Lifespan research in Kenya,Namibia,and South Africa: Cohort profile of the Africa long life study

Abstract

Most personality science is conducted with samples from wealthy Western countries, home to only 11% of the world’s population. Longitudinal studies have been particularly informative for psychology, but they are virtually absent from the rest of the world. The Africa Long Life Study (ALLS) brings African perspectives to lifespan research in personality and mental health. Large (N = ∼1000), relatively-representative samples of 18-year-olds were recruited from throughout Namibia, Kenya, and South Africa in 2022. Participants complete two surveys each year. At the time of the fifth wave, we describe here the goals of the ALLS with emphasis on personality psychology, and the contextualized measurement and methodological approaches we have developed, including the process of finding participants again at later waves. Sample characteristics and retention and attrition over the first three waves are reported. Initial lessons learned and reflections on conducting longitudinal research and personality science in Africa are discussed.

Plain language summary

Most personality science is conducted with samples from wealthy Western countries, home to only 11% of the world’s population. Longitudinal studies have been particularly informative for psychology, but they are virtually absent from the rest of the world. The Africa Long Life Study, a new longitudinal study in Sub-Saharan Africa, brings African perspectives to lifespan research in personality, mental health, and cultural mindset. Large (N = ∼1000), relatively-representative samples of 18-year-olds were recruited from throughout Namibia, Kenya, and South Africa in 2022. Participants complete two surveys each year, allowing us to test findings in personality psychology for cross-cultural relevance. We describe here the goals of the ALLS and the contextualized approach we have taken, including the process of finding participants again for later waves. Details about the particpants and how many stayed with the project into the second and third surveys are reported. We reflect on what it is like to conducting longitudinal research in Africa today.

Keywords

cohort profile longitudinal study Africa lifespan Namibia Kenya South Africa

A major problem in psychological science is overreliance on samples from a small group of closely-related, Western¹ countries, which are home to only 11% of the world’s population (Thalmayer et al., 2021). This severely limits the generalizability of our findings (Van De Vijver, 2013), as many, if not most, aspects of psychology vary across cultural settings (Henrich et al., 2010). This also means that psychology in general and personality psychology specifically have minimally addressed the question of which phenomena are human universals and which are shaped by the cultural context, and have developed little theory that takes cultural differences into account.

Longitudinal studies have been central to personality psychology, teaching us how traits develop and change over the lifespan (e.g., Bleidorn et al., 2022), how the role of the family environment decreases with age while the role of genes increases (McGue et al., 2014), which experiences or attitudes precede behaviors (e.g., Denissen et al., 2019), how life experiences precede personality change (Lodi-Smith & Roberts, 2007), and disentangling age from cohort effects (Orth et al., 2018). Long-term studies have shown that personality traits measured early in life predict health, longevity, and career and family outcomes (e.g., George et al., 2011; Hampson et al., 2013; Soldz & Vaillant, 1999). Ongoing studies also allow for psychometric explorations that depend on the administration of many variables to the same sample; the 30-year Eugene-Springfield Community Sample has had an enormous impact on the development of personality inventories for this reason.

Unfortunately, longitudinal studies on psychology are virtually absent from the ‘majority world’, i.e., Africa, Asia, the Middle East, and Latin America, home to 90% of humanity. Recent meta-analyses in specific topic areas include hundreds of longitudinal studies, for example, 331 on self-esteem (Orth et al., 2018), 205 on personality traits (Bleidorn et al., 2022), 66 on anxiety and depression (Sowislo & Orth, 2013), and 50 on recovery from psychosis (Santesteban-Echarri et al., 2017), together amounting to 652 longitudinal studies in psychology. Of these, only 4.5% were from Asian contexts, four from Israel, and one from South Africa. These few from outside the West are thus mainly from wealthy nations in East Asia. Of the 46 studies shared in the new Personality Development Collaborative, only one is from a non-Western country, Japan (https://www.personalitydevelopmentcollaborative.org/find-data/; although several include underrepresented, minority samples, e.g., the Baltimore Longitudinal Study on Aging, the California Families Project, the Karakter Project). The only longitudinal studies we identified from African contexts are the Birth to Twenty cohort in South Africa (https://www.wits.ac.za/dphru/strategic-research-resources/) and small-sample studies including two measurement points, also in South Africa (e.g., Jung et al., 2022; Wilson Fadiji et al., 2023). (Longitudinal studies on aging are more internationally diverse, https://g2aging.org/app/home).

The Africa Long Life Study (ALLS) was conceived to explore psychological development of young adults in three African countries more broadly, in larger, more diverse, and relatively-representative samples, and over at least a five-year period. It is also intended to support inclusivity, diversity, and the decolonization of psychological science by doing so with meaningful bi-directional collaborations. This report aims to introduce and describe the ALLS with regard to (1) values, goals, and the process of building and working with the multi-country team; (2) the background and study goals for the four main topic areas; (3) recruitment and the sample at Wave 1; (4) retention and challenges at later waves; (5) study materials; and (6) preliminary findings, two years and four waves into the project. We close by reflecting on the rewards, the lessons learned, and our next steps for conducting longitudinal personality science in Sub-Saharan Africa.

ALLS goals, values and team

Goals and values

The ALLS includes four broad topic areas: personality, mental health, emerging adulthood, and cultural mindset. The overarching scientific goal is to use mixed-methods and both etic (imported) and emic (localized) approaches to test findings from other contexts for replicability, and to build theory about local phenomena and topics of interest from the bottom up. ALLS samples provide a strong contrast to North American and European samples, differing in many social, cultural, ecological, and historical respects. This allows for tests of the universality of models, measures, and theories, thus helping to distinguish more universal from more culturally-specific aspects of psychology. The ALLS is also inspired by applied goals, including team members who are counseling or clinical psychologists and schoolteachers, motivated to contribute useful knowledge for local interventions, education, and policy.

A core value of the ALLS is to be shaped by input from local scholars and community members. This has meant, for example, a study structure with both a traditional longitudinal component with variables assessed once or twice annually, plus a panel component, with variables administered more flexibly. This allows collaborators and PhD students to add measures for their own projects. It also allows us to build on preliminary results as we develop theory about these topics in the African context.

ALLS team and process

From the outset, based on the team’s experiences living and/or working in Africa, it was clear that a contextualized strategy would be necessary. For example, we knew that even among digitally-inclined youth, many would lack access to a phone number, smartphone, computer, or email, and almost no one has a mailing address. We knew that methods for recruitment would have to be adapted to each site to ensure reasonably-representative samples, and the wording of surveys carefully vetted in contexts where English is used in schools but rarely at home. We would need local expertise at every level to collect high-quality data in a way that would generate support within the community, establishing and maintaining trust.

The ALLS started with Namibia, where an existing collaboration (first and third authors) and a network of teachers trained as research assistants (RAs) on prior projects made a longitudinal study conceivable. Queries to RAs made us cautiously optimistic, and a team of 15, mostly schoolteachers, representing multiple ethno-linguistic groups and regions, was assembled. The ninth author joined as lead RA, becoming a PhD student in Zurich in year two. Outreach through professional networks to recruit PhD students mentioned possible expansion, and the second author wrote to offer collaboration in Kenya. A small grant from the University of Zurich helped fund this, and 15 psychology postgraduate students and teachers from 12 Kenyan counties were recruited as RAs.

Study planning commenced in mid-2021 including two University of Zurich PhD students (tenth and eleventh authors). When we reached out to South African colleagues for consultation, they asked if South Africa could join the study. To split the work given minimal funding, colleagues from three universities (fourth through eighth authors) joined as co-investigators, recruiting students as RAs. Thanks to National Research Foundation funding in 2023, re-recruitment and hired RAs now match the structure in Namibia and Kenya. Ultimately, exceptional year-round lead RAs for each country (twelfth through fifteenth authors), who are also leading scientific studies in ALLS data, became crucial to success.

The ALLS thus developed into a three-country study, combining local expertise and networks with centralization for comparability and efficiency. This is managed through four forums. (1) At monthly leadership meetings, the authors of this report make methodological and scientific decisions and plan outreach and manuscripts. (2) At logistics meetings, the investigators managing funding, lead RAs, and UZH PhD and master’s students report on and troubleshoot data collection and processing. (3) During academic semesters, research colloquium meetings with PhD and master’s students from partner universities present study plans and results, and external collaborators visit to workshop ideas for studies in ALLS data. (4) Country teams of local RAs who manage participants met in person at study-launch trainings. Online meetings before and after each wave maintain bi-directional communication, providing feedback about experiences in the field.

ALLS topics: Background and study goals

Personality traits

As noted above, longitudinal research in the West has shown how personality traits visible early in life predict health, longevity, and career and family outcomes, and how they typically change in adaptive ways from early-to middle-adulthood, related to new roles (Lodi-Smith & Roberts, 2007). ALLS samples will allow for a cross-cultural test of these findings. The role of personality traits in people’s lives is also of interest: in more interdependent contexts dispositions are generally seen as less important in explaining behavior and outcomes, with situational factors given more emphasis (Henrich, 2020). In East Asia, traits have been seen to be less stable across situations, and consistency doesn’t have the same advantage for well-being (Suh, 2002). African views of personality consistency are not yet well explored.

Testing these questions depends on reliable assessment. The Big Five do not arise from studies of the natural language in African contexts (Thalmayer et al., 2020, 2021), and imported measures have poor reliability and validity (McCrae et al., 2005; Rossier et al., 2017; Schmitt et al., 2007). In terms of relevant traits, emic studies like the South African Personality Inventory (Fetvadjiev et al., 2015) suggest the centrality of more social-relational traits. Methodologically, item-phrases are contextually oriented to the West. Initial projects are focused on how to assess traits reliably and with cross-group comparability.

Mental health

In Sub-Saharan Africa too little is known about local mental health needs (Ministry of Health, 2015; National Department of Health, 2013; World Health Organization, 2020). We are exploring how to best assess psychological disorder symptoms for young adults in African contexts to establish a baseline and assess development from ages 18 to 23. Another important topic is the role of demographic factors in the development of depression, anxiety, and post-traumatic stress symptoms (Bambo, 2024). We are also interested in cultural characteristics relating to risk and resilience, keeping in mind that wealthier countries do not always have better outcomes when it comes to psychological disorders (e.g., Dückers et al., 2016). Based on our own and others’ work (e.g., Claudius et al., 2022; Wilson & Williams, 2013), we expect that religion and community support will be important factors in promoting mental health and wellbeing in African contexts.

Emerging adulthood

From its conception, emerging adulthood was a cultural theory: changes in Western countries after around 1960 meant later marriage, parenthood, and employment, turning the ages 18 to 29 into a period of freedom, exploration, and personal development (Arnett, 2015). Research in Japan and China suggests this is also true in Asian contexts, but with more emphasis on supporting one’s parents, and less romantic exploration (Nelson & Chen, 2007; Rosenberger, 2007; Zhong & Arnett, 2014). Two recent publications that describe emerging adulthood in Africa (Lo-oh, 2016; Obidoa et al., 2019) also emphasize caring for others as a defining marker of adulthood, consistent with the highly interdependent context. We expect, however, to find unique features with regard to romantic exploration, given the frequency and acceptance of premarital parenthood, especially in Namibia (Clark et al., 2017).

Cultural mindset: Values and worldviews

The study of the reciprocal co-constitution of the mind and culture requires valid and reliable ways to operationalize cultural differences. Early work that defined cultural-level variation (e.g., Hofstede, 1980) helped establish cultural psychology as a field. Collectivism versus individualism became the dominant contrast (Hamamura, 2012), which is useful when comparing the individualistic West to everyone else (Henrich, 2020), but it doesn’t capture differences among other world regions (Kitayama et al., 2022). Relatedly, cross-cultural psychology has relied heavily on comparing East Asia to North America (Hamamura, 2012; Kitayama et al., 2022). By administering a range of existing measures and exploring locally-relevant concepts to develop measures, we can help promote Sub-Saharan Africa as another major point of contrast and theory-building in psychology.

Recruitment and the sample at wave 1

Recruitment strategy and procedure

Ethical review was conducted at each of the five partner universities and at national levels. In Kenya this is required annually at the county level, necessitating in-person visits to over a dozen offices. In Namibia, permission and support were required from regional councillors and school systems before recruitment. The process takes substantial time and resources, but its fulfillment means that local scholars, administrators, and leaders are well informed about the study. Addressing their questions and concerns, and earning their support, enhances study quality and its reputation in the community.

To retain participants, the key strategy is local RAs, under whom participants are ‘nested’. During recruitment RAs requested detailed contact information including that of caregivers or close family members. They recruited participants in areas where they live and have wide acquaintanceship, providing credibility for the study, and bringing local know-how to re-finding participants. Engagement and goodwill are cultivated with small thank-you gifts, attentive responses to queries, communicating our interest in participants’ well-being and futures, and a survey that includes open-ended questions to allow for feedback. The wave interval of six months, for a total of 10 in the five-year study period (2022 – 2026), was chosen to increase contact and limit survey length compared to an annual interval.

Recruitment included a variety of strategies. RAs sought participants in their communities through church groups, neighbors, relatives, and friends and in a snowball approach. Schoolteacher-RAs in Namibia approached 18-year-olds at their schools or via colleagues. In Kenya, only a few RAs work at schools, and in South Africa, none, but many arranged school visits. At the University of the Witwatersrand, recruitment was mainly via an existing online survey. At other South African sites, student-RAs were close in age to participants, and some recruited on campus and through acquaintances in their hometowns. The experiences of student-RAs at University of the Western Cape are described in detail by Florence and colleagues (2023).

Participants provided written consent to join the study, all materials for which were in English, the langague of secondary education in all three countries. RAs had translations of key inventories in Owambo, Khoekhoe, and Swahili, but these were minimally used, and it was impractical to provide them for all home languages (ultimately over 50), so this was not expanded. The survey was available on an electronic tablet carried by RAs, via a link the RA could send electronically to a participant’s device, or on paper. After completion, participants were gifted an airtime voucher in Namibia and most of Kenya, or a gift voucher to a grocery store in South Africa. In the Wajir region as vouchers were not easily redeemable a small bottle of perfume was planned, but due to an outbreak of fires at schools, flammable substances were banned, and the RA instead deposited credit for participants at their school canteens.

The ALLS sample at wave 1

Demographic information collected in the first three waves is summarized in Table 1. ANOVA or Chi-square tests to assess for country-level differences indicated significant mean differences between countries for all variables tested, though not for all post-hoc comparisons. Specifically, Namibians were more likely to be in a relationship and to have a child, and South Africans were more likely to still be in school. For a detailed report see Supplemental Table S1.

Table 1.

ALLS wave 1 (early 2022) sample demographic characteristics.

	Kenya (n = 937)		Namibia (n = 1,171)		South Africa (n = 940)		Total (N = 3,048)
	n	%	n	%	n	%	n	%
Age
18	776	84.0	949	81.7	693	74.5	2418	80.2
>18	148	16.0	213	18.3	237	25.5	598	19.8
Gender = female	458	49.5	646	55.3	615	65.9	1719	56.8
Gender = other	—	—	—	—	5	0.5	5	0.2
Relationship = single	679	73.4	857	73.5	714	76.3	2250	74.3
Parent status
No children	905	97.8	1068	91.6	890	95.3	2863	94.6
One child	13	1.4	90	7.7	37	4	140	4.6
Two or more children	7	0.8	8	0.7	7	0.7	22	0.7
Full time student	772	83.5	997	85.7	774	82.9	2543	84.1
Female caregiver education
Grade 10 or less	397	42.9	629	54.1	232	25.1	1258	41.8
Grade 11 or 12	209	22.6	246	21.2	311	33.7	766	25.5
Vocational or other post-secondary	184	19.9	144	12.4	190	20.6	518	17.2
Bachelor’s degree	69	7.5	50	4.3	—	—	119	4
Post-graduate degree	34	3.7	27	2.3	68	7.4	129	4.3
No female caregiver	32	3.5	66	5.7	—	—	98	3.3
Geographic setting
Rural area/village	191	26.3	283	6.9	4	0.5	478	18
City	152	20.9	15	1.4	203	23.2	370	13.9
Town	380	52.3	488	46.3	11	1.3	879	33.1
Suburb	—	—	161	15.3	398	45.5	559	21.1
Informal settlement	4	0.6	106	10.1	47	5.4	157	5.9
Township	—	—	—	—	212	24.2	212	8
Housing
Non-permanent structure	242	26.2	198	17.2	92	9.9	532	17.7
In someone else’s home	58	6.3	113	9.8	40	4.3	211	7.0
Rented house/apartment	228	24.7	137	11.9	143	15.4	508	16.9
Owned house/apartment	395	42.8	701	61	653	70.4	1749	58.3
Electricity at home	643	69.7	845	73.5	906	97	2394	79.6
Have smartphone^a	502	53.6	523	44.7	526	56	1551	50.9
Have own bedroom	485	52.5	523	45.4	536	57.3	1544	51.3
Computer in household	301	32.6	400	34.8	602	64.4	1303	43.3
Automobile in household	218	23.6	353	30.7	544	58.1	1115	37.1

Note. Participants who did not report being single reported being in a romantic relationship; none reported being married.

^aDetermined by checking all phone numbers given for a WhatsApp account, which is ubiquitously used in these contexts.

Comparison to national characteristics

Where possible, comparisons were made to national statistics to assess the degree to which our samples represent their national populations. In all three countries, participants’ female caregivers have a slightly higher degree of education than the general population. ALLS participants also appear to be somewhat more likely to own a phone than average. Access to electricity is similar in Kenya, but higher than national averages for ALLS participants in Namibia and South Africa. ALLS participants are only half as likely to live in an informal settlement as national averages. Further details and references are in Supplemental Table S2.

Ethnolinguistic groups

Unlike contemporary Europe, where language and country overlap, the borders of African countries do not contain homogenous linguistic or ethnic groups but instead incredible diversity. Although home to only one sixth of the world’s population, Africa is home to a third of living languages (Brown & Ogilvie, 2010; United Nations, Department of Economic and Social Affairs, Population Division, 2022). This diversity was assessed following local norms. In Namibia and South Africa, the post-apartheid context means that asking about tribe is seen as essentializing group differences. Thus, we asked, “What is your home language?” or “What is your home/first language”. In Kenya the wording was, “Which tribe or ethnic background are you from?” We initially used a write-in answer format, which led to heterogeneity in spelling and many anomalous answers. For later sites in South Africa, we used a drop-down menu including the 11 main languages and a write-in ‘other’ category.

Where two or more languages were provided, we privileged the first. We used Glottolog.org (Hammarström et al., 2023) to standardize the spelling of languages. To preserve anonymity in the data, we created higher-level groupings to avoid categories with fewer than 20 participants. Frequencies are reported in Table 2, along with a superordinate grouping showing how our sample represents the three main language families in Africa. For Kenya, the percentages for ethnolinguistic groups generally match the population rank order, though not exact percentages (Statista, 2021b). For Namibia, Caprivi and Kavango languages are somewhat underrepresented (Namibia Statistics Agency, 2011; The World Factbook, 2016). For South Africa, English and Xhosa are slightly overrepresented, and Afrikaans and Sotho languages somewhat underrepresented (Statista, 2018a). Further details are in Supplemental Table S3.

Table 2.

Home language or tribal affiliation of ALLS participants by country.

Language Family	Namibia (n = 1,154)	n	Kenya (n = 910)	n	South Africa (n = 933)	n
Bantu n = 1,960	Owambo	380	Kikuyu	190	Xhosa	256
	Botatwe East Bantu languages	86	North Mara	121	Southern Sotho	179
	Herero	113	Luyia	119	Zulu	104
	Lozi	51	Kamba	60	Other Sotho-Tswana	82
	Kwangali-Gciriku languages	46	Embu	49	Other S. Bantu & Swahili	53
	Other Central Western Bantu	25	Other East Bantu	18	Other Nguni (Swati, Ndebele)	28
Khoe-Kwadi n = 369	Khoekhoe or Haiǁom	369
Nilotic n = 295			Luo	108
			Kalenjin	86
			Teso-Turkana	59
			Masai	42
Afro-Asiatic n = 58			Somali	58
Indo-European n = 315	Afrikaans	63			Afrikaans	63
	English	21			English	168

Note. Country n is based on 2,997 total participants who answered this question; 17 in Namibia, 27 in Kenya, and 7 in South Africa did not.

Getting longitudinal: Wave 2 and beyond

Procedure and retention

Wave 1 (W1) log forms detailing each participant’s contact information and linking them to a study identification code were compiled from the local RAs by the Zurich team. For W2, if the participant reported a phone number or email, loop messaging programs were used to send them a survey by text or email, followed by weekly reminders for one month. We obtained surveys from 23% of the original 3,048 participants this way, almost all by text: Email outreach resulted in only 15 surveys. Lists of unreached participants were then delivered to RAs. In-person outreach was launched via online meetings with local RAs in each country to review the survey and brainstorm strategies, and with the delivery of tablets and other materials. After nearly four months, about 77% of our original participants had completed the W2 survey. Updates to their contact information were sent from RAs to prepare for W3, as shown in Figure 1.

Figure 1.

ALLS Data Collection Process. Note. At W1 recruitment and W3 re-recruitment, participant information was collected on log forms by local RAs and delivered to the UZH team, who compiled it into the ‘Master participant list’ (MPL). At W2 and beyond, the MPL guides initial outreach from UZH. Thereafter, unreached participants and those without phone or email are sorted onto updated log forms specific to each RA. RAs update contact details as they work, which are then integrated back into the MPL for the next wave.

At W3 we did slightly better. Of the same approximately 3,000 participants sought, 2,417 (81%) provided a survey, with similar rates responding directly to UZH, and the majority found in person by the local RAs. We decided at this point to address the 19% we had lost. Of them, only 169 directly asked to leave the study; due to some changes in RAs, we simply had poor contact information for and did not believe we could again reach many of the 500 participants who were not found at W3. Luckily, thanks to new funding in South Africa, an opportunity to add an underrepresented community in Namibia, and the addition of motivated new RAs, a wave of supplemental recruitment was carried out in all countries, targeting individuals aged 18 through 21. This resulted in a total of 721 new participants and thus 3,138 total W3 surveys. The sample sizes and details for the first four waves are displayed graphically in Figure 2.

Figure 2.

ALLS Data Waves 1 Through 4. Note. 156 surveys at W1, 43 at W2, and 46 at W3 could not be definitively linked to an ID code. Thus, our fully longitudinal W1–W2 sample included 2,246 cases; W1–W3 includes 1,949 cases.

One thing that Figure 2 illustrates is the importance of in person data collection in these contexts. Some surveys can be obtained online, but there is a limit. Our improved retention at W3 compared to W2 was thanks to improvements in on-the-ground efforts. Online surveys have become the dominant method in our field, replacing or supplementing prior dependence on undergraduate student samples (Thalmayer et al., 2021). They are inexpensive and efficient, streamlining both data collection and data processing. But they assume access to a computer or smart phone and to internet connectivity, which is far from the norm in many contexts. We find it implausible that online international studies including samples from African nations have anything close to representative samples of the populations they wish to generalize to.

Contextual notes

Details of the contextual situation relevant to participants are recorded annually by co-investigators and lead RAs, and notes about experience at each wave are compiled from local RAs. For Wave 2 (W2), events noted for Kenya included, “election campaign and slight division across the country before and after a court ruling to change the constitution,” which, for example, led ALLS T-shirts that were initially enthusiastically received as gifts to become suspiciously perceived as campaign-related. Relevant to the well-being of participants but not captured by our survey: “climate change led to food shortage in most parts of the country, with Wajir being affected mostly.” In South Africa it was noted at W2 that the country was experiencing load shedding (energy blackouts for part of each day), leading many participants to have disruptions when completing the survey online. Additionally, “the COVID-19 pandemic was still rife, with restrictions still in place and most lectures still online, so it was harder to get hold of people in person.”

Challenges

The first two years of the ALLS were successful, fascinating, and bonding for the team, but also posed an endless-seeming stream of challenges. For example, after W1, what we thought would be a simple task of verifying that we had one survey per participant led to hundreds of hours of work, when we belatedly discovered that some RAs had more successfully used codes to link log forms to surveys than others. At W2, the unique Qualtrics links typically used to link longitudinal surveys in online studies did not function over African networks, so we had to ask participants to enter their code into the survey, leading to errors and dozens of calls with local RAs to sort out duplicates. Electronic gifts were needed from W2 for UZH direct outreach, but digital vouchers for airtime and groceries came with many mishaps, including erratically reachable vendors, participants who found ways to obtain more than one, and others who lost trust in us when a voucher didn’t work, in a context rife with scams. Paper surveys were often practical in the field, but quality scanners were hard to access, slowing their processing.

Local RAs dealt with the fact that a quarter of our participants had no phone, those with phones often lacked electricity or data, and those living in school hostels only access phones over weekends or holidays. Phones are sometimes shared with family members, and can be linked to debts, reasons that numbers frequently change. Calling a parent to trace the participant could lead to new scrutiny: 18-year-olds could consent to join the study without parental permission, but most were still under parental care, and African parents are often protective, especially of girls. RAs had to introduce the study and convince them it wasn’t a scam or (for male RAs) a romantic pursuit. Finding participants without a phone could mean repeated taxi rides to hostels, homes, or villages. Schoolteachers had the easiest time, as many participants were still in school, and siblings or cousins of others were in reach. But in Namibia and South Africa, W2 coincided with end-of-year exams and lead-up preparations. Other RAs had to find a participant via a chain of phone calls to neighbors and relatives, sometimes reading aloud survey questions to participants after providing a data voucher for a borrowed phone. One Kenyan RA deputized a participant working at a juice bar in town to recognize other participants from the same school and call him to hurry over with a survey when one was sighted. In South Africa some student-RAs were no longer available, and it was sometimes difficult or unsafe to visit their communities as an outsider. A young community fieldworker who helped navigate a sometimes-violent township and create alternative gifts in the form of toiletry packs, as the store was too far for vouchers to be useful, was engaged as a new RA. When he learned that a participant had been arrested, he managed to journey to the prison with the W3 survey, earning warm gratitude.

While many participants were enthusiastic about the study, others had forgotten about it or complained about its length and challenging wording. Most RAs succeeded with persistence and a patient, informative approach, but in a couple exceptional cases RAs had taken poor records and found few of their participants, leading us to reorganize and to even re-recruit before W2 in one key region.

An overarching challenge of working in Africa is lower impersonal pro-sociality: Trust in anonymous others and institutions, and assumptions of impartial fairness, are much lower outside the West (summarized by Henrich, 2020; Kitayama et al., 2022). People are wary of scams, and a data collector is not automatically trusted as to the purpose of the study, the need for contact information, and our policy of data protection. Credibility must be cultivated and guarded, by engaging RAs with local roles and networks, and sometimes with letters from regional councillors, announcements on the radio, and wearing university logos on clothing. It can easily be lost and takes effort to rebuild. Most attrition in ALLS has been associated with the few RAs who dropped off the team: many of their participants were unwilling to switch RAs.

ALLS materials

Quantitative measures

Each survey includes several scales/inventories amounting to about 140 total items. Those administered through W5 and projected for use through W10 are listed in Table 3. Some (e.g., markers of adulthood) are administered a single time, others (e.g., personality, mental health) annually, and a few (well-being and life experiences) at every wave. Every scale is scrutinized for suitability at leadership meetings, leading to adjustments in wording, instructions, and response scales. Reports of psychometric properties and adaptation are underway, details available from the authors.

Table 3.

ALLS scales used through wave 5 and projected through wave 10.

Topics and Scales	Items	Wave(s)
Demographics and Life Circumstances
Age, gender, home language, education, birth order, etc.	13	1–3
Family Affluence Scale (Boyce et al., 2006) + local items	11	1–3
Disability status	1	4
Sexual Orientation	1	5
Life Experiences
Life events: e.g., education, marital and parent status, work	13	all
Event Characteristics Questionnaire (Luhmann et al., 2021)	10	4
Open ended questions	1–2	all
Personality Traits
Cross-Cultural Big Two Inventory (Thalmayer, et al., 2024)	45	odd
HEXACO & Khoekhoe Personality Inventory markers	75	2
Honesty items (Furr et al., 2021)	3	4
Emerging Adulthood
Markers of Adulthood Scale (Arnett, 2003)	40	2
Inventory of the Dimensions of Emerging Adulthood (IDEA-15) + ‘other focus’ (Baggio et al., 2015)	18	3
Positive Youth Development (Geldhof et al., 2014)	20	2
Purpose in Life Scale (Hill et al., 2016)	4	2
Mental Health and Disorders
Satisfaction with Life Scale (Kjell & Diener, 2021)	3	3–10
International Mental Health Assessment (Thalmayer et al., 2023) + trauma history	60	odd
General Self-Reported Health (DeSalvo et al., 2005)	1	odd
Ostracism Short Scale (Rudert et al., 2020)	4	2, 4
Mental Health Continuum-Short Form (Keyes, 2009)	14	3
International Prolonged Grief Disorder Scale (Killikelly et al., 2020)	24	(4)
Cultural Mindset
Duke University Religion Index (Koenig & Büssing, 2010)	5	odd
Sustainability Attitudes	7	4
Ethical Values Assessment (Padilla-Walker & Jensen, 2016)	20	4
Emotional-expression task (Salvador et al., 2020)	48	4
Regularity-norm behaviors (Saucier et al., 2015)	5	(4), 6
Cultural tightness–looseness (Gelfand et al., 2011)	6	(4), 6
Relational Mobility (Thomson et al., 2018)	12	(4), 6
Proneness to aggress (culture of honor; Henry, 2009)	3	(4), 6

Note. List is not comprehensive after W5. Parentheses indicate branching at W4: those who reported losing a loved one saw grief items, while those who did not saw cultural-mindset items.

Qualitative interviews

Our ethical approvals additionally allow qualitative interviews in subsamples each year to explore local phenomena in a more open-ended way, to support a mixed-methods approach. Four sets of interviews have so far been conducted with ALLS participants by the PI, PhD and master’s students, on psychological disorder symptoms, drinking behavior, ubuntu values, and beliefs about the role of ancestors. Another set in an analogous sample in Namibia was conducted in the months prior to ALLS recruitment, on emerging adulthood and alcohol use. Some of these are detailed below.

Preliminary findings

Personality traits

Personality trait terms from different models were administered as single terms with a standardized response scale and stem, “I tend to (be) (a) …”, allowing for the use of verbs and nouns as well as adjectives as personality descriptors, and for exploratory analyses with a maximally-diverse pool of terms. Two models have been established. A new Cross-Cultural Big Two Inventory was developed relying in part on ALLS data, with content based on regularities in global lexical studies (Thalmayer et al., 2024). And a set of marker terms for the HEXACO six-factor model with good psychometric properties and measurement invariance across the three countries were identified (Kura et al., 2024). In both cases, associations with mental health and other life outcomes largely replicate those established in other contexts. We are now ready to address substantive and longitudinal questions using these traits and models.

Mental health

To assess mental health we adapted the International Mental Health Assessment (IMHA; Thalmayer et al., 2023), which was designed to efficiently measure common symptoms at three levels of analysis: common syndromes, overarching spectra, and general psychopathology. The IMHA facilitates cross-group comparison by avoiding reference-group effects, with a response scale referring to days in the last month, and concrete, behavioral items. An equivalent structure for the dimensions that aligns to Western models of psychopathology was found in W1 data across the three samples (Hofmann et al., under review). This provides compelling support for the cross-cultural applicability of the Internalizing and Externalizing spectra and gives us a starting point to assess causal mechanisms and longitudinal change. Cognitive interviews were used to explore interpretations of the items by ALLS participants in urban and rural Kenya in early 2023, leading to small improvements in item wording at W5 to increase comprehension in these African contexts.

Emerging adulthood

Qualitative interviews in a sample of 50 18–25-year-old Owambo-speaking Namibians were conducted shortly before the start of the ALLS, to ensure that contextually-relevant topics were included in the quantitative surveys. Preliminary results indicate the importance of family roles, especially birth order, gender and parenthood, in when and how a person is perceived as an adult (Uugwanga et al., 2024). For example, ‘first borns’ are expected to take on adult roles early, helping to care for the household and their siblings, while later borns may retain the identity of baby of the family well into early adulthood. Women especially are expected to take on caregiving roles early, and thus are seen as more adult sooner, but are also more protected by the family. It also appears that the role of romantic exploration is different from both the West and Asia, for example, before marriage, parenthood may be more common and accepted in Namibia than is living with a partner.

Cultural mindset

A first project on this topic explores the concept of ubuntu, which captures African communal values including empathy, hospitality, generosity, and respect, is considered to define a morally-celebrated African person (Mnyaka & Motlhabi, 2005; Sodi et al., 2021), with ramifications for personality expression in African contexts (Fetvadjiev et al., 2015). Semi-structured interviews (n = 14) were conducted in urban and rural Namibia and Kenya to explore the role of ubuntu-related values during emerging adulthood. Initial results suggest that such values are still the norm, implying prioritization of community and family over individual wishes and recognizing ancestral influences (Rotzinger et al., in preparation).

Taken together, the initial surveys and interviews have allowed us to test the universality of some psychological theories and models. We have identified cross-cultural regularities, for examples in terms of the Big Two, HEXACO, and psychopathology-spectra models. We have also identified ways these African samples differ from Western ones in terms of family roles in emerging adulthood and ubuntu values, and methodologically in terms of appropriate wording and framing for personality and mental health items. By integrating African scholarship, priorities, and data into the topics, methods, and outlets of interest to European and North American psychologists, we aim for these and future studies to more meaningfully include African perspectives in personality, clinical, developmental, and cultural psychology.

Rewards, reflections and next steps

At some point we had to accept that no one has conducted a longitudinal study of this nature in Africa because it’s a daunting task; we would have to learn from mistakes. Ultimately, it has required a larger team and more support at every level than originally estimated, for example year-round lead RAs in each country, rotating teams of master’s students at the University of Zürich, and logistics meetings that can go on for 2 hours.

The project is rich in rewards, however. For example, we get positive feedback from participants, who say things like, “I really enjoy completing [these] surveys as they help me reflect on myself on things I never thought about in a good way. I am really looking forward to the next one” (Kenya, W3) and “the Africa long life study has allowed me to see my personal growth as an individual and the progression of my life through time” (South Africa, W4). An RA told us, “the learners were overjoyed for wave 2 and found the survey interesting, and they were eager to answer” (Namibia, W2).

In South Africa a dozen RAs are writing a master’s or PhD thesis using ALLS data, bringing their own interests and ideas to the interpretation of ALLS data. In addition to access to this large, diverse dataset, they receive feedback and mentoring from co-investigators in the supportive scholarly community of our joint research colloquium. A dozen European and North American teams have established collaborations with us, planning studies in their areas of specialty using ALLS data, offering us advice and encouragement along the way. Together these efforts contribute both emic and etic perspectives to personality, clinical, developmental, and cultural psychology, and bi-directional cross-cultural learning among a diverse network of Western, African, and global scholars and students.

In the next years we aim to share results in international contexts and locally, where adapted measures and study results centering African experiences can benefit scholars, practitioners, and policymakers. We are seeking means to expand to Nigeria and Ghana and to continue beyond the initial five years. We hope to accompany our participants deeper into adulthood and create a truly long-term dataset like those that have shaped Western psychological science. We have learned that it takes a village to run a longitudinal study in Africa! Living in this village, sharing a sense of purpose and adventure with a diverse scholarly community, is a great joy.

Key insights

• Personality science over relies on samples from wealthy Western countries.

• Longitudinal studies, key to psychology, are absent outside the West.

• The Africa Long Life Study brings lifespan research to African contexts.

• Longitudinal research in Africa requires contextualization of methods and measures.

• Few computers and lower impersonal pro-sociality challenge research in Africa.

Relevance statement

The Africa Long Life Study, a first longitudinal study of psychology in Sub-Saharan Africa, surveys samples of young adults in Namibia, Kenya, and South Africa every six months, allowing us to test findings in personality psychology for cross-cultural relevance.

Supplemental Material

Supplemental Material - Lifespan research in Kenya, Namibia, and South Africa: Cohort profile of the Africa long life study

Supplemental Material for Lifespan research in Kenya, Namibia, and South Africa: Cohort profile of the Africa long life study in Amber Gayle Thalmayer, Stephen Asatsa, Elizabeth N. Shino, Luzelle Naudé, Sumaya Laher, Tasneem Hassem, Maria Florence, Tracey-Ann Adonis, Selma N. Uugwanga, Julia S. Rotzinger, Daniel Hofmann, John Makunda, Casey Botha, Annelisa Murangi and Catherine M. Shirima in Personality Science

Footnotes

Author note

This paper is part of the bundle Personality Science Around the World. Friedrich Götz was the handling editor.

Acknowledgements

The authors were supported in data collection by local research assistants, in Namibia (in alphabetical order): Taimi Amutse, Hilma Awala, Elveraldo Awases, Marialda Garises, Jasmine Hangara, Lorensia Kafuro, Arthur Kambambi, Monika Matakala, Grace Mubiana, Ester Muhepa, Regina Mwanyangapo, Paulus Mwetulundila, Julius Ndikwetepo, Laina Shangano, and William Tjipundi Kunjanderua. In Kenya by Lorna Ayako, Godwin Emochi, Anna Madoka, Eucabeth Manyibe, Wairimu Murichi, Dr. Sammy Mutisya, Muthoni Mwangi, Margaret Natocho, Monica Nyagah, Esther Kerebi Nyamanche, Mikhal Odhiambo, Alice Sangok, Mercy Wambui Wachira, Esther Wangu, Rose Ndanu and Willy Kiptanui. In South Africa it was supported by Matsidiso Bambo, Valentina Bantam, Salmah Chopdat, Leano Cleophas, Tashnikah Davids, Erika Erasmus, Beauty Gama, Salma Gani, Sinesipho Jenkins, Siphiwe Kobo, Nelly Koketso Mamabolo, Khulekani Magagula, Elam Maso, Kutloano Molumo, Kgabo Mphela, Noluthando Mpisane, Precious Ndebele, Yonela Ngubo, Nnenna Osonda, Adhelle van der Poll, Jana Ras, Safaa Reddy, Boitumelo Sehlabaka, Ashton Smith, Lerato Sokhaya, Amy-Jean Viljoen, Shahied Visagie, Hannah Lukic, Khelsey Fraser, Xolisa Gwadiso, Munene Nkuna, Shannen Ferreira, Warona Mateane and Zandile Viti. At the University of Zurich online data collection was supported by Sarah Muhlert, Lea Bächlin, Andrianos Michail, Myrjam Suter, and Lilly Schmidt-Tophoff. We thank Marie Kura for extensive work processing the Wave 2 data, Alena Witzlack-Makarevich for consultation on grouping the languages, and Jeffrey Jensen Arnett for support in early study planning.

Author contributions

Amber Gayle Thalmayer: Conceptualization; Resources; Writing – original draft; Writing – review & editing; Supervision; Project administration; Funding acquisition.

Stephen Asatsa: Resources; Writing – review & editing; Supervision; Project administration.

Elizabeth N. Shino: Resources; Writing – review & editing; Supervision; Project administration.

Luzelle Naudé: Resources; Writing – review & editing; Supervision; Project administration; Funding acquisition.

Sumaya Laher: Resources; Writing – review & editing; Supervision; Project administration.

Tasneem Hassem: Resources; Writing – review & editing; Supervision; Project administration.

Maria Florence: Resources; Writing – review & editing; Supervision; Project administration.

Tracey-Ann Adonis: Resources; Writing – review & editing; Supervision; Project administration.

Selma N. Uugwanga: Data curation; Writing – review & editing; Visualization; Project administration.

Julia S. Rotzinger: Data curation; Writing – review & editing; Visualization; Project administration.

Daniel Hofmann: Validation; Formal analysis; Data curation; Writing – review & editing; Visualization; Project administration.

John Makunda: Data curation; Writing – review & editing; Project administration.

Casey Botha: Data curation; Writing – review & editing; Project administration.

Annelisa Murangi: Writing – review & editing; Project administration.

Catherine M. Shirima: Writing – review & editing; Project administration.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclose receipt of the following financial support for the research, authorship, and/or publication of this article: Funding for the Africa Long Life Study was provided to Amber Gayle Thalmayer by the Swiss National Science Foundation Eccellenza fellowship, “Universal versus culturally-specific aspects of personality and mental health” PCEFP1_194552, and by a University of Zürich Einrichtungskredit. Funding was provided to Luzelle Naudé for data collection in South Africa by the National Research Foundation of South Africa (SRUG220318204) as of Wave 3.

ORCID iD

Not applicable.

Data accessibility statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

Supplemental material

Supplemental material for this article is available online. Depending on the article type, these usually include a Transparency Checklist, a Transparent Peer Review File, and optional materials from the authors.

Notes

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