telephone sampling in the middle east: issues and advancements

T e l e p h o n e S a m p l i n g i n T h e m e n a : i S S u e S a n d a d va n c e m e n T S

CARSTEN BROICH,SAMPLE SOLUTIONS

Content

4 Abstract

5 Introduction

6 Methodology

7 Coverage of telephone surveys

9 Numbering Plan

11 Sample Creation

12 Screening of Sample

13 Fieldwork Results

16 Conclusion and Follow-up Steps

16 References

4 | C O R P O R A T E A N N U A L R E P O R T

CATI surveys are a widely used method for data collection as an alternative to conventional face-to-facestudies. Especially in areas where face-to-face data collection is difficult to conduct due to cultural, financial,logistical or political issues, CATI surveys remain a predominant method of collecting data. In NorthernAmerica and many Western European countries, population studies have been replaced by online studies.Nevertheless due to cultural, polit-ical and financial aspects , CATI surveys remain a very favorable method tocollect data in the MENA region. In order to reduce the total survey error, it is important that the telephonesampling frame is setup correctly.While in Western Europe and North America most of the numbering plansare well documented, this is not the case in most of the MENA countries.

To prevent under-coverage error,the sampling plan should include all allocated numbering blocks while at the same time remove non-allocatedblocks to increase the working number rate. Furthermore, a number of allocated numbers within a numberingblock remain very low resulting in a very inefficient telephone sample in case of no further filtering beingapplied. A correct categorization of status codes of phone numbers before and after data collection is also

necessary in order to estimate the non-response bias. This paper will analyze research studies from recentyears in which landline and cell phone numbers have been pre-filtered.

Also, methods for pre-screeningof landline and mobile phone numbers will be outlined which can further increase the efficiency duringfieldwork. On the other hand, it is mandatory that working numbers are not removed by chance.e. Methods ofanalysis to prevent this are discussed. With decreasing landline phone penetration and increasing cell phonepenetration it will be required to make use of a higher share of cell phone

sample. Current ly, most researchersare hesitant to increase the share of mobile phone penetration due to the fact that no location information isavailable, in contrast to the US where an exchange code even for cell phone numbers

exists. This paper willfurther analyze ways to find estimates for the location of cell phone numbers within predetermined regions.

Abstract

“This paper will analyze research studies from recent

years in which landline and cell phone numbers have been

pre-filtered”

Telephone Sampling in the MENA: Issues and Advancements

Country Mobile in % Landline in %

Algeria 92.9 7.8

Egypt 114.3 7.6

Iran 87.8 39.1

Kuwait 218.4 14.2

UAE 178.1 22.26

Libya 161.1 11.3

Oman 157.8 9.6

Quatar 145.8 18.4

Saudi Arabia 179.6 12.33

Syria 63.86 8.5

C O R P O R A T E A N N U A L R E P O R T | 5

Introduction

Much research has been conducted on telephone sampling frames in Western countries like Germany or the USA but very little has been published about telephone sampling in the MENA region. Various studies exist with regard to face-to-face sampling in the MENA region like the Pew Global Attitude study which uses face-to-face interviews in countries like Turkey (Wike, 2014) or Lebanon (Kohut, 2012). Looking at the recent develop-ments in the Middle East and Northern Africa, it becomes also evident why telephone surveys are becoming a preferred option for data collection in the Middle East: The geopolitical situation in many MENA countries makes it difficult to collect data face-to-face. This could be due to the topics of the surveys, governmental restriction, security of interviewers and staff and many other reasons. Telephone surveys at the same time can be conducted remotely within the country or even from a different country providing fewer issues when it comes to sensitive questions that might be opposed by a government or the safety of interviewing staff. Besides that, telephone sampling frames in Western European countries or the US are well researched and documented (available numbers, dual-frame, screening, response rates etc.) while not much research exists for sampling frames in the MENA regions (Elkasabi, 2015).

In this paper telephone sampling in the MENA region is analyzed together with an outline of advancements from a sampling point of view but also from a technology point of view. While face-to-face studies are still the preferred option for data collection in the MENA region, CATI fieldwork is considered more and more as an option due to faster turnaround time, high cell phone penetration and lastly cost-efficiency. Mobile phone penetration increased rapidly during the last decade while landline phone penetration has been decreasing or stagnating. While certainly, a coverage error exists within a dual-frame telephone sample, this error is becoming smaller and smaller. Looking at table 1, it can be seen that mobile phone penetration has reached levels beyond 100%, nevertheless, this does not mean that there is no coverage error due to the fact that people might have multiple phones. In most of the MENA countries, landline penetration is in the single digit or low two digits with the exception of Iran. When comparing these countries with Western countries like the UK, US or Germany, it can be seen that the landline penetra-tion in Western countries is a lot higher but at the same time, mobile phone penetration for the MENA countries surpasses the Western countries like the UK, US or Germany, it can be seen that the landline penetration in Western countries is a lot higher but at the same time, mobile phone penetration for the MENA countries surpasses the Western countries.



Methodology

From a methodological point of view, it is important to consider the total survey error framework (TSE) and implement this partially for telephone surveys in the Middle East. Looking at coverage error, sampling error and non-response error there are certain key items that are very different for telephone surveys in the MENA region when comparing to Face-to-Face or CATI surveys in other countries. This will be outlined in the methodology section.This paper also aims on gathering information from previous research and further stresses the importance of a well documented sampling approach for CATI surveys, similar to the level that exists for face-to-face surveys.

In order to assess the issues but also advancements in telephone sampling in the Middle East, the whole process is broken down into five main aspects which support the analysis:• Coverage• Numbering Plan• Sample Creation• Screening of Sample• Fieldwork Results

Each of this section will provide an introduction to the different stages of telephone sampling. In the numbering plan section different numbering plans, formats and differences for mobile and landline frames are explained and analyzed. Furthermore, list-assisted numbering plans are elaborated on. Once a numbering plan exists it is possible to create the actual telephone sample which can be drawn by using different methods like a simple random sample or a stratified random sample as long as the probability of selection for each phone number remains equal. Additionally, geocoding variables like rural/urban can be added to allow more control during fieldwork or for post-stratification purposes. Once the sample is created, various screening methods can be applied to improve the efficiency of the sample and remove disconnected numbers. The sample is then forwarded to the fieldwork agency and used for data collection. An extra section will look at fieldwork results based on the provided RDD sample. This framework allows a systematic approach to cover the various stages and issues of telephone sampling in combination with actual fieldwork examples for the Middle East and Northern Africa. In this paper, we will make use of part of the total survey error framework by Groves \cite{groves2010total}. Figure 1 shows the various sources of error within a survey. Also, this paper the focus laid mainly on the coverage error and sampling error but to some extent also the non-response error in cases where data is available from actual fieldwork projects.

With regard to the coverage error, it is important to look at the RDD dual frame approach and analyze to which this represents the target population which is usually the general population of a target country. The error here is usually due to non-coverage. As for sampling, it will be looked at cases for dual-frame approach and single-frame approaches together with the limitations.When looking at the next type of error, the sampling error, there are several aspects to consider which adds additional errors. Important is that the basic requirements for a probability sample are met which is randomness for each unit/element, non-zero probability of selection for each unit and element and lastly known probability of selection for each unit/element. Besides that, the sample design needs to be defined which in most cases is a stratified random sample but there are also some further exceptions. Additionally, what usually is not included in the TSE framework within telephone fieldwork are the errors resulting from the usage of a dialer when pre-screening the raw RDD sample.

Target Population

D e s i g n a t e d Sampe

Final Sample

Coverage Error

Sampling Error

Nonresponse Error

Sampling Frame



Coverage of the Telephone Surveys in the MENA

This has an influence on the sampling error but also on the non-response error. When looking at unit non-response dialer codes are essential for calculating the response rate following the AAPOR guidelines. In some MENA countries however, dialer disposition codes are all but standard and need fine-tuning and checking. These three sources of error will be further discussed throughout the paper.

In this section different aspects of coverage from a telephone survey perspective are discussed. Different MENA countries are analyzed based on mobile and landline phone penetration and possible issues for non-coverage. To measure non-coverage, it is important to have access to various data sources. Different data sources can be used to retrieve or estimate possible coverage error.

1. Bureau of Statistic2. Survey data from Face-to-Face studies3. Proxy Data

To review the basic definition of the coverage bias: it is the difference between the mean for the target population that is included in the sampling frame and the mean for the entire population. Or expressed in the AAPOR calculation for response (equation for coverage).



Important are thus two factors: to which extent is the mean of the non-covered population different from our target population and furthermore what is the ratio of the non-covered population over the covered population. To find the ratio of the non-covered population it is necessary to understand to which extent the dual-frame approach excludes part of the target population, thus the population without any access to a landline or mobile phone. In many European or American countries, there is sufficient data from national statistics

bureaus or face-to-face surveys which provide the needed satistics. In comparison, information level in the MENA region various from country to country. Most of the Small Arabic Peninsula States (SAPS) have a high degree of information avail-able, eg. From the statistics bureau of Qatar, the following information can be retrieved (Gulrez, 2013).

Based on this source around 99% of all households consist of access to mobile phones and 70% have access to a landline phone. Combining a dual-frame approach will thus lead to a coverage error which is less than 1% for the overlay of sampling frame and target popula-tion. The information is retrieved either based on government data from the telecommunication provider or based on a government survey. In some countries, the official statistics does not provide any indication on mobile or landline penetration, therefore it is possible to use secondary surveys from research institutes which have conducted national representative face-to-face surveys and further have collected data on landline or mobile phone ownership. In a survey is Saudi-Arabia which was conducted by face-to-face interviews, a total of 9,151

households were interviewed of which a total of 98% of individuals owned a mobile phone while 79% of all households owned a landline phone. The third method makes use of estima-tions based on different factors:Amount of landline subscriptionsAmount of mobile subscriptionsAverage household sizeAverage amount of cell phone per personEstimated share of business numbers

For the case of Saudi Arabia, there was a total of 4.1 M landline phone subscrip-tions and an average household size of 6.4 people. Estimating that 10% of all landline phone numbers are business numbers leaves a total of 3.7 M landline subscriptions. Multiplying a number of residential landline subscriptions with the average household size yields a total of 23.7 M people with access to a landline household. With a population of 31 M people, this yields a penetration of 76.5% and is quite close to the value found in the face-to-face study.Making use of the various sources has some limitations. Government data might

deviate from the reality due to the fact that governments aim at showing a more advanced image of the country (mo-bile

have other bias or might not have been sampled well enough to account for rural area and thus over-representing rural population and thus the mobile phone or landline ownership on a household level. The proxy-data certainly has the advantage of just being an estimate and

further is relying on various estimates. All in all an additional disadvantage results from the fact that in many cases, the data is outdated and especially when looking at landline and mobile phone developments, large changes in owner-ships can be witnessed worldwide due to a quick decrease in pricing and vast improvement in coverage and speed. The

method for estimating phone penetration works well for ranged below 95% beyond that number it is difficult to estimate the low single digit that is excluded.

Access to mobile

phones: 99%

Penetration of 76.5%

Coverage error: less

than 1%

“ Government data might deviate from the reality due to the fact that

governments aim at showing a more advanced image of the country”



i. Numbering Plan Types

When starting with a numbering of a specific country, there are two main options of numbering plan:

• Closed Numbering Plan• Open Numbering Plan

In an open numbering plan, a number of digits is not fixed. So phone numbers can have a varying length. In contrast, a closed numbering plan imposes a fixed number of digits so that each phone number has the same length. In both cases it is common.A closed numbering plan imposes a fixed number of digits to every telephone number, while an open numbering plan allows variance in the numbers of digits. Many numbering plans subdivide numbering regions into specific geographic regions designated by an area code, which is a fixed-length or variable-length set of digits forming the most-significant part of the dialing sequence to reach a telephone subscriber. The area code can reflect regions but also cities.In a closed numbering plan, telephone numbers can be assigned based on numbering blocks or based on numbering ranges. For a numbering block, all possible numbering combinations are possible. As an example, a numbering block consisting of N=4 digits has a total of 10,000 numbering combinations (XXXX). On the other hand, numbering ranges provide a range within a specific numbering block eg. a range could be AA200-AA999 which provides a total of 800 assigned numbers. These two methods are important when calculating the frame size and total amount of possible number combinations. In countries where an open numbering plan is used, the numbering length can vary between a two digits and up to 9 digits. Considering the sum of possible numbering combination for these numbering ranges, it becomes evident that a numbering plan with all possible numbering combination is almost impossible due to a very low hit rate. Only the case of

1 numbering block with 9 digits already means a possibility of more than 1 billion possible numbering combination. In these cases, it is recommended to create an auxiliary sampling frame using listed phone numbers.

An example of a well-structured numbering plan is Egypt in which the name of the region, the area code but also the numbering ranges are provided. For the case of Egypt, there are no numbering blocks with specific amounts of unknown eg. N=4 digits (XXXX) but rather ranges. This information allows the sampling frame to contain all possible landline and mobile phone combination so that no numbers are included.

From these specific ranges it is possible to create sampling frame containing all possible numbering combinations.

Region Range Area Code

East Cairo 23100200-23100399 02

East Cairo 23130000-23131999 02

East Cairo 23132000-23137499 02

West Cairo 25466000-25466649 02

Numbering PlanIn this section, various forms of numbering plans are discussed. Furthermore, list-assisted numbering plans are discussed for cases where the provided numbering plan data is not sufficient to create a sampling frame. Additionally, various examples from different countries within the MENA regions are looked at for both cell phone and landline numbering plans. The numbering plan section also deals with the coverage error. By using some example numbering plans and countries, coverage error due to mismatch of the target population and sampling frame are discussed but also issues with the sampling frame due lack of information or list-assisted numbering plans.

ii. List-assisted Numbering Plans

In the previous section, list-assisted numbering plans are introduced. There are several advantages and disad-vantages for list-assisted numbering plans. First, there is an outline on the creation of list-assisted numbering plans, followed by an outline when it is useful to make use of a list-assisted numbering plan. Lastly, we will look at advantages and disadvantages for a list-assisted numbering plan together with examples from past MENA sampling projects.

List-assisted numbering plans can be created in different ways. Imagining the case where a numbering plan consists of a varying length of numbers (open numbering plan), in this open numbering plan, it would be very inefficient to sample all possible numbering combinations due to an extremely low hit rate during dialing and subsequently very high-fieldwork costs. An alternative approach here is to make use of numbering block stem which is created from available phone numbers. If a sufficiently large data pool of phone numbers is available, it can be used to create an auxiliary list-assisted numbering plan. The idea behind this list-assisted numbering plan is the fact that providers do not allocate all numbering within the numbering ranges equally but rather release numbering block by numbering block which is allocated to a specific telephone operator. These operators need to purchase numbering blocks and therefore prefer adding phone numbers to allocated blocks. Thus, when making use of a list-assisted numbering plan using working phone numbers it is possible to increase the amount of working phone numbers per numbering block significantly.Another option for a list-assisted numbering plan is by analyzing active sim cards within a provider range. Imagine the case of a provider having the following assigned phone numbering blocks: AB X XXX XXX. In this case, there is a total of 10 million different numbering possibilities. By creating a test sample of around 1% of the entire numbering size and then checking in which areas phone numbers are active, it is possible to find ranges in which numbers are allocated. With mobile phone sample, these ranges are usually in blocks of 10.000 or 100.000 numbers thus that when an active SIM within a range is found, the entire numbering block of 100.000 or 10.000 numbers can be marked as allocated. This method can significantly decrease fieldwork time and cost while merely sacrificing coverage.

The following figure shows a block density analysis for the case of Libya. There are two providers, namely Al Madar and Libyana. A total of 1.000 mobile phone numbers was tested per provider and subsequently

first two digits of the numbering block. For Libyana it is possible to derive that no numbers are assigned for the area of 10-20 and also for the area of 90-99. Similar for Al Madar, no phone numbers are assigned for area 20-29 and for 50 to 59.

Block density Analysis for Mobile operators in Lybia

This example shows that around 20% of possible numbering area can be removed, in other countries, these values can stretch up to 85% and therefore can significantly improve fieldwork performance while not introducing any further coverage error.

iii. Advantages and Disadvantages of a list-assisted numbering plan

There are several advantages and disadvantages when using a list-assisted numbering plan. First of all, a list-assisted numbering plan makes population studies via telephone feasible since in many cases there is no existing or complete numbering plan. Due to large publicly available data on numbering, directories, and social media channels, it is possible to create list-assisted numbering plans with large coverage.While coverage levels can reach up to 99%, neverthe-less there will be some coverage error due to the fact that available data sources might not show every single available phone number.

0

5

10

15

10 30 50 70 90

Block Density Analysis

Al Madar Libyana

1 0 | C O R P O R A T E A N N U A L R E P O R T

C O R P O R A T E A N N U A L R E P O R T | 1 1

Sample CreationIn this section various methods for sample creation are outlined, all of them making use of an equal probability of selection procedure.

i. Stratified Random Sampling

For most of the RDD sample in the MENA regions, a proportionate stratified random sample is used. This is used for both mobile and landline RDD sample. While the landline RDD sample is stratified based on the population sizes in the strata (based on regions), the mobile RDD sample is generated based on the provider share and a number of allocated numbers.

The strata used in most cases are the population size of specific areas of popula-tion strata (governates, regions etc) can be linked to specific telephone numbering blocks. Within a strata all possible numbering combinations are generated, subsequently, a random selection is drawn. A proportionate random sample is neces-sary in many cases since the amount of assigned numbers per numbering strata varies significantly. In urban regions, the amount of total numbers per available numbers might be far higher than the same for rural areas. If the sample is then not screened or pulsed it would lead to an underrepresentation of rural areas.

ii. Simple Random Sampling

In some countries in the MENA region, it is possible to make use of a simple random sampling since no area codes or regions exist. All possible numbering possibilities are then collected in a central database.An example of this kind of sampling is Bahrain. While no area codes exist, different provider exchange does exist which would be a stratification based on provider strata possible. However, number portability is enabled so that

enabling stratifications based on strata would not make a lot of sense.

iii. Geocoding

In most cases, additional data can be added to the telephone sample. For stratified RDD sample, it is common that a stratum is included with a region for which the phone number is sampled on. Besides that, it is possible to append more information like the size of community (population), rural/urban definitions, ruling entity (eg. IS vs. government fractions)

In some cases, the numbering plan only gives a very rough indication of the assigned landline areas. Therefore, the numbering plan would not be suitable for targeting small areas. Nevertheless, by overlaying known phone numbers together with their location (latitude and longitude), it is possible to plot a heatmap of these available phone numbers based on specific exchanges.

The generated heatmap can then be used to provide an understanding of the area which is covered by a specific exchange.

Besides a heatmap for the location of numbering exchanges, it is possible to add other socio-demographic elements to the sample. Consider the case of Syria in which response rate is certainly influenced by the ruling faction. By doing so, changes in ruling fractions can be updated with ease. But during the fieldwork asking about the ruling faction might be a very sensitive topic to ask. Based on the numbering area codes, however, it was possible

to match ruling factions such as:• Syrian Govern-ment forces• SRCC• Peshmerga• ISIS

to specific numbering areas. By doing so the sensitive question for the ruling faction was avoided. Obviously this geocoding was only possible for the landline phone numbers and not for the mobile sample.

“The mobile RDD smple is generated based on the provider share”

Telephone Sampling in the MENA: Issuesand Advancements


i. Screening of landline sample

Landline sample screening is usually done using a dialer which pulses the phone number. A ping signal is sent to the phone number and before an actual

connection is made, the connection is disconnected. Nevertheless, it is possible to check whether a phone number is active or disconnected in most of the cases.

ii. Screening of mobile phone sample

The most suitable way to screen mobile phone sample is by means of checking the sim card status and the IMSI number. If a phone number is activated, it will have received an IMSI number which shows

that the SIM card is in use. This method is effective in most of the countries and fairly cost effective on a large scale. Up to 50 numbers can be screened per second this way which is way faster than the usage of the traditional dialer. At the same time, it is possible to check for numbers which are roaming and might be located in a different country. An active sim card might however not directly imply that the phone is currently switched on and in usage. With people using multiple sim card or multiple devices, it occurs frequently that the phone is not reachable. Additionally, with the internet of things (IoT) approaching more and more, SIM cards are also used in many other devices with the primary goal of internet connectivity and not telephone. This makes it hard to detect using the provider lookup whether a phone is actually used as contact medium. While it is possible that mobile phone numbers are flagged as working using this approach, the opposite barely happens. Provider lookups are very accurate in filtering out numbers which are not activated at all. To further increase the working number

rate of mobile phone number, it is recom-mended to dial the phone number afterward and check for a sign of ringing. By doing so only active phone numbers are being sampled.

Screening of Sample Various methods exist for screening telephone sample. Fine-tuning is required in order to achieve maximum results. In this section mobile phone numbers and landline phone numbers are separated because the methods used vary greatly. Initially, the landline phone number screening is discussed followed by the mobile phone sample.

Mobile Provider Lookup

Landline Dialer

Provider lookups are very accurate

Fine tuning is required



Fieldwork ResultsIn this section, various results from fieldwork will be looked at. By the example of fieldwork from Libya, Syria, KSA and the SAPS countries. The results will show what can be reached with the methods displayed in here but at the same time displays issues with numbering plans, sampling, and filtering.

i. General fieldwork results

In this section, different fieldwork results are analyzed based on sample disposi-tion codes and response rate calcula-tions based on the AAPOR guidelines. Looking at the following table, fieldwork results from a repeated study in the Small Arabian Peninsula States (SAPS) are displayed. For this study, around 1.2 million telephone numbers were dialed to complete 4,000 interviews.

The second column depicts the net effec-tive incidence e which is the proportion of eligible units among all units in the sample for which a definitive determi-nation of status was obtained. The net effective incidence of the RDD sample was calculated using a suitable formula.

The relatively low rate of the net effec-tive incidence shows that there is a large proportion of non-eligible phone numbers in the sample but also has an effect that the incidence rate is further reduced due to the fact that up to a quarter of the sample is flagged as unknown eligibility.As an example, if the cases of Oman with unknown eligibility would be flagged as non-eligible, the response rate would change from 3.8% to 6.6%. As a further note, the mobile sample performed a lot better than the landline sample with regard to the percentage of working number.

Additionally, the response rate takes into account the phone numbers that have been screened out via screening. For the mobile sample between 50% and 70% of the raw sample could be filtered out while for the landline sample only a small fraction could be removed before fieldwork. Depending on the country a share of 50% up to 80% mobile phone sample is used which is understandable due to the high penetration of mobile phone while landline ownership is in the low 2 digits in most of the countries.

Oman Bahrein Quatar Kuwait

Completes 1008 1005 1002 999

e (NEI) 10.8% 6.1% 6.4% 5.3%

Resp. Rate 3.8% 5.3% 4.9% 5.0%

Coop. Rate 7.0% 7.5% 7.5% 6.4%

Ref. Rate 50.9% 65.2% 60.8% 72.5%

Cont. Rate 56.7% 72.3% 67.9% 80.3%



ii. Fieldwork results for countries in turmoil

For countries in turmoil, crisis or war, it is important to under-stand how telephone sampling can influence the fieldwork. In this section items such as coverage, response, and other issues are discussed by using approaches to sampling and fieldwork results from Syria and Libya.

Strata Available Sample

Refusal rate

Usable numbers

Aleppo 4217 38.5% 72.7%

Damascus 1849 90.2% 59.5%

Daraa 890 72.7% 67.9%

Deir ez Zor 1051 0.0% 73.6%

Hama 1429 81.5% 68.7%

Al Hasakah 1264 87.0% 68.5%

Homs 1497 14.3% 71.7%

Idlib 1324 100.0% 71.9%

Latakia 1051 88.1% 58.3%

Quneitra 72 83.3% 59.7%

Ar Raqqah 828 100.0% 72.4%

Rif Dimashq 2321 9.1% 70.7%

As Suwayda 337 82.9% 58.7%

Tartus 736 89.7% 60.2%

Landine 18770 92.7% 68.9%

Mobile 20575 84.0% 65.9%

Total 39345 88.4% 67.3%

In Q2 2016 a study in Syria was conducted in which Sample Solutions provided the RDD sample. A Dual-Frame RDD sampling frame is used which took into account all possible mobile phone and landline phone numbers. The mobile phone sample is stratified according to the two mobile phone providers according to their assumed mobile share penetration. The landline sample is stratified according to the 14 governates and additionally geocoded with information on governing fraction. This is possible due to the fact that the numbering plan uses area codes for cities as well so that the flagging does not just occur on a governate level but even on a city level. One of the key questions in this study was to investigate to which extent regional coverage is given considering the fact that Syria has been at war for several years and mobile or landline communications might be destroyed.When looking at the left table, two main conclusions can be drawn. In areas with severe conflicts or even sieges, the contact rate is several magnitudes smaller when comparing contact percentage versus percentage sampled.

Province Total Contact Sampling

Aleppo 11 0.9% 23.8%

Alhasakah 153 12.4% 7.5%

Al-Quneitra 8 0.6% 0.4%

Al-rakkah 4 0.3% 4.7%

Al-Swedaa 78 6.3% 1.8%

Damascus 357 28.5% 13.4%

Damascus c. 42 3.4% 9.1%

Dara 63 5.1% 5.0%

Edleb 6 0.5% 7.4%

Hamah 122 9.8% 8.2%

Homs 2 0.2% 9.0%

Latakia 212 17.1% 5.2%

Quneitra 4 0.3% 0.4%

Tartous 191 15.4% 4.1%

Grand Total 1253



This can be seen from values for Aleppo, Homs, and Rakkah. This is due to the fact that the majority of the landline phone numbers is not operating. From this, it can be concluded that in areas of conflict, the usage of landline sample can be biased towards areas which are not involved in conflicts to such an extent as others.Subsequently, the result from fieldwork in Libya is analyzed. A total of N=2010 interviews were completed making use of a dual frame RDD sample provided by Sample Solutions.

Landline Mobile

N=9861 N=29030

Disconnected 96.2% 62.1%

Unknown 3.3% 2.1%

Working 0.4% 35.8%

One of the findings for the disposition codes of Libya is the fact that around 26% of the initially working sample is marked as non-working at a later stage. A manual check of 10 numbers which are marked working in the initial stage and later as non-working actually was found to be a ringing working number in 8 out of 10 cases. The screening of the dialer can be seen in the table below. Interesting is also the fact that with every new attempt, less sample is screened out.

Attempt # Invalid # Dialed # Invalid

1 20615 38892 53.0%

2 2856 12685 22.5%

3 1317 7455 17.7%

4 764 4845 15.8%

5 474 3338 14.2%

Nevertheless even in attempt 5, there are still numbers flagged as non-working even though they seemed to be working in the previous 4 attempts. From this, it can be concluded that part of the non-working numbers might indeed be working and reverse. More accurate screening options can be a way to not only provide accurate results but also support in improving the response rate based on the AAPOR guidelines.Furthermore, from the last table it can be concluded that the landline telephone system has been severely damaged. Nevertheless, a single same sampling frame might be sufficient since adding the household frame yields little to none extra value besides extra weighting procedures and costs while not only marginally adding any extra coverage.

“A single same sampling frame might be sufficient”



This research shows the vast differences in telephone sampling when comparing the MENA countries with more traditional countries like Western Europe or the USA. While telephone sampling is still common in Western countries and at the same time will be documented, lack of information and technical difficulties still exist in many MENA countries. Furthermore, with the increase of mobile phone penetration and stagnation of landline phone penetration in the Middle East, a dual-frame frame RDD sample approach (being EPSEM or list-assisted) is very suitable due to the large coverage but most certainly requires further research into numbering plans and technological advancements when it comes to numbering screening. Also, when it comes to the screening of sample, it should be noted that screening should be considered as part similar to the fieldwork progress and should, therefore, be included in the response rate calculation. Calculation for connected / disconnected numbers but also calculation of e should include the results from pulsing. Looking at the coverage error, it can be seen from this paper that it is difficult to find sources in order to establish estimates for mobile only, landline only or neither landline nor mobile population. While different research institutes and government entities collect data from face-to-face surveys, most of the information regarding mobile or landline population is not shared publicly. By establishing a central information platform, different findings could be gathered and thus improve the knowledge of coverage using specific sampling frames. With regard to sample screening, it is suggested to use part of the sampling batch with a set of numbers that are flagged as non-working in order to estimate the efficiency of the screening for both mobile and landline sample.

Conclusion and Follow-up Steps

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Telephone Sampling in the MEMA: Issuesand Advancements

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http://www.pewglobal.org/files/2012/06/Pew-Global-Attitudes-Project-Syria-Report-FINAL-Thursday-June-21-2012.pdf

http://www.pewglobal.org/files/2014/10/Pew-Research-Center-Dangers-Report-FINAL-October-16-2014.pdf

http://www.pewglobal.org/files/2014/10/Pew-Research-Center-Dangers-Report-FINAL-October-16-2014.pdf

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telephone sampling in the middle east: issues and advancements

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