, at a fraction of what traditional research would cost making it more affordable.

SM: What has surprised you about GeoPoll and the projects we work on?  

JPM: Many things, but early on it was how suddenly, working with the same clients, we were able to be efficient in study design, especially from a questionnaire length perspective. For clients who traditionally would have 40-50-page questionnaires asking all sorts of things, we were able to conduct studies for them with 10-30 very definite questions tailored to specific objectives.

The speed of how projects can be turned around was mind-blowing at the start, but now I am used to it. Can you imagine doing a survey of n=3,000 nationally in 2-3 days, and delivering the results displayed on an online dashboard immediately once data collection is closed? This is work that would take weeks or months in other settings. In face-to-face research, interviewers would still be on the ground, with clients worrying about if an interviewer in fact went to the field. Meanwhile, GeoPoll would have reported the results, and the client made informed decisions for their businesses and moved on to other pressing issues.

SM: What are you excited about in terms of where GeoPoll will go in the future?  

JPM: I think the COVID-19 pandemic has accelerated the need to infuse technology in research. GeoPoll is at the center of this and we are already pioneering many efficient, forward-looking research solutions for customers across multiple geographies. We will continue developing these as we go into the future as this is the direction the world is taking.

SM: Do you have a favorite project or experience at GeoPoll you’d like to share?

JPM: I have always given this as an example of how best research studies can be executed in today’s world. The project was looking to reach farmers in the Kandahar region of Afghanistan. The end client was in the US; the agency implementing the client’s intervention was in Afghanistan, and I was the project lead was based in Nairobi. We conducted 1,000 successful IVR interviews in the local languages (Pashto and Dari) remotely from our Nairobi operations Hub. This done within 2 weeks, and the client had their study results and went ahead to implement the recommendations from the research.

SM: What’s a fact about yourself that people may not know right away?  

JPM: I love farming and am a smallholder crops farmer. I like giving back to society whenever I can and work with various charity organizations. I am always keen on being outdoors and visiting new places, and this year was planning to do the famous Cairo to Cape route by road joining from Kenya but was hampered by COVID-19. We will be back in 2021 inshallah!

SM: What does it take to succeed in your line of work? 

JPM: There are no excuses, and one must put in the grind – hard work and consistency are key. You also must be widely read and have a flexible mindset, as we work with clients across multiple industries. As a market research consultant, having knowledge of various industries is vital, or else you are not going to have proper engagements. Finally and most important is nurturing human relationships. We are in a people business, and humans require authentic interactions. We must be understanding, communicate effectively, and be empathetic in how we undertake our work.

The post GeoPoll’s John Paul Murunga on the Evolution of the Market Research Industry appeared first on GeoPoll.

Random digit dialing or RDD is commonly used to conduct general population studies, as it allows researchers to create a sample frame that represents everyone with access to a phone in a population, rather than only those who are listed in a phonebook or have shared their phone number with another source. As random digit dialing does not require researchers to gain access to existing lists of phone numbers, it is one of the fastest and simplest ways to create sample for researchers who do not have an existing sample source. At GeoPoll, we have access to a large database of mobile subscribers in most of the countries we work in, however, we utilize Random Digit Dialing as a sample source for certain projects or in countries where we do not have existing sample.

Pros and Cons of Random Digit Dialing Sample

While random digit dialing is a popular technique, there are some pros and cons to using RDD over a provided sample source, such as a list of specific phone numbers. Some of the pros and cons of random digit dialing include:

Pros of Random Digit Dialing

• Allows those without their own sample sources to conduct general population studies via telephone numbers
• Generating sample is fast and cost-effective, and large sample sizes can be achieved as additional telephone numbers can be easily generated
• It is a highly regarded method of sample creation and is commonly used by research organizations around the globe

Cons of Random Digit Dialing

• May be difficult to reach more targeted respondents, such as those with specific professions, as you do not have any prior information about the characteristics of each respondent, which other sample sources may provide. You also do not have information about those who do not respond to your survey (known as nonresponse error) for the same reason.
• Depending on the phone number format within a country and use of mobile phones versus landlines, targeting respondents by location can also be a challenge
• Not all generated numbers may be valid, which can lead to lower response rates than with a pre-verified list of numbers

Overall, when administered through an experienced research firm, RDD is an excellent way to gather high-quality sample, especially for projects aiming to gather a nationally representative sample.

GeoPoll’s Random Digit Dialing Process

GeoPoll has our own database of respondents in many of the countries we operate in who are profiled by demographics include age, gender, and location, but in certain circumstances, we may turn to RDD to gather sample. In these cases, GeoPoll uses our extensive knowledge of telephone samples to intelligently generate RDD sample that has response rates in-line with those found from the GeoPoll respondent database. GeoPoll’s random digit dialing has three main steps for generating and testing phone numbers:

1. Mobile number generation: Using public information, GeoPoll’s team will identify the most common prefixes for each mobile network operator operating in a market, as well as the percentage share that each mobile network operator represents. We then randomly generate lists of unique numbers that include numbers from each telecom network. This ensures that every mobile number within a country has an equal opportunity of being selected and reduces risks of selection bias.
2. Mobile number validation and testing: Once the initial files are generated, GeoPoll conducts a validation process that identifies likely active and inactive numbers, allowing us to remove numbers that are inactive before proceeding with live testing. GeoPoll’s call center teams then conduct testing with the final list of numbers. If the response rates during testing are in line with expectations based on previous work, we proceed with a survey. If we encounter high numbers of disconnected numbers, we may generate additional numbers.
3. Survey administration: Once GeoPoll has finalized the list of mobile numbers, it is handed off to our trained survey interviewers for full survey administration. During this stage, each interviewer is given a unique list of mobile numbers and is equipped with the GeoPoll Computer Assisted Telephone Interviewing (CATI) Application, which tracks the outcome of each call. The CATI application tracks the percent of phone numbers that are invalid, and the percent of respondents who refuse to take a survey. For those respondents who agree to take a survey, GeoPoll’s system securely stores demographic information along with their telephone number so they can participate in future research.

Random digit dialing is a useful method for conducting surveys via telephone calls in almost any country around the globe. To learn more about GeoPoll’s experience with RDD surveys or to get a quote for your own project, please contact us.

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Weighting is a statistical technique in which datasets are manipulated through calculations in order to bring them more in line with the population being studied. The key difference between the initial sample composition and weighting is that weights are applied after data is collected, and allow researchers to correct for issues that occurred during data collection. For this reason, weighting is also known as post-stratification, as it takes place after the sample has been selected, as opposed to pre-stratification, which is used to balance a sample before data has been collected.

Researchers applying weights are most often weighting on demographic characteristics, such as age, gender, location, and education, but weighting can also account for the differences between those who partake or do not partake in research studies (known as self-selection bias). Weights can also minimize any effects the survey design or data collection mode may have on the sample makeup and resulting data.

In addition to weighting on common demographic variables, studies have found that weighting based on other variables such as internet usage and political affiliation can further reduce bias in some cases. If conducting a phone survey, for example, weights can be applied based on mobile versus landline phone users.

Survey Weighting Methods: Raking and Cell Weighting,

There are several ways in which the actual weighting is performed. Two of the most common include cell-based weighting and raking:

Cell-based Weighting

One of the simplest types of weighting, cell-based weighting can be used when you know the number of respondents your sample should have who are, for example, males age 15-24 or females age 25-34. If your desired sample included 100 males aged 15-24 and 80 females aged 25-34 but should have included 80 males aged 15-24 and 120 females aged 25-34, you can apply simple cell-based weights as illustrated to the left.

Raking or RIM Weighting 

Raking, also known as random iterative method (RIM) weighting or iterative proportional fitting, is a slightly more complex method that can be used when you are weighting to a number of variables, but may not know how the variables interlock; For example, if you need 100 females and 120 people aged 25-34, but do not know how many females aged 25-34 are required. With raking, a researcher would first balance the sample based on one variable, such as gender, and then on the next variable, such as age. If the adjustments for one variable affect another variable too much, then more adjustments are performed until a balanced sample is achieved.

Raking is one of the most common and accepted methods of weighting for public opinion surveys, as it allows for weighting based on multiple variables and aims to adjust each variable by as small an amount as possible. It can be performed quite quickly using a statistical software such as SPSS.

Other methods of weighting include matching, in which a researcher selects a set of cases that is representative of the population from another dataset and aims to match cases from the dataset being studied. Logistic regression modelling and propensity weighting are other types of weighting that are used to account for selection bias amongst a sample. For a more in-depth explanation of various weighting methods see this paper from Pew Research and this from the Journal of Official Statistics.

Pros and Cons of Weighting Data

As with any technique used to manipulate a dataset, there are both pros and cons of weighting, and several guidelines that should be kept in mind when weighting data.

Advantages of weighting data include:

• Allows for a dataset to be corrected so that results more accurately represent the population being studied.
• Diminishes the effects of challenges during data collection or inherent biases of the survey mode being used
• Ensure the views of hard-to-reach demographic groups are still considered at an equal proportion to the population in the final data.

Disadvantages of weighting data are:

• Can over-represent the views of one or several people who may not be an accurate reflection of their entire demographic group
• Can inadvertently introduce additional biases into the dataset
• Can make the findings more variable as it increases the standard deviation of answers (check)

In order to reduce the impacts of data weighting, it’s recommended to weight by as few variables as possible. As the number of weighting variables goes up, the greater the risk that the weighting of one variable will confuse or interact with the weighting of another variable. Also, when data must be weighted, try to minimize the sizes of the weights. A general rule of thumb is never to weight a respondent less than .5 (a 50% weighting) nor more than 2.0 (a 200% weighting).

Additional Information on Data Weighting

GeoPoll provides nationally representative data through a combination of a carefully selected sample frame, use of quotas to manage the demographic composition of those who respond to surveys, and application of weights where necessary. GeoPoll can use multiple weighting methods, including both cell-based and raking, based on the project specifications. To learn more about GeoPoll’s data collection and research process, please contact us here.

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Research Methodologies

Self-Administered Surveys 

A survey is a series of simple questions that build on each other and are designed in a specific order to explore one or more topics. Depending on the survey, questions and responses can be text or multimedia. Self-administered surveys are surveys that are completed by the respondent and are often sent to and completed by SMS, web link, or mobile application. 

Respondents can complete surveys via Short Message Service (SMS or text message) one question at a time. This method is ideal for some populations, such as those found in countries in sub-Saharan Africa or Latin America, because it does not require respondents to have internet connectivity. 

Surveys can also be internet-based via a web link, mobile web link or a mobile application. With mobile web links, respondents with internet-capable phones click on a link within an initial SMS message and then complete the survey in a basic web browser. Mobile web supports video/picture questions, and question formats such as matrices which are not supported in SMS surveys.

Respondents who own smartphones can complete surveys through a mobile application which, with the respondent’s consent, has additional capabilities for GPS location, passive data collection and picture taking. Mobile applications can also facilitate tasks such as retail audits or billboard monitoring. 

Interactive Voice Interviews (IVR) are a self-administered audio interview, in which respondents listen to audio pre-recordings and answer questions using their dialpad. This method is useful for reaching illiterate populations through voice calls, without needing to train call center interviewers, however response rates can be lower than when using CATI, a method outlined below.

Interviews through CATI, CAPI, or Pen and Paper  

Similar to surveys, interviews are a series of questions that explore one or more topics. One major difference is that the interview methods discussed here are interviewer-administered, rather than self-administered. Depending on the targeted population and the amount of time needed for data collection, interviews can be done in person or over the phone. Interviewers must be trained, fluent in the respondent’s language of choice, and familiar with their cultural context.

Computer Assisted Telephone Interviewing (CATI), is done over the phone with the interviewer based in a country-specific call center. This methodology allows for interviewers with multiple language capabilities to easily speak with respondents across a large or hard-to-access region more quickly than is possible using face-to-face interviews. 

For in-person interviews, Computer Assisted Personal Interviewing (CAPI) facilitates face-to-face data collection in the field through a mobile application and removes the need for paper questionnaires or manual data collection. Pen and paper interviews are still used in some contexts but are inefficient and can lead to data input errors or interviewer error.

Observative Research

Observation is a qualitative methodology where researchers witness a respondent’s natural behavior in their usual environment. Depending on the goal, a researcher may engage with the situation or remain at a distance and only watch. The benefit of this methodology is that researchers can understand how a respondent actually acts, rather than what they self-report.

Observational research may be used as a precursor to a survey when researchers need more information about a specific question. Or, observation might be used if researchers are concerned that self-reported behaviors may differ from a person’s actions, even if this inaccuracy is unintentional. 

Focus Groups

A focus group is a small group of people (usually 6-8) who represent a larger group. In traditional focus groups,  respondents meet in one location with a researcher for up to two hours and discuss specific research topics. Similar to surveys or interviews, the researcher will lead respondents through a series of predetermined questions. This methodology allows for discussion and collaboration between respondents.

Digital focus groups can also be administered through either computer-based chats, often called Market Research Online Communities, or using mobile-based group chats, such as ones GeoPoll has facilitated with brands including Unilever. 

Big Data Analytics

Analysis of large amounts of data is a useful way to understand patterns and trends. Gartner defines big data as “data that contains greater variety arriving in increasing volumes and with ever-higher velocity.”  Big data can be valuable in identifying certain types of consumer insights. It can lead to robust decision-making around consumer needs or satisfaction and help predict future opportunities for innovation. However, the large amount of information is not infallible. Just as important is the interpretation and application of this data. While big data analysis tries to make sense of large amounts of information, market research methodologies like surveys and interviews can answer a specific research question. 

Market Research Methods During COVID-19

In-person research is one of the most traditional types of data collection and still remains popular today. However, the worldwide outbreak of coronavirus has made in-person research impossible, and researchers must find other ways to collect data that keep both themselves and their respondents safe. 

Using the aforementioned remote methodologies, which include SMS, mobile web link, CATI, and mobile-based focus groups,  data collection is still possible and safe during coronavirus. GeoPoll has experience transitioning face-to-face research to remote methodologies and has the existing infrastructure to support robust data collection. Our team are experts in remote data collection methodologies and can quickly transition an in-person study to a remote, mobile-based methodology. To speak to a member of our team about your project, please contact us today. For more information about GeoPoll’s research methodologies and conducting research throughout Africa, Asia, and Latin America, download our guide to research in emerging regions. 

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The Challenge of Gathering Mobile Penetration Data 

Although the mobile penetration rate in Asia has been increasing dramatically, it can be difficult to get accurate mobile subscriber and user numbers for the region. There are multiple factors that contribute to this difficulty including individuals having access to a phone they do not own, or people owning multiple SIM cards. An accurate count of subscribers in rural areas can be particularly challenging because counting non-users face-to-face may be impossible due to time and money. To get accurate data, some mobile penetration statistics rely in some part on numbers provided from mobile operators, as well as independent research projects. All of these factors can help account for the variation between different research sources. 

Current Mobile Penetration Rates in South Asia and Southeast Asia

To fully understand the data on mobile penetration rates in Asia, we must take a more granular view and consider statistics from specific parts of the region. Countries in South Asia and Southeast Asia provide a snapshot of this rapidly changing region.

Mobile Penetration in South Asia

Mobile penetration in South Asia varies between countries, with India having the highest reported access. The penetration rate in India was at 55% in 2018 and is projected to reach 63% by 2025. A 2018 Pew Research Center study looking at smartphone penetration found India to have a 40% rate of mobile phone ownership with a further 24% owning smartphones. Another source reported 26% smartphone penetration in 2018. While estimations may vary slightly, all show a strong growth trend.

A 2019 GSMA study on mobile internet specifically found the mobile internet penetration rate in India to be at around 35% the previous year. Bangladesh, the 5th largest mobile market in the region, had a mobile internet penetration rate of 22% in 2018 with Pakistan at 24%. Overall in South Asia, 33% of the population is connected to mobile internet, a number which has almost doubled since 2014. During these four years, an additional 50 million people have also gained access to mobile broadband coverage. 

Mobile penetration across South Asia will likely continue to grow in the coming years. South Asia has some of the most affordable access to coverage worldwide. Additionally,  significant infrastructure investments by 4G providers over the years can partially account for the growth in mobile penetration. 

Mobile Penetration in Southeast Asia

The countries in Southeast Asia have seen incredible growth in mobile penetration over the last few years. GSMA highlights Indonesia as an emerging digital economy giant and one of the top ten most improved countries since 2014. In 2017, there were 176 million unique mobile subscribers in Indonesia, which equated to a 64% penetration rate. This number is estimated to grow to a 69% penetration rate by 2025. Due to infrastructure growth, affordability of monthly data plans, increased higher education, and development of local content 25 million people started using mobile internet in one year. Two thirds of the country now own a mobile device. 

The  2018 Pew Research Center study on smartphone penetration reported 42% of the population had smartphones, with another 28% of people owning another type of mobile phone. A recent GSMA study calculates smartphone connection at 73% of total connections in Q3 of 2019. Young adults are particularly quick to adopt this technology. Only 17% of young adults (18-34yrs) owned a smartphone in 2013, but the Pew study cites a growth to 66% by 2018. 

The Philippines is another area in Southeast Asia that has seen significant growth in mobile penetration rates over the last several years. Startups are driving digital innovation in the country. A recent GSMA report on the mobile economy indicated 2018 mobile subscriber penetration at 64% of the population. Specifically for smartphones, the Pew Research Center cited 55% of adults are reported owning a smartphone, with another 22% owning another type of mobile phone. Of this 55% of adults who owned a smartphone, the largest percentage of these (74%) were young people ages 18-34. 

What is next for mobile penetration in Asia?

Mobile technology is increasingly essential for innovative businesses and international trade. Unsurprisingly, economic success is directly correlated to phone ownership. As a country’s mobile phone penetration increases, the economy becomes more successful. One commonality across Asia and other emerging regions is that younger people are more likely to have access to smartphones. 

We should continue to examine trends in mobile penetration, as well as smartphone and mobile internet adoption while simultaneously not discounting those who do not have access. GeoPoll leverages the growing power of mobile connectivity, while also utilizing face-to-face research modes in certain situations. We are able to reach all types of mobile devices and connectivity levels through SMS, voice calls, and web-based methodologies. 

For more information on our mobile methodologies, please contact us today.

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