Collecting Data During an Epidemic: A Novel Mobile Phone Research Method – Maffioli – – Journal of International Development

1 INTRODUCTION

More and more researchers collect survey data through the use of mobile phones (Toninelli, Pinter, & de Pedraza, 2015). Mobile phone technology has removed significant barriers inherent to traditional survey data collection: It helps in gathering high‐frequency panel data (Dillon, 2012; Hoogeveen et al., 2014; Ballivian, Azevedo, & Durbin, 2015), it provides timely access and monitoring of data collected through face‐to‐face surveys (Tomlinson et al., 2009; Schuster & Brito, 2011; Hughes, Haddaway, & Zhou, 2016) and it allows for fast and low‐cost data collection (Schuster & Brito, 2011; Leo et al., 2015; Mahfoud et al., 2015; Garlick, Orkin, & Quinn, 2019). Yet as mobile phones are widely used as a data collection tool in developed countries where access is common, this research method is getting more and more traction in developing settings, too (Gibson et al., 2017).

Given the rise in mobile phone penetration rates in developing economies (World Bank, 2016), mobile phone surveys are increasingly used to gather national statistics and to conduct monitoring, bio‐surveillance and disaster management (Gallup, 2012; Bauer, Akakpo, Enlund, & Passeri, 2013; Twaweza East Africa, 2013; Hoogeveen et al., 2014; van der Windt & Humphreys, 2014; Garlick et al., 2019). Researchers use mobile phones to conduct different types of mobile phone interviews (Lau, Cronberg, Marks, & Amaya, 2019): interactive voice response (IVR) surveys, which rely on a pre‐recorded voice recording to ask questions to respondents; computer‐assisted telephone interviewing (CATI), which requires trained interviewers to make live calls to respondents following a script provided by a software application; and SMS surveys, which require respondents to type an answer and send back a text message (Dabalen et al., 2016).

Yet gathering data in developing settings—where there might be weak institutions, limited resources and infrastructure, cultural constraints and low literacy—can be even more challenging than in developed countries (Grosh & Glewwe, 2000; Ganesan, Prashant, & Jhunjhunwala, 2012; Dabalen et al., 2016). In developing economies, for example, there is often no access to an initial list of contacts or publicly available data sources, and researchers need to gather baseline data themselves to have a sampling frame. Times of emergency, such as conflicts, infectious disease epidemics or weather‐related disasters, when it is hard to reach respondents in person for interviews, exacerbate these challenges.

This study tested the feasibility of a novel mobile phone data collection method to interview more than 2000 respondents during the 2014 Ebola epidemic in Liberia. The research method uses (i) random‐digit dialling (RDD) and IVR surveys to sample and screen respondents and (ii) CATI to conduct (30‐ to 45‐minute) interviews.

This method builds upon the RDD selection approach outlined by Leo et al. (2015), in which the sample was randomly selected through an online platform and the data collection was performed through an IVR survey.
1 The authors assessed whether mobile phone surveys were a feasible and cost‐effective approach to collect data in four middle‐income or low‐income countries, focusing on whether the method could reach a nationally representative sample and how to improve its survey completion rates. Similarly, L’Engle et al. (2018) used RDD and IVR surveys to collect survey data in Ghana, assessing the response rate and representativeness of the obtained sample compared with face‐to‐face national surveys.

This method also builds upon previous studies, which used CATI to collect high‐frequency data. Hoogeveen et al. (2014) provided examples of phone surveys at high frequency in Tanzania and South Sudan through a call centre. A similar approach was used by Dillon (2012) to elicit data regarding farmer expectations, production and income levels over time. Demombynes et al. (2013) also used a similar high‐frequency survey approach, where the authors randomized the level of incentives and the phone equipment to increase response rates in South Sudan. Finally, Garlick et al. (2019) compared frequencies of in‐person or CATI interviews to micro‐enterprises, and they found no difference in data quality or response rates between high‐frequency CATI interviews and low‐frequency in‐person interviews.

By combining these established methods (RDD, IVR, CATI) in a novel manner, this study developed a data collection procedure that does not require in‐person contacts, thus allowing researchers to gather survey data in challenging settings. In fact, although limitations in the application of mobile phones as unique data collection devices (Kempf & Remington, 2007) remain, evidence regarding their use—as a method both to gather survey data and to select and screen an initial sample of respondents to interview—is still lacking. This research method aims at overcoming two specific challenges related to data collection in developing countries and at times of emergency.

First, researchers begin field research by developing a sampling frame. Usually, they seek access to an initial list of contacts, such as a list of respondents from past studies or a list of phone numbers contained within the datasets from collaborating institutions (The World Bank Group, 2014; The World Bank Group, 2015). Alternatively, researchers may develop a sampling frame from publicly available data sources, such as large‐scale national household surveys or population censuses, which provide the advantage of being nationally representative; or they simply gather data themselves through a face‐to‐face baseline survey. However, getting a sampling frame is challenging when data do not exist or face‐to‐face data collection is not feasible.

Second, in a state of emergency, reaching survey respondents in person for interviews can be challenging, or the risks and costs associated with data collection in order to have a large enough sample may be insurmountable. For example, at the time of the Ebola epidemic—or currently during Covid‐19—in‐person contacts should be limited, if not avoided altogether. Similarly, in the aftermath of hurricanes, floods or earthquakes, travelling to remote areas might be impossible. As weather‐related disasters or infectious diseases remain a worldwide threat, especially in developing countries (United Nations Office for Disaster Risk Reduction, UNISDR, 2015), in‐field data collection may not be always feasible.

This study proposes to use mobile phone technology as the sole tool for all stages of data collection, in order to overcome both of the aforementioned challenges. The research method allows researchers to conduct the entire data collection while relying solely on mobile phones, precluding the need for prior data or fieldwork activities to have a sampling frame or to gather survey data. While the studies mentioned above required at least one in‐person interaction in order to facilitate data collection through mobile phones, the proposed method does not necessitate any physical contact with the respondents. This is key when a sampling frame is not available or fieldwork activities are excessively demanding, such as in the case of emergencies. Furthermore, this method was implemented and tested in a developing country where this type of technology is most needed.

The paper is organized as follows: Section 2 describes the method used to collect the data and the analysis conducted; Section 3 provides a description of the results by estimating call outcomes and rates, the representativeness of the survey sample and the implementation costs of this research method; Section 4 discusses the lessons learned; Section 5 concludes the paper.

2 METHODS

The goal of the initial project was to study the political economy of the 2014 West African Ebola epidemic (Maffioli, 2020), by gathering survey data on individuals’ level of trust and perceived corruption toward several institutions, and their opinions on the government’s actions during the response. However, successfully accomplishing this goal required surmounting significant survey data challenges. There were indeed no baseline data available to select respondents, and gathering face‐to‐face data was impossible owing to the high costs and risks at the time of the epidemic. Thus, the project relied solely on mobile phone technology for both stages of the data collection procedure: (i) sampling and screening of the respondents and (ii) data gathering.

This study tested the feasibility of this research method, by conducting more than 2200 interviews in Liberia between October 2015 and June 2016. Call outcomes and response, cooperation, refusal and contact rates were calculated according to the American Association for Public Opinion Research guidelines (AAPOR, 2016). The representativeness of the survey sample was explored using the nationally representative Demographic and Health Surveys (DHS) (Demographic Health Survey Liberia, 2013) as a benchmark. Costs were also computed in comparison with similar mobile phone approaches used in past research studies.

2.1 Data Collection

2.1.1 Sampling and screening of respondents (IVR)

Owing to the lack of access to a sample available in the pre‐Ebola period, an online platform called VotoMobile was employed to draw an initial list of respondents through RDD.
2
3

Given the known structure of the mobile phone numbers in Liberia, the platform created a list of randomly generated phone numbers that fit that structure through an algorithm. The platform was set up to select phone numbers from the two main Liberian phone companies at that time (Liberian Telecommunications Authority, 2012), LonestarCell/MTN with a share of 49.55 per cent and Cellcom with a share of 40.36 per cent.

The platform went through the randomly generated phone numbers. It was set up to attempt up to four calls to the same phone number: After the first attempt, the second call was placed after 5 minutes, while the third and fourth calls were placed after 8 hours each. The calls were made 7 days a week from 8:00 a.m. to 8:00 p.m. Once the phone number connected and a person picked up the call—implying that it was an existing Liberian phone number—a short pre‐recorded survey (IVR) informed the respondent that she/he had been selected for an interview. The IVR message asked the respondent three questions to gather her/his residence location at the beginning of the epidemic: (i) whether she/he lived in Montserrado County; (ii) if not, in which other county did she/he live; and (iii) which district did she/he live in.

The aim was to gather a heterogeneous sample of individuals from all 15 counties in Liberia, to compare individuals affected by Ebola with those unaffected by it. More specifically, the first question was set up to limit respondents from Montserrado County, the most urbanized and populous region in Liberia and where most of the Ebola cases were concentrated: Selecting respondents from other counties would allow for a heterogeneous sample to make meaningful comparisons for the analysis.
4 If the respondent answered all of the three IVR questions, then she/he would also be informed that someone would call back from the local non‐governmental organization (NGO) and that, upon completion of the CATI survey, she/he would receive $1 of free airtime for her/his phone as a sign of appreciation. The IVR survey was conducted between 21 and 31 October 2015.

As no restrictions were placed on the selection process through IVR, any person answering the phone was considered eligible. Following AAPOR (2016) guidelines, complete interviews (I) were defined as answering the three location questions to gather information on both the county and the district where the respondent resided at the beginning of the epidemic. Partial interviews (P) were defined as answering the first two questions of the survey to gather the county, but not the district. Because the IVR confirms that a real person picked up the call only when the respondent answers the first question, refusals (R) were defined as not answering the first IVR question, that is, whether the respondent resided in Montserrado County. Break‐offs (R) were defined in a similar way for two reasons: First, following the definition of break‐offs in AAPOR (2016) guidelines, the IVR survey was so short that answering the first question meant answering almost 50 per cent of the survey; second, partial and complete interviews already took into account the completion of the second and third questions in the IVR message. In practice, I conservatively categorized as break‐offs phone numbers, which were dialled, for which the phone rang but the respondent did not pick up the call. According to VotoMobile coding, this category does not allow to distinguish whether the user did or did not deliberately respond to the call.
5 In addition, unknown eligibility (UH) was classified as phone numbers which were always busy.

Finally, phone numbers that were dialled but could not be confirmed as known working numbers were classified as not eligible. A high number of ineligible calls are expected because of the automated nature of the RDD calling system.
6 This category includes (i) phone numbers that never responded because the call never rang on a person’s phone owing to an error on the provider’s end. In practice, these are non‐existing phone numbers resulting from the random dialling
7 ; (ii) phone numbers temporarily out of service; (iii) phone numbers unable to connect due to specific technological issues; and (iv) phone numbers for which the call connected at the network level, but a valid connection to an individual’s mobile phone could not be confirmed. These numbers were categorized as phone numbers that connected, but there was no or invalid selection, and correspond to quick hang‐ups.

2.1.2 Data gathering (CATI)

Screened and selected respondents through the IVR survey were called back by real enumerators from a local NGO to conduct a 30‐ to 45‐minute interview.
8 The sample for CATI was selected for the initial research project (Maffioli, 2020) among the phone numbers called in stage 1, for which the IVR survey was either complete (I) or partial (P).

The enumerators from the local NGO were instructed to call the full list of phone numbers multiple times to reach the respondents. They also had the flexibility to re‐contact the respondents at their most preferred time and to call them back when the survey was interrupted for any reason. Owing to budget constraints, the main data collection by the local NGO proceeded in two rounds. Round 1 was conducted between December 2015 and February 2016, while round 2 was conducted in June 2016.

The CATI survey was performed by 18 enumerators, five of whom were female and who had received training in human subject research and a training for mobile phone data collection. In addition to respondent and household socio‐demographics, the survey tool collected data on the following: (i) political outcomes, such as self‐reported level of trust in governmental and non‐governmental institutions and people, perceived corruption of similar institutions and past voting behaviour; and (ii) Ebola‐related questions, such as self‐reported Ebola incidence in the community, the level of information received, the experience with the response and perceptions about the government’s performance (see Maffioli, 2020; Gonzalez & Maffioli, 2020, for the use of the survey sample for other research).
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The data were collected in Kobo Toobox through mobile phone devices and then exported automatically in Excel, and quality checks were performed by the researcher daily. Data cleaning and analysis were conducted using Stata software v15. Ethical approval was obtained from the University of Liberia and Duke University.

The CATI interviews for which respondents gave verbal consent and that were more than 80 per cent completed were classified as complete (I). Because all respondents finished the entire survey, there were no CATI interviews classified as partial (P). Refusals (R) were defined as not agreeing to participate in the survey, while break‐offs (R) were defined as phone numbers that were dialled for which the phone rang but the respondent did not pick up the call. Unknown eligibility (UH) was classified as phone numbers not screened for eligibility, for example, in the case respondents reported having already been interviewed. Finally, not eligible phone numbers were classified as those numbers which (i) were ineligible because respondents were younger than 18 years: This restriction was placed on the CATI selection process because sensitive information was asked; (ii) were temporarily out of service; and (iii) never responded because the call never rang on a person’s phone owing to an error on the provider’s end; in practice, because the call did not connect at the time of the CATI—between 2 and 8 months after the IVR survey—it was impossible to know whether the number was still existing and valid.

It is important to notice that this last group of phone numbers was active during the IVR survey implemented in October 2015, but the phone numbers turned out to be non‐existing or non‐active at the time of CATI, and this is why they could be classified as not eligible (4.31) according to the AAPOR (2016) guidelines. This classification assumes the phone numbers to be existing and valid at the time of CATI to be defined eligible. However, an alternative classification could assume that these phone numbers were instead eligible, but not interviewed (non‐contacts 2.20) because they were working the first time they were contacted for the IVR, that is, between 2 and 8 months before CATI. An alternative estimation of call rates is performed under this assumption.

For both IVR and CATI, response, cooperation, refusal and contact rates were computed according to the AAPOR (2016) guidelines, as follows:

Response rate 1:

(1)

where I, P, R and UH were defined as above; NC was defined as non‐contacts, including cases in which the number was confirmed as an eligible respondent, but the selected respondent was never available or only a telephone answering device was reached (see AAPOR, 2016, categories 2.21 or 2.22); O was defined as other cases, such as instances in which there was a respondent who did not refuse the interview, but no interview was obtainable, including death or inabilities (see AAPOR, 2016 categories 2.31–2.36); UO was defined as contacts who remained of unknown eligibility, such as failure to complete a needed screener, instances in which a person’s eligibility status could not be confirmed or disconfirmed, and other miscellaneous cases in which the eligibility of the number was undetermined and which did not clearly fit into one of the other designations (see AAPOR, 2016, categories 3.2–3.9). Response rate 2 added partial interviews P to the numerator; response rate 3 multiplied (UH+UO) by e, defined as the proportion of all callers screened for eligibility who were eligible (see details below); response rate 4 added P to the numerator and multiplied (UH+UO) by e.

Cooperation rate 1:

(2)

where I, P, R and O were defined as above. Cooperation rate 2 added partial interviews P to the numerator; cooperation rate 3 did not consider other causes (O) among eligible respondents, but not interviewed (see AAPOR, 2016 categories 2.0–2.3); cooperation rate 4 added partial interviews P to the numerator and did not consider other causes (O).

Refusal rate 1:

(3)

where I, P, R, O, NC, UH and UO were defined as above. Refusal rate 2 multiplied (UH+UO) by e, defined as the proportion of all callers screened for eligibility who were eligible; refusal rate 3 excluded (UH+UO).

Contact rate 1:

(4)

where I, P, R, O, NC, UH and UO were defined as above. Contact rate 2 multiplied (UH+UO) by e, defined as the proportion of all callers screened for eligibility who were eligible; refusal rate 3 did not consider (UH+UO).

It continues to be debated which the best method is to estimate e. Most AAPOR methodologies assume that all callers are either eligible (e = 100 per cent) or all ineligible (e = 0 per cent) to define a range of minimum or maximum response rate, respectively (Smith, 2009). However, assuming that some are eligible seems to be a more plausible assumption (Martsolf, Schofield, Johnson, & Scanlon, 2012). I followed Martsolf et al. (2012) and estimated e using the most conservative method (AAPOR4), which considered all unknown eligibility refusals (quick hang‐ups) to be eligible non‐interviews. In practice, e was calculated by taking the sum of cases that were considered eligible (P,I,R) and dividing by the sum of those cases (P,I,R) plus the known ineligible non household cases (UH).
10 However, robustness checks were also implemented assuming the extreme cases of e = 100 per cent or e = 0 per cent.

3 RESULTS

4 DISCUSSION

The two‐stage data collection method proposed in this study uniquely samples and screens respondents and gathers data without any in‐person interaction with respondents, in a developing country and at the time of an epidemic. The method allowed researchers to conduct more than 2200 interviews in Liberia during the 2014 Ebola epidemic, with an average estimated cost of $24 per survey and with call rates comparable with those of similar survey research methods.

By combining established data collection methods (RDD, IVR and CATI) in a novel manner, the main strength is that, relying solely on mobile phone technology, this precludes the need for prior data or fieldwork activities to have a sampling frame, and it allows researchers to gather survey data in challenging settings. The sampling and screening of respondents through IVR (stage 1) could be useful in any country with some phone access (Liberia has on average 65 per cent of phone coverage, Table A3), and in total absence of any initial list of respondents. The data collection through CATI (stage 2) documents how sensitive information can be asked in a 30‐ to 45‐minute phone interview without compromising the response rate. Altogether, the survey data highlight how this innovative data collection method was feasible and cost‐effective in a developing country and at the time of an epidemic.

There are several meaningful lessons learned, which are important to discuss for the use of this method in future research.

First, the study found that the group of respondents was not representative of the general population of the country. In fact, in Table 3, the survey sample was compared with a nationally representative survey (Demographic Health Survey Liberia, 2013), and it was shown to be biased. Even after re‐weighting on a few selected socio‐demographic variables, most of the differences remained statistically significant. This is not surprising because the selection and screening were conducted through mobile phones and respondents needed to have access to a mobile phone in order to pick up the call. This is also in line with other research in developed (Lee, Brick, Brown, & Grant, 2010) and developing countries (Leo et al., 2015; Lau et al., 2019) where respondents interviewed through telephone surveys are different from the entire population, even after controlling for demographic characteristics. This method, then, does not appear to be the best approach if researchers are interested in a nationally representative sample, unless fixed quotas are used in the IVR survey to select respondents based on pre‐defined geographical and socio‐demographic characteristics, in order to reproduce a nationally representative sample.

Reaching sample representativeness is achievable, but it would come at the expense of higher implementation costs in the IVR message, because several screening questions would need to be added to the survey tool. As the number of questions asked through the IVR survey increases along with sample specificity (e.g. half women and half men, or a fixed number of respondents per geographical area), so do the difficulty and costs associated with finding the targeted nationally representative sample.
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The principal remaining advantage of gathering data using this innovative method (but from a non‐nationally representative sample) is to collect high‐quality data at time of emergency when in‐person interactions are impossible and no publicly available data exist to select a nationally representative sample. Several research questions are and could be answered by focusing on a specific sample of respondents selected with a small set of IVR quotas.

Second, another important lesson learned from this study to consider for future research relates to the attrition that researchers could face from the initial list of phone numbers generated by the RDD to the completion of the CATI survey.

World Bank researchers who performed phone surveys during the Ebola epidemic in Liberia reported that only 30 per cent of the initial sample completed the survey (16 per cent of the original sample, The World Bank Group, 2014). In Sierra Leone, the response rate was also lower than expected, given the nature of the survey and the difficult conditions under which it was conducted: About 69 per cent of the sample respondents with phone numbers completed the survey (45 per cent of the original sample, The World Bank Group, 2015). Other surveys, performed through face‐to‐face interviews in Montserrado County, reached 95 per cent of the respondents (Blair, Morse, & Tsai, 2016). Follow‐up phone surveys reached about 80 per cent of the original sample. The initial in‐person interaction between the field enumerators and respondents during the baseline survey seemed to have been the main factor determining the lower attrition rate. Because this project was conducted starting in late 2015, when the epidemic was not at the peak and life was going back to normal, the response rate was expected to be similar or higher than it was in the World Bank surveys and were indeed computed at 51.97 per cent for the IVR survey (Table 2), and 91.10 per cent for the CATI survey (Table 3).

Similarly, participation for confidentiality reasons was a related concern. First, although individuals never provided their phone numbers to enumerators, there was concern that respondents, once called back from the local NGO, would ask enumerators where they accessed their phone number from and refuse to participate. In Liberia, this was not a problem because people are used to receiving calls for advertisements or polls: The refusal rate of the CATI survey was in fact 8.30 per cent across the two rounds of data collection (Table 3).

Second, because of the complete lack of in‐person interaction at both stages of the data collection, there was a concern that respondents would not feel at ease to provide opinions to a stranger.
18 There was also uncertainty that, owing to the personal nature of the questions asked in this study (e.g. about their experience with Ebola or their political views), respondents would be reluctant or they would refuse to stay on the phone for a long time (the survey lasted 30–45 minutes with 100 questions). Rather, only 113 respondents refused to participate (Table 2), and once enumerators established a first phone contact and call at an appropriate time, individuals completed the CATI survey.

Third, respondents were provided with a $1 airtime incentive that was directly transferred upon the completion of the survey.
19 Respondents were informed at the time of the sampling, through the IVR survey, about this direct benefit. Recent phone surveys in developing countries found small differences in attrition when incentives were randomly varied (Gallup, 2012; Demombynes et al., 2013; Hoogeveen et al., 2014; Leo et al., 2015); therefore, the incentive may have contributed to a lower non‐response rate.

Finally, this method does not solve the problem of people sharing a phone number. For both stage 1 and stage 2, whoever answered the call was interviewed. It is then possible that whoever picked up the call during stage 1 would not be the same person in stage 2. Even if that was the case, stage 1 only collected the geographical location of the respondent, while the survey data were collected in stage 2. During the CATI surveys, the respondent was asked about her/his location again. If different from what was reported during the IVR, the respondent was confronted about it and was asked to confirm the correct location at the beginning of the epidemic. In about 16 per cent of the cases, individuals reported a different location at the two stages. Qualitatively, the majority of respondents said that they had problems with the speed of the IVR message and how they inputted their answers into the keyboard. For the analysis, the location data collected through CATI interviews were trusted more than the data collected through IVR. However, this problem should be taken into account and addressed in future uses of this method.

A last important lesson learned relates to the time waited between the sampling and screening through IVR (stage 1) and the data collection through CATI (stage 2). For budgetary reasons, a mobile phone data collection (round 2) was added at a later date, and this caused between 2 and 8 months of delay between stage 1 and stage 2. In round 2, the local NGO found that 43 per cent (634) of the numbers provided (1475) were permanently switched off and 28 per cent (408) were not ringing (Table 3). While Liberians have the habit of owning multiple SIM cards and switch between them on the basis of cost advantages for airtime, text messages and internet data, it is not clear whether this would happen in other countries. Still, it is advised that researchers limit the time window between the two stages of data collection as much as possible to reduce potential additional problems of not finding (at least temporarily) working phone numbers.
20 Despite this caveat, respondents were willing to answer long (30–45 minutes, 100 questions) CATI surveys with low refusal and break‐off rates (Table 3). This is a good indication that CATI surveys are feasible in challenging settings such as at the time of an epidemic in a developing country.

5 CONCLUSION

This study proposes and describes a novel two‐stage data collection method which combines (i) RDD of phone numbers and an IVR survey to conduct sampling and screening and (ii) data collection through CATI. This procedure was used to conduct more than 2200 interviews in the country of Liberia, at the time of the 2014 Ebola epidemic. Following the AAPOR (2016) guidelines, response, cooperation, refusal and contact rates were computed at 51.97, 52.62, 41.85 and 98.77 per cent, respectively, for the IVR survey. The CATI survey was more successful: Response, cooperation, refusal and contact rates were 91.10, 91.65, 8.30 and 99.40 per cent, respectively.

Unsurprisingly, because both stages of the data collection method were conducted by mobile phones and no quotes were set up to reach a nationally representative sample, male, educated respondents from urban areas and with access to mobile phones were more likely to be interviewed. A comparison between the survey sample and the nationally representative sample from the DHS indeed confirmed statistically significant differences in socio‐demographic characteristics. The re‐weighting of the sample on a few selected covariates reduced the gaps, but the differences remained statistically significant. Yet in an analysis of the costs compared with other past research approaches used, the study found that this method offers promise for data collection in developing countries at a low cost ($24 per survey), especially in challenging settings.

The results on call rates, sample representativeness and costs are in line with other studies that use RDD, IVR or CATI to collect data in developing countries. First, the closest studies to mine in terms of the methods used to gather data in other developing countries (L’Engle et al., 2018; Lau et al., 2019) found worse call rates. Second, similar to what I found, some research—in both developed (Lee et al., 2010) and developing countries (Leo et al., 2015; Lau et al., 2019)—found that respondents interviewed through CATI were different from the entire population, even after controlling for demographic characteristics. Third, the costs of this innovative method were lower than those of face‐to‐face surveys (Dillon, 2012; Hoogeveen et al., 2014; Ballivian et al., 2015; Lietz et al., 2015; Mahfoud et al., 2015; Dabalen et al., 2016) and within the range of studies using IVR or CATI (Schuster & Brito, 2011; Dillon, 2012; Ballivian et al., 2015; Leo et al., 2015; Mahfoud et al., 2015; Dabalen et al., 2016; L’Engle et al., 2018; Garlick et al., 2019; Lau et al., 2019), suggesting that this method is feasible and affordable.

I suggest that researchers weigh the advantages and limitations of this approach before implementing it in any specific country or context. Still, the proposed method remains the first to only rely on mobile phone technology for all stages of data collection. As the utilization of mobile phones for data collection and research is increasing in developing countries, this innovative two‐stage procedure improves the ability of researchers to gather information in emergencies in developing economies, making it a unique and feasible approach to implement in challenging settings.