Journal of Environmental Psychology 99 (2024) 102432 Contents lists available at ScienceDirect Journal of Environmental Psychology journal homepage: www.elsevier.com/locate/jep Exposure and connectedness to natural environments: An examination of Y the measurement invariance of the Nature Exposure Scale (NES) and R Connectedness to Nature Scale (CNS) across 65 nations, 40 languages, gender identities, and age groups RA Viren Swami a,b,*, Mathew P. White c, Martin Voracek c, Ulrich S. Tran c, Toivo Aavik d, Hamed Abdollahpour Ranjbar e, Sulaiman Olanrewaju Adebayo f, Reza Afhami g, OIlBi Ahmed h, Annie Aimé i, Marwan Akel j,k, Hussam Al Halbusi l, George Alexias m, Khawla F. Ali n, Nursel Alp-Dal o, Anas B. Alsalhani p, Sara Álvarez-Solas q, Ana Carolina Soar esL Amaral r, Sonny Andrianto s, Trefor Aspden t, Marios Argyrides u, John Jamir BenzNon R. Aruta v, Stephen Atkin n, Olusola Ayandele w,x, Migle Baceviciene y, Radvan Bahbouh z, Andrea Ballesio aa, David Barron ab, Ashleigh Bellard ac, SóleySesseljaA Bender ad, Kerime Derya Beydaǧ ae, Gorana Birovljević af, Marie-Ève Blackburn ag, Teresita Borja-Alvarez ah, Joanna Borowiec ai, Miroslava Bozogáňová aj,ak, Solfrid BratlandD-Sanda al, MatthewH.E.M. Browning am, Anna Brytek-Matera an, Marina Burakova ao, Yeliz Çakır-Koçak ap, Pablo Camacho aq, Vittorio Emanuele Camilleri ar, ValentinaA Cazzato ac,as, Silvia Cerea at,au, Apitchaya Chaiwutikornwanich av, Trawin Chaleera ktIraBkoon aw, Tim Chambers ax, Qing-Wei Chen ay,az, Xin Chen ba, Chin-Lung Chien bb, Phatthanakit Chobthamkit aw, Bovornpot Choompunuch bc, Emilio J. Compte bd,be, Jennifer Corrigan bf,O GetrFude Cosmas bg, Richard G. Cowden bh, Kamila Czepczor-Bernat bi, Marcin Czub an, Wanderson Roberto da Si lva bjY , Mahboubeh Dadfar bk, Simon E. Dalley bl, Lionel Dany ao,T Jesus Alfonso D. Datu bm, Pedro Henrique Berbert de CarvIalho bn,bo, Gabriel Lins de Holanda Coelho bf, Avila Odia S. De Jesus v, Sonia Harzallah Debbabi bp, Sandesh Dhakal bq, FrancescaS Di Bernardo br, Donka D. Dimitrova bs,bt, Jacinthe Dion bu, Barnaby Dixson bv, StaceRy M. Donofrio bl, Marius Drysch bw, Hongfei Du bx, Angel M. Dzhambov bt,by, Claire El-Jor bz, Violeta Enea ca, Mehmet Eskin cb, Farinaz Farbod cc, Lorleen Farrugia ar, Leonie Fian c, Maryanne L. Fisher cd, Michał Folwarczny ce, David A. FrederVick cEf, Matthew Fuller-Tyszkiewicz ax, Adrian FurnhaIm cg, Antonio Alías García ch, Shulamit Geller ci, Marta Ghisi at,cjN , Alireza Ghorbani ck, Maria Angeles Gomez Martinez cl, Sarah Gradidge a, Sylvie Graf cm, Caterina Grano aa, Gyöngyvér Gyene cn, Souheil Hallit co,cp, Motasem Hamdan cq, JonathUan E. Handelzalts ci,cr, PaulH.P. Hanel cs, Steven R. Hawks ct, Issa Hekmati cu, Mai Helmy cv,cw, Tetiana Hill cx, Farah Hina cy, Geraldine Holenweger cz, Martina Hřebíčková cm, Olasupo Augustine Ijabadeniyi da, Asma Imam cq, Başak İnce db, Natalia Irrazabal dc, * Corresponding author. School of Psychology and Sport Science, Anglia Ruskin University, Cambridge, Cambridgeshire, CB1 1PT, United Kingdom E-mail address: viren.swami@aru.ac.uk (V. Swami). https://doi.org/10.1016/j.jenvp.2024.102432 Received 20 May 2024; Received in revised form 9 August 2024; Accepted 13 September 2024 Available online 16 September 2024 0272-4944/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ). V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 Rasa Jankauskiene y, Ding-Yu Jiang dd, Micaela Jiménez-Borja ah, Verónica Jiménez-Borja de, Evan M. Johnson cf, Veljko Jovanović df, Marija Jović dg, Marko Jović dh, Alessandra Costa Pereira Junqueira di,dj, Lisa-Marie Kahle dk, Adam Kantanista ai, Ahmet Karakiraz dl, Ayşe Nur Karkin cb, Erich Kasten dm, Salam Khatib dn, Nuannut Khieowan do, Patricia Joseph Kimong bg, Litza Kiropoulos dp, Joshua Knittel cz, Neena Kohli dq, Mirjam Koprivnik dr, Aituar Kospakov ds,dt, Magdalena Król-Zielińska ai, Isabel Krug dp, Garry Kuan du, Yee Cheng Kueh dv, Omar Kujan dw, Miljana Kukić af, Sanjay Kumar dx, Vipul Kumar dy, Nishtha Lamba dz, Mary Anne Lauri ar, Maria Fernanda Laus di,dj, Liza April LeBlanc cd, Hyejoo J. Lee ea, Małgorzata Lipowska eb, Mariusz Lipowski ec, Caterina Lombardo aa, Andrea Lukács ed, Christophe Maïano i,ee, Y Sadia Malik ef, Mandar Manjary dx,eg, Lidia Márquez Baldó eh, Martha Martinez-Banfi ei,ej, Karlijn Massar ek, Camilla Matera el, Olivia McAnirlin am, R Moisés Roberto Mebarak em, Anwar Mechri en, Juliana Fernandes Filgueiras Meireles eo, A Norbert Mesko ep, Jacqueline Mills ax, Maya Miyairi eq, Ritu Modi dq, Adriana Modrzejewska er, Justyna Modrzejewska es, Kate E. Mulgrew bv, Taryn A. MyersRet, Hikari Namatame eu, Mohammad Zakaria Nassani ev, Amanda Nerini el, Félix Neto ew, Joana Neto ex, Angela Nogueira Neves ey, Siu-Kuen Ng ez, Devi Nithiya fa, Jiaqing OItB, Sahar Obeid fb, Camila Oda-Montecinos fc, Peter Olamakinde Olapegba w, Tosin Tunrayo Olonisakin f, Salma Samir Omar fd L , Brynja Örlygsdóttir ad, Emr ah Özsoy dl, Tobias Otterbring fe, Sabine Pahl c, Maria Serena Panasiti aa,ff, Yonguk Park fg, Muhammad Mainuddin Patwary fh,fi, Tatiana Pethö aj, N Nadezhda Petrova fj, Jakob Pietschnig fk, Sadaf Pourmahmoud g, A Vishnunarayan Girishan Prabhu fl, Vita Poštuvan fm,fn, Pavol Prokop fo,fp, Virginia L. Ramseyer Winter fq, Magdalena Razmus fr, Taotao RuDay,az, Mirjana Rupar cm, Reza N. Sahlan fs, Mohammad Salah Hassan ft, Anđela ŠalovAfu, Saphal Sapkota fv, Jacob Owusu Sarfo fw, Yoko Sawamiya et, Katrin Schaefer fx,fy, Michael Schulte-Mecklenbeck cz,fz, Veya Seekis ga, B Kerim Selvi gb, Mehdi Sharifi gc, Anita Shrivastava dz , IRumana Ferdousi Siddique gd, Valdimar Sigurdsson ge, Vineta Silkane gf, Ana Šimunić fu, Govind Singh dq, Alena Slezáčková gg, Christine Sundgot-Borgen gh, Gill Ten Hoor ek, PaFssagorn Tevichapong gi, Arun Tipandjan gj, Jennifer Todd a,b, Constantinos Togas m, F erOnando Tonini dc, Juan Camilo Tovar-Castro gk, Lise Katrine Jepsen Trangsrud al, Pankaj Tripathi dq, Otilia Tudorel gl, Tracy L. Tylka gmT, AnYar Uyzbayeva dt, Zahir Vally gn, Edmunds Vanags go, Luis Diego Vega gp, Aitor Vicente-Arruebarrena cl, Jose Vidal-Mollón eh, Roosevelt VIilar gq, Hyxia Villegas gp, Mona Vintilă gl, Christoph Wallner bw, Simon Whitebridge n, Sonja Windhager fx,fy, Kah Yan Wong gr, Eric Kenson Yau gs, Yuko Yamamiya gt, VictoRria SWai Lan Yeung gs,gu, Marcelo Callegari Zanetti gv, Magdalena Zawisza a, Nadine Zeeni bz, MaErtina Zvaríková fo, Stefan Stieger gw a School of Psychology, SIporVt, and Sensory Sciences, Anglia Ruskin University, Cambridge, United Kingdomb Centre for Psychological Medicine, Perdana University, Kuala Lumpur, Malaysiac Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria d Institute of PsycNhology, University of Tartu, Tartu, Estoniae Department of Psychology, Koç University, Istanbul, Turkiyef Department of Psychology and Behavioural Studies, Ekiti State University, Ado-Ekiti, Nigeria g Department of Art Studies, Tarbiat Modares University, Tehran, Iran h DepartmUent of Psychology, University of Chittagong, Chattogram, Bangladesh i Department of Psychoeducation and Psychology, Université Du Québec en Outaouais, Saint-Jérôme, Canada j INSPECT-LB: National Institute of Public Health, Clinical Epidemiology, and Toxicology, Beirut, Lebanon k School of Pharmacy, Lebanese International University, Beirut, Lebanon l Department of Management, Ahmed Bin Mohammad Military College, Doha, Qatar m Faculty of Psychology, Panteion University of Social and Political Sciences, Athens, Greece n Royal College of Surgeons Ireland-Bahrain, Adliya, Bahrain o Department of Midwifery, Faculty of Health Sciences, Munzur University, Tunceli, Turkiye p Department of Oral Medicine and Diagnostic Sciences, Vision College of Dentistry and Nursing, Vision Colleges, Riyadh, Saudi Arabia q Facultad de Ciencias de La Vida, Universidad Regional Amazónica Ikiam, Muyuna, Ecuador r Federal Institute of Education, Science, and Technology of Southeast of Minas Gerais, Barbacena, Brazil s Department of Psychology, Universitas Islam Indonesia, Yogyakarta, Indonesia t Department of Psychology, Aberystwyth University, Ceredigion, United Kingdom 2 V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 u Department of Psychology, Neapolis University Pafos, Paphos, Cyprus v Department of Psychology, De La Salle University, Philippines w Department of General Studies, The Polytechnic, Ibadan, Nigeria x Department of Psychology, University of Ibadan, Ibadan, Nigeria y Health Research and Innovation Science Centre, Faculty of Health Sciences, Klaipeda University, Klaipeda, Lithuania z Department of Psychology, Faculty of Arts, Charles University, Prague, Czech Republic aa Department of Psychology, Sapienza University of Rome, Rome, Italy ab School of Social Sciences, Heriot-Watt University Malaysia, Putrajaya, Malaysia ac School of Psychology, Faculty of Health, Liverpool John Moores University, Liverpool, United Kingdom ad Faculty of Nursing and Midwifery, University of Iceland, Reykjavik, Iceland ae Department of Nursing, Faculty of Health Sciences, Yalova, University, Yalova, Turkiye af Department of Psychology, Faculty of Humanities and Social Sciences, University of Rijeka, Rijeka, Croatia ag ÉCOBES-Research and Transfer, Cégep de Jonquière, Québec, Canada ah Colegio de Ciencias Sociales y Humanidades, Universidad San Francisco de Quito USFQ, Quito, Ecuador ai Department of Physical Education and Lifelong Sports, Poznań University of Physical Education, Poznań, Poland Y aj Institute of Social Sciences of the Centre of Social and Psychological Sciences, Košice, Slovakia ak Faculty of Humanities and Natural Sciences, Institute of Pedagogy, Andragogy, and Psychology, University of Prěsov, Prěsov, Slovakia al Department of Sports, Physical Education and Outdoor Studies, University of South-Eastern Norway, Bø in Telemark, Norway R am Department of Parks, Recreation and Tourism Management, Clemson University, Clemson, SC, USA an Institute of Psychology, University of Wrocław, Wrocław, Poland A ao Laboratory of Social Psychology, Aix-Marseille University, Aix-en-Provence, France ap Department of Midwifery, Faculty of Health Sciences, Bartın University, Bartın, Turkiye aq RCentroSan Isidoro University Center, Seville, Spain ar Department of Psychology, University of Malta, Msida, Malta as Department of Cognitive Sciences, Psychology, Education, and Cultural Studies, University of Messina, Messina, Italy B at Department of General Psychology, University of Padova, Padova, Italy I au Department of Biomedical Sciences, University of Padova, Padova, Italy av Faculty of Psychology, Chulalongkorn University, Bangkok, Thailand L aw Department of Psychology, Faculty of Liberal Arts, Thammasat University, Pathumthani, Thailand ax School of Psychology, Deakin University, Geelong, Australia ay Lab of Light and Physio-Psychological Health, National Center for International Research on Green Optoelectrics, South China Normal University, Guangzhou, China az Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China ba ADepartment of Psychology, Graduate School of Arts and Science, New York University, New York, USA bb Department of Psychology, Soochow University, Taipei, Taiwan bc Department of Educational Psychology and Guidance, Faculty of Education, Mahasarakham University, MahDa Sarakham, Thailand bd School of Psychology, Universidad Adolfo Ibáñez, Penalolen, Chile be Comenzar de Nuevo Treatment Center, Monterrey, Mexico A bf School of Applied Psychology, University College Cork, Cork, Ireland bg Faculty of Psychology and Education, Universiti Malaysia Sabah, Kota Kinabalu, Malaysia bh Human Flourishing Program, Harvard University, Cambridge, USA B bi IDepartment of Pediatrics, Pediatric Obesity and Metabolic Bone Diseases, Faculty Fof Med ical Sciences in Katowice, Medical University of Silesia, Katowice, Polandbj Graduate Program in Food, Nutrition, and Food Engineering, São Paulo State University, São Paulo, Brazilbk Department of Addiction, School of Behavioral Sciences and Mental Health (Tehran Institute of Psychiatry), Iran University of Medical Sciences, Tehran, Iranbl Department of Psychology, University of Groningen, Groningen, the Netherlands bm Academic Unit of Human Communication, Learning, and Development, Faculty of Education. The University of Hong Kong, China bn Body Image and Eating Disorders Research Group, Federal University of JOuiz de Fora, Juiz de Fora, Brazil bo Institute of Psychiatry, University of São Paulo, São Paulo, Brazil bp Faculty of Medicine, University of Sousse, Sousse, Tunisia bq Central Department of Psychology, Tribhuvan University, KathmYandu, Nepal br Department of Psychology, University of Campania Luigi Vanvitelli, Caserta, Italy bs Department of Health Management and Healthcare EcIonoTmics, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgariabt Environmental Health Division, Research InstituSte at Medical University of Plovdiv, Medical University of Plovdiv, Plovdiv, Bulgariabu Department of Psychology, Université Du Québec à Trois-Rivières, Trois-Rivières, Canadabv School of Health, University of the Sunshine Coast, Moreton Bay, Australia bw Department of Plastic and Hand SurgerRy, BG University Hospital Bergmannsheil Bochum, Ruhr University Bochum, Bochum, Germanybx Department of Psychology, Beijing Normal University at Zhuhai, Zhuhai, Chinaby Health and Quality of Life in a GEreen and Sustainable Environment Research Group, Strategic Research and Innovation Program, Medical University of Plovdiv, Plovdiv, Bulgariabz Department of Natural ScViences, School of Arts and Sciences, Lebanese American University, Beirut, Lebanonca Department of Psychology, Alexandru Ioan Cuza University, Iași, Romaniacb Department of Psychology, Koc University, Istanbul, Turkiye cc Department of NTextile aInd Fashion Design, Alzahra University, Tehran, Irancd Department of Psychology, Saint Mary’s University, Halifax, Canadace Discipline of Marketing, J.E. Cairnes School of Business & Economics, University of Galway, Galway, Ireland cf Crean CUollege of Health and Behavioral Sciences, Chapman University, Orange, USAcg Department of Leadership and Organizational Behaviour, Norwegian Business School, Oslo, Norwaych Department of Education, University of Almería, Almería, Spain ci School of Behavioral Sciences, The Academic College of Tel Aviv-Yaffo, Tel Aviv-Yafo, Israel cj Unità Operativa Complessa (UOC) Hospital Psychology, Padova University Hospital, Padova, Italy ck Department of Social Sciences, Payam Noor University, Tehran, Iran cl Faculty of Psychology, Pontifical University of Salamanca, Salamanca, Spain cm Institute of Psychology, Czech Academy of Sciences, Brno, Czech Republic cn Doctoral School of Psychology, Eötvös Loránd University, Budapest, Hungary co School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon cp Applied Science Research Center, Applied Science Private University, Amman, Jordan cq Faculty of Public Health, Al-Quds University, East Jerusalem, Palestine cr Department of Psychiatry, University of Michigan, Ann Arbor, USA cs Department of Psychology, University of Essex, Colchester, United Kingdom ct Department of Kinesiology and Health Science, Utah State University, Logan, USA 3 V. 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J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 cu Department of Psychology, Faculty of Human Science, University of Maragheh, Maragheh, Iran cv Department of Psychology, College of Education, Sultan Qaboos University, Muscat, Oman cw Department of Psychology, Faculty of Arts, Menoufia University, Shebin El Kom, Egypt cx Hertfordshire Business School, University of Hertfordshire, Hatfield, United Kingdom cy Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom cz Department of Consumer Behavior, University of Bern, Bern, Switzerland da Department of Sociology and Social Justice, Afe Babalola University, Ado-Ekiti, Nigeria db Centre for Research in Eating and Weight Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, United Kingdom dc Faculty of Social Sciences, University of Palermo, Buenos Aires, Argentina dd Department of Psychology, National Chung Cheng University, Chia Yi, Taiwan de Colegio de Comunicación y Artes Contemporáneas, Universidad San Francisco de Quito USFQ, Quito, Ecuador df Department of Psychology, Faculty of Philosophy, University of Novi Sad, Novi Sad, Serbia dg Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia dh Faculty of Medicine, University of Belgrade, Belgrade, Serbia Y di Department of Psychology, University of São Paulo, Ribeirão Preto, Brazil dj Department of Nutrition, University of Ribeirão Preto, Ribeirão Preto, Brazil dk Faculty of Life Sciences, Medical School Hamburg, Hamburg, Germany R dl Sakarya Business School, Sakarya University, Sakarya, Turkiye dm Practice for Psychotherapy, Am Krautacker 25, Travemünde, Germany A dn Faculty of Health Professions, Al-Quds University, East Jerusalem, Palestine do Asian Studies Department, Faculty of International Studies, Prince of Songkla University, Phuket Campus, Phuket, Thailand dp RSchool of Psychological Sciences, University of Melbourne, Melbourne, Australia dq Department of Psychology, University of Allahabad, Prayagraj, India dr Institute of Anton Martin Slomsek, Primary School Montessori, Maribor, Slovenia B ds Department of Sociology and Social Work, Al-Farabi Kazakh National University, Almaty, Kazakhstan I dt Department of General Education Disciplines, Astana IT University, Astana, Kazakhstan du Exercise and Sport Sciences Programme, School of Health Sciences, Universiti Sains Malaysia, Kubang Kerian, Malaysia L dv Biostatistics and Research Methodology Unit, School of Medical Sciences, Universiti Sains Malaysia, Kubang Kerian, Malaysia dw Oral Diagnostics and Surgical Sciences, UWA Dental School, The University of Western Australia, Nedlands, Australia dx Department of Psychology, D.A.V. College, Muzaffarnagar, India N dy Department of Psychology, Kashi Naresh Government Post-Graduate College, Gyanpur, India dz Department of Psychology, Middlesex University Dubai, Dubai, United Arab Emirates ea ADepartment of Counselling Psychology and Social Welfare, Handong Global University, Pohang, South Korea eb Institute of Psychology, University of Gdańsk, Gdańsk, Poland ec Faculty of Social and Humanities, University WSB Merito, Gdansk, Poland D ed Faculty of Health Sciences, University of Miskolc, Miskolc, Hungary ee Cyberpsychology Laboratory, Department of Psychoeducation and Psychology, Université Du Québec eAn Outaouais (UQO), Saint-Jérôme, Canada ef Department of Psychology, University of Sargodha, Sargodha, Pakistan eg M.M.D. Public School, Brahmpuri, Muzaffarnagar, India eh Department of Research Methods and Diagnosis in Education, University of València, ValènciaB, Spain ei IFaculty of Legal and Social Sciences, Simón Bolívar University, Barranquilla, Colombia ej Life Science Research Centre, Simón Bolívar University, Barranquilla, Colombia ek Department of Work and Social Psychology, Maastricht University, Maastricht, the Netherlands el FDepartment of Education, Languages, Intercultures, Literatures, and Psychology, University of Florence, Florence, Italy em Department of Psychology, Universidad Del Norte, Barranquilla, Colombia en Faculty of Medicine of Monastir, Eya Medical Centre, Monastir, TunisiaO eo Department of Family and Community Medicine, School of Community Medicine, University of Oklahoma, Tulsa, USA ep Institute of Psychology, University of Pécs, Pécs, Hungary eq Department of Health Sciences, DePaul University, Chicago, USYA er Department of Medical Anthropology, Faculty of MediIcal TSciences in Katowice, Medical University of Silesia, Katowice, Polandes Institute of Pedagogy, University of Bielsko-Biala, Bielsko-Biala, Polandet Department of Psychology, Virginia Wesleyan University, Virginia Beach, USA eu Faculty of Human Sciences, University of Tsukuba, Tsukuba, Japan ev Department of Restorative and Prosthetic Dental Sciences, College of Dentistry, Dar Al Uloom University, Riyadh, Saudi Arabia ew Faculty of Psychology and Educational Sciences, University of Porto, Porto, Portugal ex REMIT – Research on Economics, Management and Information Technologies, Universidade Portucalense, Porto, Portugal ey Division of Research, Physical EdEucatioRn College of the Brazilian Army, Rio de Janeiro, Brazilez Physical Education Unit, Chinese University of Hong Kong, Chinafa Department of Physiology,V Mahatma Gandhi Medical College and Research Institute, Puducherry, Indiafb Social and Education SIciences Department, School of Arts and Sciences, Lebanese American University, Jbeil, Lebanonfc Institute of Social Sciences, Universidad de O’Higgins, Rancagua, Chilefd Department of Dermatology, Venereology, and Andrology, Alexandria University, Alexandria, Egypt fe Department of MNanagement, University of Agder, Kristiansand, Norwayff Santa LuUcia Foundation, Scientific Institute for Research and Healthcare, Rome, Italyfg Department of Psychology, Kyungnam University, Changwon, South Koreafh Environment and Sustainability Research Initiative, Khulna, Bangladeshfi Environmental Science Discipline, Life Science School, Khulna University, Khulna, Bangladesh fj Department of Human Anatomy, Histology and Embryology, Faculty of Medicine, Medical University of Plovdiv, Plovdiv, Bulgaria fk Department of Developmental and Educational Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria fl Industrial and Systems Engineering, University of North Carolina, Charlotte, USA fm Slovene Centre for Suicide Research, Andrej Marusic Institute, University of Primorska, Koper, Slovenia fn Department of Psychology FAMNIT, University of Primorska, Koper, Slovenia fo Department of Environmental Ecology and Landscape Management, Faculty of Natural Sciences, Comenius University in Bratislava, Bratislava, Slovakia fp Institute of Zoology, Slovak Academy of Sciences, Bratislava, Slovakia fq School of Social Work, University of Missouri, Columbia, USA fr Institute of Psychology, Marie Curie-Skłodowska University, Lublin, Poland fs Department of Counseling, School, and Educational Psychology, Graduate School of Education, University at Buffalo-SUNY, Buffalo, USA ft Arabic Program Department, Modern College of Business and Science, Muscat, Oman fu Department of Psychology, University of Zadar, Zadar, Croatia 4 V. 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J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 fv KOSHISH-National Mental Health Self-Help Organization, Kusunti, Lalitpur, Nepal fw Department of Health, Physical Education and Recreation, University of Cape Coast, Cape Coast, Ghana fx Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria fy Human Evolution and Archaeological Sciences, University of Vienna, Vienna, Austria fz Max Planck Institute for Human Development, Berlin, Germany ga School of Applied Psychology, Griffith University, Gold Coast, Australia gb Department of Psychology, Eskişehir Osmangazi University, Eskişehir, Turkiye gc Department of Psychology, Islamic Azad University, Bandar Gaz, Iran gd Department of Psychology, University of Dhaka, Dhaka, Bangladesh ge Department of Business Administration, Reykjavik University, Reykjavik, Iceland gf Faculty of Social Sciences, Vidzeme University of Applied Sciences, Valmiera, Latvia gg Department of Medical Psychology and Psychosomatics, Faculty of Medicine, Masaryk University, Brno, Czech Republic gh Regional Department for Eating Disorders, Oslo University Hospital, Oslo, Norway gi Department of Psychology, Faculty of Humanities, Chiang Mai University, Chiang Mai, Thailand gj International Centre for Psychological Counselling and Social Research, Puducherry, India Y gk Department of Psychology, University of the Andes, Bogotá, Colombia gl Department of Psychology, West University of Timişoara, Timişoara, Romania gm Department of Psychology, The Ohio State University, Columbus, USA R gn Department of Clinical Psychology, United Arab Emirates University, Al Ain, United Arab Emirates go Faculty of Education, Psychology, and Art, University of Latvia, R̄ıga, Latvia A gp Vice-rectory for Teaching, Research, and Extension, Universida Latina de Costa Rica, San José, Costa Rica gq Department of Psychology, Massey University, Auckland, New Zealand gr RSchool of Psychology, University of Nottingham Malaysia, Semenyih, Malaysia gs Department of Psychology, Lingnan University, China gt Department of Undergraduate Studies, Temple University, Japan Campus, Tokyo, Japan B gu Wofoo Joseph Lee Consulting and Counselling Psychology Research Centre, Lingnan University, China I gv Department of Physical Education, São Judas Tadeu University, São Paulo, Brazil gw Department of Psychology and Psychodynamics, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria L N A R T I C L E I N F O A B S T R A C T A Handling Editor: L. McCunn Detachment from nature is contributing to the environmental crisis and reversing this trend requires detailed monitoring and targeted interventions to reDconnect people to nature. Most tools measuring nature exposure and Keywords: attachment were developed in high-Aincome countries and little is known about their robustness across national Nature exposure scale and linguistic groups. Therefore, we used data from the Body Image in Nature Survey to assess measurement Connectedness to nature scale invariance of the Nature ExpoBsure Scale (NES) and the Connectedness to Nature Scale (CNS) across 65 nations, Measurement invarianceCross-cultural 40 languages, gender ide ntIities, and age groups (N = 56,968). While multi-group confirmatory factor analysis Multi-group confirmatory factor analysis (MG- (MG-CFA) of the NES supported full scalar invariance across gender identities and age groups, only partial scalar CFA) invariance was supFported across national and linguistic groups. MG-CFA of the CNS also supported full scalar invariance across gender identities and age groups, but only partial scalar invariance of a 7-item version of the CNS across nOational and linguistic groups. Nation-level associations between NES and CNS scores were negli-gible, like ly reflecting a lack of conceptual clarity over what the NES is measuring. Individual-level associations between both measures and sociodemographic variables were weak. Findings suggest that the CNS-7 may be a usefYul tool to measure nature connectedness globally, but measures other than the NES may be needed to capture nature exposure cross-culturally.T 1. Introduction SI their actual exposure – have more positive attitudes towards the natural environment, ecological behaviours, and nature protection (for At the turn of the century, thRe United Nations Millennium Declara- meta-analyses, see Barragan-Jason et al., 2022; Whitburn et al., 2020). tion recognised that insufficient respect for nature is a fundamental They also exhibit better well-being and mental health (Capaldi et al., challenge for international Erelations and global sustainable development 2014; White et al., 2021).(United Nations General Assembly, 2000). Despite ongoing efforts, The mechanisms underpinning these relationships are thought to anthropogenic-relaItedV climate change, biodiversity loss, and land, reflect a combination of genetic inheritance (Kellert & Wilson, 1993), water, and air pollution are accelerating (e.g., Goudie, 2019). Some personal experience and associative learning (e.g., Yannick & de Block, observers havNe suggested that this is, at least in part, a consequence of a 2011), and salient sociocultural norms (Bourassa, 1990). In addition, growing detachment from the natural world, especially among nature exposure and nature connectedness are likely to be mutually increasiUngly urbanised populations (Beery et al., 2023; Soga & Gaston, reinforcing. Positive contact with the natural world can increase feelings 2016). Reversing these trends requires an understanding of the drivers of connectedness to nature (e.g., Fränkel et al., 2019; Lengieza & Swim, and barriers of ecological and pro-environmental behaviours, and using 2021; Martin et al., 2020; Swami, Barron, et al., 2020), with a this knowledge to promote widespread behaviour change (Grilli & meta-analysis of experimental manipulations and field interventions Curtis, 2021; Schultz & Kaiser, 2012). reporting a moderate positive mean effect of nature contact on nature One important avenue of research concerns people’s physical contact connectedness (g = .44, 95% CI = .31, .58; Sheffield et al., 2022). with, and psychological connectedness to, the natural world. In terms of Conversely, greater nature connectedness can encourage people to seek the former, the relationship between recreational nature exposure (e.g., more nature exposure (Martin et al., 2020; Stehl et al., 2024). Positive leisure visits to parks, woodlands, and beaches) and a range of public experiences may then strengthen nature connectedness over time. and private pro-environmental behaviours has been shown to be Although improving our understanding of these processes remains consistently positive (De Ville et al., 2021; Martin et al., 2020). important, our objective here was to consider how physical contact and Regarding psychological connectedness, robust evidence shows that psychological connectedness with nature are measured and, in partic- people who feel more connected to the natural world – independent of ular, how generalisable existing measures are across linguistic and 5 V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 national contexts. Given that the environmental crisis is a global phe- This is particularly important as some work has suggested that de- nomenon (Goudie, 2019), adequate measurement and monitoring mographic factors affect responding on the NES (Picanço et al., 2024). across contexts requires instruments that can be deployed reliably in multiple settings to make robust comparisons. Crucial to this issue is the 1.2. Connectedness to nature concept of measurement invariance, the notion that a measurement tool should measure the same underlying construct in the same way across Although many measures of nature connectedness exist (Martin & different groups (Swami & Barron, 2019; Vandenberg & Lance, 2000), Czellar, 2016; Richardson et al., 2019), one of the most widely used is which in turn ensures that measurement biases leading to artefactual, the Connectedness to Nature Scale (CNS; Mayer & Frantz, 2004; cited inaccurate, or irreplicable results are avoided (Fischer et al., 2023). more than 3500 times based on Google Scholar citations up to August Measurement invariance can be determined at different levels, with 2024). In the original study reporting on the development of the 14-item scalar or partial scalar invariance typically considered a minimum CNS (see Appendix 1 for items), Mayer and Frantz (2004 threshold for comparison of latent means (Chen, 2007). based on exploratory factor analyses (EFAs) – that scores To date, however, determining measurement invariance of key na- RY ) reported – were unidi- mensional in two community and three college samples from the United ture exposure and nature connectedness instruments has been hampered States. The unidimensional model of the 14-item CNS scores has also because most research – including the development of instruments to been supported in other national and linguistic contexts, such as Brazil measure these constructs – has been conducted in a small handful of (Pessoa et al., 2016), China (Li & Wu, 2016), ItAaly (Lovati et al., 2023), countries in the Global North (Tirri et al., 2021; Zhang et al., 2020). and Spain (Mattas-Terrón & Elósegui-Bandera, 2012). Here, we aimed to address this shortcoming. Specifically, using data However, not all studies have demoRnstrated adequate fit of the from 65 countries, we aimed to explore the measurement invariance of unidimensional model of CNS scores, and in some national contexts, a two well-known instruments across multiple linguistic and national unidimensional model was only sBupported once several items were contexts. The first was a measure of nature exposure – the Nature eliminated: one item in SpainL (OliIvos et al., 2013), three items in France Exposure Scale (NES; Kamitsis & Francis, 2013) – while the second was a (Navarro et al., 2017) and Australia (Pearce et al., 2022), four each in measure of nature connectedness, the Connectedness to Nature Scale Kenya (Marczak & Sorokowski, 2018) and South Korea (Gim et al., (CNS; Mayer & Frantz, 2004). We briefly review what is currently 2019), and seven items in Poland (Strzelecka et al., 2023). Likewise, known about these instruments. Anđić and ŠuperinaN (2021) reported difficulties translating four CNS items into CroaAtian, resulting in a 10-item, single factor, instrument. In 1.1. Nature exposure Turkey, the CNS reduced to two dimensions reflecting integration with nature (tDwo items) and feeling part of nature (six items; Bektaş et al., Many definitions of nature exposure, or nature contact, exist 2017). (Holland et al., 2021). Much of the research linking nature exposure to These equivocal findings may reflect the fact items on the CNS health and well-being uses remote sensing data to estimate the per- conAtain two verbal structures: items that include the word “feel” as an centage of vegetation around the home using various radial buffers (e.g.,I Bemotional component and other items that more closely reflect a Browning & Lee, 2017) or distance to local green and blue spaces (e.g., cognitive belief in one’s connection to nature (Lee & Oh, 2021; Perrin & Geary et al., 2023). Others consider vegetation around other core l o- Benassi, 2009). Alternatively, it is possible that some CNS items either cations, such as work/school (e.g., Dadvand et al., 2015), or explore function poorly or are redundant in some national contexts. Based on more deliberative, intentional nature contact, such as leisure vFisits to Item Response Theory, Pasca et al. (2017) suggested that seven CNS natural settings (e.g., Garrett et al., 2023). Finally, man y Oof the benefi- items (Items #1, 3, 4, 8, 12, 13, and 14) were either redundant or lacked cial effects of nature contact on health and pro-environmentalism may adequate fit in a sample of Spanish adults. In subsequent analyses, the depend on psychological awareness of exposure, oYr a certain degree of same authors also suggested that a truncated, 7-item version of the CNS mindfulness of this contact with nature (e.g., Macaulay et al., 2022; (i.e., the CNS-7) had adequate composite reliability, although factorial Richardson, Hamlin, Butler, et al., 2022). T validity was not assessed. In a more recent study with Brazilian uni-One self-report measure that attempts to address all three aspects – versity students, Rosa et al. (2022) reported that scores on the CNS-7 that is, everyday nature around the hoSme/wIork, recreational visits, and were unidimensional and that the CNS-7 had slightly improved fit nature awareness – is the 4-item Nature Exposure Scale (NES, Kamitsis & indices compared to the full version.Francis, 2013; see Appendix 1 fRor items). Although the instrument has To date, however, assessments of the measurement invariance of the been utilised in diverse national groups (e.g., Arroz et al., 2022; Bace- CNS across national groups in the same study, conducted at the same viciene et al., 2021; PicaEnço et al., 2024; Stieger et al., 2022), its time, remain rare. One study using samples from seven nations (Spain, factorial validity has been infrequently assessed. Studies with adults the Netherlands, Turkey, Portugal, Germany, France, and Hungary) from the United StatesV (Swami et al., 2016), Portugal (Arroz et al., 2022; utilising the CNS-7 supported metric, but not scalar invariance, once the Picanço et al., 2024I), and Lithuania (Matukyniene et al., 2021) suggest loading associated with Item #7 was relaxed (Navarro et al., 2022). In scores are unidimensional, whereas a study with an online sample (na- terms of the 14-item CNS, Pasca et al. (2018) examined measurement tionality unreNported) found that it was necessary to drop one item (Item invariance across samples from Spain and the United States (the latter #1) to achieve unidimensionality (Wood et al., 2019). representing data from Mayer & Frantz, 2004). Their analyses indicated The Uequivocal nature of findings vis-à-vis the factorial validity of the support for configural, but not metric, invariance. Based on Item NES may reflect the fact that a single instrument is trying to measure Response Theory, Pasca et al. (2018) further noted that seven of the CNS very different types of exposure using different response outcomes. items showed differential functioning across groups. These studies Thus, while some authors have suggested that overall scores on the 4- suggest that the latent connectedness to nature construct is not equiv- item NES demonstrate adequate indices of face validity (e.g., Picanço alent across national groups (Navarro et al., 2022). et al., 2024; Swami et al., 2019), others have implied that it is only the Beyond invariance across national groups, very little work has two items that assess direct contact with nature that truly assesses nature assessed invariance of the CNS across other sociodemographic charac- exposure (Goh et al., 2023). As scholars increasingly seek brief teristics. For instance, only two studies have examined the measurement self-report measures of nature exposure, a fuller understanding of the invariance of the CNS across gender identities. In samples of Italian factorial validity of the NES, including item behaviour, is vital (Swami, adults, Di Fabio and Rosen (2019) and Lovati et al. (2023), respectively, 2024). In the same vein, more can be done to understand the psycho- reported that the CNS achieved full scalar invariance across women and metric properties of this instrument beyond singular national groups, men. However, it remains possible that gendered experiences – partic- including in terms of gendered identities, age groups, and languages. ularly across national or cultural groups – shape one’s understanding 6 V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 and manifestations of connectedness to nature. Drawing on gender majority of recruitment taking place online. The overall project received socialisation theories, for instance, McCright (2010) suggested that ethics approval from the School Research Ethics Panel at the first au- women, more so than men, are expected to demonstrate an ethic of care thor’s institution (approval code: PSY-S19-015) and, unless exempt by for the natural environment and exhibit both greater environmental national laws, all collaborating teams additionally obtained ethics concern (e.g., Xiao & McCright, 2015) and dispositional empathy with approval from local institutional ethics committees or review boards. A nature (Tam, 2013). list of nations, associated sample sizes, data collection methods, ethics As such, there is a need to more carefully and comprehensively assess approvals, and survey languages is presented in Supplementary the extent to which the CNS is invariant across gender identities in Table S1. Sample sizes ranged from 204 in the United Arab Emirates multiple cultural contexts. Likewise, we are unaware of any previous (Arabic) to 3275 in Thailand. work that has examined invariance of the CNS across age groups. Existing studies have reported equivocal results in terms of the associ- 2.2. Participants ation between CNS scores and age, with some studies reporting that CNS scores increase with older age (Swami et al., 2016) and other studies The BINS dataset consists of 56,968 respondents, of wYhom 58.9% reporting no significant association (e.g., Swami et al., 2016). Other were women, 40.5% were men, and 0.6% reported another gender work has suggested that connectedness to nature dips in adolescence Ridentity. Ages ranged from 18 to 99 years (M =A33.10, SD = 13.79). In before returning to pre-adolescent levels in early adulthood (Richardson terms of financial security compared to others of their age in their et al., 2019), where it then remains relatively stable (Anderson & country, 25.5% and 49.6% felt more orR equally secure, respectively, Krettenauer, 2021). More work is needed to explore these patterns with 24.9% feeling less secure. Most (84.5%) lived in an urban rather across different cultural contexts. than a rural (15.5%) area, and theI majority reported at least completing secondary education (72.6%). In tBotal, 53.0% were in a committed 1.3. The present study relationship including marriage. The majority (74.2%) self-identified as being part of a racialised majority in their country, whereas 11.3% Large, multinational studies offer the best opportunity to deal with identified as part of a racia liLsed minority group (13.5% were uncertain many of the issues noted above, particularly given that research on and race data were not collected in France due to prohibition of the nature exposure and connectedness to nature often centres the experi- collection and storagNe of race-related data). Table 1 presents detailed ences of respondents in the Global North (Barragan-Jason et al., 2023; sample descripAtion data for all individual nations. In six countries, data Soga & Gaston, 2023). Thus, in the present study, we utilised data from was collected in either two (Canada, Iceland, India, the Philippines, the the Body Image in Nature Survey (BINS; Swami, Tran et al., 2022), a United Arab Emirates [UAE]) or three (China) languages. collaborative, 253 researcher-crowdsourced project that gathered CNS D and NES data between 2020 and 2022 from participants in 65 nations 2.3. Measures across 40 language groups with variance across gender identities and A age groups. In terms of the NES, we considered whether a unidimen- B2.3.1. Nature exposuresional model with all four items, as well as multidimensional models, The 4-item self-reported Nature Exposure Scale (NES; Kamitsis & would offer optimal fit. Given that there are few assessments of th eI Francis, 2013) covers perceptions about everyday nature exposure, factorial validity of this instrument and the limits of cross-sectional data frequency of more distal visits (“nature exposure”; Items #1 and #3), for establishing the dimensionality of measures, we do not advanFce any and attention paid to nature in both settings (“noticing nature”; Items specific hypotheses here. In terms of the CNS, we adopted an exploratory #2 and #4; see Appendix 1 for English wording). Response anchors framework, considering the extent to which either the fu ll O14-item CNS, varied depending on the item, but all used 5-point scales. The NES was or the truncated CNS-7, would balance item retention and measurement translated for use in the present project using the back-translation pro- invariance across groups. As a preliminary hypothesis, we expected that cedure (Brislin, 1986; for further information, see Swami, Tran et al., the CNS-7 would demonstrate superior fit comTpareYd to the full CNS and 2022) unless it was presented in English or a validated, localised version would also evidence scalar or partial scalar invariance across groups. was available for use. A list of the 40 languages in which the BINS survey A second objective was to assess wShethIer, and the extent to which, package was presented is reported in Supplementary Table S1 and all nature exposure is significantly associated with connectedness to nature translations are available from the first author.across nations. Our expectation, based on previous work, was of a small, positive correlation in the r ~ .3R0 range (Sheffield et al., 2022; Swami 2.3.2. Connectedness to natureet al., 2016; Swami, BarronE, et al., 2020). Finally, we also assessed the The 14-item Connectedness to Nature Scale (CNS; Mayer & Frantz, extent to which sociodemographic variables included in the BINS (i.e., 2004; items in English are presented in Appendix 1) uses a 5-point financial security, urbVanicity, educational qualifications, marital status, response scale: 1 (strongly disagree) to 5 (strongly agree). Unless pre-and racialised statuIs) were associated with both nature exposure and sented in English, or where a previously validated translation was not connectedness to nature. Although this aspect of our study was more available, the CNS was also translated for use in the BINS using the exploratory, bNased on the available evidence, we expected that greater parallel back-translation procedure (see Supplementary Table S1).nature exposure and connectedness to nature would be significantly associated with greater financial security, rural residence (e.g., Carrus 2.3.3. Urbanicity et al., 2U020; Richardson et al., 2019), higher educational qualifications To assess urbanicity, participants were asked about their current (e.g., Nesbitt et al., 2019), being married/in a committed relationship place of residence, with response options adapted from Pedersen and (Pasanen et al., 2023), and racialised majority status (e.g., Murdock, Mortensen (2001) as follows: capital city, capital city suburbs, provincial 2019). city (more than 100,000 residents), provincial town (more than 10,000 residents), and rural areas. Response options were assigned values 1 to 5 2. Materials and methods (in the above order) for statistical analysis and collapsed into urban versus rural for descriptive purposes. This measure of urbanicity has 2.1. Overview of the Body Image in Nature Survey been used in previous cross-national work (Swami et al., 2020). Full details of the Body Image in Nature Survey (BINS) are published 2.3.4. Financial security elsewhere (Swami, Tran et al., 2022). Data were collected between Following previous cross-national work (Swami et al., 2012, 2020), November 2020 and February 2022 with community sampling, with the participants were asked to self-report how financially secure they felt 7 V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 Table 1 Sample Descriptions of Data from the Body Image in Nature Survey (BINS). Nation Sample Mean age % Mean financial %Urban %Secondary/tertiary %In committed %Racialised size (SD) Women security (SD) residence education relationship or married minority Argentina 670 35.36 57 2.13 (.7) 98 81 50 9 (13.6) Australia 1038 35.23 71 1.90 (.8) 93 77 55 18 (13.1) Austria 1279 41.99 54 2.08 (.7) 67 62 63 9 (16.5) Bahrain 441 30.47 74 1.98 (.6) 98 87 51 8 (9.8) Bangladesh 460 29.30 42 1.78 (.8) 88 80 51 13 (8.6) Y Bosnia & Herzegovina 406 43.93 64 2.15 (.7) 87 90 70 16 (10.9) R Brazil 1462 36.77 58 2.21 (.7) 99 86 66 12 (12.0) Bulgaria 248 33.52 62 2.16 (.6) 92 54 52 A 4 (14.1) Canada (English) 336 24.61 83 2.10 (.7) 82 36 48 R 14 (10.0) Canada (French) 806 38.22 88 2.29 (.7) 78 95 72B 7 (12.8) I Chile 422 36.14 79 2.28 (.8) 94 73 L41 8(13.6) China (Cantonese) 409 20.50 58 2.18 (.7) 100 96 2 2 (5.9) China (English) 349 21.93 65 1.79 (.7) 97 62 (5.3) N 26 6 China (Mandarin) 1231 35.00 69 1.82 (.6) 95 92 86 4 (7.3) A Colombia 793 27.15 60 2.01 (.8) 96 D57 22 7(11.5) Croatia 898 39.10 59 2.08 (.7) 71 91 69 2 (12.1) Cyprus 363 34.31 65 2.09 (.7) 87 A 69 64 4 (9.6) Czechia 700 38.10 66 2.29 (.6) B82 75 62 2 (17.0) I Ecuador 863 30.97 53 1.81 (.8) F 86 65 33 11(12.3)Egypt 1627 23.62 72 2.0O6 (.6) 98 86 27 6(8.7)Estonia 449 38.93 63 2.10 (.7) 80 64 58 2(14.1)France 562 36.01 76 Y2.08 (.7) 64 67 47 NA(14.2)Germany 620 31.01 62 2.18 (.8) 83 64 58 12 (11.9) Ghana 434 21.97 IT41 2.08 (.8) 84 72 32 26(4.5) Greece 556 31.49S 65 2.03 (.7) 91 63 55 5R(11.8)Hungary 654 32.80 69 2.07 (.6) 72 69 63 2(13.4) Iceland (English) 1149E 38.50 50 2.27 (.7) 92 61 65 11(17.5) Iceland (Icelandic) IV432 54.91 54 2.05 (.6) 75 81 78 3(15.5)India (Hindi) 1664 32.07 45 2.14 (.8) 73 78 45 13(11.8) India (Tamil)N 376 36.78 52 1.71 (.6) 57 65 70 37(12.1) IndonesUia 292 19.79 72 1.76 (.5) 87 43 14 3(3.2) Iran 1318 33.46 60 1.99 (.6) 95 82 61 29 (11.3) Iraq 405 34.13 33 1.49 (.5) 100 97 45 53 (12.1) Ireland 351 33.73 50 2.11 (.8) 76 80 62 5 (12.4) Israel 493 30.77 62 2.13 (.7) 87 67 32 7 (11.6) Italy 2307 33.17 62 1.95 (.6) 81 67 61 6 (14.0) (continued on next page) 8 V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 Table 1 (continued ) Nation Sample Mean age % Mean financial %Urban %Secondary/tertiary %In committed %Racialised size (SD) Women security (SD) residence education relationship or married minority Japan 360 49.44 100 1.79 (.6) 90 81 61 8 (16.6) Kazakhstan 380 30.07 53 2.04 (.6) 94 76 48 11 (11.3) Latvia 827 41.04 66 2.02 (.7) 74 82 69 4 (12.8) Lebanon 1295 25.74 67 1.93 (.7) 70 63 33 16 (12.3) Lithuania 491 40.34 51 2.05 (.6) 72 84 74 3 (12.8) Malaysia 1193 27.81 69 1.74 (.6) 76 84 29 30Y (8.7) Malta 347 35.52 72 2.10 (.7) 78 71 60 R7 (15.4) Nepal 353 25.78 50 1.77 (.7) 82 98 28 A 5(6.0) Netherlands 1004 46.81 53 2.05 (.6) 61 98 69 9 (16.3) R Nigeria 1274 31.64 34 1.85 (.8) 93 64 63 14 (9.2) Norway 360 41.24 77 2.17 (.7) 78 92 L7I7 B 4 (11.6) Pakistan 267 20.59 28 2.16 (.9) 100 47 83 49(2.7) Palestine 401 27.64 25 2.01 (.6) 81 90 42 7 (9.5) Philippines (English) 350 24.87 0 2.03 (.7) 97 56 N 24 13 (11.2) Philippines (Tagalog) 504 37.43 73 1.83 (.7) 97 D89A 65 16(11.9)Poland 1954 30.51 62 1.99 (.7) 74 63 56 3(11.9) Portugal 363 36.53 68 2.05 (.7) 85 A 81 37 5(17.9) Romania 1819 26.94 53 2.05 (.7) 80 49 60 5 (10.8) B Russia 206 39.94 71 1.84 (.5) I 97 84 67 8 (11.8) Saudi Arabia 380 28.02 55 2.03 (.7) F 94 83 33 20(9.7) Serbia 650 30.72 56 2.2O0 (.7) 95 65 65 10(11.3)Slovakia 814 37.79 54 1.92 (.6) 65 75 67 4(14.7) Slovenia 452 36.84 59 Y2.16 (.7) 49 87 66 2(14.9) South Africa 318 35.15 IT53 1.74 (.8) 78 73 45 31(16.1)South Korea 381 27.60S 48 1.89 (.6) 98 54 43 52(9.7)Spain 1266 34.54 52 2.17 (.8) 88 82 43 5 (16.3) Switzerland 377ER46.48 52 1.98 (.7) 62 51 66 5(15.2)Taiwan V529 41.36 60 2.48 (.7) 90 92 67 7(13.6)Thailand I 3275 25.85 62 1.76 (.6) 87 45 23 6(10.8)Tunisia N 374 41.62 55 2.10 (.6) 96 90 63 0(15.2)TürkiyeU 2518 31.63 57 1.98 (.8) 97 61 57 14(11.5)Ukraine 141 39.00 59 1.74 (.6) 95 87 71 9 (11.7) United Arab Emirates 204 26.37 73 2.07 (.4) 99 35 39 10 (Arabic) (6.7) United Arab Emirates 904 27.50 36 2.13 (.8) 98 73 43 31 (English) (11.8) United Kingdom 1243 37.99 54 2.03 (.7) 84 87 68 23 (13.9) United States of 2531 35.35 62 1.93 (.7) 85 82 61 20 America (12.7) Note. SD = standard deviation. 9 V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 relative to others of their own age in their country of residence (1 = less for the CNS-7 than the CNS-14, but followed this strategy to determine secure, 2 = same, 3 = more secure). which version of the instrument should be used in further analysis. Second, in the six countries where data was collected in multiple 2.3.5. Demographics languages, measurement invariance of the NES and CNS was tested with Highest educational qualification was assessed as follows: 1 = no multi-group CFAs (MG-CFAs) for cross-language survey presentation. formal education, 2 = primary education, 3 = secondary education, 4 = still Data from the same nations were merged in the subsequent measure- in full-time education, 5 = undergraduate degree, 6 = postgraduate degree, 7 ment invariance analysis of nations only if scalar measurement invari- = other; marital status was assessed as: 1 = single, 2 = single but in a ance held within nations (i.e., all linguistic versions of the scales showed committed relationship, 3 = married, 4 = other; and racialised status satisfactory invariance). This was only true for China. Where this was relative to their country of residence was assessed as: 1 = ethnic/racial not the case, data for the two different language versions of the survey majority, 2 = ethnic/racial minority, 3 = not sure. The latter item provides were kept separate in further analysis (i.e., Canada, Iceland, India, the a common metric of categorising ethnicity/race across diverse nations Philippines, and the UAE). We therefore use the term “national groups” (Swami, Barron, et al., 2020). For descriptive purposes at the national rather than “countries” where we are reporting results Ythat include level and for analyses, response options for highest educational quali- multiple languages within countries. fication were collapsed into secondary/tertiary (secondary education, Third, measurement invariance of the CNS aAnd NERS was tested with undergraduate degree, postgraduate degree) versus other (all remaining MG-CFAs for: national groups, languages, gender identities (women vs. categories) and response options of racialised status were collapsed into men vs. other gender identities), and age gRroups based on Arnett (2000)racialised minority (racial minority) versus other (all remaining and Erikson (1968), namely young adulthood (18–24 years), middle categories). adulthood (25–44 years) and oldIerB adulthood (≥ 45 years). Note that testing measurement invariance of languages implied mixing different 2.4. Procedures, ethics, and data sharing national groups with the same language in analysis (e.g., English was the survey language in the UK, the USA, but, inter alia, also in one of the Full procedural information about the BINS is provided in Swami Chinese samples). Assumin g Lscalar or at least partial scalar measurement et al. (2022). The BINS project was conducted in accordance with the invariance, we then examined latent means in these groups and, principles of the Declaration of Helsinki and following all local institu- assuming at least meNtric invariance, we examined associations between tional guidelines. In brief, once local ethics approval had been obtained the NES and CANS across national groups, using factor scores.or collaborators confirmed that approval was not required as per na- Fourth, assuming scalar or at least partial scalar measurement tional laws (see Supplementary Table S1), researchers recruited partic- invariance, sociodemographic correlates of the NES and CNS factor ipants from the community in their respective nations between scores weDre investigated with multilevel models (MLMs) across national November 2020 and February 2022. Inclusion criteria were being ≥ 18 groAups. Predictors were financial security, urbanicity (urban vs. rural), years of age, a resident and citizen of the particular nation in which education (secondary/tertiary vs. other), marital status (committed/ recruitment took place, and being able to complete a survey in the married vs. other), and racialised identity (racialised minority vs. other). language in which it was presented. In all but nine locales (see Sup-IBMplus 8.8 (Muthén & Muthén, 2022) was used for the CFAs, plementary Table S1), data collection was conducted online. All par- MG-CFAs, and MLMs. Significance was set to p < .05. For the structural ticipants were presented with a standardised information sheFet an d analyses, the weighted mean- and variance-adjusted weighted least provided (digital or written) informed consent before completing an squares estimator (WLSMV) was used to account for the anonymous version of the BINS package. Upon completionO, participants ordered-categorical item response formats of the NES and CNS. WLSMV received debriefing information, which included contact information for estimates one loading parameter for each item, but #response options – the first author as well as a local researcher. TheY BINS data and our 1 threshold parameters (one for each transition of one response option to analytic codes are available on the Open Science Framework at htt the next) instead of a single intercept parameter per item. To account for ps://osf.io/rfhwe/?view_only=dc87d4d3088bT4f62922177fbbe06e8b6. missing data (0.4% in the NES, 1.4% in the CNS-14), full information I maximum likelihood was used.2.5. Analytic strategy Measurement invariance analyses tested for configural invariance (i. S e., same loading patterns across groups), metric invariance (i.e., same The general analytic plan, iRncluding structural and measurement unstandardised loadings across groups), and scalar invariance (i.e., same invariance analyses of the key variables of the BINS (including the NES unstandardised loadings and threshold parameters across groups). Based and CNS) is described in thEe BINS study protocol (Swami et al., 2022). on the configural invariance models, reliability estimates (ω total) are Further analyses not covered in the study protocol were not preregis- presented for all groups. If full scalar measurement invariance could not tered separately. V be assumed, we aimed for partial scalar measurement invariance instead The analysis proIceeded in four steps, in a similar fashion for both the (i.e., equal item parameters across some groups and items, but not all). NES and CNS: first, CFA models were fitted to the total sample to For this, the alignment method (e.g., Asparouhov & Muthén, 2023) was determine theN structure of the NES and test both the full CNS (henceforth used for guidance. Alignment does not require exact measurement “CNS-14”) and the CNS-7 for unidimensionality. For the NES, unidi- invariance, but instead seeks a solution that minimises the differences in mensionUal and 2-factor models were fitted, testing for the possible scale loadings and threshold parameters across groups, while still retaining multidimensionality (nature exposure in everyday life and environ- identical fit to the configural invariance model. The method provides for ments: Items #1 and #2; nature exposure outside everyday environ- each item parameter information on the groups for which invariance ments: Items #3 and #4). As the items further allude to a conditional holds and an R2 measure that indicates the amount of invariance structure (with Items #1 and #3 assessing levels of “nature exposure” in (typically, the more invariant, the higher the R2; however, in some each domain and Items #2 and #4 assessing “noticing nature” in each special cases, R2 is a poor measure of invariance; Asparouhov & Muthén, domain), a unidimensional model with correlated error variances be- 2023). This information was used to (a) select two items per scale (an- tween Items #1 and #2, and Items #3 and #4, respectively was also chor items) for which invariance was assumed (two anchor items being fitted on the data (an analogous 2-factor model could not be tested, as it sufficient for the comparison of latent means; Pokropek et al., 2019) and was under-identified with only 4 items). The best-fitting model was then to (b) exclude groups that violated invariance the most. Additionally, we used for further analysis. In the unidimensional model of the CNS, error looked at the reliability of the scales in each group and the contributions variances of Items #5 and #7, and Items #5 and #10 were allowed to of each group to the overall χ2 values of the MG-CFAs. If composite re- correlate (following Rosa et al., 2022). We expected a better model fit liabilities were low and/or the contributions to the χ2 value were high, 10 V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 compared to sample size, groups were excluded from the final partial and #3 had lower standardised loadings (.54 and .67) than Items #2 and scalar measurement models. #4 (.84 and .72; all ps < .001). Including correlated errors between For the assessment of model fit, commonly used fit indices were Items #1 and #2, and #3 and #4, respectively, to accommodate the consulted: the comparative fit index (CFI; good/acceptable fit: ≳ .95/ model for their conditional structure improved the model fit consider- .90), the Tucker-Lewis index (TLI; ≳ .95/.90), the root-mean square ably (to keep this model identified, the strength of the residual associ- error of approximation (RMSEA; good fit: ≲ .06) and its 90% confidence ation between the two items needed to be constrained to equality across interval, and the standardised root mean square residual (SRMR; good pairs). The standardised items loadings in this model were .44, .86, .60, fit ≲ .08; Hu & Bentler, 1999). The cut-off for the RMSEA was set to .15 and .67, with residual correlations between Items #1 and #2, and #3 for MG-CFAs with more than 10 groups (Rutkowski & Svetina, 2014). and #4, of .35 and .27 (all ps < .001). For the measurement invariance analyses, we present ΔCFI and In contrast, a correlated 2-factor model (setting equality constraints ΔRMSEA values, and Δχ2 tests, but primarily interpreted the former two for the similar loadings in each pair of Items #1 and #3, and #2 and #4 as they were not affected by sample size. For the comparison of metric to to obtain admissible parameter estimates of the residual variances) configural, and scalar to metric invariance models, the cut-offs ΔCFI ≲ resulted in a poorer model fit (Table 2). In this modelR, ItemYs #1 and #2 .020/.010 and ΔRMSEA ≲ .030/.015 were used to indicate good fit of loaded on one factor (nature exposure in everyday life and environ-the respective stricter model (Rutkowski & Svetina, 2014). ments; standardised loadings = .62 and .87) aAnd Items #3 and #4 on For the MLMs, Bayesian estimation (using diffuse priors as specified another (nature exposure outside everyday environments; .63 and .89). in Mplus default settings) was used. This allowed obtaining correctly The latent factor intercorrelation was .76 (all ps < .001). standardised (cf. van Assen et al., 2022) parameter estimates that were Thus, even though there was some inRdication of potential multidi- interpreted as measures of effect size (comparable to Pearson’s r). For mensionality in the NES, a unidiImeBnsional model that accommodated the dichotomous predictors, these estimates were further transformed the conditional structure of the items fitted the data best and was, into the metric of Cohen’s d as well. The predictors of financial security, therefore, used in all subsequent analyses. However, according to their urbanicity, education, marital status, and racialised identity were threshold parameters, some Lresponse options were relatively uninfor-groupmean-centred on Level 1, and their cluster-level means were mative, because item categories were so close to one another (which was further used as Level-2 predictors. Thereby, associations on the indi- also reflected in theN rela tive sparseness of response option endorse-vidual level (Level 1) and on the cluster-level (national groups; Level 2) ment). Thus, response options 1 and 2, and 4 and 5 were each combined could be optimally distinguished. However, to avoid overfitting on Level for Items #1 aAnd #3, and response options 1 and 2 for Item #4. Even 2, we only kept significant Level-2 predictors in the final models. though this measure only slightly increased some model fit indices and slightly dDecreased others (Table 2), it ensured that sparseness of data did 3. Results not further complicate the subsequent multigroup analyses. Thus, resAponse options were also combined in all multigroup analyses.3.1. Nature exposure scale 3.1.2. Invariance of the cross-language results in the six countries with 3.1.1. Structural analysis in the total sample Bmultiple survey languages The unidimensional model had poor to borderline fit to the data,I The MG-CFA invariance test results are presented in Supplementary judging by its CFI value (and disregarding TLI and RMSEA value s, Table S2. Configural and metric invariance was found for all six coun- because of the small degrees of freedom of this model; Table 2). IteFms #1 tries, but scalar invariance only for China. Thus, only the data from O Table 2 Analyses of the NES in the total sample and invariance oYf the NES concerning national groups, language, gender identity, and age.Grouping variable and type of model χ2(df) IT CFI TLI RMSEA 90% CI SRMR Model comparisonsΔCFI ΔRMSEA Configural MetricTotal sample 1F 636S9.45(2) .944 .831 .236 [.232, .241] .0431FCE R1754.73(1) .984 .907 .175 [.169, .182] .0211FCE + CRO 1493.96(1) .984 .902 .162 [.155, .169] .0272FConL E 4434.23(2) .961 .882 .197 [.192, .202] .0422FConL + CRO 3367.73(2) .963 .889 .172 [.167, .177] .048National groups Configural invariance 1423.31(139) .987 .961 .107 [.102, .112] .034 Metric invariance 6951.05(346) .934 .920 .153 [.150, .156] .066 .053 .046 5300.73(207) Scalar invariance IV 19559.39(691) .812 .886 .183 [.181, .185] .084 .124 .030 17980.50(552) 13946.75(345) Language Configural inNvariance 1579.37(79) .985 .954 .115 [.111, .120] .032MetriUc invariance 5970.23(196) .942 .929 .144 [.141, .147] .055 .043 .029 4266.41(117)Scalar invariance 17242.02(430) .831 .906 .166 [.164, .168] .072 .023 .022 15544.82(351) 12088.40(234)Gender identity Configural invariance 1052.28(5) .989 .959 .105 [.100, .110] .027 Metric invariance 863.35(11) .991 .985 .064 [.060, .068] .027 − .002 − .041 23.19(6) Scalar invariance 989.69(23) .989 .992 .047 [.045, .050] .028 .002 − .017 134.03(18) 120.26(12) Age Configural invariance 1206.13(5) .987 .952 .112 [.107, .118] .027 Metric invariance 1048.65(11) .989 .981 .070 [.067, .074] .028 − .002 − .042 150.04(6) Scalar invariance 1473.44(23) .984 .987 .058 [.055, .060] .031 .005 − .012 536.33(18) 414.80(12) Note. 1F = 1-factor model; 1FCE = 1-factor model with correlated errors between Items #1 and #2, and #3 and #4; 1FCE + CRO = 1FCE model with combined response options in Items #1, #3, and #4 (see main text for details); 2FConL = correlated 2-factors model with equality constraints on the loadings of Items #1 and #3, and #2 and #4; 2FConL + CRO = 2FConL model with combined response options in Items #1, #3, and #4 (see main text for details). All ps of χ2 and Δχ2 tests (comparisons of the multigroup models) were <.001. Gender identity compared groups of women, men, and other gender identity, age compared groups of partic- ipants with 18–24 years, 25–44 years, ≥45 years of age. 11 V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 China (i.e., Mandarin, Cantonese, and English versions) were pooled for 3.1.3. Invariance across national groups, languages, gender identities, and the analysis of national groups. This analysis therefore included 70 age groups “national groups” from 65 countries. 3.1.3.1. Overall findings. The results of the MG-CFA analyses are pre- sented in Table 2. The NES showed configural, but neither metric nor scalar invariance, across the 70 national groups and 40 languages. RY BR A LI AN AD F IB Y O T RS I E NI V U Fig. 1. Ordering and Magnitude of Standardised Latent Mean Differences (Cohen’s d) in the NES Between National Groups (as Compared to the UK; Left) and Languages (as Compared to English; Right). Note. Data from UAE (Arabic version), Ecuador, Iran, Iraq, and Spain were excluded either due to poor scale reliability or poor fit in the partial measurement model (see main text). 12 V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 However, it showed configural, metric, and scalar invariance across for current purposes. Data from the UAE (Arabic version) were excluded gender identities and age groups. Scale reliability was not strong. Me- in all further analyses of national groups and in the partial measurement dian scale reliability (ω total) across all national groups was .65, ranging model for languages. from just .16 (UAE [Arabic version] though only reliability below .44) to .87 (Saudi Arabia; P25 = .60, P75 = .74). Nevertheless, with only 4 items 3.1.3.2. National groups. The alignment method (Table S3) suggested and two correlated errors, scale reliability was judged to be sufficient in the following descending ordering of the four NES items concerning all national groups (except the UAE Arabic version) for further analyses their invariance: #2, #4, #3, #1. Item #3 had a higher summed R2 value RY BR A L I DA N IB A OF IT Y RS IV E UN Fig. 2. Ordering and Magnitude of Standardised Latent Mean Differences (Cohen’s d) in the CNS-7 Between National Groups (as Compared to the UK; Left) and Languages (as Compared to English; Right). Note. Data from Iraq and Bosnia and Herzegovina were excluded due to the poor scale reliabilities in these countries. 13 V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 than Item #4; however, Item #4 had a higher loading and exhibited national groups. Thus, this analysis included 67 national groups from 65 invariance in its parameters in a larger number of national groups than nations. Item #3. Items #2 and #4 were, therefore, used as item anchors in a partial scalar measurement model. Note that this indicated that the two 3.2.3. Invariance across national groups, languages, gender identities, and “nature noticing” items (#2 and #4) were more invariant across na- age tional groups than the two “nature exposure” items (#1 and #3). In addition to the UAE (Arabic version), data from Ecuador, Iran, Iraq, and 3.2.3.1. Overall findings. The results of the MG-CFA analyses are pre- Spain were also subsequently excluded, as they disproportionately sented in Table 3. The CNS-7 showed configural, metric, and scalar decreased the fit of the partial scalar measurement model, relative to invariance across gender identities and age groups, but only configural their sample sizes. and metric invariance for the 67 national groups and 40 languages. For the remaining 65 national groups (including 61 single language Median scale reliability (ω total) across all national groups was consid- countries and four multi-language countries), the partial scalar mea- erably higher than the NES (.92), ranging from .64 (Iraq) to .97 (Spain; surement model (using items #2 and #4 as anchors) had acceptable fit, YP25 = .89, P75 = .93). Bosnia and Herzegovina (.73) was the only other χ2 = 7722.24, df = 449, CFI = .913, TLI = .924, RMSEA = .141 (.138, country below .80. .144), SRMR = .051. Compared to the UK as a reference category (for R purely nominal reasons [the first author is based there]), the largest 3.2.3.2. National groups. Using the alignmentA method, we obtained positive differences in self-reported nature exposure, as reflected by the information on items and item parameters that were most invariant latent means on the NES, were for (in descending order) Bosnia and among the 67 national groups (Table S7).R According to the summed R2 Herzegovina, Croatia, and Lithuania, and were in the range of d = .98 to values across the loading and all threshold parameters per item (to get 1.18 (see Fig. 2, left panel; for individual Cohen’s d values, see Table S4). an indication of overall item invariaBnce), Items #7, #5, and #2 (in this The largest negative differences compared to the UK were observed for order) appeared to be the most invIariant. However, it was Items #2 and Lebanon, South Korea, and, Brazil and were in the range of d = − .97 to #7 whose loadings were also invariant amongst the largest number of − 2.21. national groups (ItemN #5 h ad L the lowest number here). Hence, we opted for using Items #2 and #7 as anchor items in a partial scalar measure- 3.1.3.3. Languages. Using Items #2 and #4 again as anchor items, the ment model. Considering the low scale reliabilities in the Iraq and partial scalar measurement model had an acceptable fit to the data of the Bosnia and HeArzegovina data, we excluded these national groups from 40 language groups, χ2 = 6959.85, df = 274, CFI = .926, TLI = .935, this analysis. Data of these two countries were also excluded in all RMSEA = .131 (.128, .134), SRMR = .048. Here, the largest positive further aDnalyses of national groups and in the partial measurement differences in country-level latent means on the NES, compared to En- model of languages. glish (the original scale language), were observed for (in descending The partial scalar measurement model had a good fit to the data, χ2 order) Bosnian, Lithuanian, and Croatian and were in the range of d = B= 1A0264.83, df = 1164, CFI = .982, TLI = .979, RMSEA = .095 (.094, 1.04 to 1.18 (see Fig. 2, right panel; for individual Cohen’s d values, see .097), SRMR = .026. Comparing all other national groups to the UK, the Table S5). The largest negative differences were observed for Cantones e,I ordering and magnitude of standardised latent mean differences Korean, and Portuguese and were in the range of d = − .87 to − 1.68. (Cohen’s d) are provided in Fig. 2 (individual Cohen’s d values are These rankings mostly matched the rankings of the analysis of national provided in Table S8). The largest positive differences were observed for groups (see above; but note that languages could contain a Fmix of (in descending order) Nepal, Iran, and South Africa and were in the different national groups, if the language in which the scOale was pre- range of d = 1.20 to 1.39 (see Fig. 1, left panel), suggesting that par-sented there was the same; this specifically applied to English). ticipants from these nations reported higher connectedness to nature compared to the United Kingdom. The largest negative differences were 3.1.3.4. Gender identities and age groups. Men reYported lower nature observed for Israel, Japan, and Spain and were in the range of d = − .30 exposure (i.e., had lower NES latent means) tThan women (Cohen’s d = to − .61.− .14, p < .001), as did those with other genIder identities (d = − .25, p <.001). Reported nature exposure also increased with age: age groups 3.2.3.3. Languages. Using Items #2 and #7 as anchor items again, the 25–44 years vs. 18–24 years differed by d = .14 (p < .001), whereas age partial scalar measurement model had a good fit on the data of the 40 groups ≥ 45 years vs. 18–24 yeaRrs byS d = .48 (p < .001). language groups, χ 2 = 8666.51, df = 716, CFI = .985, TLI = .983, RMSEA = .088 (.086, .090), SRMR = .023. Comparing all other lan- E guages to English (again the original scale language), the ordering and 3.2. Connectedness to nature scale magnitude of standardised latent mean differences (Cohen’s d) are V provided in Fig. 2 (individual Cohen’s d values are provided in Ta-3.2.1. Structural anIalysis of the CNS-14 and CNS-7 in the total sample ble S9). The largest positive differences were observed for (in descend-A unidimensional model had a poor fit on the CNS-14, χ2 =N ing order) Nepali, Bangla, and Farsi and were in the range of d = .63 to 56589.17, df = 75, CFI = .921, TLI = .904, RMSEA = .115 (.114, .116), .88 (see Fig. 2, right panel). The largest negative differences were SRMR = .048, compared to the CNS-7, χ2 = 5583.20, df = 12, CFI =U observed for Dutch, Hebrew, and Japanese and were in the range of d =.987, TLI = .978, RMSEA = .090 (.088, .092), SRMR = .017. Addi- − .40 to − .69. These rankings mostly matched the rankings of the tionally, the standardised loadings of Items #12 and #13, and of the analysis of national groups (see above). negatively worded Items #4 and #14, were low (≤ .36). Hence, the CNS- 7 was used in all subsequent analyses. 3.2.3.4. Gender identities and age groups. Men reported slightly lower connectedness to nature (CNS-7 latent means) than women (Cohen’s d 3.2.2. Invariance of the cross-language results in the six countries with = − .08, p < .001). Other gender identities reported similar connected- multiple survey languages ness to women (d = .02, p = .79). Connectedness to nature also increased The MG-CFA invariance test results of the cross-language survey with age: age groups 25–44 years vs. 18–24 years differed by d = .17 (p presentation of the CNS-7 in Canada, China, Iceland, India, the < .001), whereas age groups ≥ 45 years vs. 18–24 years by d = .36 (p < Philippines, and the UAE are presented in Supplementary Table S6. Full .001). scalar invariance could be assumed for all countries, except Canada and Iceland, for which metric invariance was upheld. In all countries, except Canada and Iceland, the available data were pooled for the analysis of 14 V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 Table 3 Invariance of the CNS-7 concerning national groups, language, gender identity, and age. Grouping variable χ2(df) CFI TLI RMSEA 90% CI SRMR Model comparisons ΔCFI ΔRMSEA Configural Metric National groups Configural invariance 8001.09(804) .986 .976 .103 [.101, .105] .024 Metric invariance 9076.08(1200) .985 .982 .088 [.086, .090] .028 .001 − .015 2679.88(396) Scalar invariance 33207.52(2520) .941 .967 .120 [.119, .121] .044 .044 .032 25335.58(1716) 25786.21(1320) Language Configural invariance 6625.49(480) .988 .980 .095 [.093, .097] .020 Metric invariance 7376.45(714) .987 .985 .081 [.079, .083] .024 − .005 − .014 2167.62(234) Scalar invariance 27691.05(1494) .951 .972 .111 [.110, .112] .038 .036 .020 21099.45(1014) 21599.26(780) Gender identity Y Configural invariance 5361.43(36) .988 .979 .088 [.086, .090] .017 Metric invariance 3851.56(48) .992 .989 .065 [.063, .066] .017 − .004 − .023 120.35(12) Scalar invariance 4026.67(88) .991 .994 .049 [.047, .050] .018 .001 − .016 766.62(52) R710.21(40) Age Configural invariance 5784.07(36) .987 .977 .092 [.090, .094] .017 A Metric invariance 3981.35(48) .991 .988 .066 [.064, .067] .017 − .004 − .026 150.24(12) Scalar invariance 4236.02(88) .991 .993 .050 [.049, .051] .018 .000 − .016 837R.15(52) 757.37(40) Note. All ps of χ2 and Δχ2 tests (model comparisons) were <.001. Gender identity compared groups of women, men, and otheIr Bgender identity, age compared groups of participants with 18–24 years, 25–44 years, ≥45 years of age. 3.3. Associations between NES and CNS-7 factor scores within national However, higher cluster-le veLl means of nature exposure were associated groups with lower cluster-leNvel means (i.e., lower prevalence rates) of living in urban (vs. rural) settings and of being a member of a racial minority (vs. In the 65 national groups for which both CNS-7 and NES factor scores other), and thus included in the final model. In other words, countries could be computed, the association between nature exposure and (including those with multiple languages) with more rural respondents connectedness to nature ranged from r = − .13 (Cyprus) to .22 (Malta), had highDer naAture exposure and those with more racial minority re-with r = .03 in median. That is, the median correlation between these spondents had lower overall nature exposure, echoing the Level-1 re-two constructs across the 65 national groups was practically nil. sulAts. These cluster-level associations were sizeable and, combined, explained 26% of the Level-2 variance. 3.4. Sociodemographic correlates of NES and CNS-7 factor scores B4. Discussion At the individual (Level-1) level, higher financial security, living inI rural (vs. urban) settings, secondary/tertiary (vs. other) educFation al Here, we used the BINS dataset – with data from 56,968 respondents qualification, and being in a committed relationship or married (vs. across 65 nations and 40 languages – to conduct the most comprehen-other) were all associated with both greater nature exposure and sive assessment of the factorial validity and measurement invariance of connectedness to nature (see Table 4). Being a member oOf a racialised the NES and CNS. In terms of the NES, reliability across countries was minority (vs. other) was associated with lower nature exposure. All as- highly variable with many countries falling below the usual thresholds sociations at this individual level were, however, sYmall and statistically of acceptability. A unidimensional model of the NES did, however, show explained only small amounts of the Level-1 variance. Expressed as full scalar invariance across gender identities and age groups; partial Cohen’s ds, the effect sizes for the dichotIomTous predictors of nature scalar invariance was also found for all languages and all but five na-exposure and connectedness to nature, respectively, were as follows: tional groups. In terms of the CNS, our results are consistent with pre-urban vs. rural living setting, .25/-S.10; secondary/tertiary vs. other vious work suggesting that the 14-item, unidimensional model of this −educational qualification, .02/.05; and committed/married vs. other, instrument has poor factorial validity (Pasca et al., 2017; Rosa et al., .07/.11. For racial minority (vs.R other), Cohen’s d was .04 for nature 2022). Conversely, the CNS-7 showed full scalar invariance across −exposure and non-significant for nature connectedness. gender identities and age groups, and partial scalar invariance across all At the national group clEuster-level (Level 2), connectedness to nature languages and all but two national groups. Associations between nature was not associated with any of the sociodemographic variables. exposure and connectedness to nature across nations were negligible, Table 4 V SociodemograpNhic coIrrelates of nature exposure and connectedness to nature.Predictor Nature exposure Connectedness to natureU Estimate (posterior SD) 95% credibility interval p (one-tailed) Estimate (posterior SD) 95% credibility interval p (one-tailed)Level 1: Individual level Financial security 0.07 (0.004) [0.06, 0.08] < 0.001 0.02 (0.005) [0.01, 0.03] < 0.001 Urbanicity ¡0.12 (0.004) [-0.13, -0.12] < 0.001 ¡0.05 (0.004) [-0.06, -0.04] < 0.001 Education 0.01 (0.004) [0.005, 0.02] < 0.001 0.03 (0.004) [0.02, 0.04] < 0.001 Marital status 0.04 (0.005) [0.03, 0.05] < 0.001 0.05 (0.004) [0.05, 0.06] < 0.001 Racialised status ¡0.02 (0.005) [-0.03, -0.01] < 0.001 .002 (.004) [-.005, .01] .29 Level 2: Cluster-level means Urbanicity ¡0.35 (0.098) [-0.51, -0.14] < 0.001 Racialised status ¡0.30 (0.103) [-0.46, -0.05] < 0.001 Random Effects Intercept residual variance .74 (.09) [.56, .92] <.001 1.00 (.00) NA <.001 Explained variance (Level 1/Level 2) 2%/26% .7%/0% Note. Estimates are on a standardised scale. SD = standard deviation. Significant (one-sided p < .025) estimates are highlighted in boldface. 15 V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 although we did find significant (albeit weak) individual level associa- NES generally increased from emerging to older adulthood. It is likely tions between both constructs and several sociodemographic factors. that the latter group has greater time and opportunities to engage with nature (Freeman et al., 2019) and may also have a fuller understanding 4.1. Nature exposure of the natural world, which in turn may enhance their abilities to “notice” the natural environment (Ojala, 2009). We tested both a unidimensional model of the NES with all four items and a correlated 2-factor model, with equality constraints set for the two 4.2. Connectedness to nature items that most closely assess “nature exposure” (#1 and #3) and “noticing nature” (#2 and #4), respectively. In the total sample, the 2- The CNS results indicated that a unidimensional model including all factor model had poor fit to the data, with item loadings reflecting 14 items had poor fit. Although the 14-item model demonstrated “nature exposure” in everyday environments (#1 and #2) and outside adequate fit in some previous research (e.g., Li & Wu, 2016; Mayer & everyday environments (#3 and #4), rather than an “exposure-noticing” Frantz, 2004; Pessoa et al., 2016), our findings corroborateY more recent split. In contrast, a unidimensional model had adequate fit to the data, difficulties replicating this model (e.g., Navarro et al., 2017; Olivos although we did find that the “nature exposure” items had lower et al., 2013; Pearce et al., 2022). As Pasca et al. (2017)Rhave suggested, it standardised loadings than the “noticing nature” items. Including is highly likely that some CNS items either perform poorly – for lin- correlated errors between two item pairs (#1 and #2, and #3 and #4, guistic, conceptual, or semantic reasons – or Aare redundant in some respectively) substantially improved fit of the unidimensional model. In national contexts. Adopting an Item Response Theory approach also short, although the NES does present some indication of possible sta- suggested that some items may inadequaRtely discriminate between in- tistical multidimensionality, this does not obviously align with face dividuals who vary in their degree of connectedness to nature (Pasca validity of the items, and a unidimensional model presented the best fit et al., 2017). For these reasons, PascBa et al. (2017) recommended using a to the data anyway. truncated version of the CNS thatI includes only seven of the 14 original Although this unidimensional model of the NES showed configural items, which has been foun d Lto have a unidimensional factor structure in invariance, it did not show metric or scalar invariance across national previous work (Rosa et al., 2022). groups and languages. This suggests that, while there may be a near- Using this CNS-7N, our results suggested that configural and metric universally plausible basic organisation of the nature exposure invariance was supported across nations, languages, gender identities, construct – as measured using the NES – each item does not contribute to and age groups. In terms of national groups and languages, the lack of the latent construct in the same way across nations or languages, making full scalar invaAriance is consistent with previous work in seven European it a problematic measure to use in cross-cultural research. It was none- nations, Dwhere only configural and metric invariance was found theless possible to achieve partial scalar invariance across all but five of (Navarro et al., 2022). One possible reason for the lack of scalar the national groups (i.e., UAE [in Arabic], Ecuador, Iran, Iraq, and invariance of the CNS-7 across national groups and languages is that this Spain) represented in the analyses and all 40 languages, suggesting that BinsAtrument – and indeed the construct of connectedness to nature itself – it may tap a common latent construct across national groups and lan-I is steeped in a boundary distinction between humans and the natural guages, albeit with some variation in meaning. environment (i.e., that one can be distinct or disconnected from nature). There were also large inter-nation and inter-language differences in As Fletcher (2017, p. 228) has suggested, however, this view is grounded NES scores. These results may reflect actual cross-national differeFnces in in a “culturally specific … conceptual dichotomy between opposing exposure to natural environments, as evidenced by the large variations realms of ‘nature’ and ‘culture’” that is characteristic of wealthy, seen in Fig. 1. Content-wise, these differences appeared tOo be particu- Western nations. In other words, the suggestion that humans can be larly driven by differences in “noticing nature” (Items #2 and #4 were separate from nature – or, indeed that humans are not nature – may be a the primary contributors to latent NES scores) butY may have also been cultural worldview with limited global application, which in turn may affected by differences in the meaning of “exposure to nature” across explain the lack of scalar invariance in relation to the CNS-7 in the national groups (McPhie & Clarke, 2020). HeTre, we highlight Brazilian present study.participants as an outlier in terms of their low latent NES scores (see Having said this, full scalar invariance is often an unrealistic goal for Fig. 1). Reasons for this are unclearS but mIay reflect difficulties that datasets with a large number of groups (Marsh et al., 2018). Impor-Brazilian participants experience in noticing natural environments, tantly, our results also suggested that it was possible to achieve partial despite their proximity to such environments (see Profice et al., 2023). scalar invariance of the CNS-7 across all but two national groups (i.e., Alternatively, it may reflect lowR levels of nature exposure that many Iraq and Bosnia and Herzegovina, respectively) and all 40 languages urban dwellers in low- andE lower-middle income nations have due to a represented in the BINS. This is important theoretically because it sug-lack of time, money, and nearby natural environments (Awoyemi et al., gests that the CNS-7 can be used to measure a latent construct of 2024) or a perceived Vlack of safety in natural environments (e.g., due to connectedness to nature that may have near-universal applicability, social unrest). I albeit with some possible loss of meaning. From a practical Conversely, there was evidence that the unidimensional model of the point-of-view, achieving partial scalar invariance means that we were NES achievedN full scalar invariance across gender identities and age able to compare latent CNS-7 means across national groups and lan-groups. In terms of gender identities, women reported significantly guages, with our results showing large cross-national and cross-language greater Unature exposure than men and individuals who identified their differences in CNS-7 latent means. Understanding why such differences gender in another way, although effect sizes were small. This is a curious exist is, however, more difficult and only preliminary explanations can finding, particularly as studies in Western nations have generally iden- be put forward based on the present data. tified a gender gap in nature contact, with women visiting natural en- Richardson, Hamlin, Elliott, and White (2022), for instance, sug- vironments less frequently than men (e.g., Boyd et al., 2018), possibly gested that lower country-level nature connectedness may reflect his- because of experiences of fear and vulnerability in natural environments torical interactions with, and attitudes towards, the natural world. or due to societal and gendered norms that mean women often feel a lack Observing that all six of the countries with the lowest nature connect- of entitlement to leisure time in general (Day, 2000). It is possible that edness scores in an 18-country study by White et al. (2021) were at some this discrepancy across studies is due to the inclusion of “noticing na- time subject to British rule, Richardson, Hamlin, Elliott, and White ture” in the NES and possibly women having a greater tendency to (2022) wondered whether early industrialisation and urbanisation of “notice nature” because they may be more sensitive to particular settings the UK, and resource extraction and exploitation of its colonies, that may evoke fear, anxiety, and negative emotions (van den Berg & ter encouraged seeing nature as external and separate to human lives. Heijne, 2005). In terms of age, self-reported nature exposure using the However, although the UK again had very low levels of CNS in the 16 V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 present research, as did some of its former colonies (e.g., Australia, collapsed into a unidimensional scale representing a single underlying Canada [English], Ireland, and the USA), other former British colonies, factor, it was perhaps not surprising that Items #2 and #4 were the notably on the African continent, including South Africa, Nigeria, and strongest since they were the only two measuring the same thing in the Ghana, were among the countries with the highest connectedness to same way. By contrast, the 4-item scale lacks clear conceptual or face nature. An alternative suggestion, therefore, is that it is the English validity. So, if the NES is not measuring nature exposure, what is it language version that results in lower CNS scores, an idea supported by measuring? One possibility is that it is heuristically measuring a general the English versus French/Icelandic data from Canada and Iceland data, attitude towards the natural world with people generally interested in but undermined by the fact that these three African countries also used nature answering relatively positively to the four items and vice versa. If the English language version. this were the case, however, one might still expect stronger associations More broadly, therefore, it appears that the variation in connected- between NES scores and connectedness to nature. Another possibility is ness to nature across nations and languages may reflect differences in that the NES is measuring different types of “exposure” in a single the ways that connectedness to nature is constructed, negotiated, and metric, and which form of exposure is most salient or relevant may experienced by different communities (see McPhie & Clarke, 2020). depend on the national or linguistic context. Y That is, what it means to be “connected to nature” or experience a sense In terms of relations of the two instruments to selected de- of oneness with nature likely varies across nations and/or cultures, mographics, both greater nature exposure and AconneRctedness to nature which results in the type of variation that can be seen in Fig. 2 when were significantly (but weakly) associated with greater financial secu- individuals are asked to self-report their experiences. It is also possible rity, rural residence (versus urban residence), higher education, being in that cultural practices and traditions that foster both connectedness to a committed relationship (versus being Rsingle), and being in a racial nature, as well as the ability to articulate that connectedness, varies majority in a specific country. Broadly speaking, the significant associ- across national groups (Keaulana et al., 2021). For instance, there is ations with financial security, highBer education, and racial majority some evidence to suggest that individuals in Nepal and Bangladesh – two status reflect known socioecLonomIic inequities in terms of the distribu-nations that had very high latent CNS-7 scores in the present study – live tion of, and outcomes of exposure to, natural environments (e.g., Gerrish in ways that are intimately connected to the natural world (Widdop & Watkins, 2018; Murdock , 2019). Likewise, the significant association Quinton & Khatun, 2020). Further work is clearly needed to explore with rurality is perhNaps to be expected and consistent with previous these issues in greater depth. findings (e.g., Carrus et al., 2020; Martin & Czellar, 2017). The finding In terms of gender identities, we found that the CNS-7 showed full that those in coAmmitted relationships were more likely to report greater scalar invariance across women, men, and individuals who identified connectedness to nature and nature exposure than those who were their gender in another way. The CNS-7 taps a common underlying single is aDlso consistent with existing research (e.g., Teixeira et al., 2023) construct of connectedness to nature that is not differentially affected by and may reflect the beneficial impact of social support in nature gender identities. Women reported higher nature connectedness than engagement. Racialised minority individuals may also experience nat- men, consistent with previous work showing that women have greater BuraAl environments differently to majority groups (e.g., in terms of environmental concern (e.g., Xiao & McCright, 2015) and dispositionalI perceived safety, comfort, a sense of belonging) due to historic and empathy with nature (Tam, 2013), though effect sizes were relative ly contemporary inequities stemming from structural racism (Collier, small. Additionally, we found that the CNS-7 was fully invariant across 2022; Roberts et al., 2023), which impacts ongoing nature exposure.age groups, with connectedness to nature generally increasing from emerging adulthood to older adulthood, though again group diffeFrences 4.4. Constraints on generalisability and directions for future research were relatively small. O Although the present work provides one of the largest cross-national 4.3. Correlates of nature exposure and connectednesYs databases on nature exposure and connectedness to nature, our findings should be considered in light of a number of constraints on their gen-Contrary to our hypothesis, the median coTrrelation between nature eralisability (Simons et al., 2017). First, our sampling strategy was exposure and nature connectedness, as mIeasured using the NES and opportunistic in most cases and, as such, the individual samples should CNS, across nations was practically nilS. This finding stands in contrast to not be considered representative of a particular nation. This may reduce previous cross-sectional (e.g., Fränkel et al., 2019; Martin et al., 2020) the generalisability of our findings, particularly when making compar-and experimental studies (SheffiReld et al., 2022) that have shown these isons across nations or linguistic groups. Relatedly, although one of the constructs to be weakly-to-moderately correlated. Moreover, where strengths of the BINS dataset is the focus on operational equivalence studies have assessed theseE constructs using the NES and CNS, respec- across research sites (Swami et al., 2022), we cannot entirely rule out tively, reported associations have tended to be moderate-to-large small differences in recruitment and survey completion (e.g., in terms of (Baceviciene et al., 2V021; Picanço et al., 2024; Swami et al., 2016; online versus offline completion, participant remuneration, specific Swami, Barron, et aIl., 2020). In these studies, however, the significant recruitment methods). Also related to recruitment, because the BINS associations may have been inflated through the use of ecological cor- dataset was researcher-crowdsourced, our data was under-represented relations in siNngular nations. in several world regions (e.g., Africa, Central Asia, the Caribbean, Instead, the present results suggest that the true variation in the as- Central America), though this is a common limitation of many sociatioUn between nature exposure (measured using the NES) and large-scale, cross-national studies (Krys et al., 2024).connectedness to nature (measured using the CNS-7) across nations may Another constraint on generalisability was that specific conditions be relatively wide. This raises questions about what exactly is being during the period of data collection – which extended over 15 months measured by the NES, and how. While we acknowledge the rationale for and took place in the shadow of the COVID-19 pandemic – may have the scale to want to incorporate the three types of exposure explored in varied substantially across nations. This is particularly important when the literature (local, active visits, and awareness) into a single measure, considering that pandemic-related policies may have directly impacted the way this is operationalised is logically problematic because there is one’s ability to spend time in nature (e.g., due to periods of lockdown; no longer any single underlying latent construct. While Items #2 and #4 Stieger et al., 2021). These varying conditions make it difficult to know both tap into “nature-noticing” using the same response options, Items to what extent our data are temporally reliable and whether specific #1 and #3, by design, are interested in different types of “nature- pandemic-related experiences (e.g., being in lockdown, severity of the exposure” and use different response options. It is perhaps not surpris- pandemic, national and international responses to the pandemic, none ing, then, that the reliability across many countries was low. of which were measured in our survey) may have affected our findings. Although there was some statistical evidence that the items could be Still, given the consistency of factor structures across groups, any biases 17 V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 in results are likely to be reflected in latent group differences. Investigation, Data curation. Nursel Alp-Dal: Writing – review & edit- Finally, because the BINS dataset consists of self-reported data, we ing, Investigation, Data curation. Anas B. Alsalhani: Writing – review & cannot rule out common-method biases. On this note, there was some editing, Investigation, Data curation. Sara Álvarez-Solas: Writing – evidence of response-scale spareness in terms of the NES (i.e., some review & editing, Investigation, Data curation. Ana Carolina Soares response options were rarely utilised), which may have affected our Amaral: Writing – review & editing, Investigation, Data curation. findings. Yet, even though some response options had to be combined to Sonny Andrianto: Writing – review & editing, Investigation, Data make the multigroup analyses feasible, this measure is not needed when curation. Trefor Aspden: Writing – review & editing, Investigation, utilising the instrument. We still recommend using its 5-point response Data curation. Marios Argyrides: Writing – review & editing, Investi- scale, but expect that some response options will only be seldomly used. gation, Data curation. John Jamir Benzon R. Aruta: Writing – review & Also, there was evidence of insufficient fit of a unidimensional model for editing, Investigation, Data curation. Stephen Atkin: Writing – review the NES in a number of countries, which may further limit its & editing, Investigation, Data curation. Olusola Ayandele: Writing – applicability. review & editing, Investigation, Data curation. Migle Baceviciene: Our findings also raise several important questions that could be Writing – review & editing, Investigation, Data curatiYon. Radvan more fully answered in future research. For instance, it is unclear at Bahbouh: Writing – review & editing, Investigation, Data curation. present why instrument composite reliabilities were less-than-adequate Andrea Ballesio: Writing – review editing, InR& vestigation, Data in some linguistic or national groups (e.g., the Arabic NES in the United curation. David Barron: Writing – review & ediAting, Investigation, Data Arab Emirates or the CNS in Iraq and Bosnia and Herzegovina). Like- curation. Ashleigh Bellard: Writing – reRview & editing, Investigation, wise, while our work provides a useful starting point, much more can be Data curation, Writing – review & editing, Investigation, Data curation. done to interrogate and understand latent national and linguistic dif- SóleySesselja Bender: Writing – reBview & editing, Investigation, Data ferences in nature exposure and connectedness to nature. More gener- curation. Kerime Derya Beydaǧ: Writing – review & editing, Investi- ally, it remains important to explore the diverse ways that “nature” and gation, Data curation. Gorana BirIovljević: Writing – review & editing, “nature connectedness” are conceptualised, understood, and lived in Investigation, Data curatio n.L Marie-Ève Blackburn: Writing – review & diverse cultural contexts, as well as the ways in which nuances in un- editing, Investigation, Data curation. Teresita Borja-Alvarez: Writing – derstandings of “nature” affect human behaviour (Droz et al., 2022). review & editing, Investigation, Data curation. Joanna Borowiec: Doing so will help researchers and practitioners ensure that voices from Writing – review &N editing, Data curation. Miroslava Bozogáňová: diverse populations worldwide are not rendered invisible or muffled in Writing – review & editing, Investigation, Data curation. Solfrid Brat- the scientific literature. land-Sanda: WAriting – review & editing, Investigation, Data curation. MatthewH.E.M. Browning: Writing – review & editing, Investigation, 4.5. Conclusion Data curation. Marina Burakova: Writing – review & editing, Investi- gatAion, Data curation. Yeliz Çakır-Koçak: Writing – review & editing, These constraints on generalisability notwithstanding, the present work suggests that the CNS-7 is a useful tool for assessing a latent connectedness to nature construct across national and linguistic group s,I B Investigation, Data curation. Pablo Camacho: Writing – review & editing, Investigation, Data curation. Vittorio Emanuele Camilleri: Writing – review & editing, Investigation, Data curation. Valentina gender identities, and adult age groups. While our statistical analyses Cazzato: Writing – review & editing, Investigation, Data curation. Silvia also pointed to the potential use of the NES to generate a uniFvariate Cerea: Writing – review & editing, Investigation, Data curation. score representing nature exposure, important questions remain around Apitchaya Chaiwutikornwanich: Writing – review & editing, Inves- this instrument’s face and nomological validity. While we conclude that tigation, Data curation. Trawin Chaleeraktrakoon: Writing – review & the CNS-7 can be used in cross-national and cross-ling uOistic research editing, Investigation, Data curation. Tim Chambers: Writing – review with no substantive loss of meaning, we also caution that our work was & editing, Investigation, Data curation. Qing-Wei Chen: Writing – re- not set up to investigate and understand localised, cultural meanings view & editing, Investigation, Data curation. Xin Chen: Writing – re- and experiences of connectedness to nature. FTurthYer work is needed to view & editing, Investigation, Data curation. Chin-Lung Chien: Writing robustly capture nature exposure. One mIay flip our methodological – review & editing, Investigation, Data curation. Phatthanakit Chob-design and utilise more emic approaches in the future to fully under- thamkit: Writing – review & editing, Investigation, Data curation. stand geographic, cultural, and linguSistic variations in how nature is Bovornpot Choompunuch: Writing – review & editing, Investigation, experienced and how connectedRness to nature manifests (e.g., Sedawi Data curation. Emilio J. Compte: Writing – review & editing, Investi-et al., 2021). gation, Data curation. Jennifer Corrigan: Writing – review & editing, E Investigation, Data curation. Getrude Cosmas: Writing – review & CRediT authorship contribution statement editing, Investigation, Data curation. Richard G. Cowden: Writing – V review & editing, Investigation, Data curation. Kamila Czepczor-Ber-Viren Swami: IWriting – original draft, Project administration, nat: Writing – review & editing, Investigation, Data curation. Marcin Methodology, Investigation, Data curation, Conceptualization. Mathew Czub: Writing – review & editing, Investigation, Data curation. Wan-P. White: WNriting – original draft, Investigation. Martin Voracek: derson Roberto da Silva: Writing – review & editing, Investigation, Writing – review & editing, Validation, Methodology, Investigation, Data curation. Mahboubeh Dadfar: Writing – review & editing, Data cuUration, Conceptualization. Ulrich S. Tran: Writing – review & Investigation, Data curation. Simon E. Dalley: Writing – review & editing, Visualization, Methodology, Investigation, Formal analysis, editing, Investigation, Data curation. Lionel Dany: Writing – review & Data curation, Conceptualization. Toivo Aavik: Writing – review & editing, Investigation, Data curation. Jesus Alfonso D. Datu: Writing – editing, Investigation, Data curation. Hamed Abdollahpour Ranjbar: review & editing, Investigation, Data curation. Pedro Henrique Ber- Writing – review & editing, Investigation, Data curation. Sulaiman bert de Carvalho: Writing – review & editing, Investigation, Data Olanrewaju Adebayo: Writing – review & editing, Investigation, Data curation. Gabriel Lins de Holanda Coelho: Writing – review & editing, curation. Reza Afhami: Writing – review & editing, Investigation, Data Investigation, Data curation. Avila Odia S. De Jesus: Writing – review curation. Oli Ahmed: Writing – review & editing, Investigation, Data & editing, Investigation, Data curation. Sonia Harzallah Debbabi: curation. Annie Aimé: Writing – review & editing, Investigation, Data Writing – review & editing, Investigation, Data curation. Sandesh curation. Marwan Akel: Writing – review & editing, Investigation, Data Dhakal: Writing – review & editing, Investigation, Data curation. curation. Hussam Al Halbusi: Writing – review & editing, Investiga- Francesca Di Bernardo: Writing – review & editing, Investigation, Data tion, Data curation. George Alexias: Writing – review & editing, curation. Donka D. Dimitrova: Writing – review & editing, Investiga- Investigation, Data curation. Khawla F. Ali: Writing – review & editing, tion, Data curation. Jacinthe Dion: Writing – review & editing, 18 V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 Investigation, Data curation. Barnaby Dixson: Writing – review & Investigation, Data curation. Aituar Kospakov: Writing – review & editing, Investigation, Data curation. Stacey M. Donofrio: Writing – editing, Investigation, Data curation. Isabel Krug: Writing – review & review & editing, Investigation, Data curation. Marius Drysch: Writing editing, Investigation, Data curation. Garry Kuan: Writing – review & – review & editing, Investigation, Data curation. Hongfei Du: Writing – editing, Investigation, Data curation. Yee Cheng Kueh: Writing – re- review & editing, Investigation, Data curation. Angel M. Dzhambov: view & editing, Investigation, Data curation. Omar Kujan: Writing – Writing – review & editing, Investigation, Data curation. Claire El-Jor: review & editing, Investigation, Data curation. Miljana Kukić: Writing Writing – review & editing, Investigation, Data curation. Violeta Enea: – review & editing, Investigation, Data curation, Writing – review & Writing – review & editing, Investigation, Data curation. Mehmet editing, Investigation, Data curation. Sanjay Kumar: Writing – review Eskin: Writing – review & editing, Investigation, Data curation. Farinaz & editing, Investigation, Data curation. Vipul Kumar: Writing – review Farbod: Writing – review & editing, Investigation, Data curation. & editing, Investigation, Data curation. Nishtha Lamba: Writing – re- Lorleen Farrugia: Writing – review & editing, Investigation, Concep- view & editing, Investigation, Data curation. Mary Anne Lauri: Writing tualization. Leonie Fian: Writing – review & editing, Investigation, Data – review & editing, Investigation, Data curation. Maria FernYanda Laus: curation. Maryanne L. Fisher: Writing – review & editing, Investiga- Writing – review & editing, Investigation, Data curation. Liza April tion, Data curation. Michał Folwarczny: Writing – review & editing, LeBlanc: Writing – review & editing, Investigation, Data curation. Investigation, Data curation, Writing – review & editing, Investigation, Hyejoo J. Lee: Writing – review & editing, Inve Data curation. David A. Frederick: Writing – review & editing, Inves- Małgorzata Lipowska: Writing – review & ediAstigatiRon, Data curation. ting, Investigation, Data tigation, Data curation. Adrian Furnham: Writing – review & editing, curation. Mariusz Lipowski: Writing – reRview & editing, Investigation, Investigation, Data curation. Antonio Alías García: Writing – review & Data curation. Caterina Lombardo: Writing – review & editing, editing, Investigation, Data curation. Shulamit Geller: Writing – review Investigation, Data curation. AndIreBa Lukács: Writing – review & edit-& editing, Investigation, Data curation. Marta Ghisi: Writing – review & ing, Investigation, Data curation. Christophe Maïano: Writing – review editing, Investigation, Data curation. Alireza Ghorbani: Writing – re- & editing, Investigation, Data curation. Sadia Malik: Writing – review & view & editing, Investigation, Conceptualization. Maria Angeles editing, Investigation, Data Gomez Martinez: Writing – review & editing, Investigation, Data view & editing, Investiga tioLcuration. Mandar Manjary: Writing – re-n, Data curation. Lidia Márquez Baldó: curation. Sarah Gradidge: Writing – review & editing, Investigation, Writing – review & editing, Investigation, Data curation. Martha Mar- Data curation. Sylvie Graf: Writing – review & editing, Investigation, tinez-Banfi: WritingN – review & editing, Investigation, Data curation. Data curation. Caterina Grano: Writing – review & editing, Investiga- Karlijn Massar: Writing – review & editing, Investigation, Data cura- tion, Data curation. Gyöngyvér Gyene: Writing – review & editing, tion. Camilla MAatera: Writing – review & editing, Investigation, Data Investigation, Data curation. Souheil Hallit: Writing – review & editing, curation.D Olivia McAnirlin: Writing – review & editing, Investigation, Investigation, Data curation. Motasem Hamdan: Writing – review & Data curation. Moisés Roberto Mebarak: Writing – review & editing, editing, Investigation, Data curation. Jonathan E. Handelzalts: Writing Investigation, Data curation. Anwar Mechri: Writing – review & edit- – review & editing, Investigation, Data curation. PaulH.P. Hanel: BingA, Investigation, Data curation. Juliana Fernandes Filgueiras Meir-Writing – review & editing, Investigation, Data curation. Steven R.I eles: Writing – review & editing, Investigation, Data curation. Norbert Hawks: Writing – review & editing, Investigation, Data curation. Is sa Mesko: Writing – review & editing, Investigation, Data curation. Jac-Hekmati: Writing – review & editing, Investigation, Data curation. Mai queline Mills: Writing – review & editing, Investigation, Data curation. Helmy: Writing – review & editing, Investigation, Data curation. Teti- Maya Miyairi: Writing – review & editing, Investigation, Data curation. ana Hill: Writing – review & editing, Investigation, Data cuFration. Ritu Modi: Writing – review & editing, Investigation, Data curation. Farah Hina: Writing – review & editing, Investigation, Data curation. Adriana Modrzejewska: Writing – review & editing, Investigation, Geraldine Holenweger: Writing – review & editing, Inv estOigation, Data Data curation. Justyna Modrzejewska: Writing – review & editing, curation. Martina Hřebíčková: Writing – review Y& editing, Investiga- Investigation, Data curation. Kate E. Mulgrew: Writing – review & tion, Data curation. Olasupo Augustine Ijabadeniyi: Writing – review editing, Investigation, Data curation. Taryn A. Myers: Writing – review & editing, Investigation, Data curation. Asma TImam: Writing – review & & editing, Investigation, Data curation. Hikari Namatame: Writing – editing, Investigation, Data curation. BaşaIk İnce: Writing – review & review & editing, Investigation, Data curation. Mohammad Zakaria editing, Investigation, Data curation. Natalia Irrazabal: Writing – re- Nassani: Writing – review & editing, Investigation, Data curation. view & editing, Investigation, DataS curation. Rasa Jankauskiene: Amanda Nerini: Writing – review & editing, Investigation, Data cura-Writing – review & editing, Investigation, Data curation. Ding-Yu tion. Félix Neto: Writing – review & editing, Investigation, Data cura- Jiang: Writing – review & editiRng, Investigation, Data curation. Evan tion. Joana Neto: Writing – review & editing, Investigation, Data M. Johnson: Writing – revEiew & editing, Investigation, Data curation. curation. Angela Nogueira Neves: Writing – review & editing, Inves-Veljko Jovanović: Writing – review & editing, Investigation, Data tigation, Data curation. Siu-Kuen Ng: Writing – review & editing, curation, Writing – Vreview & editing, Investigation, Data curation. Investigation, Data curation. Devi Nithiya: Writing – review & editing, Marija Jović: WritIing – review & editing, Investigation, Data curation. Investigation, Data curation. Jiaqing O: Writing – review & editing, Marko Jović: Writing – review & editing, Investigation, Data curation, Investigation, Data curation. Sahar Obeid: Writing – review & editing, Writing – revNiew & editing, Investigation, Data curation. Alessandra Investigation, Data curation. Camila Oda-Montecinos: Writing – re-Costa Pereira Junqueira: Writing – review & editing, Investigation, view & editing, Investigation, Data curation. Peter Olamakinde Ola-Data cuUration. Lisa-Marie Kahle: Writing – review & editing, Investi- pegba: Writing – review & editing, Investigation, Data curation. Tosin gation, Data curation. Adam Kantanista: Writing – review & editing, Tunrayo Olonisakin: Writing – review & editing, Investigation, Data Investigation, Data curation. Ahmet Karakiraz: Writing – review & curation. Salma Samir Omar: Writing – review & editing, Investigation, editing, Investigation, Data curation. Ayşe Nur Karkin: Writing – re- Data curation. Brynja Örlygsdóttir: Writing – review & editing, view & editing, Investigation, Conceptualization. Erich Kasten: Writing Investigation, Data curation. Emrah Özsoy: Writing – review & editing, – review & editing, Investigation, Data curation. Salam Khatib: Writing Investigation, Data curation. Tobias Otterbring: Writing – review & – review & editing, Investigation, Data curation. Nuannut Khieowan: editing, Investigation, Data curation. Sabine Pahl: Writing – review & Writing – review & editing, Investigation, Data curation. Patricia Jo- editing, Investigation, Data curation. Maria Serena Panasiti: Writing – seph Kimong: Writing – review & editing, Investigation, Data curation. review & editing, Investigation, Data curation. Yonguk Park: Writing – Litza Kiropoulos: Writing – review & editing, Investigation, Data review & editing, Investigation, Data curation. Muhammad Mainuddin curation. Joshua Knittel: Writing – review & editing, Investigation, Patwary: Writing – review & editing, Investigation, Data curation. Data curation. Neena Kohli: Writing – review & editing, Investigation, Tatiana Pethö: Writing – review & editing, Investigation, Data curation. Data curation. Mirjam Koprivnik: Writing – review & editing, Nadezhda Petrova: Writing – review & editing, Investigation, Data 19 V. Swami et al. J o u r n a l o f E n v i r o n m e n t a l P s y c h o lo g y 99 (2024) 102432 curation. Jakob Pietschnig: Writing – review & editing, Investigation, curation. Stefan Stieger: Writing – review & editing, Investigation, Data Data curation. Sadaf Pourmahmoud: Writing – review & editing, curation. Investigation, Data curation. Vishnunarayan Girishan Prabhu: Writing – review & editing, Investigation, Data curation. Vita Appendix A. Supplementary data Poštuvan: Writing – review & editing, Investigation, Data curation. Pavol Prokop: Writing – review & editing, Investigation, Data curation. Supplementary data to this article can be found online at https://doi. Virginia L. Ramseyer Winter: Writing – review & editing, Investiga- org/10.1016/j.jenvp.2024.102432. tion, Data curation. Magdalena Razmus: Writing – review & editing, Investigation, Data curation. Taotao Ru: Writing – review & editing, Appendix 1 Investigation, Data curation. Mirjana Rupar: Writing – review & edit- ing, Investigation, Data curation. Reza N. Sahlan: Writing – review & editing, Investigation, Data curation. Mohammad Salah Hassan: The Nature Exposure Scale (NES) Writing – review & editing, Investigation, Data curation. Anđela Šalov: Y Writing – review & editing, Investigation, Data curation. Saphal Sap- Two of the four NES items ask about nature exposure in one’s kota: Writing – review & editing, Investigation, Data curation. Jacob Reveryday life and environments: Item #1 “In your everyday home, Owusu Sarfo: Writing – review & editing, Investigation, Data curation. travel, and work environments and activities, pAlease rate your level of Yoko Sawamiya: Writing – review & editing, Investigation, Data exposure to ‘natural environments” with options ranging from ‘High curation. Katrin Schaefer: Writing – review & editing, Investigation, (most of my everyday environment is nBaturalR)’ (5)’, to ‘Low (very little of my Data curation. Michael Schulte-Mecklenbeck: Writing – review & everyday environment is natural)’ (1), and Item #2 “How much do you editing, Investigation, Data curation. Veya Seekis: Writing – review & notice the natural environments in your everyday life?” with options editing, Investigation, Data curation. Kerim Selvi: Writing – review & ranging from ‘A great deal’ (5), toI ‘Not much’ (1). Two further items ask editing, Investigation, Data curation. Mehdi Sharifi: Writing – review & about nature exposure during excursions outside of one’s everyday en- editing, Investigation, Data curation. Anita Shrivastava: Writing – re- vironments: Item #3 “PleaseL rate the frequency (how often) of exposure view & editing, Investigation, Data curation. Rumana Ferdousi Sid- to nature-rich envirN onments outside of your everyday environment” dique: Writing – review & editing, Investigation, Data curation. with options ranging from ‘High (once a month or more often)’ (5), to ‘Low Valdimar Sigurdsson: Writing – review & editing, Investigation, Data (once a year or curation. Vineta Silkane: Writing – review & editing, Investigation, of nature in thAless)’ (1), and Item #4 “How much notice would you take ese environments?” with the same response options as Data curation. Ana Šimunić: Writing – review & editing, Investigation, item 2. Data curation. Govind Singh: Writing – review & editing, Investigation, D Data curation. Alena Slezáčková: Writing – review & editing, Investi- The Connectedness to Nature Scale (CNS) gation, Data curation. Christine Sundgot-Borgen: Writing – review & A editing, Investigation, Data curation. Gill Ten Hoor: Writing – review & The 14 items of the CNS are as follows, with response options ranging editing, Investigation, Data curation. Passagorn Tevichapong: WritingI Bfrom 1 (strongly disagree) to 5 (strongly agree): #1 “I often feel a sense of – review & editing, Investigation, Data curation. Arun Tipandjan: oneness with the natural world around me”, #2 “I think of the natural Writing – review & editing, Investigation, Data curation. Jennif er world as a community to which I belong”, #3 “I recognize and appre- Todd: Writing – review & editing, Investigation, Data curationF. Con- ciate the intelligence of other living organisms”, #4 “I often feel stantinos Togas: Writing – review & editing, InvestigatioOn, Data cura- disconnected from nature”, #5 “When I think of my life, I imagine tion. Fernando Tonini: Writing – review & editing, Investigation, Data myself to be part of a larger cyclical process of living”, #6 “I often feel a curation. Juan Camilo Tovar-Castro: Writing – review & editing, kinship with animals and plants”, #7 “I feel as though I belong to the Investigation, Data curation. Lise Katrine Jepsen TYrangsrud: Writing – Earth as equally as it belongs to me”, #8 “I have a deep understanding of review & editing, Investigation, Data curTation. Pankaj Tripathi: I how my actions affect the natural world”, #9 “I often feel part of the web Writing – review & editing, Investigation, Data curation. Otilia of life”, #10 “I feel that all inhabitants of Earth, human, and nonhuman, Tudorel: Writing – review & editing, Investigation, Data curation. share a common ‘life force’”, #11 “Like a tree can be part of a forest, I Tracy L. Tylka: Writing – review & Sediting, Investigation, Data cura- feel embedded within the broader natural world”, #12 “When I think of tion. Anar Uyzbayeva: Writing – review & editing, Investigation, Data my place on Earth, I consider myself to be a top member of a hierarchy curation. Zahir Vally: Writing – review & editing, Investigation, Data that exists in nature”, #13 “I often feel like I am only a small part of the curation. Edmunds VanagEs: WrRiting – review & editing, Investigation, natural world around me, and that I am no more important than the Data curation. Luis Diego Vega: Writing – review & editing, Investi- grass on the ground or the birds in the trees”, and #14 “My personal gation, Data curation. 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