RAPID GENDERASSESSMENT
ON THE IMPACT
VIOLENCE AGAINSTWOMENTECHNICAL REPORT - KENYA SURVEY FIELDWORK
MAY - JUNE 2021

1 Introduction
Prompted by strong concern that the COVID-19 pandemic has intensified the risk of different forms ofviolence against women (VAW) and girls, particularly intimate partner/domestic violence, and realizing theneed to collect reliable data that are in line with methodological, safety and ethical standards, UN Womencommissioned Ipsos to conduct a study on the impact of COVID-19 on women’s well-being and safety in 13
countries across several regions all over the world. The participating countries were Albania, Bangladesh,
Cameroon, Colombia, Côte d’Ivoire, Jordan, Kenya, Kyrgyzstan, Morocco, Nigeria, Paraguay, Thailand andUkraine.
The core objectives of the study were to:•collect data on the impacts of COVID-19 on violence against women in low- and middle-income
countries.

•

strengthen measurement and methodologies for violence against women data collection and use.

•strengthen available knowledge on all forms of violence against women and improve capacities tocollect, analyse and use data on violence against women in accordance with globally agreed
methodological, safety and ethical standards.

•

inform where the policy and programmatic response of UN Women should focus and support
UN system-wide efforts to scale up actions to address violence against women in the context of
COVID-19.

The following groups contributed to the project’s management and execution:1.

the UN Women project team, comprising statisticians, researchers and policy specialists;

2. the Technical Advisory Group – an external reference group established by UN Women to provide
independent technical advice and expertise to the project;
3. the Ipsos project team involved in the set-up and delivery of the study; and4. national fieldwork partners.

1

Methodology of the study
Target group
TimingData collection method
Sampling approach
Sample Size

Questionnaire length andtype

Women aged 18 +•Two-phased approach: Phase I (four countries) took place May–June 2021;
Phase II (nine countries) took place August–September 2021

Computer-Assisted Telephone Interview (CATI)•

Random probability sampling with monitoring of quota on age and region•

Random-digit dialling to mobile sample frames•

1,200 women aged 18+ per country•

Target duration of max. 20 minutes•

No open-ended answers•

Informed consent asked to each participant at the beginning of the interview•

Awareness of safety, privacy and confidentiality of women respondents•

Guided by the “do no harm” principle applicable to the women respondents
and the interviewers/research team•The questionnaire was adapted in each country by localizing characters’
names and cultural references (i.e., popular food, music, sports).

ScriptingCentral scripting in software applications - iField and Dimensions

Language

At least one main language per country

The country chapters provide information on the set-up of the data collection process (obtaining of ethicalclearance, sampling and translation), description of the fieldwork, main characteristics of the achieved
sample by several indicators (age group, region, area type, educational attainment), technical information(interview duration, contact outcomes, screen-outs, temporary interview suspension at safety questions,survey dropouts, use of safety word, and item non-response), key field observations and relevant lessons
and recommendations.

22 Country information
2.1

Kenya

2.1.1 Obtaining ethical clearance
UN Women sought general support or tacit approval for the study from the Kenya National Bureau of
Statistics (KNBS). The ‘no objection’ from KNBS was granted on 11 March 2021 in support of the project.

2.1.2 Sampling plan
A single-frame cell-phone random-digit dialling (RDD) survey design was used to reach a nationally
representative sample of women aged 18+. The sample mirrored the telephone database distribution (framesize) by provider and the gross (unscreened) sample included numbers for each of the providers in the
country to the same proportions as in the frame size.1 In 2017, 80% of Kenya’s population owned a mobile
phone.2
Of the 103,000,000 numbers available in the sample frame, 49,501,430 (48%) were expected to be working
numbers (based on cell subscriptions in 2018). The sample was pulsed, meaning that a signal was sent to
the randomly generated phone number to verify if the number existed (i.e., if a connection could beachieved). The upfront pulsing of the gross sample closely reflected the market shares of the different
operators (in terms of subscriptions). The sample was also screened against business registers so as to
exclude numbers that were used by companies. Further details on the sampling frame are provided in
section 2.4.1 of the main technical report.
Due to challenges in reaching older women (60+), and with approval from UN Women, the field team
supplemented the RDD sample with a contact database that they had built the previous year. The database
comprised people who had participated in general population CATI surveys and consented to being
recontacted for future studies. It included about 12,000 contacts, spread nationwide (of which about 2% fellin the group of 60+ women), and included mobile and landline numbers, as well as information on the age
and gender of the contacts. This database was used only for the purposes of reaching respondents aged 60
and above. Further, only mobile phones were called to adhere to the design of the survey and ensure that
ethical and safety considerations were in place.
To ensure a representative sample, quotas were set on age (18–29, 30–39, 40–49, 50–59, 60+) and region(first-level administrative division), using the following statistical sources:•

Age: KNBS. 2019 Kenya Population and Housing Census.3•

Region: KNBS. 2019 Kenya Population and Housing Census.4

1 Airtel Networks 37,000,000; Jamii Telecoms 1,000,000; Safaricom 54,000,000; Telkom Kenya 11,000,000.
2 https://www.pewresearch.org/global/2018/10/09/majorities-in-sub-saharan-africa-own-mobile-phones-but-smartphone-adoption-is-modest/
3 https://www.knbs.or.ke/?wpdmpro=2019-kenya-population-and-housing-census-volume-i-population-by-county-and-sub-county
4 Ibid.

3

2.1.3 Translation
The questionnaire was localized in English and translated into Swahili. No issues related to equivalence(conceptual equivalence, semantic equivalence, etc.) were encountered during translation.
The questionnaire required localization for several items in the text of the questions and answers, including
widely known, yet neutral in meaning, male and female names, which were used in the vignettes in the
Safety Section of the survey. In Kenya, the male name used was John and the female, Mary.

2.1.4 Fieldwork period
Fieldwork took place between 30 April and 21 May 2021, with 10 interviewers working on the project.
Approaching half (42%) of the target was achieved by the end of the second week (9 May), and 84% by theend of the third week (16 May). The team interviewed throughout Eid al-Fitr on 14 May. The last week offieldwork was devoted to reaching older age groups, using the database created by Ipsos Kenya, describedabove. During this time, the regional quotas were relaxed to facilitate reaching the age quota.

2.1.5 Achieved sample
In total, 1,224 valid interviews were completed in Kenya. The targets for the age groups 50–59 and 60+ were
not quite reached with a shortfall of 2 percentage points and 1 percentage point, respectively.
Table 1: Achieved sample, by age group (Kenya)Age group

TargetTarget %Completed(unweighted)

Completed %(unweighted)

Weighted %5

Difference
unweighted %vs. target %Difference
weighted %vs. target %

18–29

483

40%

516

42%

40%

2%

0%

30–39

290

24%

307

25%

24%

1%

0%

40–49

180

15%

185

15%

15%

0%

0%

50–59

108

9%

91

7%

9%

-2%

0%

60+

136

11%

125

10%

11%

-1%

0%

The field team had difficulties quota towards the end of fieldwork, to give priority to interviewing older
women, meant that the shortfalls were not addressed during this time. Nonetheless, they were able to reachthe target in all regions, and especially the North Eastern and Western regions, where the number of theachieved interviews in the sample were 2 percentage points below the set targets. With the relaxation of theregional quota, the number of completed interviews per region still allowed for effective weighting.

5 Weighting procedures are discussed in more detail in section 4.4.2 of the main technical report.

4

Table 2: Achieved sample, by region (Kenya)

RegionTargetTarget %Completed(unweighted)

Completed %(unweighted)

Weighted%

Difference
unweighted% vs. target %Difference
weighted %vs. target %Central

162

14%

193

16%

14%

2%

0%

Coast

107

9%

121

10%

9%

1%

0%

Eastern

177

15%

185

15%

15%

0%

0%

Nairobi

130

11%

165

14%

11%

3%

0%

North Eastern

44

4%

18

2%

4%

-2%

0%

Nyanza

156

13%

147

12%

13%

-1%

0%

Rift Valley

301

25%

292

24%

25%

-1%

0%

Western

122

10%

103

8%

10%

-2%

0%

The distribution across rural (51% weighted) and urban (49% weighted) areas (as self-reported byrespondents) deviated from World Bank estimates, which put the total rural population at 72%.6 Of course,
it needs to be borne in mind that a cell-phone sampling frame was used. Latest available data suggest thatmobile Internet use in sub-Saharan Africa – which can be used as an indicator for cell phone use in general –stands at 16% in rural areas, compared to 40% in urban areas. In low- and middle-income countries, there isalso a gender gap in mobile Internet use: In 2019, women were 20% less likely than men to use mobile
Internet.7
Table 3: Achieved sample, by area type (Kenya)
Area

Completed (unweighted)

Completed % (unweighted)

Weighted %

Urban

636

52%

49%

Rural

588

48%

51%

A quarter of respondents had completed primary school or had no formal education. One-third had attended
a tertiary education programme, which suggests a relatively well-educated sample compared to incidencesof women’s tertiary education in the population.
Table 4: Achieved sample, by educational attainment (Kenya)
Education level

Completed (unweighted)

Completed % (unweighted)

Weighted %No formal education

44

4%

5%

Completed primary school

255

21%

22%

Completed secondary school

418

34%

34%

Technical & vocational training

85

7%

7%

6 World Bank staff estimates based on the United Nations Population Division's World Urbanization Prospects: 2018 Revision.
7 GSMA. 2020. The state of mobile internet connectivity 2020. https://www.gsma.com/r/wp-content/uploads/2020/09/GSMA-State-of-MobileInternet-Connectivity-Report-2020.pdf

5Completed university/college

403

33%

32%

Completed post graduate

15

1%

1%

Don't know

3

0%

0%

Refused10%0%2.1.6 Quality control and data processing
No interviews had to be removed from the Kenya sample because they did not meet the quality criteria interms of length, straight-lining and/or item non-response. (Section 4.4 of the main technical report provides
a detailed description of the implemented quality control procedures and how data were processed).

2.1.7 Technical information
The interview duration was above the envisaged 20 minutes, at 23 minutes on average (compared with amean of 25 minutes across the four countries).
Table 5: Interview duration (Kenya)
MeanMedianStandard deviation

22.87

20.7

10.35

The response rate in Kenya was 16%, calculated using the first American Association for Public Opinion
Research (AAPOR) definition – that is, the number of complete interviews divided by the number ofinterviews (complete plus partial) plus the number of non-interviews (refusal and break-off plus noncontacts plus others) plus all cases of unknown eligibility (unknown if housing unit, plus unknown, other).8
The table below shows the incidence of each outcome code.
Table 6: Contact outcomes (Kenya)
Contact outcomesCount% of all outcomes

Fixed appointment

119

1%

Soft appointment

303

2%

Incomplete interview with call-back toresume later

130%

Telephone answering device / voicemail/ message service

2, 08316%

Busy / engaged

305

2%

No answer

2, 40319%

Complete

1, 224

10%

8AAPOR. 2015. Standard Definitions. https://www.aapor.org/AAPOR_Main/media/MainSiteFiles/Standard-Definitions2015_8thEd.pdf

6Screen-out

4, 45035%

Incomplete interview with no call-back(terminated)

620%

Immediate refusal (before eligibility of
respondent has been confirmed)

467

4%

Refusal by eligible respondent (after
eligibility of respondent has been
confirmed, but before the actualinterview started)

220

2%

Respondent never available forappointment

117

1%

Deceased respondent

90%

Respondent physically or mentally
unable / incompetent370%Respondent or household language
problem

137

1%Call blocking / barring / do not call list

199

2%

Fax / data line / modem / pager

130%

Non-working / disconnected / dead /bad number

400

3%

Non-residential number (business,government office, other organization)

25

0%

Technical issue / CATI system error

187

1%

Table 7 provides an overview of specific call outcomes (completed interview, partial interview and refusals)
by the time of the day (morning, afternoon, evening) and the day of the week. The figures should beinterpreted with caution, as they do not reflect the percentage of all contact attempts performed during thistime/on that day, but the split of these specific outcomes across the time of day/day of the week. Feedback
from the field team on the best time and the best day to obtain an interview is provided in section 2.1.8.
Table 7: Call outcome, by time of the day and day of the week (Kenya)

SundayMondayCompleted interviews

Partial interviews

Refusals

Count

%

Count

%

Count

%

Morning

0

0%

0

0%

0

0%

Afternoon

0

0%

0

0%

0

0%

Evening

0

0%

0

0%

0

0%

Morning

50

4%

4

4%

0

0%

Afternoon

67

5%

9

10%

1

100%

Evening

0

0%

0

0%

0

0%

7

TuesdayWednesdayThursdayFridaySaturdayMorning

55

4%

11

12%

0

0%

Afternoon

167

14%

21

24%

0

0%

Evening

11

1%

1

1%

0

0%

Morning

104

8%

8

9%

0

0%

Afternoon

191

16%

13

15%

0

0%

Evening

1

0%

0

0%

0

0%

Morning

76

6%

1

1%

0

0%

Afternoon

158

13%

5

6%

0

0%

Evening

17

1%

0

0%

0

0%

Morning

88

7%

2

2%

0

0%

Afternoon

96

8%

8

9%

0

0%

Evening

0

0%

0

0%

0

0%

Morning

68

6%

0

0%

0

0%

Afternoon

75

6%

6

7%

0

0%

Evening

0

0%

0

0%

0

0%

Twenty-six per cent of the sample called were men and thus screened out at the very beginning of the survey.
Thereafter, only a small number of people were screened out – seven because they were not yet 18 years old,
one because she did not want to participate, and one because she did not fully understand the confidentiality
measures.
Table 8: Screen-outs (Kenya)
Question

AnswerCount% of total answered

S3

Sex: Male

603

26%

S4_1

Age: Below 18 years

7

1%

S1

Willing to participate: No

1

0%

S6

Understand purpose: No

0

0%

S7

Understand confidentiality: No

1

0%

The interview was temporarily discontinued in seven cases because the respondent answered “no” to one of
the five ‘safety’ questions (listed in Table 9 below).

8

Table 9: Temporary interview suspension due to responses to safety questions (Kenya)
Question

AnswerCount% of total answered

S2

Can you let me know if the speaker phone is off? No

3

0%

S5

Can you confirm if now is a good time? No

0

0%

B01

Can you confirm that there is no one around you over the age of
2 that can overhear our conversation? No

0

0%

C18

Alternatively, if someone over the age of 2 is listening to our callor is nearby that can hear, please say no. No

4

0%

C27

If someone over the age of 2 is listening to our call or is nearby
that can hear, please say no. No00%

Eighty-nine respondents dropped out of the survey. The table below shows the questions at which the
dropouts occurred. Eighteen respondents dropped out at S11, which asked respondents whether they lived inan urban or rural area. The initial survey pilot had shown that the terms “rural” and “urban” were not wellunderstood. Therefore, explanations of these terms were added to the script for the mainstage fieldwork.
According to the field team, this measure worked well, which suggests that the dropout rate at the question
during the mainstage may simply reflect a spontaneous decision at the beginning of the survey not toparticipate, rather than face ongoing issues of comprehension.
Another question with a relatively high number of dropouts was A01 (marital status). The interviewing teamflagged that it was sometimes difficult to get an answer to this question, with respondents appearing
reluctant to disclose this personal information.
Table 10: Survey dropouts, by question (Kenya)
Question

Question text

No. of participants
dropped out% of total
dropouts

S9

And what district do you live in?

7

7.9%

S11

Would you consider the area you live in to be urban or rural?

18

20.2%

A01

What is your marital status?

12

13.5%

A02

What is the highest level of education that you completed?

8

9.0%

A03

Which of the following best describes what you mainly do eachday...?

6

6.7%

A06

How many people including yourself, live in your household?

6

6.7%

A07

How many of the people living in your household are age 0–17?

2

2.2%

A08

How many of the people living in your household are elderly, aged
65 or over? Please include yourself if that is appropriate.

1

1.1%

A09

Who do you consider to be the head of your household?

2

2.2%

9

Question

Question text

No. of participants
dropped out% of total
dropouts

A10

What was the monthly income of your household BEFORE
COVID-19?

3

3.4%

A11

Has this changed at all in the past year?

1

1.1%

A12

During the last month, was there a time when, because of lack ofmoney or other resources, you or others in your household…

6

6.7%

B02

For each question, I will ask how often you thought or felt acertain way – please answer with never, almost never, sometimes,
fairly often, or very often. In the last month, how often did you
feel…

7

7.9%

B03

Thinking about these topics I've mentioned, would you say the
COVID-19 situation has made these difficulties better, worse or
the same?

1

1.1%

C02

To what extent, if at all, do you feel safe when walking alone in
the area where you live during the day?

1

1.1%

C05

Discrimination happens when you are treated less favourablycompared to others or harassed because of your sex, age,disability, socioeconomic status, place of residence, politicalopinion, or any other characteristics. Have you personallyexperienced any form of discrimination against you SINCECOVID-19?

2

2.2%

C06

Do you feel that discrimination, prejudice or racism in the area
where you live have changed SINCE THE ONSET OF COVID-19?

1

1.1%

C07

To what extent do you think that violence, abuse or harassment
are a problem in the area where you live?

1

1.1%

C09

Have you personally experienced physical harm or threats ofphysical harm, abuse or harassment specifically by the police or
security agents in the context of implementing restrictions to
respond to COVID-19 (movement restriction, curfew, closure ofcertain premises)?

1

1.1%

C10

How common do you think this story is for women in the area
where you live?

2

2.2%

C18

Just to make sure, before we continue, can you confirm that there
is no one around you over the age of 2 that can overhear ourconversation?

1

1.1%

Total

89

100%

10

As previously explained, and as part of the safety protocols, respondents were given a safe word, a specificpopular local food (PLF), that they could use if they were disturbed by a third party or otherwise felt unsafe
at any point during the interview. In Kenya, the food used was “MCHELE NA MAHARAGWE” (rice and beans).
This word was used 13 times during the course of fieldwork. The table below shows the questions at which
this occurred.
Table 11: Use of popular local food, per question (Kenya)
Question

Question text

PLF mentioned

S9

And what district do you live in?

1

A02

What is the highest level of education that you completed?

1

A10

What was the monthly income of your household BEFORE COVID-19?

1

B03

Thinking about these topics I've mentioned, would you say the COVID-19 situation
has made these difficulties better, worse or the same?

1

C03

To what extent, if at all, do you feel safe when walking alone in the area where
you live at night?

1

C05

Discrimination happens when you are treated less favourably compared to othersor harassed because of your sex, age, disability, socioeconomic status, place ofresidence, political opinion, or any other characteristics. Have you personallyexperienced any form of discrimination against you SINCE COVID-19?

1

C06

Do you feel that discrimination, prejudice or racism in the area where you live
have changed SINCE THE ONSET OF COVID-19?

2

C07

To what extent do you think that violence, abuse or harassment are a problem in
the area where you live?

1

C12

Do you think [Scripter: insert Female name] would seek help for this situation?

1

C14

How common do you think this story is for women in the area where you live?

1

C24

How often, if at all, did you go out of your house in the last month?

1

C25

Do you still continue to see or speak with your friends and social groups?

1

Total

13

In terms of item non-response (INR), this varied across the survey but was highest at A10 (25%). A high INR
for questions that ask about the monthly household income is not unusual. This information is often
considered as too sensitive to share in a survey, and respondents frequently do not know the exact income
range applicable to them. Asking about the time before the COVID-19 pandemic adds another layer ofdifficulty because respondents might not remember their income from early 2020. Other questions had avery low INR.

11

Table 12: Item non-response (Kenya)
Question

Question text

Item nonresponse

A02

What is the highest level of education that you completed?

0.33%

A09

Who do you consider to be the head of your household?

0.08%

A10

What was the monthly income of your household BEFORE COVID-19?

24.84%A11

Has this changed at all in the past year?

3.92%

A12_2

During the last month, was there a time when, because of lack of money or otherresources, you or others in your household…were unable to eat healthy and nutritious
food?

0.16%

A12_5

During the last month, was there a time when, because of lack of money or otherresources, you or others in your household…ate less than you thought you should?

0.08%

A12_6

During the last month, was there a time when, because of lack of money or otherresources, you or others in your household…ran out of food?

0.25%

A12_7

During the last month, was there a time when, because of lack of money or otherresources, you or others in your household…were hungry but did not eat?

0.08%

A12_8

During the last month, was there a time when, because of lack of money or otherresources, you or others in your household…went without eating for a whole day?

0.16%

B02_1

In the last month, how often did you feel…that you were unable to control theimportant things in your life?

0.65%

B02_2

In the last month, how often did you feel…confident about your ability to handle yourpersonal problems?

0.57%

B02_3

In the last month, how often did you feel…difficulties were piling up so high that you
could not overcome them?

0.25%

B03

Thinking about these topics I've mentioned, would you say the COVID-19 situation has
made these difficulties better, worse or the same?

0.08%

B04

Overall, would you say your own mental or emotional health (e.g., stress, anxiety,confidence, etc.) has been affected AS A RESULT OF COVID-19?

0.16%

C03

To what extent, if at all, do you feel safe when walking alone in the area where you liveat night?

4.00%

C04

How, if at all, has your feeling of safety while walking alone at night changed SINCE THE
ONSET OF COVID-19?

2.29%

C05

Discrimination happens when you are treated less favourably compared to others or
harassed because of your sex, age, disability, socioeconomic status, place of residence,
political opinion, or any other characteristics. Have you personally experienced any formof discrimination against you SINCE COVID-19?

0.16%

C06

Do you feel that discrimination, prejudice or racism in the area where you live havechanged SINCE THE ONSET OF COVID-19?

3.51%

C07

To what extent do you think that violence, abuse or harassment is a problem in the area
where you live?

2.37%

12

C08

How, if at all, do you think physical harm, abuse or harassment in the area where youlive have changed SINCE COVID-19?

4.49%

C09

Have you personally experienced physical harm or threats of physical harm, abuse or
harassment specifically by the police or security agents in the context of implementing
restrictions to respond to COVID-19 (movement restriction, curfew, closure of certain
premises)?

0.08%

C10

How common do you think this story is for women in the area where you live?

2.37%

C11

Would you say the situation of COVID-19 has made things better, worse or the same for[Scripter: insert Female name] from this story?

1.72%

C12

Do you think [Scripter: insert Female name] would seek help for this situation?

2.37%

C13

Who would [Scripter: insert Female name] most likely seek help from?

1.47%

C14

How common do you think this story is for women in the area where you live?

1.47%

C15

Would you say the situation of COVID-19 has made things better, worse or the same for[Scripter: insert Female name] from this story in terms of her feelings of safety?

3.27%

C16

Do you think [Scripter: insert Female name] would seek help for this situation?

3.10%

C17

Who would [Scripter: insert Female name] most likely seek help from?

1.39%

C19

How often in the last six months would you say that adults in your household haveargued or have had some sort of conflict among themselves?

0.65%

C20

Would you say the situation of COVID-19 has made conflict between adults in yourhousehold more frequent, less frequent or about the same?

1.72%

C21

In the last six months, did you ever feel unsafe in your home?

0.25%

C22

Would you say COVID-19 has made things better, worse or the same in terms of how
safe you feel in your home?

0.41%

C23_4

Why do you feel unsafe in your home? I am living with people I cannot trust

0.08%

C23_8

Why do you feel unsafe in your home? Other adults in the household have hurt me

0.08%

C23_11

Why do you feel unsafe in your home? Something else

0.08%

C24

How often, if at all, did you go out of your house in the last month?

0.16%

C28A

Please tell me how many of the following statements you regard as true SINCE THE
ONSET OF COVID-19:

0.08%

C28B

Please tell me how many of the following statements you regard as true SINCE THE
ONSET OF COVID-19:

0.08%

C29A

Please tell me how many of the following statements you regard as true SINCE THE
ONSET OF COVID-19:

0.08%

C29B

Please tell me how many of the following statements you regard as true SINCE THE
ONSET OF COVID-19:

0.16%

C30_1

Which, if any, of the following have you or any other woman you know experienced
SINCE THE START OF COVID-19? Been slapped, hit, kicked, had things thrown at them, orother physical harm

0.49%

13

C30_2Which, if any, of the following have you or any other woman you know experienced
SINCE THE START OF COVID-19? Been yelled at, called names, humiliated

0.41%

C30_3

Which, if any, of the following have you or any other woman you know experienced
SINCE THE START OF COVID-19? Had resources (like healthcare) or basic needs (likemoney, food, water, shelter) denied

0.49%

C30_4

Which, if any, of the following have you or any other woman you know experienced
SINCE THE START OF COVID-19? Been forced to stay alone for long period of time ordenied communication with other people

0.41%

C30_5

Which, if any, of the following have you or any other woman you know experienced
SINCE THE START OF COVID-19? Been the subject of inappropriate jokes, suggestive
comments, leering or unwelcome touch/kisses

0.57%

C31_3

Do you have any suggestions or recommendations of what we could do for women
suffering from violence during the pandemic? Medical and financial support

0.08%

C31_4

Do you have any suggestions or recommendations of what we could do for women
suffering from violence during the pandemic? Someone to talk to like professional
mental health experts

0.16%

C31_5

Do you have any suggestions or recommendations of what we could do for women
suffering from violence during the pandemic? Legal support or help in reporting theincident or dealing with police

0.25%

C31_6

Do you have any suggestions or recommendations of what we could do for women
suffering from violence during the pandemic? Other, specify

0.16%

2.1.8 Field observations
This section describes the observations made by Field Managers and interviewers in the course of fieldwork.
The observations relate to the most productive days for fieldwork; the main reasons for refusals to take partin the survey; challenges of reaching certain age groups; and the clarity of terms, concepts and questions,among other topics. The observations effectively complement the fieldwork statistics included in theprevious section, providing a more nuanced picture of the fieldwork experience.
•

The best days of the week to obtain an interview were working days, between 9 a.m. and 1 p.m.

•

Appointments were scheduled in cases where respondents could not take part in an interview
straight away. However, only a few appointments actually resulted in a completed interview; inmost cases, the respondents could not be reached at the agreed appointment time.

•

The main reason for refusal to take part in the survey was the topic, which was perceived as too
sensitive.

•

Women aged 50–59 and 60+ were less represented in the RDD sample frame and it was therefore
more difficult to achieve the target set for both age groups – hence the use of the contact database,described above.

•

Language barriers were experienced in the North Eastern region because individuals contacted
spoke Somali. Across all regions, 137 cases of language problems were recorded.

14•In four cases, a man took the telephone from the women respondent and terminated the interview
by saying they were not interested in participating. In all four cases, this happened at the beginningof the interview, after the woman expressed interest in participating. In three of the cases, the callwas ended right after the man talked to the interviewer. Therefore, the interviewers do not knowwhat was said between the woman and the man afterwards. In the other case, the interviewer
briefly overheard the conversation before the phone was hung up but did not understand what wassaid because the woman and man talked in Somali. Interviewers expressed concern about thesafety of the women involved.

•

About half of respondents asked for the telephone number of the local support organization.

In terms of observations relating to the survey questionnaire:•

In some cases, it was difficult to obtain an answer to the questions on age and marital status,
because this information was considered too personal to share.

•

At the beginning of the interview, respondents were generally more reserved and hesitant about
giving personal information. As the interview progressed, however, they appeared to grow more
comfortable sharing such details.

•

Respondents sometimes did not understand right away the purpose of the popular local food and it
often required further explanation on how to use the codeword.

•

As noted above, the survey pilot showed that the terms “urban” and “rural” were not wellunderstood by respondents. During mainstage fieldwork a short explanation of urban and rural
areas was given, and no further issues of comprehension were observed.

•

In a very small number of cases respondents asked for clarification on the interpretation of thewords “harassment” and “discrimination”, but terms and concepts used in the questionnaire wereoverall well understood and conceptual equivalence was confirmed by the field team.

•Respondents sometimes replied in a lower tone of voice to questions that asked about violenceagainst women, or when they wanted to be discrete about their participation in the survey. Butquestions were answered and not refused.

•

There were 15 cases of strong emotional reactions (such as despair, anger or sorrow) to questions on
discrimination, physical violence, not feeling safe at home and violence at home. Interviewers usedtechniques and guidance provided in the training and the interviewer manual (e.g., listening,staying neutral but empathetic, and not acting as counsellors). They waited until the respondent
was able to continue the survey or rescheduled it to another time if needed.

2.1.9 Lessons learned and recommendations
This section draws upon the fieldwork, statistics and observations outlined in the previous sections to provide
recommendations for future surveys on violence against women in Kenya. Specifically, whether remote datacollection can be considered as an appropriate approach to develop an evidence base on the perceptions andincidence.

15

•

CATI and the use of a single-frame cell phone RDD design worked well in many respects. That said,
it might be necessary to complement this frame with another database to specifically target
women aged 60 and above. Further consideration will also need to be given to how it can be more
representative in terms of both area type (rural and urban) and education. Again, an additional
database might help to overcome barriers in this regard, but, as noted in section 5.3 of the maintechnical report, it must be borne in mind that non-probability sampling methods like the use of
databases by definition carry the potential to introduce bias into a survey sample because certain
sociodemographic and/or attitudinal profiles might be overrepresented.

•

The challenges reaching older women and those living in certain regions should be reflected in thetime dedicated to active data collection in future surveys with age- and region-based quotas. A
longer fieldwork period would help to ensure the quotas are uniformly achieved. In the case of aface-to-face approach, a longer fieldwork period would be absolutely necessary owing to the extra
time required for physical visits to prospective respondents’ homes.

•

Given the field team’s observations that working days were among the most productive days forfieldwork, it is recommended that the data-collection activities via CATI be concentrated on working
days, but Saturday or Sunday should still be covered to ensure segments of society that are notreachable on working days have a probability of being invited to participate in the survey.

•

To minimize language barriers and achieve the target number of interviews in the North Easternregion, consideration should be given to offering the survey in Somali, as well as in English andSwahili. This would also help to ensure that different social or socioeconomic groups in the region
are included and the achieved sample is not biased by excluding Somali speakers.

•

Given that a quarter of respondents did not answer the question about their monthly householdincome before the outbreak of the COVID-19 pandemic,9 the researchers recommend that future

surveys contain a question on subjective economic welfare (such as the Deleeck Question or the
Income Evaluation Question that are commonly used to construct subjective poverty lines.10) Thismay help to increase the amount of data collected and thus enable the analysis of the survey
results across different socioeconomic profiles.

9 Analysis of the data shows that a high proportion of the non-responses to this question comprised ’don’t know’ responses (96% of non-responses).
Refusals to answer the question were comparatively infrequent (4% of non-responses). Not knowing the monthly household income before theoutbreak of the COVID-19 pandemic might have various causes, including simply not remembering, an irregular income, or not managing thehousehold budget. Indeed, 74% of those who did not know the answer to the income question did not consider themselves as head of thehousehold (alone or together with the husband), and the head of the household is most often responsible for managing the household income andassets
10 See for example, Lačný, M. 2020. “Approaches to subjective poverty in economic and sociological research.” Human Affairs. 30, 3. pp. 413–427.
https://doi.org/10.1515/humaff-2020-0035https://doi.org/10.1515/humaff-2020-0035.

16

© 2022 UN Women. All rights reserved.
The views expressed in this publication arethose of the author(s) and do not necessarily
represent the views of UN Women, the United
Nations or any of its affiliated organizations.
Produced by the Women Count programme
Research partner: Ipsos
Editor: Jen Ross
Design: Ouissal Hmazzou

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