DOI: 10.5061/dryad.j3tx95xdx
title: Understanding relational values in cultural landscapes in Romania and Germany

description: Relational values recently emerged as a concept to comprehensively
 understand and communicate the many values of nature. Relational values
 can be defined as preferences and principles about human-nature
 relationships and focus both on human-nature connections, as well as
 human-human connections. Here, drawing on 819 face-to-face questionnaires,
 we analysed relational, intrinsic and instrumental values across a total
 of six agricultural landscapes in Transylvania (Romania) and Lower Saxony
 (Germany). The landscapes described a gradient of land use intensity,
 within and across the countries. Our results suggest a bundling of values
 into four groups: those concerned with individual cognition (including
 intrinsic values), those that focus on nature as a place for social
 interaction and relaxation, those that capture cultural identity and
 spiritual values and one bundle that only includes instrumental values.
 These different values, in turn, were strongly related to (i) respondents’
 attitudes towards environmental conservation and the (ii) frequency with
 which respondents used nature as a resource.  Instrumental values
 have the tendency to be inversely related with relational values and were
 found to increase with the land use intensity of the focal landscapes.
description: <i>Data
 collection</i> Preparation for our quantitative survey
 included extensive theoretical and literature studies on relational values
 and human-nature relationships. Building upon prior empirical work in the
 region (see e.g. (Balázsi et al., 2019; Hartel et al., 2016; Riechers et
 al., 2019) the questionnaire development included two focus groups with
 laypersons to improve structure and wording of the questionnaire and a
 pilot study with <i>n</i> = 20. The questionnaire contained
 parts on (1) utilisation of nature (Visiting frequency of natural areas in
 the vicinity from “daily” to “never”; distance travelled to these places
 from “up to 1km” to “over 10km”, use of different natural products such as
 water, wood, decorative material from “always” to “never”) (2) attitudes
 towards nature and nature conservation (importance from “very important”
 to “not important” of the conservation of specific natural attributes in
 the landscape), (3) relational, intrinsic and instrumental values and (4)
 socio-demographic information (see Supplementary Material S1 for the full
 questionnaire). In our study we focused on nine relational values that
 were seen as important from our prior research, instrumental and intrinsic
 values. An overview of the values used in this paper and their description
 can be found in Table 1. Data were collected through face-to-face surveys,
 within randomly chosen villages within the focal landscapes. We used
 proportionate sampling based on the population density of the villages in
 the focal landscapes. Within the villages the streets and households were
 sampled randomly. Surveys were conducted on various days of the week.
 After a second unsuccessful try, selected households were marked as
 dropouts. To decrease the dropout rate we did not randomly select
 respondents within a given household. All respondents were asked for an
 oral consent to participate in this study, as a personal signature was
 deemed to create discomfort and increase drop-out rates, especially in
 Romania. Data were collected between April and July 2017. This resulted in
 a total sample size of <i>n</i> = 819 across 52 villages
 (Romania <i>n</i> = 22, Germany <i>n</i> = 30).
 The ethics approval of this research was granted by the Leuphana
 University.  
 <i>Data
 analysis</i> Exploratory factor analysis
 Our relational value data frame had
 a size of N = 819 observations of 18 variables. We imputed missing data
 with the method of predictive mean matching. Cronbach’s α for these
 variables was 0.83, while Kaiser-Meyer-Olkin’s measure of sampling
 adequacy was 0.93, well above the recommended value of 0.6, Bartlett’s
 test of sphericity was significant (χ² (153) = 5583.0, p &lt; .001).
 All of these diagnostics suggest reasonable factorability. We considered three, four and five-factor
 models using oblimin rotation and a minimum residual factoring method.
 Associated scree plots and fit statistics indicated that the four-factor
 model was sufficient (RMSEA = 0.071, Tucker-Lewis-Index = 0.885). The four
 factors explained 29%, 7%, 5% and 4% of the variance respectively for a
 total of 45%. We refrained from removing items with factor loadings
 &lt;0.4 because of our sample size of well above 300 (Stevens, 2002
 :395). We provide the full loadings matrix in Table 3. We created
 composite scores for each factor by adding the scores of the items loading
 onto each factor for subsequent regression analysis.   Candidate modeling We modelled the response of the three
 latent factors to a set of socio-demographic variables using beta
 regression models (Cribari-Nieto and Zeileis, 2010; Grün et al., 2012) on
 the latent factor scores that we transformed to the open standard unit
 interval (0, 1). The transformation applied was the one recommended by
 Smithson and Verkuilen (Smithson and Verkuilen, 2006), so that
 <i>y</i>’ = (<i>y</i> × (<i>n</i> – 1)
 + 0.5) / <i>n</i> where <i>y</i> is the data of
 length <i>n</i>. We based the set of candidate models on
 grouping explanatory variables into three categories: personal
 characteristics of the respondent (‘P’: gender, age), nature-based
 variables (‘N’: distance travelled, attitude towards conservation,
 visiting frequency, frequency of use of natural products) and focal
 landscape (‘L’). We constructed the following set of eight candidate
 models, which may be seen as our hypotheses regarding what variables might
 explain the latent factor scores observed: Null, N, P, L, N + P, N + L, L
 + P, N + P + L. We based model selection on AICc values and used the full
 average method where model averaging was required (Grueber et al., 2011;
 Nakagawa and Freckleton, 2011). We conducted our analyses using the R
 programming language (R Core Team 2019). We present the coefficients of
 the best-fitting models for each latent factor in Tables 4 and 5 and in
 the supplementary tables A1-A4.
description: Data has been anonymised.