Sowing Q methodology in the rural global South: a review of challenges and good practices

Figures & data

Figure 1. Stages and steps of Q, adapted from (Zabala et al., 2018).

Table 1. Data extracted from the 50 selected studies.

Nr. (Ref.)	Location	Aim	Concourse		Q-set size	P-set		Sorting grid		Administration	Sorting time	Analysis	Factors			Salient reported difficulties
			Sources	RT		ST	Size	Criteria	Scale				Results	Presentation	Labelling
1 (Alexander et al., 2018)	Laos: Savannakhet and Champasak provinces	To identify drivers and constraints affecting smallholder farmers’ decision-making when considering introduced technologies	PD: NE-UN/SD: GL-UN	EJ	16	SS	T: 35 F: 16 M: 19 RD: 100%	Not important to Very important	−3 to +3 (7p)	T: Face-to-face L: N/A A: By-step guidance; clarification of statements	∼1 h	S: PQMethod E: PCA R: Varimax	NF: 2 VE: 40%	Z-score table	Long societal scenarios	Semi-literacy of respondents
2 (Anderson and Jacobson, 2018)	Ecuador: Tabuga village (Manabí province)	To identify and categorize barriers to teaching environmental education	SD: SL-GL	EJ	25	PS, CS	T: 25 F: 7 M: 18 RD: N/A	Very unimportant barriers to Very important barriers	−4 to +4 (9p)	T: Face-to-face L: Schools A: Pre-sort instructions	∼35 m	S: PQMethod E: N/A R: N/A	NF: 3 VE: 44%	Factor table	Explanatory labels	N/A
3 (Astari and Lovett, 2019)	Indonesia: South Sumatra, Riau and West Kalimantan provinces	To analyze discourses around the Indonesian Sustainable Palm Oil policy	PD: EX-PR/SD: SL-GL	DD, MM, SW	54	N/A	T: 27 F: N/A M: N/A RD: 0%	Most disagree to Most agree	−5 to +5 (11p)	T: Face-to-face L: N/A A: Clarification of statements	N/A	S: PQMethod E: PCA R: Varimax	NF: 5 VE: 48%	Factor table	Long societal scenarios	N/A
4 (Barbosa et al., 2020)	Brazil: Lagoa Grande village (Mato Grosso do Sul state)	To identify rural women’s viewpoint about the elements that would facilitate them to eventually take over the family farm	SD: SL-GL	CD	30	CS, SS	T: 28 F: 28 M: 0 RD: 39%	Strong disagreement to Strong agreement	−4 to +4 (9p)	T: Face-to-face, individually L: N/A A: N/A	N/A	S: PQMethod E: PCA R: Varimax	NF: 5 VE: 64%	Factor table	Societal scenarios	Time restrictions did not allow to conduct post-sorting interviews
5 (Brannstrom, 2011)	Brazil: Far west of Bahía state	To analyze discourses regarding environmental governance held amongst key actors in a region of expanding high-input, high-output agriculture	PD: EX-PR	CT	26	PS, SS	T: 21 F: 4 M: 17 RD: 24%	Most disagree with my views to Most agree with my views	−4 to +4 (9p)	T: N/A L: N/A A: N/A	∼25 m	S: PQMethod E: N/A R: Varimax	NF: 4 VE: 67%	Z-score table	Societal scenarios	Anguishing of respondents after some difficult choices in sorting
6 (Bumbudsanpharoke et al., 2009)	Thailand: Ping river basin (Chiang Mai province)	To explore perspectives of environmental best practices in Thai agriculture	PD: EX-OR/SD: UN	MM	36	PS	T: 72 F: N/A M: N/A RD: 100%	Disagree to Agree (not clear)	−4 to +4 (9p)	T: Face-to-face L: N/A A: By-step guidance	N/A	S: PQMethod E: PCA R: Varimax	NF: 4 VE: N/A	Factor table	Behavioral adjectives	N/A
7 (Cammelli et al., 2019)	Brazil: Paragominas municipality	To explore smallholders’ perceptions of fire use, fire control, and firefighting in a post-frontier region of the eastern Amazon, and how these relate to fire risk perceptions and governance preferences	PD: NE-PR/SD: UN	MM	17	SS	T: 51 F: N/A M: N/A RD: 100%	Strongly disagrees to Strongly agrees	−2 to +2 (5p)	T: Face-to-face, individually L: Farmers’ houses A: By-step guidance; reading of statements	∼1/2 h	S: N/A E: PCA R: Varimax	NF: 4 VE: 55%	Modified Z-score table	Long societal scenarios	Low literacy levels of respondents
8 (Carmenta et al., 2017)	Indonesia: Riau province	To illustrate the diverse stakeholders’ perceptions of peat fires and fire management interventions across sectors and scales of governance	PD: EX-PR/SD: SL-GL	IR	70	PS	T: 219 F: N/A M: N/A RD: 80%	Least important to Most important/Least effective to Most effective	−3 to +3 (8p)	T: N/A L: N/A A: On-demand reading of statements	N/A	S: qmethod for R E: PCA R: Varimax	NF: 9 (4/5) VE: 47%/39%	Factor table and Z-score diagram	Long societal scenarios	N/A
9 (Forouzani et al., 2013)	Iran: Marvdasht county	To study stakeholders’ perceptions toward agricultural water poverty	PD: EX-PR/SD: UN	MM	54	PS	T: 75 F: N/A M: N/A RD: 67%	Strongly disagree to Strongly agree	−2 to +2 (5p)	T: Face-to-face, individually L: N/A A: N/A	N/A	S: PQMethod E: CFA R: Varimax	NF: 7 (4 – farmers/3 – specialists) VE: 42%/45%	Z-score table	Long behavioral adjectives	N/A
10 (Frate and Brannstrom, 2015)	Brazil: Andalucía village (Nioaque municipality, Mato Grosso do Sul state)	To determine empirically social perspectives regarding ethanol-agrarian reform conflicts, focusing specifically on food security and safety	PD: EX-PR	EJ	26	PS	T: 26 F: N/A M: N/A RD: N/A	Most agree with my views to Most disagree with my views	+4 to −4 (9p)	T: Face-to-face L: N/A A: By-step guidance	∼40 m (sorting); ∼55 m (interview)	S: PQMethod E: N/A R: N/A	NF: 4 VE: 63%	Combined Z-score and factor table	Long societal scenarios	N/A
11 (Giannichi et al., 2018)	Brazil: Mato Grosso state	To identify viewpoints of sellers and buyers of an emerging forest certificate trading scheme	PD: EX-UN/SD: SL-GL	CD	39	PS, SS	T: 59 F: N/A M: N/A RD: 100%	Strongly disagree to Strongly agree	−4 to +4 (9p)	T: Face-to-face L: N/A A: Pre-sort instructions	∼1 h	S: qmethod for R E: PCA R: Varimax	NF: 6 (3/3) VE: 44%/46%	Factor table	Behavioral adjectives	Remote and nonaffiliated farmers were likely undersampled
12 (Hamadou et al., 2016)	Niger: N’dounga, Namaro and Bitinkodji communes	To examine the viewpoints of stakeholders in the management and conservation of farm animal biodiversity in Niger	PD: EX-NR/SD: SL-GL	N/A	48	PS	T: 20 F: N/A M: N/A RD: 35%	I totally disagree to I fully agree	−3 to +3 (7p)	T: Face-to-face L: N/A A: Pre-sort instructions	N/A	S: qmethod for R E: PCA R: Varimax	NF: 3 VE: 61%	Factor table	Behavioral adjectives	Low educational level of several respondents
13 (Hilhorst et al., 2012)	Nepal: Beldangi I refugee camp (Damak municipality)	To explore the mutual imaging of aid workers and refugees in humanitarian crisis contexts	PD: EX-PR/SD: UN	N/A	47	SRS	T: 28 F: N/A M: N/A RD: N/A	Strongly disagree to Strongly agree	N/A (11p)	T: Face-to-face L: N/A A: N/A	N/A	S: PQMethod E: N/A R: N/A	NF: 4 VE: N/A	N/A	Generic labels	N/A
14 (Hu et al., 2018)	China: Rizhao, Tai’an, Xuzhou, Lu’an, Xinyang, Zhumadian, Fuyang, Shangqiu and Luoyang cities	To evaluate the attitudes of Chinese farmers in the period of agricultural supply-side structural reform	N/A	N/A	33	N/A	T: 61 F: 27 M: 35 RD: 100%	Strong disagreement to Strong agreement	−4 to +4 (9p)	T: Face-to-face L: N/A A: N/A	30 m–3 h	S: PQMethod E: PCA R: Varimax	NF: 3 VE: 44%	Combined Z-score and factor table	Behavioral adjectives	N/A
15 (Huaranca et al., 2019)	Argentina: Salta province	To identify and describe social perspectives on deforestation, land use change and economic development	SD: SL-GL	CT	68	PS	T: 60 F: N/A M: N/A RD: 17%	Less agreement to More agreement	−5 to +5 (11p)	T: Face-to-face/Online (POETQ) L: N/A A: N/A/Software-led	N/A	S: PQMethod E: PCA R: Varimax	NF: 4 VE: 54%	Z-score table	Societal scenarios	N/A
16 (Hugé et al., 2016)	Malaysia: Mangrove Forest Reserve, Perak state	To identify perspectives on the management of Matang Mangrove Forest Reserve	PD: EX-NR/SD: SL-GL	CT	48	SS	T: 29 F: N/A M: N/A RD: 31%	Least agreement to Strongest agreement	−3 to +3 (7p)	T: Face-to-face L: N/A A: N/A	N/A	S: PQMethod E: CFA R: Varimax	NF: 3 VE: 41%	Factor arrays	Long societal scenarios	Participants had difficulties at times while sorting Q was difficult or impossible to administer to some rural dwellers, due to social, educational and cultural barriers Some respondents tended to please researchers rather than answering authentically
17 (Jaung et al., 2016)	Indonesia: West Lombok district	To analyze diverse perspectives on the certification of a payment for watershed services scheme	PD: EX-NR/SD: SL	N/A	48	PS, SS	T: 19 F: N/A M: N/A RD: 21%	Most disagreed to Most agreed	−6 to +6 (13p)	T: Face-to-face L: N/A A: By-step guidance	N/A	S: PQMethod E: PCA R: Varimax	NF: 3 VE: 51%	Factor table	Long societal scenarios	N/A
18 (Jiren et al., 2020)	Ethiopia: Southwestern Ethopia (Jimma zone)	To examine alternative approaches to food security and biodiversity conservation	PD: EX-NR/SD: SL-GL	CT	32	PS	T: 50 F: N/A M: N/A RD: 2%	Most important to Least important	+4 to −4 (vertical) (9p)	T: Face-to-face, individually L: N/A A: Pre-sort instructions	N/A	S: qmethod for R E: PCA R: Varimax	NF: 4 VE: 48%	Z-score table	Societal scenarios	N/A
19 (Kopytko and Pruneddu, 2018)	Ukraine: Kyiv, Kherson, Ternopil, Lviv and Chernihiv municipalities	To reveal barriers to crop rotation	PD: EX-PR	N/A	27	PS, SS	T: 10 F: N/A M: N/A RD: 20%	Strongly agree to Strongly disagree	+3 to −3 (7p)	T: Online (Q sortware) L: N/A A: Software-led	N/A	S: qmethod for R 32.2 E: N/A R: Varimax	NF: 3 VE: 53%	Factor table and Z-score diagram	Societal scenarios	Respondents were difficult to reach
20 (Lairez et al., 2020)	Laos: Leng, Lé, Xay, Nadou villages (Kham district, Xieng Khouang province)	To understand farmers’ perceptions of soil fertility	PD: NE-UN	N/A	47	N/A	T: 19 F: N/A M: N/A RD: 100%	Disagree to Agree	−5 to +5 (11p)	T: Face-to-face L: Respondents’ houses A: By-step guidance; reading of statements; on-demand clarification	N/A	S: PQMethod E: CFA R: Varimax	NF: 3 VE: N/A	Factor table	Mixed (scenarios and adjectives)	N/A
21 (Lansing, 2013)	Costa Rica: Talamanca region	To examine perspectives about baseline constructions of land-use change, and the potential role of offsets in an indigenous community	PD: EX-PR/SD: SL-GL	CT	30	PS	T: 15 F: 4 M: 11 RD: 67%	Most disagree to Most agree	−4 to +4 (9p)	T: Face-to-face L: N/A A: N/A	N/A	S: PQMethod E: N/A R: Varimax	NF: 3 VE: 46%	Factor table	Behavioral adjectives	N/A
22 (Leite et al., 2019)	Brazil: Santa Catarina region	To analyze the perception of farmers on the landscape and its dynamics	SD: SL-GL	N/A	16	SS	T: 90 F: 45 M: 45 RD: 100%	Higher agreement to Less agreement	+3 to −3 (7p)	T: Face-to-face L: N/A A: N/A	N/A	S: PQMethod E: PCA R: Varimax	NF: 3 VE: 48%	Factor table	Societal scenarios	N/A
23 (Mayett-Moreno et al., 2017)	Mexico: La Preciosita Sangre de Cristo village (Puebla state)	To understand young farmers’ views of a community-based ecotourism project	PD: EX-OR	CA	36	PS	T: 16 F: 5 M: 11 RD: 100%	I don’t identify with to I identify with	−5 to +5 (11p)	T: Face-to-face, collectively L: N/A A: N/A	N/A	S: PQMethod E: PCA R: Varimax	NF: 3 VE: N/A	Z-score table, factor arrays and factor table	Societal scenarios	N/A
24 (Moros et al., 2020)	Colombia: Bogotá, Cali, Buenaventura, Medellín municipalities	To explore the discourses that underpin Payment for Ecosystem Services debates and practice in Colombia	SD: SL-GL	CD, CT, EJ	36	PS, SS	T: 41 F: N/A M: N/A RD: N/A	Strongly disagree to Strongly agree	−4 to +4 (9p)	T: Face-to-face L: N/A A: Pre-sort instructions; by-step guidance; on-demand clarification	∼1 h	S: qmethod for R E: PCA R: Varimax	NF: 3 VE: 59%	Combined Z-score and factor table	Societal scenarios	N/A
25 (Nguyen et al., 2018)	Vietnam: Phuoc Duc and Tam Lanh communes (Quang Nam province)	To examine community perceptions concerning the impact of gold mining	SD: UN	N/A	25	SS	T: 137 F: 49 M: 88 RD: 88%	Least important to Most important	−4 to +4 (9p)	T: Face-to-face, individually L: N/A A: Pre-sort instructions	N/A	S: N/A E: PCA R: Varimax	NF: 8 (5/3) VE: 51.23%/43%	Factor table	Generic labels	N/A
26 (Nhem and Lee, 2020)	Cambodia: Oddar Meanchey province	To assess the perceptions of local stakeholders towards the quality of governance of the first REDD + pilot project in Cambodia	SD: SL-GL	CD	40	PS	T: 52 F: N/A M: N/A RD: 100%	Most strongly disagree to Most strongly agree	−4 to +4 (9p)	T: Face-to-face, individually L: Respondents’ houses A: Pre-sort instructions; by-step guidance	N/A	S: PQMethod E: PCA R: Varimax	NF: 4 VE: 35%	Z-score table	Societal scenarios	N/A
27 (Nhem and Lee, 2019)	Cambodia: Oddar Meanchey province	To investigate the views of local experts on the sustainability of community-based forestry management	SD: SL-GL	EJ	43	SS	T: 52 F: 8 M: 44 RD: 100%	I Most Disagree to I Most Agree	−5 to +5 (11p)	T: Face-to-face, individually L: N/A T: By-step guidance	∼50 m	S: PQMethod E: PCA R: Varimax	NF: 4 VE: 37%	Modified Z-score table	Societal scenarios	N/A
28 (Nordhagen et al., 2017)	Papua New Guinea: Eastern Highlands and Morobe provinces	To examine the motivations for growing diverse crops amongst semi-subsistence rural farmers in Papua New Guinea	PD: NE-PR	CD	31	RS, SS	T: 92 F: 50 M: 42 RD: 100%	Don’t agree at all to Agree very much	−3 to +3 (7p)	T: Face-to-face L: N/A A: By-step guidance; reading of statements	N/A	S: PQMethod E: PCA R: Varimax	NF: 5 VE: N/A	Factor table	Behavioral adjectives	Low literacy level of respondents
29 (Pereira et al., 2016)	Brazil: Mato Grosso do Sul state	To assess the diversity of values and goals amongst Brazilian commercial-scale progressive beef farmers	SD: UN	N/A	49	PS, SRS	T: 26 F: 0 M: 26 RD: 100%	Strong disagreement to Strong agreement	−4 to +4 (9p)	T: Face-to-face L: N/A A: N/A	∼1.5 h	S: PQMethod E: PCA R: Varimax	NF: 4 VE: 62%	Factor table	Behavioral adjectives	Respondents were all male; they were the main decision makers, although some women were the legal owners of the farms
30 (Pirard et al., 2016)	Indonesia: East Kalimantan province	To study the general perceptions of rural populations toward industrial plantations	PD: NE-PR	EJ	30	SRS	T: 31 F: 12 M: 19 RD: 100%	Most disagree with to Most agree with	−3 to +3 (8p)	T: Face-to-face, three-person rounds L: N/A A: Pre-sort instructions; on-demand clarification	20 m–1 h	S: PQMethod E: PCA R: Varimax	NF: 3 VE: N/A	Factor arrays and factor table	Mixed (scenarios and adjectives)	Respondents had different levels of fluency in Indonesian
31 (Rijneveld and Marhaento, 2020)	Indonesia: Getas village (Blora district), Pitu village (Ngawi district)	To explore stakeholder perceptions of the effects of agroforestry and mono-cropping systems on water use	SD: SL-GL	N/A	17	N/A	T: 33 F: N/A M: N/A RD: N/A	Fully agree to Fully disagree	+2 to −2 (5p)	T: Face-to-face; individual and collective L: N/A A: N/A	N/A	S: qmethod for R E: N/A R: N/A	NF: 3 VE: N/A	Z-score diagram	Generic labels	N/A
32 (Rodriguez-Piñeros et al., 2012)	Mexico: La Preciosita Sangre de Cristo village (Puebla state)	To identify perspectives on sustainable management of a community-owned forest reserve	PD: EX-OR	CT	36	N/A	T: 20 F: 9 M: 1 RD: 100%	Unlike me to Like me	−4 to +4 (9p)	T: N/A L: N/A A: N/A	N/A	S: PQMethod E: PCA R: Varimax	NF: 3 VE: 65%	Z-score and factor tables	Societal scenarios	N/A
33 (Rodríguez-Piñeros et al., 2018)	Colombia: Cimitarra, Magangué, Guapi, Pasto and Tuluá municipalities	To understand stakeholders’ values about Colombian forest legislation and its implications for legal timber trade	PD: EX-UN	EJ, SW	34	PS	T: 27 F: 5 M: 22 RD: 19%	Less affect me to Most affect me	−4 to +4 (9p)	T: Face-to-face, individually L: N/A A: N/A	N/A	S: PQMethod E: PCA R: Varimax	NF: 4 VE: 52%	Z-score table	Long societal scenarios	N/A
34 (Rodríguez-Piñeros and Mayett-Moreno, 2015)	Mexico: La Preciosita Sangre de Cristo village (Puebla state)	To examine perceptions of small forest owners to build infrastructure in their forest as part of their community-based ecotourism project, and, to explore their underlying reasons to retain the forest for ecotourism	PD: EX-OR/SD: SL-GL	CA	32	PS	T: 14 F: 7 M: 7 RD: 100%	Unlike me to Like me	−4 to +4 (9p)	T: Face-to-face L: Respondents’ houses A: N/A	N/A	S: PQMethod E: PCA R: Varimax	NF: 3 VE: 56%	Z-score table	Long societal scenarios	N/A
35 (Rust, 2017)	Namibia	To investigate whether a diverse range of stakeholders could agree on how to mitigate conflict between carnivores and livestock farmers in Namibia	PD: EX-PR/SD: GL	CD	36	PS, SS	T: 35 F: N/A M: N/A RD: 40%	Strongly disagree to Strongly agree	−3 to +3 (7p)	T: Online (SurveyMonkey) L: N/A A: Software-led	N/A	S: PQMethod E: PCA R: Varimax	NF: 2 (2 in each of three rounds) VE: 51%/45%/57%	Z-score table	Generic labels	N/A
36 (Schneider et al., 2015)	Brazil: São Felix do Xingu (Pará state)	To study small-scale farmers’ needs to end deforestation	PD: NE-UN	N/A	29	SS	T: 14 F: 0 M: 14 RD: 100%	Strongly disapproves to Strongly approves	−2 to +2 (5p)	T: Face-to-face L: Farmers’ villages A: N/A	∼30 m	S: N/A E: PCA R: Varimax	NF: 3 VE: N/A	Z-score table	Behavioral adjectives	Due to remoteness researchers had to rely on an organization to reach participants Communications difficulties hampered higher participation rates Some women farmer refused to participate due to shyness Some participants found difficult to make sorting choices
37 (Schuman et al., 2018)	South Africa: Jouberton and Ikageng communities, Ventersdorp municipality	To study the influence of religious beliefs on the understanding and experience of climate change adaptation	PD: EX-OR	N/A	40	PS, SS	T: 25 F: N/A M: N/A RD: N/A	Strongly agree to Strongly disagree	+3 to −3 (7p)	T: Face-to-face L: N/A A: Pre-sort instructions	N/A	S: PQMethod E: PCA R: N/A	NF: 5 VE: 58%	Factor table	Mixed (scenarios and adjectives)	N/A
38 (Stoudmann et al., 2017)	Madagascar: Alaotra-Mangoro and Analanjirofo regions	To gain a better understanding of farmers’ definitions and experiences of change	PD: EX-OR	N/A	52	SS	T: 30 F: N/A M: N/A RD: 100%	Most disagree to Most agree	1 to 11 (11p)	T: Face-to-face, individually L: N/A A: N/A	N/A	S: PQMethod E: N/A R: Varimax	NF: 5 VE: 38%	Combined Z-score and factor table	Societal scenarios	Possible dissimilarities in terms translated between languages and local dialects
39 (Sumberg et al., 2017)	Ghana: Tepa (Ashanti region), Savelugu (Northern region)	To understand young people’s attitudes towards farming; and, to understand what should be done about rural young people and farming	PD: NE-UN/SD: SL-GL	N/A	32	SS	T: 38 F: 20 M: 18 RD: 100%	Most disagree to Most agree	−3 to +3 (7p)	T: Face-to-face L: N/A A: Pre-sort instructions; by-step guidance; on-demand clarification	N/A	S: PQMethod E: PCA R: Varimax	NF: 8 (4/4) VE: N/A	Factor table	Long societal scenarios	N/A
40 (Taheri et al., 2020)	Iran: Khuzestan province	To understand how farmers perceive the impact of dust phenomenon on agricultural production activities	PD: EX-PR/SD: GL	CT, EJ	48	PS, SS	T: 60 F: 0 M: 60 RD: 100%	Strongly disagree to Strongly agree	−2 to +2 (5p)	T: Face-to-face L: N/A A: N/A	N/A	S: PQMethod E: CFA R: Varimax	NF: 3 VE: 54%	N/A	Behavioral adjectives	N/A
41 (Truong et al., 2017)	Vietnam: Long An and Tay Ninh provinces	To explore farmers’ perceptions of foot-and-mouth disease vaccination	PD: EX-OR	N/A	46	STS	T: 46 F: 13 M: 29 RD: 100%	Strongly in disagreement to Strongly in agreement	−3 to +3 (7p)	T: Face-to-face, individually L: Usually farmers’ houses A: Pre-sort instructions	>1 h	S: qmethod for R E: PCA R: Varimax	NF: 3 VE: 57%	Factor arrays and Z-score table	Societal scenarios	Low degree of farmer participation Some participants did not follow instructions Too short post-sorting answers due to tiredness after long sorting times Possible response bias due to pre-sorting discussions Some participants complained about unclear or complicated statements
42 (Truong et al., 2019)	Vietnam: Dong Thap province (Mekong delta)	To identify poultry farmers’ drivers to use high amounts of antimicrobials	PD: EX-PR	CT	46	RS, SS	T: 54 F: 6 M: 48 RD: 52%	Extremely disagree to Extremely agree	−3 to +3 (7p)	T: Face-to-face L: Usually farmers’ houses A: Pre-sort instructions	∼1 h	S: R E: PCA R: N/A	NF: 7 (4/3) VE: 55%/50%	Factor arrays and factor table	Societal scenarios	Sampled target population changed due to unpredictable field constraints Some participants misunderstood instructions or were not willing to complete the sorting Husbands were usually the respondents, although wives were the ones conducting the farm work
43 (Tuokuu et al., 2019)	Ghana: Tarkwa-Nsuaem municipality	To understands areas of agreements and disagreements amongst critical stakeholders as a way to improve environmental policy development and implementation	PD: EX-PR/SD: SL-GL	CD	31	PS	T: 15 F: N/A M: N/A RD: N/A	Least effective to Most effective	−3 to +3 (7p)	T: Face-to-face L: N/A A: By-step guidance	N/A	S: R E: N/A R: Varimax	NF: 3 VE: 46%	Factor table and factor arrays	Societal scenarios	N/A
44 (Vargas et al., 2019)	Colombia: Luruaco and Santa Catalina municipalities	To explore the understanding of the conservation-development relationship by a rural community in Colombia	PD: EX-UN	CT	40	RS, PS	T: 38 F: 20 M: 16 RD: 100%	Disagree most to Agree most	−4 to +4 (9p)	T: Face-to-face, collectively L: N/A A: N/A	N/A	S: PQMethod E: PCA R: Varimax	NF: 4 VE: 51%	Crib sheets	Societal scenarios	N/A
45 (Vela-Almeida et al., 2018)	Ecuador: Quito municipality; Tundayme and Los Encuentros parishes (Zamora Chinchipe province); San Juan Bosco municipality (Morona Santiago province)	To assess subjective perceptions of key stakeholders regarding mining projects	SD: UN	N/A	30	SS	T: 47 F: N/A M: N/A RD: N/A	Agree the most to Disagree the most	N/A	T: N/A L: N/A A: Pre-sort instructions	N/A	S: PQMethod E: CFA R: Varimax/Judgmental	NF: 4 VE: 62%	Factor table	Societal scenarios	N/A
46 (Weldegiorgis and Ali, 2016)	Rwanda: Murambi and Masoro communities (Rulindo district)	To quantitatively analyze qualitative perspectives regarding impacts of mining-led development at a district level in Rwanda	PD: EX-PR/SD: GL	CT	46	PS, SS	T: 49 F: 15 M: 34 RD: 71%	Fully Disagree to Fully Agree	−6 to +6 (13p)	T: Face-to-face L: Communities, offices A: Pre-sort instructions	N/A	S: PQMethod E: PCA R: Varimax	NF: 3 VE: 41%	Factor arrays and factor table	Long societal scenarios	Some respondents sorted out of the distribution grid; at times they were unable to prioritize statements Some respondents criticized the forced distribution
47 (Wijaya and Offermans, 2019)	Indonesia: North Luwu, Bondowoso and Pelalawan districts	To identify sustainability perspectives in agriculture	PD: EX-PR/SD: SL-GL	CT, CD	37	PS	T: 64 F: 13 M: 51 RD: 52%	Strongly disagree to Strongly agree	−4 to +4 (9p)	T: Face-to-face L: N/A A: Pre-sort instructions; by-step guidance; reading of statements; on-demand clarification	N/A	S: PQMethod E: PCA R: Varimax	NF: 2 VE: 33%	Modified Z-score table	Behavioral adjectives	Gender diversity was hard to accomplish in the field due to cultural constraints
48 (Yeboah et al., 2017)	Ghana: Tepa (Ashanti region), Savelugu (Northern region)	To explore perspectives of job desirability	PD: NE-UN/SD: SL-GL	N/A	59	SS	T: 72 F: 29 M: 43 RD: 100%	Most disagree to Most Agree	−4 to +4 (9p)	T: Face-to-face L: N/A A: Pre-sort instructions; by-step guidance; on-demand clarification	65 m (students); 50 m (parents)	S: PQMethod E: PCA R: Varimax	NF: 19 (8/11) VE: N/A	Factor summary table	Long societal scenarios	The initial aim was to interview students and their parents; because some parents could not or were unwilling to participate, the authors resorted to parents of non-participant students
49 (Zabala et al., 2017)	Mexico: Chiapas state	To identify smallholders’ views with regard to silvopastoral practices in a community in the forest frontier in Chiapas	PD: EX-PR/SD: UN	CT	26	PS	T: 32 F: 0 M: 32 RD: 100%	Most disagree to Most agree	−3 to +3 (7p)	T: Face-to-face L: N/A A: N/A	N/A	S: qmethod for R E: PCA R: Varimax	NF: 3 VE: N/A	Z-score diagram	Behavioral adjectives	Lack of female respondents because male head of household is the decision maker
50 (Zobeidi et al., 2016)	Iran: Hamidiyeh district	To investigate and understand farmers’ perceptions regarding climate change	PD: EX-PR/SD: SL-GL	MM	42	PS, SS	T: 46 F: N/A M: N/A RD: 100%	Most disagree to Most agree	−2 to +2 (5p)	T: Face-to-face, individually L: Extension offices, homes, farms A: By-step guidance	∼80 m	S: PQMethod E: CFA R: Varimax	NF: 3 VE: 56%	Factor table	Mixed (scenarios and adjectives)	N/A

List of acronyms in the table:

In Concourse – Sources column: primary data (PD); not-exclusive for the study (NE); exclusive for the study (EX); unspecified sources (UN); partially rural dwellers sources (PR); only rural dweller sources (OR); no rural dweller sources (NR); secondary data (SD); grey literature (GL); scientific literature (SL).

In Concourse – Reduction Technique (RT) column: expert judgement (EJ); division in discourses (DD); matrix method (MM); software (SW); categorization (CT); iterative refinement (IR); combination and deletion of similar statements (CD); content analysis (CA).

In P-set – Sampling Technique (ST) column: snowball sampling (SS); purposive sampling (PS); convenience sampling (CS); random sampling (RS); stratified random sampling (SRS); structured sampling (STS).

In P-set – Size column: total (T); female (F); male (M); percentage of rural dwellers (RD).

In Sorting grid – Scale column: sorting points (p).

In Administration column: administration technique (T), location (L), assistance (A).

In Analysis column: software (S); factor extraction method (E); factor rotation method (R); Principal Component Analysis (PCA); Centroid Factor Analysis (CFA).

In Factors – Results column: number of retained factors (NF); total variance explained (VE).

Figure 2. Characteristics of the selected studies. (a) Number of studies across disciplines of agriculture (AG), international development (ID), environmental studies (ES), conservation (CO), forests and forestry (FF), veterinary sciences (VS), and others (OT). (b) Number of studies per category of Q study [as defined by (Zabala et al., 2018)] as conflict resolution (CR), management alternatives (MA) and policy appraisal (PA). (c) Number of studies published as (non)open-access documents. (d) Number of studies per geographical region, across Australasia (AA), East Asia (EA), Eastern Europe (EE), Middle East (ME), Central America (CA), South America (SA), South Asia (SAs), Southeast Asia (SEA) and sub-Saharan Africa (SSA); solid dark gray, solid light gray and diagonal-line patterns on each bar represent the proportions of low-, lower-middle- and upper-middle-income countries, respectively.

Figure 3. Geographic location of main authors’ affiliations and studies per the theme of Q study [as defined by (Zabala et al., 2018)].

Figure 4. Characteristics of the concourse. (a) Number of studies per source for concourse construction, based on primary data (PD), secondary data (SD) and mixed sources (PD/SD). (b) Size of the constructed concourse in a number of statements across studies. (c) Number of studies per concourse reduction technique, comprising software (SW), matrix method (MM), iterative refinement (IR), expert judgement (EJ), division in discourses (DD), categorization (CT), combination and deletion of similar statements (CD), content analysis (CA). (d) Concourse reduction ratio across studies, expressed as the decreasing percentage between the concourse and the Q-set.

Figure 5. Characteristics of research design. (a) Q-set size in a number of statements across studies. (b) Number of studies per P-set sampling techniques, including convenience sampling (CS), purposive sampling (PS), random sampling (RS), stratified random sampling (SRS), snowball sampling (SS) and structured sampling (STS). (c) P-set size in number of participants across studies. (d) Q-set/P-set ratio across studies.

Figure 6. P-set size and genders. (a) Number and gender of participants per each of the 50 studies. Numbers on the X-axis correspond to the references. Solid dark gray, solid light gray and diagonal-line patterns on each bar represent the proportions of female participants, male participants, and gender-unspecified participants, respectively. Dashed line, dotted-dashed line and dotted line represent the average of female, male and total participants across studies, respectively. (b) Female/Male ratio across the selected studies. Barbosa et al. (2020) considered female participants only, thus is not represented here.

Figure 7. Sorting grids of the selected studies. ¹(Anderson and Jacobson, 2018; Brannstrom, 2011; Forouzani et al., 2013; Huaranca et al., 2019; Hugé et al., 2016) did not report the orientation of their grids. They were assumed as inverted distributions. ²The shaded area with thicker border represents the second grid used in (Carmenta et al., 2017). ³(Hilhorst et al., 2012) did not use a quantitative scale; instead, its authors reported graphical hints (happy/sad faces). ⁴Reported grids of (Rodríguez-Piñeros and Mayett-Moreno, 2015; Schneider et al., 2015) did not match with their respective number of statements. ⁵(Sumberg et al., 2017; Yeboah et al., 2017) resorted to two grids; only information of one of them was provided.

Table 2. Characteristics of sorting grids with regard to their size and kurtosis.

Q-set	Platykurtic (flat)	Mesokurtic	Leptokurtic (steep)
10–30	(Kopytko and Pruneddu, 2018)	(Alexander et al., 2018; Anderson and Jacobson, 2018; Barbosa et al., 2020; Brannstrom, 2011; Cammelli et al., 2019; Lansing, 2013; Nguyen et al., 2018; Pirard et al., 2016; Zabala et al., 2017)	(Schneider et al., 2015)
31–50	(Moros et al., 2020; Weldegiorgis and Ali, 2016)	(Bumbudsanpharoke et al., 2009; Giannichi et al., 2018; Hilhorst et al., 2012; Jaung et al., 2016; Jiren et al., 2020; Lairez et al., 2020; Mayett-Moreno et al., 2017; Nhem and Lee, 2020; Nhem and Lee, 2019; Nordhagen et al., 2017; Rodríguez-Piñeros et al., 2018; Rodríguez-Piñeros and Mayett-Moreno, 2015; Sumberg et al., 2017; Tuokuu et al., 2019)^b	(Hugé et al., 2016; Taheri et al., 2020; Truong et al., 2017; Truong et al., 2019; Zobeidi et al., 2016)
51–70	(Astari and Lovett, 2019; Carmenta et al., 2017)^a^,^b	(Huaranca et al., 2019; Stoudmann et al., 2017; Yeboah et al., 2017)^b	(Carmenta et al., 2017; Forouzani et al., 2013)^a^,^b

^a Carmenta et al. (2017) was the only study that offered two sorting grids from two different kurtosis categories.

^b Carmenta et al. (2017), Sumberg et al. (2017), and Yeboah et al. (2017) provided two Q-sets to the participants; the table registers the sum of their statements.

Figure 8. Sorting times across the selected studies. (a) Absolute sorting time in minutes. (b) Sorting time ratio, expressed in seconds per statement. (c) Sorting time ratio, expressed in seconds per statement per sorting points.

Table 3. Summary of discussed challenges due to the implementation of Q in RGS settings and respective good practices.

Stage	Aspect	Challenge	Good practices
Q stage 1: Research design	Concourse development and reduction	^aInaccessibility to RGS dwellers as primary data sources	Resort to reliable secondary data
			Reuse of primary data from larger research program
			Rely on proxies’ discourses
		^aUnbalanced representation of discourses, in detriment of less-empowered individuals	Apply matrix reduction method
	Q-set	Too large Q-sets might discourage participation	Keep small number of statements without compromising representativeness
		^aIlliteracy of participants	Use other (visual) techniques (e.g. pictures/illustrations)
		Incompatibility of non-Latin script languages of Global South cultures with certain electronic platforms	Use of hand-written material, which can be ultimately digitalized
			Development and definition of non-Latin script languages
		Multiple languages or dialects involved	Apply a reliable translation method (Buil et al., 2012)
			Iterative piloting of statements in the local context
	P-set	^aToo homogeneous P-set due to biased snowball/purposive sampling	Use variations of snowball sampling (Cohen and Arieli, 2011; Sadler et al., 2010)
		Underrepresentation of female respondents	Adoption of cross-cutting gender-sensitive approaches in the research process
	Sorting grid	Confusion due to mismatches between qualitative scale and wording of statements	Make sure both elements hold to one another consistently
		Burden caused by (too) many sorting points in quantitative scale	Limit the number of sorting points depending on the topic under study and characteristics of respondents
		Mismatch between direction of quantitative scale and the sociolinguistic context of participants	Define a qualitative scale sensitive to the linguistic context
		Inappropriate shape of sorting grids	Define the shape of sorting grids based on nature of topic and level of knowledge of participants
Q stage 2: Data collection	Location and materials	^aInappropriate locations and unavailability of adequate furniture	Use of right materials for boards (sorting grid) and cards (statements)
	Administration technique	Data collection is too time consuming for researchers	Administer sorts simultaneously to small groups of (Lang and Lingnau, 2015; United Nations, 2021) respondents
		Undesired interactions between respondents in collective sorts	Consider small groups of (Lang and Lingnau, 2015; United Nations, 2021) respondents, preventing their interaction
		^a(Digital) gap between researchers and studied populations	Long-term Q capacity building in the Global South
		^aLimited use of online-administered Q	Development of open-source, mobile-friendly Q platforms
	Assistance and facilitation	Biases in responses due to prolonged assistance	Provide concise pre-sort instructions and clear (short) statements
			Limit assistance to the sorting mechanism rather than interpretation of statements
			Use the three-pile technique (or its nine-pile variant) as auxiliary sorting method
		Biases due to status of researchers	Training and empowerment of local researchers and/or assistants
		Biases due to translation and interpretation	Proper training of interpreters about both the method and the topic
			Long-term Q capacity building in the Global South
	Sorting times	Too long sorting times affecting response rates and validity	Control number and type of statements, as well as the number of sorting points
	Complementary information	Poor quality of collected information	Use structured instruments (surveys, forms) to reduce total interaction times and burden of respondents
	Data recording	Possible loss of data	Use of context-sensitive recording means
			Rely on mutually complementary and redundant recording methods
Q stage 4: Interpretation	Validation	^aImpossibility of validation due to remoteness	(Remote) validation with at least the highest loaded respondent per factor
Post-Q: Knowledge production	Access to knowledge	Q studies with limited access to Global South researchers	Open-access publishing
		^aSocio-geographical decoupling of researchers and studied populations	Long-term Q capacity building in the Global South

^aChallenges that may be more profoundly present in RGS settings.

Table

Author	Statement
Juan Carlo Intriago Zambrano	Conception and design of the work; acquisition, analysis, and interpretation of data Drafting of the work Final approval of the version to be published Agreement to be accountable for all aspects of the work
Jan Carel Diehl	Interpretation of data Critical review for important intellectual content Final approval of the version to be published Agreement to be accountable for all aspects of the work
Maurits W. Ertsen	Interpretation of data Critical review for important intellectual content Final approval of the version to be published Agreement to be accountable for all aspects of the work

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Data availability statement

The dataset related to this study can be found at https://doi.org/10.34894/K252KB, an open-source online data repository hosted at DataverseNL (Intriago Zambrano, 2020).