The Eco-Generativity Scale-Short Form: A Multidimensional Item Response Theory Analysis in University Students

Abstract

The Eco-Generativity Scale (EGS) is a recently developed 28-item scale derived from a 4-factor higher-order model (ecological generativity, social generativity, environmental identity, and agency/pathways). The aim of this study was to develop a short-scale version of the EGS to facilitate its use with university students (N = 779) who will determine the future of our world’s ecosystem. Data analyses included removing misfitting items and assessing the psychometric properties of the EGS short form. The Eco-Generativity Scale-Short Form (EGS-SF) showed a good fit for a higher-order model composed of four factors and sixteen items (four items for each factor).

Keywords

eco-generativity Eco-Generativity Scale-Short Form psychology of sustainability and sustainable development eco-anxiety university students

Introduction

Climate change and environmental issues are among the most significant challenges facing 21st-century societies (Heeren & Asmundson, 2023). The negative consequences of climate change (e.g., global warming, forest degradation/fires, diminished freshwater supplies, and biodiversity loss) have downstream effects that adversely influence human health and well-being and underlie such specific negative psychological phenomena as eco-anxiety (a chronic fear of environmental disaster) (Clayton & Karazsia, 2020). Even though eco-anxiety is considered an adaptive reaction to environmental threats, it may lead to more significant mental health issues (Usher et al., 2019). Research points to greater vulnerability among younger generations (Hickman et al., 2021) including university students (Searle & Gow, 2010), adolescents (Sciberras & Fernando, 2022), and children (Léger-Goodes et al., 2022), who have an amplified exposure to environmental issues and associated concerns via social media (Searle & Gow, 2010).

Di Fabio and Svicher (2023) have recently proposed a paradigm shift to increase resilience associated with environmental concerns and occurrences by advancing the construct of eco-generativity and developing a new measure: the Eco-Generativity Scale (EGS). The EGS integrated previous constructs (see Di Fabio and Svicher, 2023) in line with both the psychology of sustainability and sustainable development (Di Fabio & Cooper, 2023; Di Fabio & Rosen, 2018) and strength-based prevention perspectives (Di Fabio & Saklofske, 2021) which emphasize the building of psychological health and well-being in individuals.

The 28-item EGS self-report scale assesses eco-generativity as a resource to contribute to environmental preservation for the benefit of future generations by means of sustainable practices, awareness, and responsibility towards the environment, communities, and the common good (Di Fabio & Svicher, 2023). The EGS demonstrated a higher-order factor of eco-generativity composed of four factors: ecological generativity, social generativity, environmental identity, and agency/pathways. The ecological-generativity factor encompasses the careful use of energy, protecting nature and animals, and living sustainably. The social-generativity factor focuses on taking care of the community and future generations, highlighting the relevance of one’s actions for the common good. The environmental identity factor refers to perceiving oneself as embedded in nature, experiencing calmness in natural settings, allocating efforts and resources for the environment, and having sustainable habits. Finally, agency/pathways reflects the individual’s self-perception of achieving goals and developing successful plans. The EGS showed adequate factor structure, internal consistency, and positive correlations in relation to measures of hedonic and eudaimonic well-being (Di Fabio & Svicher, 2023). However, the authors highlighted the need to further examine the psychometric properties of the scale using Item Response Theory models (Di Fabio & Svicher, 2023). In this line, Multidimensional Item Response Theory (MIRT) (Reckase, 2006) is a psychometric approach that extends IRT to multidimensional scales involving item-level statistics. The present study aimed to obtain a psychometrically sound short form of the EGS for university students via MIRT analysis.

Methods

Participants

University students from Central Italy volunteered to participate in this study including 53.7% female (n = 418) and 46.3% male (n = 361) with a mean age (SD) of 21.01 (3.84) years. All participants signed informed consent forms in line with Italian privacy laws (DL-196/2003; EU 2016/679). The scale was administered in English, and all participants had the B2 certification in English.

Measures

The self-report 28-item Eco-Generativity Scale (EGS) (Di Fabio & Svicher, 2023) employs a 7-point Likert rating scale (1 = strongly disagree; 7 = strongly agree). Previous research identified four factors: Ecological Generativity (EG), Social Generativity (SG), Environmental Identity (EID), and Agency/Pathways (AP) that regressed onto a higher-order factor. Cronbach’s alpha coefficients ranged from .94 (ED) to .80 (EG) (Di Fabio & Svicher, 2023).

Data Analysis

The full EGS was investigated using half the sample; n = 386; females = 52.6%; males = 47.4%; mean age (SD) = 21.03 (3.46). Item Factor Analysis via Graded Response Model (GRM) (Samejima, 1969) was performed and a higher-order model was tested where EG, SG, EID, and AP factors were regressed onto a higher-order factor of Eco-generativity. CFI and TLI > .97 and RMSEA ≤ .05 were considered good (Schermelleh-Engel et al., 2003). Item reduction was then conducted by inspecting Orlando and Thissen’s (2000) signed chi-squared (S-X2), and statistically significant items were removed.

The resulting short form was then investigated with the second group of participants; n = 393; females = 54.7%; males = 45.3%; mean age (SD) = 21.02 (3.18). Factor loadings, Mokken coefficients of scalability (H_ij) (Sijtsma & Verweij, 1992), Cronbach’s alphas (α), discrimination (a), and difficulty (b) parameters were calculated. Guidelines were applied as follows: H_ij > .30 and α > 70 were adequate. Values of a were considered according to the criteria: <.64 unacceptable; .65–1.34 moderate; 1.35–1.69 high; ≥1.70 very high. EGS’s items are rated on a 7-point Likert scale with 6-item thresholds (b₁, b₂, b₃, b₄, b₅, and b₆) reflecting the level of the measured construct when participants have a 50/50 likelihood to choose either one or another option on the Likert scale. Items' thresholds ranked from the easiest to the most difficult were judged optimal (Samejima, 1969). Analyses were performed using mirt 1.38.1, mokken 3.0.6, and psych 2.3.3 R packages. The R Studio 2022.12.0 for Macintosh was implemented.

Results and Discussion

Table 1 shows the results of Full-Information Item Factor Analysis applying MIRT GRM for the EGS performed on the first half of participants (n = 386). The initial 28-item higher-order model provided an adequate fit to the data (CFI = .971; TLI = .972; RMSEA = .049). However, two items of the SG factor, seven items of the EID factor, and three items of the AP factor showed statistically significant S-X2 (Table 1). Therefore, these twelve items were removed, and the shortened scale was subjected to another factor analysis with responses from the second half of participants (n = 393). A 16-item higher-order model provided excellent fit to the data (CFI = .984; = TLI = .987; RMSEA = .033). Therefore, this solution was retained as the Eco-Generativity Scale-Short Form (EGS-SF). Table 2 shows the factor loadings for EGS-SF, reporting good factor loadings for all items. Similarly, Mokken coefficients of scalability and Cronbach alphas were adequate. Lastly, Table 3 reports the difficulty and discrimination parameters of EGS-SF. Each item showed adequate discrimination parameters, ranging from moderate to high, and ordered item thresholds, reflecting that a seven-point Likert scale is adequate (Table 3).

Table 1.

Eco-Generativity Scale (EGS) and Eco-Generativity Scale-Short Form (EGS): Multidimensional Item Response Theory (MIRT) Analysis. Item Fit Statistics for the Grade Response Model.

	EGS 28-item higher-order model (n = 386)		EGS-SF 16-item higher-order model (n = 392)
Item	S-X2 (df)	p	S-X2 (df)	p
EG1	136.490 (141)	.592	107.795 (103)	.354
EG2	127.647 (126)	.442	109.082 (99)	.229
EG3	138.000 (144)	.625	85.250 (72)	.136
EG4	124.541 (125)	.495	90.473 (75)	.108
SG1	222.509 (203)	.166	171.099 (154)	.164
SG2	265.927 (215)	.010†	–
SG3	285.691 (211)	.000†	–
SG4	232.235 (213)	.174	142.452 (142)	.474
SG5	199.288 (198)	.461	126.834 (155)	.953
SG6	247.545 (235)	.274	175.129 (154)	.117
EID1	158.724 (148)	.259	129.915 (116)	.178
EID2	189.376 (195)	.600	134.672 (143)	.678
EID4	232.165 (193)	.028†	–
EID5	216.670 (180)	.032†	–
EID8	251.855 (199)	.007†	–
EID9	278.407 (230)	.016†	–
EID10	232.165 (193)	.028†	–
EID11	197.086 (237)	.972	163.953 (148)	.175
EID12	260.959 (192)	.001†	–
EID13	262.623 (259)	.426	130.491 (140)	.706
EID14	186.272 (151)	.027†	–
AP1	261.138 (220)	.030†	–
AP2	245.047 (240)	.398	168.909 (172)	.552
AP4	242.045 (204)	.035†	–
AP6	233.393 (241)	.625	152.558 (166)	.765
AP8	204.856 (214)	.661	166.206 (175)	.671
AP10	230.479 (226)	.405	199.067 (201)	.525
AP12	293.439 (245)	.018†	–

Note. EG = Ecological Generativity; SG = Social Generativity; EID = Environmental Identity; AP = Agency/Pathways. S-X2 = Orlando and Thissen’s signed chi-squared test; † and bold p-values indicate the removed items.

Table 2.

Eco-Generativity Scale-Short Form (ECG-SF). Multidimensional Item Response Theory (MIRT). Full-Information Item Factor Analysis and Mokken Analysis (n = 392).

	Factor analysis				Mokken analysis
Item	Factor analysis				H_ij (SE)	Cronbach’s alpha	M(SD)
EG1	.508				.40 (.026)
EG2	.577				.53 (.022)
EG3	.552				.47 (.021)
EG4	.436				.35 (.030)
EG					.42 (.023)	.81	19.08 (4.63)
SG1		.521			.46 (.020)
SG2		.608			.53 (.021)
SG5		.525			.48 (.022)
SG6		.469			.40 (.022)
SG					.46 (.021)	.85	18.81 (4.72)
EID1			.512		.55 (.023)
EID2			.612		.53 (.026)
EID11			.662		.52 (.027)
EID13			.754		.56 (.023)
ED					.55 (.023)	.88	21.38 (4.73)
AP2				.649	.38 (.022)
AP6				.731	.35 (.025)
AP8				.647	.37 (.026)
AP10				.486	.32 (.025)
AP					.36 (.023)	.78	20.86 (4.68)
ECG-SF					.40 (.016)	.82	80.13 (12.90)

Note. EG = Ecological Generativity; SG = Social Generativity; EID = Environmental Identity; AP = Agency/Pathways; H_ij(SE) = Mokken coefficient of scalability.

Table 3.

Eco-Generativity Scale-Short Form. Multidimensional Item Response Theory (MIRT): Discrimination and Difficulty Parameters (Grade Response Model) (n = 392).

Item	a₁	a₂	a₃	a₄	b₁	b₂	b₃	b₄	b₅	b₆
EG1	1.61	.00	.00	.00	−3.12	−1.47	.59	1.90	3.22	5.44
EG2	1.53	.00	.00	.00	−2.11	−.79	.75	1.59	2.71	4.26
EG3	1.73	.00	.00	.00	−1.05	−.81	1.19	3.41	3.94	5.22
EG4	1.11	.00	.00	.00	−2.49	−.77	.43	1.84	2.78	4.44
SG1	.00	1.50	.00	.00	−2.17	−.28	1.22	2.33	3.76	5.53
SG2	.00	1.52	.00	.00	−1.41	−.13	2.15	3.57	4.33	5.47
SG5	.00	1.32	.00	.00	−2.29	−1.27	−.31	.92	2.72	3.25
SG6	.00	1.12	.00	.00	−2.57	−.93	.91	2.31	4.21	5.87
EID1	.00	.00	1.72	.00	−1.18	.35	1.79	3.22	4.88	5.13
EID2	.00	.00	1.34	.00	−1.12	.57	2.12	3.24	4.61	5.76
EID11	.00	.00	1.19	.00	−1.55	.50	2.47	4.28	5.56	6.04
EID13	.00	.00	1.43	.00	−1.78	−.59	.38	1.55	2.47	3.70
AP2	.00	.00	.00	1.65	−1.91	.26	2.42	4.11	5.14	7.03
AP6	.00	.00	.00	1.09	−2.13	−.48	.90	2.25	4.51	6.88
AP8	.00	.00	.00	1.21	−2.17	.44	1.22	2.27	4.45	6.13
AP10	.00	.00	.00	1.47	−.81	1.03	2.55	3.48	4.67	5.31

Note. a = discrimination parameter for a₁ = Ecological Generativity (EG); a₂ = Social Generativity (SG); a₃ = Environmental Identity (EID); a₄ = Agency/Pathways (AP). b_n = 0 medium difficulty; b_n < 0 less difficulty; b_n > 0 more difficulty.

The present research focused on creating a shorter version of the EGS for use with university students to facilitate a quick and effective measure of eco-generativity. An MIRT analysis showed an excellent fit for a higher-order model composed of four factors with four items each comprising the 16-item EGS-SF. Adequate homogeneity, good internal consistency, adequate discrimination, and ordered difficulty parameters provided preliminary evidence of the psychometric properties for the EGS-SF. This brief scale takes into account both the traditional accountability frame, suggesting short scales to preserve cost-effectiveness during administrations (Whiston, 2001), and the current parsimonious perspective (Duffy et al., 2023), aimed to decrease the participants’ burden using reliable short measures. However, limitations have to be highlighted. Participants in our study were university students from Central Italy. Future research should investigate the EGS-SF in university students as well as youth and young adults from different regions in Italy and from other countries. Future studies should also take into account the relationship between the EGS-SF and hedonic and eudemonic well-being and positive psychological constructs such as emotional intelligence (Petrides & Furnham, 2000), resilience (Wilson et al., 2019), humor (Marunic et al., 2023), and critical individual differences such as perfectionism (Feher et al., 2020). Nevertheless, the EGS-SF showed good psychometric properties opening new opportunities for research and interventions in both the psychology of sustainability and sustainable development and strength-based prevention perspectives. In this line, promoting and supporting eco-generativity among university students may be a major factor in ensuring the planet’s health, protecting the common good for communities and next generations, and promoting personal health by constructively addressing eco-anxiety.

Supplemental Material

Supplemental Material - The Eco-Generativity Scale-Short Form: A Multidimensional Item Response Theory Analysis in University Students

Supplemental Material for The Eco-Generativity Scale-Short Form: A Multidimensional Item Response Theory Analysis in University Students by Annamaria Di Fabio, and Andrea Svicher in Journal of Psychoeducational Assessment.

Footnotes

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Annamaria Di Fabio

Andrea Svicher

Supplemental Material

Supplemental material for this article is available online.

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