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Understanding relational values in cultural landscapes in Romania and Germany

Citation

Riechers, Maraja et al. (2021), Understanding relational values in cultural landscapes in Romania and Germany, Dryad, Dataset, https://doi.org/10.5061/dryad.j3tx95xdx

Abstract

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.

Methods

Data collection

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 n = 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 n = 819 across 52 villages (Romania n = 22, Germany n = 30). The ethics approval of this research was granted by the Leuphana University.

 

Data analysis

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 < .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 <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 y’ = (y × (n – 1) + 0.5) / n where y is the data of length n. 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.

Usage Notes

Data has been anonymised.

Funding

Volkswagenstiftung and the Niedersächsisches Ministerium für Wissenschaft und Kultur, Award: A112269

Romanian National Authority for Scientific Research and Innovation, Award: BiodivERsA-FACCE2014-47